In the following video, Eric interviews Bruce Clay of Bruce Clay, Inc. gives his thoughts on Google and personalization:

Key Points

1. Web history has existed for 5 years, and Google uses this information to tailor search results to individuals.
2. Google is trying to overcome ambiguity. When you search for a term like “hammer,” what do you mean?
3. Two people might be searching for the same word and get different results, depending on their location and web search history.
4. While personalization may lead to less false positive results (and therefore lower total site traffic), it also leads to more highly targeted visitors to your website. This can actually increase conversion rates as a percentage of search traffic in some cases.
5. In some cases, personal history may give Google incorrect signals, and actually increase the amount of false positives.
6. Ranking is no longer the best way to track SEO efforts, as ranking will vary from person to person.
7. In the past, SEO professional’s were optimizing around one word, whereas in the future, they will have to optimize for communities.

Full Interview Transcript

Eric Enge, CEO, Founder, Stone Temple Consulting: Hi, I’m Eric Enge with Stone Temple Consulting. We are an internet marking optimization firm that does SEO, paid search, social media, and a variety of other things for fun.

I’m here today with Bruce Clay. We are going to take on an interesting topic, which is that of personalization. But, Bruce, why don’t you introduce yourself for us?

Bruce Clay, President, Founder, Bruce Clay, Inc.: Thank you. Bruce Clay Inc. We’ve been in business since January of 1996. We focus on internet marketing optimization. That is: SEO, PPC, analytics, social conversion, and information architecture.

We sponsor a lot of conferences that many people probably have gone to. I am sure at one point or another they’ve had a drink ticket with my name on it. So, that’s an important networking activity we support.

Eric Enge: You know how to make friends.

Bruce Clay: I do. Certainly, sponsoring drinks is one sure-fire way of doing it. It’s the ultimate social media platform at a conference.

Eric Enge: Right!

Bruce Clay: So, we do that. We have offices internationally and we’re (centrally) located in Southern California.

Eric Enge: Awesome! So, today we wanted to talk, like I said, about personalization a bit.

Boy, there are a lot of dimensions to that. But we’re going to try to hit the high points here, I guess, and maybe you could give me your sense of what personalization involves at Google today and where you think it might go.

Bruce Clay: Personalization is a little bit like the blind man and the elephant. No matter what part of the elephant you touch, it looks like a different kind of an animal. Unless you see the whole thing, you don’t know it’s an elephant. I think personalization fits into that.

I think, in the beginning, Google had; well, if you remember back, Google had 10 blue links, one algorithm, one-size-fits-all, no matter where you were in the world you got the same 10 results.

For about five years, they’ve been attempting to auto-localize. They’ve been attempting to look at your web history and web history has existed for about five years.

I think the impact on search results has been growing over time, but I have slides in my training course where we talk about web history from five years ago. I have samples of those slides.

What I think has been going on is Google has been fighting the battle against something called ambiguity. Ambiguity is where somebody puts in a word and Google doesn’t know what you meant.

What I really like is my example for a search for “hammer.” Now, we all know what a hammer is. But if you search for hammer in Google, the number one result is a vitamin. The number two result is the Armand Hammer art museum at UCLA. And the number three result is a bowling ball known as “The Hammer.” Number four is Wikipedia, number five is MC Hammer. Nowhere in there are you really dealing with striking instruments.

But, they didn’t know what you meant by “hammer.” Now, we can keep going. Guns have hammers. Pianos have hammers. I mean, there’s a lot of different things going on. So, Google has attempted to give you nutrition and art and sports. The best hammer in each of them, because they’re the popular categories.

Well, that ambiguity becomes a problem for Google. So, where web history is played, and this personalization, is they’ve started to look at where are you and what have you previously searched for? So, if I search for equipment and tools and pliers and screwdrivers and then hammer, they’re gonna know what kind of hammer I’m likely looking for.

Eric Enge: Right.

Bruce Clay: Personalization, then, has migrated us from the 10 blue links one-size-fits-all to something that is individualized. It is what I want, based on what I searched for in front of it. And that personalization is changing the top 10 results for each individual, even if they search for the same word.

Now, the good news is, if somebody actually does go to my site for hammer, it’s going to be a more targeted visitor. The bad news is, I’m not going to get as many false positives, if you will; people that will find my site my site when they didn’t really want the tool.

Eric Enge: Right. A little less serendipity.

Bruce Clay: Yeah. So, my traffic is going to drop, but my conversion, perhaps, won’t. And my conversion as a percentage may actually improve.

So, I think Google is using personalization as a way to address “how do I better target the search results by eliminating ambiguity?” And I think that’s really part of the battle.

Eric Enge: Right. Well, that makes a lot of sense.

On January 10th, actually, we got the Search Plus Your World update from Google, which introduced a new level of personalization to search results, didn’t it?

Bruce Clay: Yes. I refer to it as Search Rocks Your World. There were so many things that changed that people saw dips in traffic. A lot of ecommerce sites saw the traffic drop without actually seeing a conversion change, which is what I had alluded to. But they immediately freaked out because of the traffic drop. Now, I think that that’s a natural kind of a thing.

But I’ll tell you where the problem comes in, is that nobody knows how to get rid of web history. That means that I’m getting even more false positives. If I search for baby gifts because one of my employees had a baby, and then I search, because I’m going to a conference in Las Vegas, if I search for Las Vegas hotels the last thing I want is Las Vegas hotels catering to babies. Trust me. That’s not what I’m after.

So, you know, it could backfire on me finding results, and we’ve heard many people; many people indicate that the Google results are not any better; that they’re actually worse.

I think that one of the reasons they’re worse is that they’re using mixed queries. They’re searching for this gift and then that destination and then this travel means, and the signals are actually creating false positives. And I think that makes it a little bit harder.

The other thing you have to understand is, you know, when Google does all this, the intent is to improve. They don’t always get to improve.

Eric Enge: Right. It’s not as simple as you might think it is. Just because we can take one scenario and design an algorithm for that, we need to remember it’s being designed for, really, literally billions of scenarios.

Bruce Clay: Yeah. How do you anticipate what somebody would have searched for before they searched for your keyword?

Eric Enge: Right. And I think when I interviewed Google’s Jack Menzel last, one of the things that he commented on is that is exactly one of the reasons why they don’t reach that far back into the web history and they keep it fairly localized just to try to minimize some of the fallout from that kind of problem.

Bruce Clay: But it also impacts the ability to use, for instance, in SEO, a lot of people using ranking as a determining factor of how well am I doing.

And unless you have an apples and apples and apples way of determining ranking, it turns out that you can say “I’m in position five” and everybody else sees you in position 20. They don’t even see you at all. So, it does mess up the SEO methodology a little bit.

Eric Enge: Yes, it does.

Any other thoughts that we should add on personalization before we finalize?

Bruce Clay: Well, I think that personalization is being comingled with other things. I think we’re gonna see it actually change the way pay-per-click works as a part of that algorithm.

You know, Google makes money on pay-per-click. They obviously want that to work.

We know that localization is occurring in the Google Places realm. Places was occasional, then it’s 30 percent, and now I expect it, within a year and a half, two years, to be 70 percent of all queries that have places on them.

Auto-localization is happening. I think that SEO becomes harder. I think that we as search engine optimization professionals have got to understand and wrap our head around the persona of the actual target.

We try to anticipate the keywords that they’re going to use and then content has to be changed. So many people for so long have written content around one keyword, not around a community. And I think that that change is going to impact how the whole world optimizes for traffic. And I expect it to be a very exciting period.

Eric Enge: Well, that’s awesome. That’s a big thought to leave this off on, but actually a really good one for people to think about.

Thanks for taking the time to be with us today, Bruce.

Bruce Clay: Thank you.

Key Points

1. In 2011 Google had billions of ads that were submitted. Of these, roughly 130 million ads and 800,000 advertiser accounts were disapproved/suspended for violations of policy.
2. Some of the spam problem is handled algorithmically while some is assisted with manual review. The system is constantly under work and revision.
3. One of the biggest areas of bad ads is counterfeit. In 2011, of the 800,000 advertisers, 150,000 were for counterfeit violations.
4. Of the counterfeit removals, 95% were proactive measures that Google has in place and 5% were from user complaints.
5. Economic downturn gives way to new scams, as people are looking to save money and thus are easily tricked into deceptive billing practices or schemes.
6. Bad ads are attacked with a 3 pronged approach:
   1. Looking for bad ad text or landing pages.
   2. Looking at sites in an industry, agnostic of which advertiser is advertising the site, to see if there is a whole class of policy that is being violated.
   3. Looking at individual advertising accounts (all of the ads and everything they advertise)
7. The account review portion has a Risk Model where Google predicts the risk of a certain account to violate policy based on account history and a variety of account signals.
8. Many complaints are from users reporting their competitors after they have been banned.

Full Interview Transcript

Eric Enge: Hi, I’m Eric Enge. I’m the CEO and founder of Stone Temple Consulting, an internet marking optimization firm that does SEO, pay per click, social media, and lots of other wonderful stuff.

And I’m here today with David Baker from Google. David, can you introduce yourself, please?

David Baker: Sure. My name’s David Baker. I’m a director of engineering with Google and I’ve been working with Google going on eight years now.

Eric Enge: Awesome. Thank you. And we’re going to talk about some fun stuff today. We’re going to talk about the spam police, but it’s not the organic spam police. It’s the ad spam police.

That’s probably not the term you use internally for it, but I’m going on the fly here. Sounds pretty good, doesn’t it?

David Baker: Yes, indeed.

Eric Enge: OK. Great.

So, I think a great place to start is you had shared with me some great metrics about, you know, some of the things that sort of characterize how you guys are attacking the problem with bad advertisers.

You’ve talked about how many advertisers you’ve shut down; things like that. Can you talk about some of that?

David Baker: Absolutely. So, you know, the numbers bring to life just how much a challenge of scale it is that we have here at Google. And in 2011 we had billions of ads that were submitted to us, and of those, 130 million were disapproved.

And the number of advertisers that we shut down for policy reasons was 800,000 advertisers.

Eric Enge: Wow.

Yeah. And I think there were a lot of accounts that were shut down as well, isn’t that right?

David Baker: Yeah. So, then when I say “suspended advertiser,” that is shutting down an account. So, the number of accounts was 800,000 last year that we shut down.

Eric Enge: That’s amazing. And 130 million ads; that’s a significant percentage of the total. I mean, it’s not 20 percent but it’s, you know, it’s several percentage points, it sounds like.

David Baker: Yeah, you know, 130 million of billions is a large number. It’s a lot of policy enforcement that requires a lot of hard work; a lot of good engineering and a lot of good people behind it.

Eric Enge: Yeah, and I saw that, I think you have hundreds of Googlers working in this area?

David Baker: That’s correct. So, you know, we spend millions of dollars building systems and applications and the staffing behind this is hundreds of Googlers. You know, both engineering and specialists who are able to enforce policies but, you know, manually looking at the edge cases when our; the systems that I build and my team builds, when we don’t have a high degree of confidence in a result, we send it to a real person to make a decision.

And that human feedback is essential to all the technology that I and my team built. We learn from the things that real people tell us, specialists tell us, about what’s good and what’s bad.

Eric Enge: Right. So, it sounds like some percentage of the problem is addressed purely algorithmically and then some falls into the class of what I’ll call algorithmically-assisted manual review. Or algorithmically-inspired manual review might be a better way to put it.

So, you kind of flag it, you say, “OK, we’re not quite sure but this looks like it might be a problem. Let’s have someone look at it.”

David Baker: That’s correct. That’s correct. And, you know, it’s; we believe that we’ve got an industry-leading solution here, but it is also something that is constantly under work and under revision.

The people that are trying to do bad things to our end users or to violate our policies are not giving up and I doubt will ever give up. And so it’s a constant battle and arms race where we are constantly revising our systems, building new components, or rebuilding existing components, trying to make them better.

And one of the key things is, you know, making sure that we’ve got good metrics to know how we are doing and whether or not we are doing better.

Eric Enge: Right. And can you classify for me some of the kinds of spam that you end up seeing and dealing with?

David Baker: I think one of the biggest areas of the bad ads that we see is counterfeit. It’s a very lucrative market where a well-known brand can be established and is known for having quality goods, but people can create very cheap knockoffs and trick unsuspecting users into thinking they’re getting a really good deal, when, in fact, they’re not.

And so, with counterfeit, we see various brands from clothing to electronic goods, being counterfeited and various attackers trying to get these ads in front of users. And that one is, you know, a big area that we see lots of people trying to make money and we work very hard to shut them down.

I believe that in 2011, of the 800,000 advertisers that we shut down, 150,000 of those were for counterfeit. Ninety-five percent of that was caught by our proactive systems; the other 5 percent from complaints from the outside.

Eric Enge: So that’s a case like someone makes a cheap knockoff of some Nike sneakers and they put an ad up which makes it sound like they’re either advertising real Nike products or that they’re Nike themselves or. . .

David Baker: Yeah, that’s correct. And counterfeit is a particularly tricky space because the attacker is trying to fool not just Google but also the user. And they’re working very hard to make it look like they are a legitimate business.

The same techniques that they would use to fool the user make it difficult for Google to distinguish between a real good and a counterfeit good. But, you know, we’ve done a pretty darn good job.

Eric Enge: And are there other classes of spam that happens in the ad world beyond the counterfeit ads?

David Baker: Yes. Illegal goods are some of them. Scams that try to trick users into deceptive billing practices. A variety of different things.

My observation is that the problem has gotten much worse over the past few years, correlating with an economic downturn and that scams and the allure of something free or cheap to get is much more attractive to people who are hurting for money.

And so we’ve had to redouble our efforts over the past few years in order to progressively and dramatically improve the situation.

Eric Enge: And you referred to it as an arm race, I think, in a recent post, you did.

David Baker: Yes. It never ceases to amaze me how this is a constant battle that we’re just never going to be satisfied with how good we’re doing and how persistent the attackers are.

And, you know, we have reasonable data to infer that they are paying very close attention to what we’re doing. When we adopt new protections, they adapt. And one of things that, being steeped in the work that we do, that you learn very quickly, is that simple rules, simple solutions just don’t work. Because simple solutions are brittle, the attackers adapt very quickly and it requires a more sophisticated, nuanced approach and multiple approaches applied at once in order to solve this problem.

Eric Enge: Right. And can you describe to me the new risk model that you spoke about in your recent post?

David Baker: Sure. So, we attack the problem of bad ads using a three-pronged approach. One is; and the different dimensions are looking at the ads themselves; the specific ad text and the specific landing page that the ad goes to.

The second dimension is looking at sites agnostic of which advertiser is advertising the site, we aggregate all sites that are advertised and inspect the sites to see if there is a whole class of policy that applies to the entire site.

And the third is looking at individual advertiser accounts. Looking at all of their ads, all of the sites that they advertise. And the risk model that we talked about was addressing that third approach. The account review, the third prong, is oftentimes called a risk model because we’re kind of trying to predict the risk of this account to violate our Google policies.

And so the risk model looks at a variety of different signals, constantly evaluating advertiser accounts, and when we get a new piece of information like a new keyword from an advertiser or an advertiser logs in from a new IP address, we reevaluate the riskiness of that account.

And we’re able to calculate, you know, what is the percentage of bad ads on a particular keyword, or what is the percentage of bad ads on a particular; from advertisers who came from a particular IP address.

And when you send all of these signals into a machine learning model, we’re able to come up with very good predictions on whether or not this advertiser is somebody that we should take a very close look at or is somebody who is pretty good.

Eric Enge: Right. That sounds great. One last thread. When you get a manual complaint from someone: I saw some metrics on how quickly you respond to that and talk also a little bit about how you respond.

David Baker: Sure. I’m not sure of the turnaround time guarantees that we have for complaints. But we look at every single complaint. I myself take a look at individual complaints (not all of them), but a lot of complaints to see what’s going on.

But the way that we handle is it goes into a queue. We have a staff of people who take a look at the complaint, try to identify which advertiser is this impacting, and see whether or not they are violating policy.

It’s; an interesting anecdote is that we get a lot of complaints from people who have been shut down for violating policies. It’s interesting how they sometimes rat out themselves. And that can be helpful.

But we also get some very useful complaints from end users and other outside agencies.

Eric Enge: It’s kind of interesting. So, if I get torched then I’m gonna get vengeance on everybody else? Is that kind of what’s going on when that person who’s been banned is complaining about others?

David Baker: Well, you know, we treat complaints from people who have been banned with no more credibility than we would any other. And a lot of times their complaints aren’t legitimate.

But these counterfeiters and other attackers know their space and know their competitors; they’re competing with each other. A lot times they do rat out each other when it comes down to it.

Eric Enge: Well, thanks a lot, David. I appreciate your taking the time to speak with me today. Some great information. And thanks much.

David Baker: Thank you very much, Eric, and I hope we get to talk some time in the future.

Eric Enge: I imagine we will.

David Baker: Great.

This is a hot topic! If you have been hit by Penguin, or are worried about future variations of the algorithm, this is a video you can’t miss. In fact, regardless of your current situation, pruning the worst links out of your profile is a rock solid idea.

Key Points

1. Link pruning refers to the identification and removal of unnatural, non-organic, or generally spammy links from a link profile.
2. In many cases, questionable link building tactics (buying links, spamming links) can result in an eventual loss of rankings.
3. In the event of a loss of rankings (or warning from Google) due to unnatural link profiles, it sometimes makes sense to investigate a link profile, find the low quality links, and send requests to their webmasters for removal.
4. Many times, getting a link removed from a webmaster can be as difficult, if not more difficult, then getting the link in the first place.
5. The key to this process is persistence and communication with Google.
6. Sometimes, the best you can do is send a list of the links you are trying to remove, and ask Google to discount them. Showing an effort to Google is always a good plan of action.
7. When removing links, don’t expect to return to your pre-penalty rankings. You must replace the spammy links that were detected with quality links, which can sometimes be a lengthy process.
8. Link pruning should be considered every month. Ask yourself: What is the bottom 5 percent of links that I have in terms of quality? How can I remove these and replace them with quality links?

Full Interview Transcript

Eric Enge: Hi, I’m Eric Enge. I’m the CEO and founder of Stone Temple Consulting. We are an internet marking optimization firm doing pay per click, social media, and SEO.

And I’m here today with Bruce Clay. Bruce, take this moment and tell us a little bit about yourself.

Bruce Clay: Bruce Clay has been in business since 1996. We started out as a pure play SEO company. We’ve now expanded into other areas of internet marketing optimization. We do SEO, PPC, analytics. We’re Omniture-certified, as an example. We do conversion rate optimization, social media, and a lot of design architecture work.

We are probably well-known for speaking at conferences. We wrote the Wiley book Search Engine Optimization All-In-One for Dummies. We do a lot of courses. I sponsor a lot of conferences, the Search Engine Strategies and Search Marketing Expo. I think I’m either notable there for being a speaker, a teacher, an exhibitor, a sponsor.

I think mostly people remember me because I provide drink tickets.

Eric Enge: That’s an important benefit that you bring to attendees.

Bruce Clay: Yes. So, I think networking is important. So, we’re pretty well-established, I think, in the industry and we hope to continue to, obviously, grow within that space.

Eric Enge: Yes, super. So, Bruce, you and I chatted a little bit offline here about this notion of link pruning. And, you know, what we’re really talking about here is a site that may or may not have gotten the problem in terms of being flagged by Google. But they know they have a problem in terms of their link profile. Too many links from unrelated sources, or purchased, or whatever they might be. Can you talk a little bit about how you think about link pruning?

Bruce Clay: Yes. We started doing what we call link pruning. It wasn’t really; it’s still not a mainstream term, but it really does apply to what we do.

Perhaps six, eight months ago, we had a client at one point that was very, very well-ranked, but they felt that links were; they bought into the Kool-Aid, if you will. They understood that links were important, and they thought it was important and they talked to us and we’ve always been a firm that attracts links instead of recruiting links, if you will. We don’t buy them. We don’t do that.

So, really, about a year and a half; two years ago, they bought into that concept. Somebody sold them the bill of goods. They had been with us for awhile, so they said, “Oh, new eyes. Let’s try this.” They went out and that person immediately started buying links and spamming links. Blog links. All sorts of really spammy kinds of things.

They took their rankings, which were significant, and they lost them all. They came back to us and then all of sudden we’ve got a site that we were familiar with, on page looked good, but off page was absolutely a disaster. And we felt by evaluation that what they had done was burned their trust scores.

So, what had happened is their PageRank had gone up and their trust had gone down. They found themselves nowhere in the first three, four, or five pages of ranking. It was time to do a repair.

And in the world of finance, there’s two ways to make money. You either sell more or you spend less. Because the difference is where you’re at. And as any good numerator/denominator system seems to be, in this particular case, the biggest bang for the buck was actually in removing the bad links instead of trying to overcome them with more good links.

Eric Enge: Right.

Bruce Clay: So, we invented this term called link pruning, and in the link pruning process what we do is we attempt to determine the quality of the sites that actually link to you. We started by subscribing to Majestic SEO. We used their ACRank score to determine the probable lower match kind of things.

And then we manually went in to all of the lowest-scored pages and we determined whether or not they were organic or inorganic inbound links. And where we determined that they were off-topic or spammy or low-quality blog links or things that looked like they had been acquired instead of earned.

We solicited a either no-follow or removal of the link themselves. That turned out to be a rather lengthy process. For anybody that has ever tried to, “Dear Webmaster, please link to me,” getting rid of links is twice as bad.

Eric Enge: Yes, exactly. Even if you paid for it originally, they don’t want to spend the time to take it off.

Bruce Clay: Yes, because it’s going away. I mean, they make no money if you do that. They’re in no hurry to do that. In some of the cases, we found that even though, through this link network they bought into, they were paying for the links, even when they stopped paying for the link, the link didn’t go away. So, the penalty remained because the link was still there. They stopped paying for it, but that didn’t eliminate it.

Eric Enge: So, how do you deal with that problem, then?

Bruce Clay: Well, you have to be somewhat persistent. You have to request in writing a couple of times. If you can determine who it was that did it through this network, sometimes going to them, they have more juice with those individual sites than you ever would. So, you know, we would attempt to determine where the originator of the link request came from and work through them as an intermediary.

The problem you also face (and we ran into this) is some of them knew they were junk sites. And this is something really bizarre, because I didn’t expect it. When they knew that they were a junk site, as soon as we requested that they remove the link, they sent us an email saying, “We will remove your links for $10 a link.” Almost as if they put the link in knowing that eventually you would have to ask them to remove and it and that would be how they would make their money.

Eric Enge: Yes.

Bruce Clay: I think it’s kind of hard to tell. When things like that happen, you know, there’s only one thing you do is you take that email and you forward it off to Google. Because it’s a good reason why I can’t get rid of my links.

Eric Enge: Right. Well, and that, I think, is a big part of the strategy, right? Because at the end of the day you can’t get all your links removed. It’s not gonna happen. So, what we’ve seen work is you create a Google Doc, you send in a reconsideration request, you point to the Google Doc, and say, “Hey, I tried to get these removed. I couldn’t. Please just discount them.”

Bruce Clay: Yes. And, quite frankly, Google hasn’t; again, there’s no ruler for measuring how many I have to get rid of. It’s a subjective kind of a thing. In some cases, they say that you have demonstrated, on a good-faith basis, that you are removing them, continue to remove them, let us know later. In some cases, you’re demonstrating it, you’ve done a good enough job, we’ll let you back in.

The belief is that if Google actually penalizes you for these bad links, unfortunately, this is a customer belief, that if I was ranked on page 3, I buy the links, I move up to page 1. I get penalized to page 5. We remove the links, you move back up to page 3. The customer is generally upset because they didn’t move back up to page 1.

Well, the only reason you were on page 1 is because you were pre-spam. But we have customers that were upset because, “Hey, I really enjoyed being there. I asked you to fix my links and you haven’t gotten me back to page 1.” Which is two entirely different situations. Sometimes getting back to where you were before the bought the links is all you can really expect.

Eric Enge: Right. So, we won’t have time for it in today’s video, but obviously, then, that says that the next thing you do is you work on building good quality organic links to get you back there. But at least by having gone through this process will have taken the problem out of the mix.

Bruce Clay: Well, we hope. As an ongoing thing, by the way, there’s maintenance to this. I mean, every month you should say: What is my 5 percent worst links, how do I prune that 5 percent and add in 5 percent at the top? How do I churn my link network to have a generally better quality?

Because, believe me, Google’s gonna keep turning that dial. It’s gonna tighten down and what made it through last month may not make it through this month. So, the surprise of Google.

Eric Enge: Awesome. Well, great talk, Bruce. I enjoyed it a lot. Thanks very much.

Bruce Clay: Thank you.

My Take: This is a classic win-win-win.  Users get better results, Advertisers expand coverage with good performance, and Google increases the relevance of their search results and ads.  Of course, they are most likely also increasing keyword competition, i.e.: profitability for the auctioneer.

Eric Enge and I had the opportunity to catch up with Jen Huang, a Product Manager on the Google AdWords team responsible for the new matching technology.  Ms. Huang filled us in on some of the details of what exactly is changing.

"Jen Huang: The technology is attempting to expand Advertiser Keyword coverage for Phrase and Exact Match when we can match to the same user intent. Keywords that reflect similar user intent."

Background

Search Engines use matching technology to match a limited set of Advertiser Keywords to an infinite set of possible search queries.  Match Types help Search Engines match Keywords beyond their literal, character-for-character match, to all of those possible search queries.  While Phrase and Exact Match are as close as Advertisers can get to literal control, Search Engines also use normalization (or canonical form). That is a broader topic, which I covered previously on Search Engine Land with this article – Canonical Form: The Hidden Keywords in Paid Search.  Normalization has always normalized Phrase and Exact Match on capitalization, for example. [nasa] has always matched [NASA], before and after this release. This release takes that further, to closely related variants, based on the notion of user intent.

When Search Engines match various keywords, they have to play a balancing act.  If they can maintain relevance and also expand the coverage, then they increase competition and profit as a result.  However, if they expand without preserving relevance, then Advertisers would notice the decline in value, competition would decrease, and falling profitability would soon follow.

Interestingly, Broad Match Modifier already uses a similar version of this technology.  This AdWords Help Center Topic, states:

Each word preceded by a + has to appear in your potential customer’s search exactly or as a close variant. Close variants include misspellings, singular/plural forms, abbreviations and acronyms, and stemmings (like "floor" and "flooring"). However, synonyms (like "quick" and "fast") and related searches (like "flowers" and "tulips") are not considered close variants.

"Jen Huang: This new matching technology is similar to Normalization, and to the technology that finds close variants in Broad Match Modifier.  The whole point is to match to keywords with the same intent, helping users find what they are looking for, and Advertisers serve more ads."

I remember when broad-match was introduced with AdWords. It was not originally received well.  Advertisers found the technology was often way too far-reaching, and lacked relevance.  This was long before search query reports, when we actually had to do real work to parse server logs, extract the query string from the referrer_URL field… but I digress.  Most recently, AdWords added Broad Match Modifier, and adCenter is rumored to be following suit soon.

Long term, I am eagerly anticipating this shift towards intent-based advertising.  It will be an important shift that should enrich the user experience, provide some welcome simplification for Advertisers, and hopefully attract more direct-marketing advertising spend in an industry that has historically been dominated by branding and other non-ROI-focused advertising spend.

When Is This Happening?

The opt-out feature is live as of today, at the Campaign Level, giving Advertisers an opportunity to opt-out, or otherwise prepare.  The matching technology is planned to go live starting sometime in Mid-May, presumably in a rolling release.

Expected Impact

AdWords tested this feature in the marketplace with various Advertisers, across numerous verticals.  On average, with accounts already receiving greater than 33% of their traffic from Exact and Phrase Match Types, Advertisers generated 3% more clicks.  Performance metrics such as CTR, Conversion Rate, and Cost Per Acquisition were consistent.

In practice, we can expect there to be some variability between industries, and certainly amongst individual Advertisers.  With our clients, we will be advising that we take this one cautiously, and monitor performance closely.

Another impact for Direct Advertisers would be the ability to compress conversion data into one tracked keyword, while matching for more traffic on closely related searches.  This might be especially interesting for Advertisers with sparse conversion data.  On the other hand, Advertisers with a surplus of Conversion data, and the tools to support it, might choose to stay with the old technology, and the precision it allows.

What is Changing, Exactly?

Pardon the pun…

The technology uses the Google.com search engine to map similar keywords, and will generate a match along one of the following manipulations:

• Spelling Correction
• Word Stemming
• Plurals
• Acronyms
• Abbreviations
• Accents

It is worth noting that nothing else is changing as a direct result of this release.  In particular, Negatives remain unchanged.  They still get minimal normalization, and will continue to work with the same level of precision they do today.

Spelling Correction

This will work similarly to Google.com’s auto spell correction feature:

Essentially, Advertisers should not have to invest extra time in misspelling keywords.  Hopefully, it recognizes fat-thumb spelling errors from smart phones and helps get relevant searchers pointed in the right direction.

Word Stemming

This will match a root word with its various prefixes and
suffixes.  [snare] might match "snared", "ensare" etc.

This one has more potential to conflict with the Advertiser’s intention when advertising on a word.

Plurals

For example, "car" will match "cars". Advertisers interested in distinguishing between singular and plural versions of their keywords might consider opting out.

Acronyms

For example, "NYC" and "New York City"  might be considered equivalent in this new technology.  Watch out for acronyms that might not work as intended, such as the state Abbreviations for Indiana (IN), Delaware (DE), Los Angeles (LA), etc.

Abbreviations

For example, "abbrev." might match with
"abbreviation", and vice versa.

Accents

This shouldn’t impact US-English traffic as much as it might Canadian traffic, or US-Hispanic traffic.  Of course, internationally this should provide some interesting results.

How Can We Measure the Impact?

As Advertisers, our ability to measure the impact will for the most part be limited to a "before" and "after" analysis.  We should be looking for an increase in traffic from Phrase and Exact Match
Keywords, with steady or tolerable performance metrics like CTR, CPC, ROI, etc.

My inner Excel Nerd is pondering if a more detailed analysis might work? Could we pull a Search Query Report? It might tell us the Keyword and the various expanded Search Queries? The current ones do not – they show us the normalized Search Query for Exact and Phrase. We may have to wait and see about that one…

Quality Score

Quality Score will continue to be calculated based on the performance of the original exact match keyword. Any close variants added by this new expanded matching technology will not impact Quality Score for keyword that triggered the ad.

Improving exact match and phrase match

Search Query Reports

Close variants matched by this technology will shows as a new derived Match Type &quote;Exact Match (close variants)" in the Search Query reports, once the technology is live.

Who Should Avoid It?

Advertisers who measure a decline in performance, for starters.  Additionally, Advertisers who use Brand terms with very different bids or ads, especially Brands that are intentional misspellings.  In this case, Advertisers may find the precision offered by the older technology allows them to maintain the control they need to treat Branded versus Non-Branded terms appropriately.  Likewise, any Advertiser who derives enough value from the precision of the old technology to offset the opportunity in the new version, should opt-out.

Summary

The opt-out feature is live as of today as an advanced Campaign Setting (you are auto-opted-in already), the matching technology changes in Mid-May.  You should expect good results, but I recommend keeping a close eye on things, as every Advertiser, and every Account, is different.

Key Points from Interview with Sagar Kamdar

This interview transcript is from the March 13th, 2012 conversation I had with Sagar Kamdar, who heads up the authorship program at Google. This provided some simple, straightforward clarity on how it works, and some of the various scenarios for implementation. Here are some of the key points from the interview:

1. Google sees the authorship program as a way to connect authors and readers who are a fan of their work.
2. The original 3 link method is still supported. This is documented in detail in the main interview, along with a graphic image of how it works.
3. They came up with the 2 newest methods because people were having trouble executing the 3 link method, in some cases because it was too confusing.
4. The 2 link method only requires that the content on the site link to the author’s Google profile with a ?rel=author parameter, and the Google profile lists the site in the “Contributor To” section of the profile. This is documented in detail in the main interview, along with a graphic image of how it works.
5. The email method only requires that your email address be verified and use the domain name of the site where the article is published, and that the email be included in the attribution for the article. This is documented in detail in the main interview, along with a graphic image of how it works.
6. The email method was created because some authors do not have the ability to add a link as specified in the 2 link method.
7. It is OK to put sites in the Contributor To section (provided you contribute to them) even if you don’t get rel=author links back.
8. The Authorship Request functionality has been retired, though there is a simple form there now for verifying an email for the Email method.
9. Anyone who properly implements rel=author is eligible for participation. It only requires that you have a high quality photo in your Google profile.
10. When you first show up depends primarily on crawling and indexing time. This may take days or weeks depending on your site.

Full Interview Transcript

Eric Enge: Can you provide us an overview of your background and your role at Google?

Sagar Kamdar: Sure, I am a product manager on the search team. I have been at Google for about four years, spent most of my time on search, spent my time on a variety of projects like real-time search, and webmaster tools. Actually, my first project was external evangelism with search engine specialists and most recently I have been focused on authorship and some of our social search efforts that you have seen out in the wild.

Eric Enge: There has been this longstanding problem of people who are the original author of content not necessarily being the first to show up for their content, and I think that it is really great that we are starting to have a method by which authors are identified. Can you provide some thoughts on what the authorship program is all about?

The main thing that we are trying to address is the faceless nature of the web.

Sagar Kamdar: The main thing that we are trying to address is the faceless nature of the web. For many years people have been clicking on content not knowing who created it, and not knowing who commented on it. What we are seeing is that users really want to know who created that bit of content. Users know who their favorite authors are, and we’re trying to make it easy for them to communicate with those author(s).

We want to make it easy for authors to get setup and then for searchers to be able to find the content they create. So if they search for “iPad” and they like David Pogue, they are more likely to see what David Pogue has written about the iPad. Authors benefit as well because they get attributed to their content and also they can engage with the users in a way they haven’t before.

Eric Enge: I think it is particularly interesting when you look at it because many well-known authors are published on many different sites.

Now we have this feature where users can say show me all content by Ben Parr, for example.

Sagar Kamdar: It is actually one of the coolest features we have for authors. We have all these people that are publishing on five or six different publications and their own blog and users can’t keep track of all that content. Now we have this feature where users can say show me all content by Ben Parr, for example.

Editor: This is how it looks in Google’s search results:

Editor: Here is the Author Page for Ben Parr on Google:

A user could actually type in a query and see all the content he has written about that subject matter if he has linked his content appropriately. It is a really cool way of aggregating content by an individual.

Eric Enge: Great, there are a few ways to set this up. Let’s talk a bit about those. There is the original method which was what people called the three-link method. This is where the Google profile, the author page and the article page were interlinked with one another. Is that method still supported?

The reason we actually changed to our two most current methods is just because the original method was hard for many people.

Sagar Kamdar: Yes, we still support the original method that we had blogged about. The reason we actually changed to our two most current methods is just because the original method was hard for many people.

We have been in pilot mode for a while because this is something that is new to us and we wanted to make sure that we are doing this in a way that our authors can understand and users can understand, and it was just too hard for people to implement the approach that we had originally discussed.

Eric Enge: Let me outline briefly what the three-link method is and then we will do it for the other two methods. The three-link method was as follows:

1. The author’s Google profile page would point to your Author page on the site with the content with a rel=”me” tag specified in the “Other Profiles” section.
2. The author page on the site with the content points to your Google profile page with the rel=”me” tag also.
3. Finally, each individual article page would need to point to your author page with a rel=”author” tag.

Is that correct?

Sagar Kamdar: Yes, that is correct.

Eric Enge: Okay, so that was the 3 link method. Can you outline the details of the way the other two methods work then?

Sagar Kamdar: We have the 2 link method which works as follows:

1. You link directly to your Google profile from the content you create and append the ? rel=”author” parameter at the end of the URL.
2. In your Google profile you just say you write content for this site. You do that by filling in the “Contributor To” section of your profile.

Eric Enge: In the Contributor To box, does that point to the homepage of the site or the blog section of the site?

Sagar Kamdar: To the homepage of the site.

Eric Enge: If it points to the blog section, is that bad or does that still work?

Sagar Kamdar: That still works.

Eric Enge: If you write for multiple sites, is it possible that you would get rel=author tagging from some sites, but not from others, even if everything is properly tagged?

Sagar Kamdar: I don’t think so. It is most probably a problem in the configuration and best thing to do is to go to the Rich Snippets testing tool, put in a sample URL from one of the articles and see where it may have identified an incorrect setup.

Eric Enge: Can you talk through how the email method works?

Note that we just launched a simple tool to verify your email address.

Sagar Kamdar: This method assumes you have an email address on the domain that you write for. So, if you are writing for CNET, it assumes you have an email address like yourname@cnet.com. If you do have such an email address, you can verify that email address on your profile and that gives us a signal that you may have some association with CNET, and then we try to figure out using heuristics whether the name on your profile or the name you claim to have actually matches up with articles that are written by that same individual. Note that we just launched and email address verification tool.

Eric Enge: At this point, the 2 link method and the email methods are the ones that you recommend. What are the advantages and disadvantages of each?

Sagar Kamdar: Sometimes authors don’t have the ability to add additional links from the bio portion of their article or they need to request their webmaster to make some tweaks to enable that. The email method doesn’t require any modification to the website to get setup, so it is possible that you could get setup a little bit faster for that than the 2 link method. In addition, with email verification, it is far more dependent upon our heuristics and analysis to figure out if content is associated to your Google profile and that’s a science that we are constantly tuning.

Eric Enge: Right, presumably, you can look at signals such as whether or not a piece of content is something that a person would’ve written, is it in the right place on the CNET site where they typically write. Of course, if it is an article on CNET then CNET probably isn’t going to allow other people to use your email address on their article for example.

Sagar Kamdar: Correct. We do a bunch of things and we are still learning since we are in pilot mode. We launched email verification relatively recently, a couple of months ago, and as we get broader adoption we learn new things from our users of what they expect. Of course, you need to have an email address on the domain that you write for. It doesn’t really work for a contributing guest columnist that does not have email on that domain. In that case the writer would have to use the 2 link method.

Eric Enge: If you are writing in multiple locations, you would have to have multiple email addresses, one associated with each domain.

Sagar Kamdar: Yes.

Eric Enge: Do either these have an advantage where you have multiple authors in the same site or they are both relatively equivalent for that?

Sagar Kamdar: That should not make much difference. It is more about what is easiest for you to implement.

Eric Enge: You already talked about “Contributor To” and how that works, is there any harm if you put things you are a contributor to when you yet don’t have links back from the articles on those sites?

Sagar Kamdar: No, that’s totally fine. I think people may visit your profile and want to see what other sites you write for, and it is a good way for you to fill out your profile and let people to see that. So, it may not impact authorship but just having a nice profile that has all the information about you that you feel is relevant to somebody that would visit you I think is totally cool.

Eric Enge: The Contributor To box is relatively new, right, when did that go up?

Sagar Kamdar: That went up four or five months ago, and we have already been though a learning process. Originally, you had to put in a link in the Other Profiles box and then click a checkbox to say, this is about me. If you clicked on

that checkbox, you would actually click the rel=”me” tag. People get confused by different options like this, and they would say everything on my profile is about me, why do I have to click on the checkbox? We decided to create three distinct sections on the profile page to make it easier for people visiting your profile to understand it, and also for authors to get setup.

Anything you add in other profile sections will be a rel=”me” link, anything you add in Contributor To will be a contributor link and then recommended links are links you think are cool that people should check out.

Eric Enge: If you originally filled in the rel=”me” links as part of Other Profiles, and then added them to “Contributor To”, but did not delete the original stuff from Other Profiles, is that a problem?

Sagar Kamdar: No problem at all.

Eric Enge: Can you talk a bit about the Rich Snippets testing tool?

Sagar Kamdar: The Rich Snippets testing tool can be used to tell you if you have implemented your rel=”author” markup correctly. For example, it will tell you if you have linked from your article to your Google profile but haven’t put a Contributor To link back to the site. This works well with the 2 link method, or the 3 link method, but it does not yet work for testing the email verification method. We are working on adding that.

Eric Enge: To test your markup, you should take an individual article URL and see how that does, correct?

Sagar Kamdar: Yes.

Eric Enge: Once things are properly implemented, what decides which authors will end up getting their properly tagged articles show the modified search results? And, how long does it generally take before the modified results show up?

Sagar Kamdar: The basic criteria is that you are setup correctly, you provide a high quality photo of yourself, and then based on our algorithm when your content shows up, we just try to make sure the photo would be relevant to the user. In terms of timeline, it just depends on the frequency of how often we crawl and index your content which is variable, based on sites. We just follow the natural progression of our crawling and indexing technology and it could be setup in days or it could take weeks.

Eric Enge: In terms of the photo, you mean it needs to be something in your Google profile, right?

Sagar Kamdar: Yes, the photo you upload as part of your profile.

Eric Enge: You might have a different photo on each of the individual sites that you write in, those photos don’t need to be the same, you are mostly concerned about the one in the profile, correct?

Sagar Kamdar: Yes.

Eric Enge: Basically most people, as long as they do some reasonable amount of writing online, ought to be able to qualify for this, it is just a matter of crawling time and proper setup.

Sagar Kamdar: Yes.

Eric Enge: That’s excellent. What did I leave out?

Sagar Kamdar: We are really excited about this program as it provides authors and readers an opportunity to connect. It is also great for users to see the author’s picture associated with their content, and it provides a whole new way for authors to engage with their audience. We believe the authors are starting to see the benefit and so are the readers.

Eric Enge: I imagine that this will feed into social signals and author authority in the long term. Does that make sense?

Sagar Kamdar: Yes, you could eventually see that type of thing happening.

Eric Enge: Thanks Sagar!

Sagar Kamdar: Yes, thank you Eric!

Key Points from Interview with Andrew Shuman

I had a fascinating chat with Andrew Shuman about the adCenter UI and the integration of search and social media. He provided a lot of great insights on what some of the opportunities are, and some of the challenges as well. Here are some of the other key points from the interview:

1. re: search and social integration: “There is also a very interesting scale problem there. As with traditional search, we have to query a massive numbers of servers, but then we also have to take my social graph and overlay that on the search results, modify them, and return this all in milliseconds.”
2. “You also have the case where all the people in Seattle seem to be Liking this document. That’s less about me (as a searcher) specifically. Some of those micro populations become very interesting.”
3. re: the deeper meaning behind a particular search query: “I am not just searching for a restaurant, but I am planning a whole night, and what those things mean for a user, how we can bring together the data in a related fashion.”
4. “A restaurant is related to movie theatres that are nearby, right. This creates a new kind of linkage between objects that goes beyond the link graph or the social graph.”
5. “It would be really nice that as you just get to restaurant intent, the related intents are there in part of the experience so that as I mouse over or hover over or whatever we come up with it’s probably just another tab then.”
6. “Related searches is something where we think we can really experiment a lot more with the placement. For some searches it may make sense to be more aggressive in the UI with that, as it may be more common for the user to perform follow on searches.”
7. “You also have to differentiate a person who does the same search frequently, such as they like to search on Bellevue restaurants a lot. Perhaps the third result is always the restaurant they go to.”
8. re: the strength of social as a signal: “It’s an interesting challenge though, because the more generic signals across the whole web are a much stronger signal. You have billions of clicks versus a hundred friends on Facebook – there is a different science involved in that.”
9. re: how varied people’s intents are: “… not everyone comes to Bing with a specific task in mind, sometimes they are in an exploration mode …”

Full Interview Transcript

Eric Enge: Can you provide a little background on yourself?

Andrew Shuman: I’ve been in Microsoft about nineteen years. I started in Outlook as a developer, and also spent some time with the MSN team. But, now I am at Bing, and it’s been by far my favorite job in Microsoft.

It’s a pretty unique place where you get to work with so many people of varied background in terms of linguistics, and statistics, and math, as well as computer science and that’s fairly fascinating. But then, the problems faced are so rich when you think about the fact that you can type in a three word search query, and we will show you several results.

But, from a user interface point of view and an application point of view, we are so far away from the joy of a traditional library where you get a sense of the volume of data around you and a sense of where you are within the system.

To help with that, we are constantly looking at different UI models; things like speech and touch that help create the feeling.

Eric Enge: Can you talk a little bit about the challenges of integrating social data and search?

… just because a friend of mine liked a restaurant doesn’t necessarily make it more relevant to me.

Andrew Shuman: One of the interesting areas I was thinking about a little bit this morning is the challenges of tying together the social signal with the other signals, and that it is a very interesting scale problem for us. It obviously is a very different signal to the relevance engine and to the user just because a friend of mine liked a restaurant doesn’t necessarily make it more relevant to me.

The thing that we’ve struggled with in the user interface is how to allow the user to decide if they want those opinions. We have started to play with UI models, and we implemented the Facebook annotations so you could digest the information on your own.

… when it’s a friend who likes a specific article that’s relevant, it’s incredibly useful.

But then, you run into some very interesting poor user experiences. Just because a friend likes Fox News doesn’t necessarily help me at all in my search results. Yet, when it’s a friend who likes a specific article that’s relevant, it’s incredibly useful.

There is also a very interesting scale problem there. As with traditional search, we have to query a massive numbers of servers, but then we also have to take my social graph and overlay that on the search results, modify them, and return this all in milliseconds. When you think about how we manage our performance and manage our ability to do the calls and the infrastructure that becomes a very interesting technical challenge for us.

We must have the right serving technology to map out the social graph of the user, and then overlay it on the web documents we got back. We may have crawled the document a while ago, but your friend only liked it a minute ago.

Eric Enge: So, if somebody liked it a minute ago, does that mean that you have to recheck the content of the document?

Andrew Shuman: We keep them separate so we have the document in the index, and then at run time we can do the matching of which users Like the documents.

You also have the case where all the people in Seattle seem to be Liking this document. That’s less about me (as a searcher) specifically. Some of those micro populations become very interesting.

Eric Enge: Let’s talk about user intent.

Andrew Shuman: This is a big area for us. We think a lot about how we can get beyond just the documents to more of the underlying tasks and start to make sure that a segment of tasks really lights up for a user. I am not just searching for a restaurant, but I am planning a whole night, and what those things mean for a user, how we can bring together the data in a related fashion.

This creates a new kind of linkage between objects that goes beyond the link graph or the social graph.

That’s where the ideas of entities underlying the data is really interesting so that I understand that this document references this restaurant, and this restaurant is a first class thing in our system. You can also understand what things are related to that. A restaurant is related to movie theatres that are nearby, right. This creates a new kind of linkage between objects that goes beyond the link graph or the social graph.

Eric Enge: So someone searches on “Seattle Italian Restaurant”. They book something on Open Table, and when they search for a movie you show them the movie theatre close to the restaurant?

Andrew Shuman: Yes, but there are different usage scenarios and this is the struggle we had. Some people just want the website of the restaurant.

But, it’s nice to also allow them to explore, so, in a way we would like to get beyond the basic results without the user even having to type anything more, especially on tablet devices, where typing is such a pain in the butt.

It would be really nice that as you just get to restaurant intent, the related intents are there in part of the experience so that as I mouse over or hover over or whatever we come up with it’s probably just another tab then. I can see the related things, I can find the movies; I can do things very natively without having to do very much.

Eric Enge: How do you approach that?

Andrew Shuman: One approach is bottom’s up – what do the clicks on a query show us. Given that a user typed in a restaurant, and then clicked on a restaurant, and then typed in a movie, and clicked on a movie, we might not even care what those things were. We just know that this query, and this site, we are all related enough of a population that it could be golf courses and golf balls.

It could any kinds of related things. We just recognize this through session analysis, and that’s a very powerful mechanism although you can’t maybe delight the user as much with that. You can’t say that it’s a thing that’s related in a certain way and set the user expectation the right way.

We can recognize specific user scenarios and deliberately implement a UI solution for that.

The other approach is top down, where we recognize common scenarios that will happen that we can treat as a traditional software project. We can recognize specific user scenarios and deliberately implement a UI solution for that.

Amazon has a very interesting model where they show you people who bought this product also bought these other products. They can remove the mystery about why you are seeing something, and we’ve struggled with how we can do something similar in search.

Eric Enge: A simple version of that is the related searches logic.

Andrew Shuman: Exactly, that’s a very bottom’s up example. We are thinking a lot about this. I would say none of us in any of these sites have done a very good job of this. We basically have the auto complete models, or the related search modules, or the “did you mean” modules.

Related searches is something where we think we can really experiment a lot more with the placement.

Related searches is something where we think we can really experiment a lot more with the placement. For some searches it may make sense to be more aggressive in the UI with that, as it may be more common for the user to perform follow on searches.

They will try more and more words when in fact less is often more useful because their intent is often too broaden, but it doesn’t come across.

People get really frustrated when searching when they are looking for things. They will try more and more words when in fact less is often more useful because their intent is often too broaden, but it doesn’t come across. That’s what related search is for.

Eric Enge: You could certainly see a scenario where for certain types of queries the related searches concept gets presented differently in the UI.

Andrew Shuman: Exactly. We do a little bit of this today. You’ll notice at times when we detect a result page that has more diverse click through, so it’s not a clear intent at all, or there is a high impediment. We show related searches right in line at the top.

Eric Enge: What would an example query be?

Andrew Shuman: Queries for places often have it.

Eric Enge: Do you see that happening in the case of related intent queries? Like the example you gave before of a search on “restaurant”, so perhaps you show some related searches for movies. This would be a class of anticipated intent queries.

Andrew Shuman: It’s a really good question. It’s a very hard UI problem to solve in a search engine. We are trying a lot of things around this because the related intent has to happen after your primary intent has been satisfied in many of those cases. We’ve already done some of this with follow-on queries. As you outlined before, right after you’ve found a restaurant in Downtown Bellevue, and you search on movies, we focus on Downtown Bellevue movies theatres.

One thing that I think we would like to play with a lot more is the idea of using different gestures or UI queues to do this (customize UI of the SERPs).

One thing that I think we would like to play with a lot more is the idea of using different gestures or UI queues to do this. For example, what does a scroll mean? It doesn’t necessarily mean you want to see more pages with this text on it.

We could show you more related search suggestions, or even show the results of those related searches on the page.

Eric Enge: You can also imagine a different experience UI where you are inviting people into more of a dashboard experience, such as planning my evening. Then you get a menu of options that shows up, such as a “things to do in Bellevue” box.

Andrew Shuman: Yes. I think the idea of letting you understand what we understand is a strong one.

You also have to differentiate a person who does the same search frequently, such as they like to search on Bellevue restaurants a lot. Perhaps the third result is always the restaurant they go to.

Eric Enge: It’s a complicated problem!

Andrew Shuman: It’s a problem we’ve had since we invented libraries, and we will continue to have it for a long time.

Eric Enge: What if someone searches for nursing schools, and you know from my Facebook profile that I have just graduated high school – you might not show the masters in nursing responses. You might focus on associate programs, or certificate programs. Do you see the potential to draw in other kinds of data sources like that?

Andrew Shuman: Yes, absolutely. There are some really interesting things you could do just based on demographics.

Eric Enge: Right. There is this notion of Bing having a contract with the user where they share that information with you.

Andrew Shuman: Right. It’s an interesting challenge though, because the more generic signals across the whole web are a much stronger signal. You have billions of clicks versus a hundred friends on Facebook – there is a different science involved in that.

Eric Enge: It gets back to just what percentage of audience is sending useful signals from social sites. You have your hundred friends that might be sending out signals that might help you with search for a restaurant in the local area.

Maybe I’ll ask them about it, maybe I’ll check it out, and you create some follow-on action. It gets a little more complex if you are looking for tips on some surgical procedure. You are unlikely to have a friend that had that same procedure.

Do you see any major UX changes coming in the near future?

The home page image with the mouseovers we created had a great response from users.

Andrew Shuman: We are always looking for new things. The home page image with the mouseovers we created had a great response from users. We are eager to do another turn of the crank and how we think of the search results page and really jump out to people again.

Eric Enge: People have really responded to the homepage image?

Andrew Shuman: Yes, we get endless nice feedback on that, it’s worked very well. The nice thing is it promotes the idea of exploration. People really are fascinated, not everyone comes to Bing with a specific task in mind, sometimes they are in an exploration mode, and they can click on the hotspots, they can learn more about the image, and go off on a whole educational or entertaining diversion.

Eric Enge: It’s a little bit serendipity. Have the home page videos worked well for that purpose?

Andrew Shuman: Yes, we had a huge bump in feedback of people loving it. There are a lot more Facebook Shares of the homepage since we started doing that. We only do the videos periodically so we don’t overdo it.

Eric Enge: Thanks Andrew!

Andrew Shuman: Thank you Eric!

Key Points from Interview with Rathna Sharad

The first person I met with during my week at Microsoft in January was Rathna Sharad. This was a great start because she set the tone for all of the interviews I did. In the discussion below you will see key insights into the thinking behind the overall adCenter strategy. Here are some of the other key points from the interview:

1. “The Web UI is good for a couple thousand keywords, maybe even up to ten thousand keywords in an account.”
2. “If you have tens of thousands of keywords, the adCenter Desktop Tool works very well.”
3. “Anyone that has hundreds of thousands of keywords, or more, is a great candidate for the API.”
4. adCenter strices to make all the same features available in the Web UI, the desktop tool, and via tha API.
5. adCenter keeps old version of the API alive for 6 to 9 months to allow people using it time to update their tools, but they do need to make the switchover.
6. Rathna indicates that there are no known latency issues with the API, but they recently did a desktop tool release to make that more efficient with very large campaigns.
7. Filtering functionality was added to the Web UI in late 2011. You can filter on any of the columns in the reports.
8. The adCenter team is working on features to make recommendations to advertisers based on their goals.
9. “Our marketplace is different; our consumers are different, the way they interact within the ecosystem is different.”
10. “What it (Microsoft Advertising Intelligence or MAI) does at a high level is really provide insights into the monetization aspect, which is adCenter, as well as the forecasting aspect which is based on historical performance. For example, here is what we think you should be bidding for mainline, sidebar, first page placement, those types of recommendations.”
11. One of the key capabilities in MAI is the keyword estimation capability, which shows traffic you can expect.
12. adCenter provides information on share of voice (aka impression share), including why it is your share is currently being limited.
13. The two major points of focus for adCenter are Return on Time Spent and industry standardization (parity with Adwords) to make it easier for advertisers to spend incremental ad budgets with adCenter.

Full Interview Transcript

Eric Enge: Can you tell us a little bit about yourself?

Rathna Sharad: My role currently is director of product management for demand. Essentially that I oversee the web UI, API, adCenter Desktop and the Microsoft Advertising Intelligence tool. I’ve been with Microsoft for almost six years now, and I’ve been working on search for that duration. The first two and a half years I was on the engineering side, so actually I’ve been through a lot of the products.

The latter half of the six years I’ve been on the product management team. Ultimately, I play a role in redefining our product strategy and the roadmap for advertisers. Prior to Microsoft I worked on transportation and supply chain solutions for UPS and freight forwarding companies.

The way I have my team organized is around the different areas that advertisers interact with. One of the product managers on my team is responsible for campaign management and the Web UI. We also have product managers that look after the adCenter Desktop tool as well as the Microsoft advertising intelligence tool which is basically around forecasting and prediction in terms of traffic and volumes, and also been in traffic estimation for specific keywords.

I also have people that take care of billing, and customer management, and agency management, capabilities within the platform. And, finally we have reporting, so essentially all of the recommendation engine as well as the reports generation if you will through the tools.

Eric Enge: Can you tell me a little bit about why people would choose the Web UI or the adCenter Desktop tool or the API?

Rathna Sharad: One factor is the size of their campaigns. The Web UI is good for a couple thousand keywords, maybe even up to ten thousand keywords in an account. Anything more than that, it gets a little tricky to navigate through it and find what you are looking for in a short amount of time.

We actually think about it as three different segments of advertisers. If you have tens of thousands of keywords, the adCenter Desktop Tool works very well. It’s more about offline management, and it provides easy filters. Even though we have that in the Web UI, it’s a lot faster when you bring it to your own adCenter Desktop, and then work on it.

When you get to APIs, it’s all about skill.

When you get to APIs, it’s all about skill. Anyone that has hundreds of thousands of keywords, or more, is a great candidate for the API. However, to use the API you need either to use a third party tool (such as Kenshoo, Marin, or Acquisio) or you have to have in-house development capability to be able to code against the APIs. With the adCenter Desktop Tool and the Web UI you don’t need to have those capabilities.

Eric Enge: What third-party tools do you work with?

Rathna Sharad: Most of the well known tools. We certainly work with Marin, Kenshoo, and Acquisio, but we also work with Efficient Frontier and a bunch of others.

Eric Enge: Are there additional capabilities if you get at the API level versus the adCenter Desktop versus the UI?

… we want to have feature parity across all the entry points …

Rathna Sharad: Our philosophy is that we want to have feature parity across all the entry points as we call it. So, it’s really saying that if there is a platform capability we want to make those features available to all of the segments of advertisers that are accessing through any of those entry points or all of those entry points.

Some advertisers obviously do one or more. So they may use the API, the desktop tool, and the Web UI. For that reason, it’s important to maintain consistency across the board. The challenge we sometimes have is around the timing. For example, when we update the API, we certainly don’t want to do that without getting advance notice to advertisers – because those changes impact their code and product.

If we deploy those changes without giving people dependent on the API advance warning we might break their tools. We try not to do that and stick to a cadence for API releases. We also keep the older version of the API alive for 6 to 9 months to give them time to respond.

During that time our customers should migrate to the new version. The other consideration we have is that sometimes we test pilots. We don’t really deploy the changes through the adCenter Desktop or the API until it has been validated because we tend to learn from advertisers and learn from the marketplace performance, make changes and tweak it along the way. So, we wait until it’s a valid product before we actually deploy it through APIs.

That said, the goal is to have parity as quickly as possible across all of them.

Eric Enge: That makes sense, as the tool providers have a lot of enterprise customers themselves, and breaking their tools would result in a lot of upset people.

Rathna Sharad: Yes. We keep our tool providers and large enterprise users of the API up-to-date on what to expect. We try to do these massive changes once a year, but we have, our planning process is basically we are in an agile cycle where we have monthly releases for adCenter.

Eric Enge: Are their latency issues with the API? For example, if you are dealing with a million keywords and making massive changes on a daily basis, will you run into delays?

… the APIs should be able to handle millions of keywords, it is what they are designed for.

Rathna Sharad: No, the APIs should be able to handle millions of keywords, it is what they are designed for. We monitor this closely to ensure that there are no performance issues. If anyone does experience such issues, we certainly would work with them to see if they are using the APIs correctly.

Where we are working on something in terms of performance, especially for the million keywords scenario, is on the adCenter Desktop. The latest release that we did on the adCenter Desktop was to allow for the million keyword scenario. We did not handle that very well in the past releases of the adCenter Desktop. It was more designed for smaller datasets.

Eric Enge: Even if they use the API, some people are still going to want to tinker with their accounts using adCenter Desktop.

Rathna Sharad: Absolutely. As of the last refresh of the adCenter Desktop tool, we believe it’s a lot faster for large keyword sets now.

With the latest refresh of the Web UI that went out just prior to the holidays, we added the ability to filter. So, prior to the update you had to navigate into every single ad group to see the keywords. You couldn’t, for example, look at all similar keywords across an account or across multiple campaigns.

Now, you can actually do that, but it could mean a really large dataset. And so, to eliminate performance issues there, we have a filter option just like if you were to go into Outlook and have a set of filters. Our filtering criterion allows you to narrow it down to just the set of keywords you want to look at.

Eric Enge: What type of filters are available?

Rathna Sharad: Any of the columns that you have for your keyword view or your campaign view in the UI are available, it depends on what you are looking for. You could filter based on budget, performance metrics, ROI, or bids, match type, or any of a large number of metrics.

Eric Enge: Or every keyword that contains a certain phrase in it?

Rathna Sharad: Yes. This has been around for at least two years. As a search option in adCenter where you can just go search for a phrase, and it will give you all the keywords, the ads, and campaign names that have that phrase in it, and that can act as a filter.

Eric Enge: This is useful because some companies are budget-constrained rather than ROI-constrained. For example, you may have a public company that is assigning revenue values based on a year’s worth of revenue from a customer, but they are a public company so they report quarterly. In this scenario, they have to manage by budget, right not just ROI.

Eric’s note: As you can see in the image below, if you are willing to accept 200% ROI on your PPC campaign when looking at it annually, but want 100% ROI on a quarterly basis, the two are incompatible. The 200% annual goal results in lousy ROI for the quarter – something that is not acceptable in public companies.

Eric’s note: In the following image you can see that we need to adjust the ROI to 700% for the annually based mearurement of campaign ROI to hit the quarterly goal on this scenario.

Eric’s note: The final note is that the other way to make this work is to run the PPC campaign at 200% and just restrict the budget enough that the ROI on the rest of your marketing efforts can pull the net result up to your 100% target on a quarterly basis. This is what I meant by saying that some campaigns are budget constrained rather than ROI constrained – you can only spend so much at the target ROI because spending more messes up the quarterly results.

What they would like to be able to do is pull down their high ROI keywords, when they are trying to increase spend and up the bids for those keywords. When it’s time to ramp down the spend, they would like to access the lower ROI keywords and cut their spend on those, or delete the words entirely. The result is that each budget adjustment should arguably increase overall ROI.

We are also investing in an area that we call opportunities. This is the recommendations part of adCenter.

Rathna Sharad: That’s exactly what the filtering is designed to allow. We are also investing in an area that we call opportunities. This is the recommendations part of adCenter. We think we have a lot of work to do in that area, but we are working hard on what we can recommend to advertisers based on their goals? They may be managed through a budget, it a CPA, or managed to bids.

We plan to make recommendations and then allow the advertiser to approve them or not as they see fit. We are going to expose this through APIs as well. It is not necessarily just a bid management tool. It is about the recommendations of what you can do to improve your campaign as a whole.

There are a lot of different things that we know about how their campaign is performing, and where they could be in terms of opportunity versus share. We are really trying to make it easier by surfacing those recommendations via the API for tool providers to use that they want to take those recommendations as well, or for a lot of those smaller advertisers just go into the UI and they can get recommendations as well.

We find that the optimizations aren’t really the same across engines, and doing the same kind of optimization for specific campaigns that are not within your own account is not a great strategy.

Eric Enge: Right, even if the tools were identical, the customer in the market is not.

It’s really about ROI on time spent, that’s our mantra around here now.

Rathna Sharad: Absolutely. Our marketplace is different; our consumers are different, the way they interact within the ecosystem is different. There are lots of dynamics that we need to tune for, but we also know that given that we are not the leader in the market, folks are strapped for time. We want to make the limited time that they have for adCenter the most valuable. It’s really about ROI on time spent, that’s our mantra around here now.

We’ve heard the demand for that from our advertisers, and we are trying to take that to heart. Every time we do things whether it’s API, the UI, or the adCenter Desktop, we really are thinking about how we can make things simpler.

Eric Enge: Can you talk a little bit about the intelligence tools?

Rathna Sharad: We have what we call the Microsoft Advertising Intelligence tool or MAI. It is an Excel add in tool that’s for analysts and marketers to really understand forecasting. We are leveraging a lot of the great capabilities of Excel that’s already built in and surfacing key insights from the combined marketplace through that tool.

What it does at a high level is really provide insights into the monetization aspect, which is adCenter, as well as the forecasting aspect which is based on historical performance. For example, here is what we think you should be bidding for mainline, sidebar, first page placement, those types of recommendations.

Eric Enge: Just to clarify, by mainline, you mean the ads that appear above the organic results?

Rathna Sharad: Yes, that is correct. MAI also gives you insight into traffic estimation. So, looking at Bing and Yahoo combined estimates in terms of forecasting for a specific keyword. In addition, for a match type, what can you expect?

What we are also doing is exposing a lot of those capabilities through the web interface, the APIs or the adCenter Desktop, but the Excel tool is really the tool we believe most people will use since they like to work in Excel anyway. We expect multiple different ways of slicing and dicing keywords to become available to the advertisers.

Eric Enge: How much of this will be available through the adCenter Desktop or through the UI?

Rathna Sharad: We have some basic capabilities that are available through UI and adCenter Desktop. One of the aspects that we expose today is the keyword expansion. So, use a set of seed keywords and get an expanded set of keywords. Advertisers can then decide which of those keywords they want to incorporate in their adCenter campaigns.

Eric Enge: The Excel version is obviously deeper and richer. What are some of the additional things that you get with that?

Rathna Sharad: You can also do additional pivots like targeting. So, if you want to filter by geography, by demographic data, you can do all of that. You can also customize the recommendations in terms of the size of datasets, is it more specific to match types, do you just want mainline or do you just want sidebar bids? So, those types of options are much easier in Excel as well.

Eric Enge: Do you also provide some visibility on how big a difference is to go from the right rail to the top?

Rathna Sharad: Yes, we do. You can also see that if you were to spend a dollar more, how that would affect your position, and sometimes you may find that between position three and four (for example) there isn’t that much of a difference.

What we are also finding is that very often when you go run a report, there are a bunch of things that you do to run a report, and then you have to look at the data, and then going back and making any changes based off of the report is really disjointed.

We are trying to provide as much transparency into the platform as possible so that advertisers can make the right decisions.

We really are working towards ease of use in the area. And, the other investment within reporting is the types of data that we are exposing to advertisers. We are trying to provide as much transparency into the platform as possible so that advertisers can make the right decisions. A great example of that is the share of voice.

Eric Enge: By share of voice, if you’ve got an ad with the potential to be shown ten thousand times, how many times do you show up?

Rathna Sharad: Yes, but in addition, we give insight into why is your share limited. Is it because of budget, is it because of rank, is it quality score, or is it because of other factors?

Eric Enge: For many advertisers increasing share of voice may actually be the biggest opportunity to increase traffic. They don’t actually realize that they are actually not showing 100% of the time. They are busily adding into tons of new keywords, when they might gain more just by addressing share of voice.

Rathna Sharad: So, more investment in that area in terms of trying to get more granular, more specific, providing more transparency so that they know what’s happening with their account.

Eric Enge: Let’s step back a little bit, where is most of your organizations’ mindshare going?

Rathna Sharad: First and foremost, my team thinks about return on time spent. How can we make sure that the investment in adCenter has the biggest ROI for advertisers? To do that we focus on making things a lot simpler and making innovative changes that allow for advertisers to spend less time and get more value-add with us.

The second area we focus on is what I would call industry standardization. In the past we did things a different way in adCenter and the industry standard was different, and that’s caused friction. One example of that would be negative keyword union. We are moving towards addressing that request from advertisers.

Another example is the lowest granularity we had in the system was the keyword, whereas we know that advertisers want to be able to exercise control at the match type level. For example, being able to have a specific URL at the match type level and having that as the lowest granular entity. We are trying to address those types of things so that we don’t cause any friction for advertisers in trying to adopt adCenter.

Eric Enge: As a result, for example, advertisers don’t have to rethink their entire negative keyword strategy.

Rathna Sharad: Correct. The third area that we are focusing on which is a big one for us is the international plan. We are currently in North America, and we have an international roadmap to be in thirty plus countries in the next eighteen months to two years. As a result, we are looking at how we take the platform global.

This includes localization, globalization, and rolling out to those markets. Those are some very big areas that we are focusing on. Another large area is ad format innovation. This may be deep links, images, videos, and other things of that type. We have Bing travel, Bing shopping, Bing core results, and we really are looking at what are the different ad formats would be make sense.

Eric Enge: Do you see any level of social integration as well?

Rathna Sharad: That is an area that we are exploring, but I don’t know that there is anything definitive at this point.

Eric Enge: What about local search?

Rathna Sharad: We are very focused on providing much more relevant results when that is appropriate.

Eric Enge: Right. If I search on Peet’s Coffee, I should get one that’s round the corner from here, not one in Seattle.

Rathna Sharad: Yes. That’s the relevance aspect in terms of algorithmically you want to get the most relevant result. But also, think about what is it that I want to see in the ad, something, such as the phone number; links to a picture of the store, other things that would make that ad much more useful for the consumer. Or you could even enter your zip code in the ad and do a search those types of things.

Eric Enge: Thanks Rathna!

Rathna Sharad: Yes, thank you!

Key Points from Interview with Paul Yiu

Tons of discussion in here about how social media can impact search results. While the author authority discussion is near the end I am going to highlight it first here (Twitter was the specific social network discussed), because there is a LOT of confirmation of how author authority works:

1. Bing does look at the number of followers you have.
2. They also look at the number of people you follow. If you follow 200 people and 8,000 follow you this might indicate more authority than if you follow 9,000 people and 8,000 follow you.
3. “We can actually analyze the follow graph and tell if you are trying to game the system.”
4. Relevance of the followers is used as a signal: “who you are connected to says something about you. You don’t want to get into the wrong crowd; It’s not good if you hang out with the bad group at the high school.”
5. The relevance of who you follow also matters.
6. You don’t hurt your Twitter stream by talking about irrelevant stuff. What matters more is what happens to your relevant stuff.
7. Retweeting patterns are tracked and used as a signal – especially for your relevant tweets.
8. The relevance of the re-tweeters matters too.
9. There are many iterations if signals that oculd be tracked, but as you get deeper and deeper into it the strength of the signal diminishes, so a limited number of factors (such as those above) are considered.

Here are some of the other key points from the interview:

1. (re: social media): “The behind the scenes signals are pretty useful for us, as search engines always need to find fresh content, and it’s always hard to rank fresh content.”
2. “We are trying to merge a little bit of the search and browsing intent into one, and have your friends help you navigate the web a little bit better. In a way we are bringing the office water cooler to the search engine.” (the emphasis is mine).
3. (regarding using “wisdom of the crowd” to move content higher in the results): “… people tend to like gossipy things, such as who got pregnant or was in a scandal, or something like that, so it tends to work in those cases, but not so much in the case of navigational searches …”
4. “If the content doesn’t earn its spot its placement gets modified” (confirming again that Bing uses CTR which was done for the first time in this interview with Duane Forrester.
5. As of February 22, 2012 users could associate articles of interest with people they know (the “subject” person). The subject can then use Facebook to decide if they want that article associated with them or not. If they let it be associated that article will now be highlighted in the search results for friends of the subject.
6. Social media can provide some useful enhancements to search, but currently is not in danger of reshaping the structure of search (my paraphrase of a conversation below).
7. Bing currently does not analyze Facebook updates to collect information that could be used to personalize search results. For one thing, there are serious privacy concerns with this.
8. “The typical network on Twitter has characteristics that are hard for people to emulate artificially. These (artifical networks) are unnatural, and when we see networks like this you can tell these people are trying to sell teeth whitening or whatever.”
9. “… when you say stuff where people tend to re-tweet you it behaves bit like a link.”
10. The level of effort to make a social media action affects the signal strength. For example, a Like is very little effort, and a Share requires a bit more effort.

Full Interview Transcript

Eric Enge: Can you start off with an overview of Bing and social to date?

Paul Yiu: Over two years ago we went down this path of integrating Twitter into search. Much of what we’ve done with Twitter is actually really interesting even though you may not visibly see everything. Here is what the UI looks like.

We use Twitter and public Facebook information to improve our search engine. You can see a list of topics that are trending and based on the topic what are the links people are showing on the web, and what are the updates. It could be a tweet, or a Facebook update.

The behind the scenes signals are pretty useful for us, as search engines always need to find fresh content, and it’s always hard to rank fresh content. When I worked at Yahoo three years ago, we were pretty happy if a user searched for up to the date news, such as an earthquake, and they got the right page back within a day of the event. Now, users are a lot more demanding. They want up to the minute accurate information, and we use Twitter and Facebook to help us provide that.

It really helps us in terms of finding pages that people are sharing out there, and getting the right content and even ranking. Recently, there was a premier league championship game and someone kicked this great goal. Without social signals, we would have likely shown more of the facts of who won the game. But, with social signals we were able to show the video of that really beautiful shot that everybody on the web was sharing. Because of these signals, our results can better reflect what people are doing on the web.

So today, the algorithm is flavored by people.

So today, the algorithm is flavored by people. That was our first step. As we worked more with Facebook the conversation became more and more interesting. We came up with some neat ideas to make search a little more personalized. What if you could see what your friends are paying attention to when you are searching? Just as if you are having a conversation in a coffee shop, and someone walks in, the natural tendency is all of us will look. We want to mimic that in search. We started a project a year and a half ago that we called Sergeant Pepper, because the idea was that you want to get by with a little help from your friends (that was one of the songs on the Sergeant Pepper album by the Beatles).

Suppose you search for YouTube. One guess might be that your likely intent is to check out cool stuff to look at when you get there. But maybe I have a friend in Indonesia who shared this other link on YouTube, and maybe we should let you know that. We are trying to merge a little bit of the search and browsing intent into one, and have your friends help you navigate the web a little bit better. In a way we are bringing the office water cooler to the search engine.

Eric Enge: A few months back I did a search on the New York Post, and what showed up was a wisdom of the crowd based result which was the content that had received the most likes on your post.

I believe that Bing did this because I didn’t actually have a direct friend that had liked something, so it was trying to show me the most popular content on the site. However, that no longer happens when I do that search now.

Paul Yiu: Yes, you can still see those types of results, but we did some tuning of the way that works.

In some cases people really engaged in those types of results, so we still show those, and in others they did not, so we stopped showing it in those cases. For example, people tend to like gossipy things, such as who got pregnant or was in a scandal, or something like that, so it tends to work in those cases, but not so much in the case of navigational searches as in your example.

Eric Enge: So, in the case of the New York Post, people aren’t really as interested in the most liked articles.

If the content doesn’t earn its spot its placement gets modified.

Paul Yiu: Yes. The way we think about it is not different from any other search engine. If the content doesn’t earn its spot its placement gets modified.

Let’s look at a case where the intent is a little more specific, such as a search on “family-friendly hotels in Maui”.

I am going to Maui in two weeks. Some of our friends liked some family-friendly hotels. The intent here is for people to help me make decisions where all these results are great, but maybe my take of the web is different from your take of the web like my friends are, I’ve got lots of friends who are parents.

Another scenario is when people search on their own name. Now, why is that interesting? Well, people actually do search for themselves online quite a bit for our sense of vanity. When I do that on my name there is this cool math professor that comes up, that’s definitely not me. So, one of the things we started thinking about is wouldn’t it be cool if I can actually alter this result for me and maybe my friends who likely are looking for the Paul Yiu that works on Bing. Then there is Harry Shum, he is my boss, and he shares a name with the guy on Glee.

On February 22, 2012 we released a way to improve those results for yourself.

You get a mixture of the one that’s the PhD that’s with Microsoft, you get one that’s a great dancer, that’s younger, and handsomer, and prettier. So then, how do we make this a little better? On February 22, 2012 we released a way to improve those results for yourself.

Let’s say I search on Harry Shum, and I see the Glee guy, But I also see that the one I know won an award as an Asian-American exec of the year. The Harry Shum I know is not going to share it on Facebook, because he is a humble guy. What I can do is link that to him.

Now, the idea here is as soon as I do that that it actually links over to Facebook and it shows up in his timeline. Facebook will tell him that I linked him to this article, or Harry has the choice to indicate if he prefers to still not share that with people, or he can just let it be. If he lets it be, any of us that know Harry Shum, and search on his name will get this particular link.

As more friends do this, the more we can make this search result page about him.

As more friends do this, the more we can make this search result page about him. The Glee guy, or his buddies, can also pick and choose things that they think make him shine as well. For his friends, his search results would be about the Glee guy, and stuff more about him. This helps people find information on their friends more quickly. We are hoping this will change the way people do people search.

Eric Enge: This is good stuff. It’s a great example of what you can do with social data. Do you have another example?

Paul Yiu: Yes, Bing knows that Sean and I are friends on Facebook, for example.

As you can see the Scott I know gets premium placement. In fact, friends, and friends of friends will get a special treatment right now.

We can do that because we are pretty confident. When you go beyond that, the signal gets a little weaker.

It’s really interesting when you stumble across that person that you knew in high school, that you haven’t seen for a long time, and you end up friending him/her on Facebook, and then you wonder what they have been up to. We can help you find that out quickly.

Paul Yiu: Exactly. Harry’s award result might have ranked #15 in the results naturally, because many of the results are being taken up by the Glee guy. But now, for Harry, and his friends something that would have been ranked at #15 will show up higher.

Eric Enge: Of course, sometimes you really do mean the guy from Glee, even though you know Harry at Microsoft as well.

… we limit the number of items that can be promoted this way to three.

Paul Yiu: You are totally right. How we handle that is that we limit the number of items that can be promoted this way to three.

Eric Enge: Let’s talk about the richness of the dataset. Obviously, there is enormous amount of data available, but it seems to me that we have 50% of the US population on Facebook, which means that 50% are not on it. Of those that are on it, it’s probably 10% that are very active. Perhaps 20% dabble and contribute content or Likes from time to time, and 70% who do more or less nothing at least in terms of sharing content or Liking things.

Paul Yiu: I would have to ask Facebook that question. The reason I say that is we have by and large the publicly shared information on Facebook, so your Likes and your public shares and your profile information, and your friends. We don’t have all the private stuff, such as the things I only shared with my mom. So, it’s hard for us to gage whether or not it’s ten, twenty, or seventy.

However, Twitter publishes data like that, and a tiny percentage of people on Twitter account for lot of the huge chunk of content.

Eric Enge: What I am getting at is that the limited data does require you to look for different scenarios where you can leverage the data to tell you something unique and useful. Social media is not in danger of replacing web search algorithms just yet. What you can do is leverage Facebook to provide you with enhancements to the search results. Does that make sense?

Paul Yiu: Yes.

Eric Enge: One interesting scenario is when your friends Like some content. Let’s say you are looking for a restaurant in the Bellevue area. The search results might show me that my friend Liked a particular Italian restaurant, and maybe I’ll ask him what it’s about.

Paul Yiu: That’s a great example. I live in California all the guys I know that live around here have restaurants they have Liked, and it’s super helpful. News is an interesting example because it offers a hybrid between search and browse. A friend of mine Liked this article in New York Times on decision fatigue, how we get worn out by the end of the day. When I searched for the New York Times I saw that, and it’s what he paid attention to.

In a way it’s like the water cooler was brought to my search result page. That’s a hybrid between search and browse that can be super helpful.

Another is travel and shopping because. If I go on vacation and have a good experience, we may indicate that we Like the hotels and places to go. Perhaps for family-friendly hotels in Maui.

Of course, how this adds value to you depends on what your friends actually do.

Doing this in search mimics what we do in the non-search world. Maybe there are things you happen to want to ask your friends about. Of course, how this adds value to you depends on what your friends actually do.

My friends are mostly nerds. When these crazy thin ultra notebooks were all the rage, I searched for that and I saw a lot of recommendations right in the search results, so that was really cool for me.

If I were in the alternative music scene, I might see different things. So, it depends on what my friends are into. A lot of my friends are in tech, so that influences my queries. Sports queries are interesting too. I may want more than the super unbiased ESPN article about the big game. I may want to know from the Alabama perspective what was great about the game.

This all acts to tweak the web to give it a little more flavor and personality.

Eric Enge: You probably serve the unbiased article and the Stanford article.

Paul Yiu: Yes, you still get the regular news.

Eric Enge: What if someone types in “hotels” and you see that they’ve been, talking to their friends about going to Rome in Facebook updates or Twitter tweets. Is that a possible source of data at some point you think?

Microsoft is super careful from the privacy and user control perspective.

Paul Yiu: Not currently. We do something based on your IP address if you search on things like “restaurants”, we try to find restaurants that are near your location. We haven’t done what you are suggesting yet. It would be interesting to do, but Microsoft is super careful from the privacy and user control perspective. We would have to be super transparent about that, and even then, there might be many people who object about the way their data was being used.

Eric Enge: It really does get down to all of these scenarios. It speaks to how complex the problem is because you have all these data sources and Facebook is a disjoint data source from search data, and, you are trying to find points of intersection. It will probably be a systematic process that will unfold over many years. It will also evolve as the younger generation gets older and more people are engaged with the social sites.

Paul Yiu: Yes, there is a long way to go. We’ve been at this just for a couple of years, and Google just got started.

Eric Enge: What about the challenges that you have with the scale of the data. You have this enormous scaling problem with having a search engine, and, now you have this other enormous database of social information.

Paul Yiu: Yes, it’s hard. When we did it with Twitter it was really challenging because the whole idea of Twitter is being fresh. You can’t wait for hours for a Tweet to make its way onto our search result page. So, that really pushed us.

One of the advantages we have working at a company that believes in search like Microsoft is that we have really good infrastructure and a really good platform.

As a result, we have built the right infrastructure for this. When you create a Tweet, or a Facebook update, we want to reflect that in our system very, very quickly. We did not have to build a whole lot of new infrastructure, as Bing already knows how to index content and serve it and rank it very quickly.

In addition, how you think about ranking is also different. But fortunately, we work for a company that believes in search. There are a lot of people here that we can collaborate with to make this thing happen.

Author Authority

Eric Enge:Can you outline a bit about how you determine the value of a tweet?

One metric is how many people follow you, but that can be gamed a little bit. We can actually analyze the follow graph and tell if you are trying to game the system, because your network on Twitter looks disjointed.

The typical network on Twitter has characteristics that are hard for people to emulate artificially. These are unnatural, and when we see networks like this you can tell these people are trying to sell teeth whitening or whatever.

We look at the way people are connected, and often we correlate that to the quality of a Tweet.

We look at the way people are connected, and often we correlate that to the quality of a Tweet. We can also analyze the content the Twitter account links to. What does that mix look like, and how do people interact with the content you are tweeting. That’s just on the Twitter side of things.

On the Facebook side in a way we are still working on it; with Facebook most of the time it’s your true identity. On Facebook right now is just stuff from your friends, so it’s a different problem.

Eric Enge: One example additional metric for Twitter is an account with 8,000 followers, but they follow 9,000 people, that looks like an awful lot of swapped following. In contrast you have another account where someone has 8,000 followers and only follows 300.

Paul Yiu: Yes. We look at that.

Eric Enge: What about the relevance of the followers?

… who you are connected to says something about you.

Paul Yiu: Yes that matters too. Just like it is in the real world, who you are connected to says something about you. You don’t want to get into the wrong crowd; It’s not good if you hang out with the bad group at the high school.

Eric Enge: I suspect their history of tweeting matters too. Maybe they have a history of tweeting about country music singers, and so it would matter that they have a good following and they only follow relevant people themselves.

Paul Yiu: Correct.

Eric Enge: What about ReTweets?

Paul Yiu: Yes. We actually look at how often those tweets make their way beyond just you. That seems to be a good signal.

Eric Enge: So, you don’t hurt yourself if 80% of your tweets are talking about what you had for lunch today, and those are just more or less ignored as long as the ones you do about country music get a lot of action on average.

Paul Yiu: That’s right. You can talk about the weather all day long.

Eric Enge: I assume the authority and the relevance of the people who are doing the re-tweeting matters too?

Paul Yiu: Yes, there are almost an infinite number of things you can consider.

Eric Enge: This kind of thing goes on and on. Just like standing in front of a mirror with another mirror directly behind you. The eighth mirror doesn’t matter so much, right?

Paul Yiu: That’s a great analogy the mirror in mirror thing.

Eric Enge: Ultimately, it matters that you have good followers, that you follow good things, that you tweet relevant things to a topic area and get a good response from people who are relevant themselves, right?

Paul Yiu: Yes.

Eric Enge: It’s all self-reinforcing.

… when you say stuff where people tend to re-tweet you it behaves a bit like a link.

Paul Yiu: Yes. You don’t want to be connected to a bunch of junk, and when you say stuff where people tend to re-tweet you it behaves a bit like a link.

Eric Enge: I also believe that the level of effort in the action matters. So, clicking on a Like button does indicate interest, but it’s a very low level of effort.

Paul Yiu: That’s right, it’s easy to Like something.

Eric Enge: Compared to a share, which is more effort. Then there are links implemented to your web pages from other web pages. These still carry more weight because you actually have to own and operate a website.

Paul Yiu: Yes, that’s right.

Eric Enge: Of course, this assumes all other factors are equal and I understand there are a lot of moving parts in that conversation.

Paul Yiu: Yes. That’s right.

Eric Enge: Thanks Paul!

Paul Yiu: Thank you Eric!

Prior to Google, Frederick was an engineer at Sapient and a part-time wedding photographer who found new customers through AdWords. He joined Google in 2002 to help bring AdWords to the Dutch and Belgian markets. He earned his B.S. degree in electrical engineering from Stanford University in 2000.

Key Points from Interview with Frederick Vallaeys

1. ValueTrack is the AdWords feature that allows advertisers to tag their URLs with parameters. The resulting URL can then be used within the advertiser’s own tracking systems.
2. Too many advertisers settle for global level reporting and do not look further. Even if your top level metrics are OK, you can still get great gains in overall campaign performance by digging into more detailed reports.
3. Segmentation is the biggest power reporting feature that is not used by many advertisers.
4. Types of segmentation can include times of day, days of week, device type, social signals, and more.
5. (Fred): “… no matter from which channel the +1 comes in, it all aggregates at the URL level.”
6. (Fred): “In the social segmentation, you can actually see what the impact is of having each of these different variations.”
7. You can run multiple segments via the downloadable reports or the API.
8. (Fred): “… at the end of 2011, half of American consumers had a Smartphone in their pocket.”
9. Google has a site at howtogomo.com that you can use to see how your site renders on a mobile device.
10. (Fred): “Google Analytics offers multichannel funnels, and what these allow you to do is see what touch points people have with your online campaigns before a conversion happens.”
11. (Fred): “One tool that we have is the AdWords Campaign Experiments. That’s a great way for an advertiser to explore how to improve their ROI. They can send 10%, 20%, 30%, whatever percentage they want of their traffic to that experiment.”
12. (Fred): These (new ad formats) were a big thing for us in 2011, and will continue to be a big thing in 2012.
13. The Bid Simulator tool will show you what to expect for different types of increases (or decreases) in bids.
14. The Ad Preview Tool allows you to see whether or not your ads are running. It also allows you to test geotargeting in areas other than your current location, or various types of mobile devices.
15. Top of Page bid estimates show you what your bid would have to be to show up in the space above the organic results.
16. Impression share is a way to see what percentage of the time your ads are running. Tuning your campaign to increase impression share can be one of the best ways to get additional traffic.
17. Google Analytics is planning to expand its social reporting to include more than just the data from Google owned properties – i.e. data such as Facebook Likes.

Full Interview Transcript

Eric Enge: Can you tell me some great power reporting features in the AdWords interface that people rarely use?

Frederick Vallaeys: When you look at AdWords, there are three high-level types of reports that we make available for our customers. You can go into the campaign management interface and pull reports right there in your campaigns. Then, we also have Google Analytics which goes a little bit deeper into some of the data, for example, with real-time reports, social reports and cross-channel reports that look at how ALL your campaigns are contributing to your success and your ROI. The third one is making reporting available for people who prefer using APIs or building their own reporting systems using our URL tagging feature, ValueTrack.

That is a way for us to attach some additional information to each click that comes to your website so that your own reporting software can capture that and then process it. If you look specifically at what is available in the AdWords interface, it’s really gotten very sophisticated in terms of segmentation. And, I think one of the biggest mistakes that advertisers make is they look at their reports at too high a level.

There are probably all of these micro-segments within your campaign where things are performing fantastically well …

There are probably all of these micro-segments within your campaign where things are performing fantastically well, but you don’t know it because you looked at things are an aggregated level. On the flip side you also have elements of your campaign that just aren’t working well. Examples of segments that you could be looking at are the specific time of the day, and the specific day of the week. You may for some reason find people just aren’t buying your product at certain times of day or days of the week.

Eric Enge: What are some of the other segments you offer have?

Frederick Vallaeys: One of my favorite ones is the social segments. When we introduced Google+, part of that was the +1 button, and now the +1 button will show up next to ads. +1s are being collected from the ads, from the organic results, but also from having the +1 button on your own website.

… no matter from which channel the +1 comes in, it all aggregates at the URL level …

Eric Enge: The +1 is associated with a web page, and not the ad or organic results, isn’t that right?

Frederick Vallaeys: Exactly, no matter from which channel the +1 comes in, it all aggregates at the URL level, and will show up in any channel where the consumer is then looking for your business. As an advertiser, when you put the +1 button on your site, you start getting some +1s, and now when somebody is looking for your business or your service, they see your ad as you would have seen it in the past but now there is also a count of +1s next to it.

It might indicate that Eric and five other friends have +1d this page, or, you have the more generic one where it just says 500 people have +1d this. In the social segmentation, you can actually see what the impact is of having each of these different variations.

If you knew that this could drive a lot more clicks, a lot more conversions, then you could make a bit of an effort within your company to get more +1s for those specific URLs …

As an advertiser, what you can start to see is that probably the most powerful results are the ones that have the personal recommendations, and you could start finding some URLs on your sites in your campaigns that you are advertising for, that don’t have a lot of these personal recommendations. If you knew that this could drive a lot more clicks, a lot more conversions, then you could make a bit of an effort within your company to get more +1s for those specific URLs that you have in your ad that need more of the personal +1 recommendations.

Eric Enge: Very cool. What are the sub-flavors that go into the social reports then?

Frederick Vallaeys: When we moved all of reporting into the campaign management interface, the whole notion that we had was to make it really easy for people to immediately act on the information that we show them. Just imagine your traditional campaign management page, it’s down to the ad group level, you can see the results for that. And any time horizon of course that you want as well. Ryan, is there anyway to run multiple segments at the same time?

Ryan:Ryan Voccola: Not in the UI, but you can do it with a downloaded report, or with the API where you would have to add on additional segments that will come out in the CSV export.

Frederick Vallaeys: So you can see how social is effecting your performance, but then correlate that to how certain times of day, or the day of the week, is also impacting your results. That’s where you really get small micro segments where you could start figuring out some pretty interesting things. I think most advertisers will stay at that first level of segmentation, because that’s really going to give you some pretty good returns and that’s also where they have enough data to make statistically sound decisions.

Eric Enge: You can also segment by device, right?

… at the end of 2011, half of American consumers had a Smartphone in their pocket.

Frederick Vallaeys: Yes, that’s a huge one right now because at the end of 2011, half of American consumers had a Smartphone in their pocket. There is a lot of web usage occurring on these mobile devices. One interesting thing we see is that mobile device usage really spikes early in the morning and late at night, so literally the first thing people do in the morning is take out their mobile devices and check their email or research something and it is also the last thing they do before they go to sleep.

As that behavior becomes more common and the usage numbers go up dramatically, it’s really important for an advertiser to look at how they are performing differently on these different devices. Your performance could vary based on whether or not searchers are on a Smartphone, tablet device, desktop or laptop. Perhaps you should make a mobile website to drive up your conversions.

One really cool tool that we launched a little while ago that maybe not many people know about is howtogomo.com. On that site we do an evaluation of people’s websites and how they would render on a mobile device. It’s a really easy for someone to see if somebody came to the site from a mobile phone, would it even make sense to them, would they be able to click the links or are they too small, or how does the page render on these smaller screen devices.

Eric Enge: Have you seen examples of people where there are drastic differences in time of day in terms of conversion rates?

Frederick Vallaeys: That is a little bit industry specific for the most part, but in the travel space in the morning people tend do a lot of research, and then during their lunch break they call their spouse or significant other, and in the afternoon you might see a little bit more booking behavior. Obviously, you do have to be careful with this because those clicks and those visitors that were doing research may be just as important to getting the final conversion.

… a large percentage of conversions involve multiple clicks.

You probably look at Google Analytics to see the multiple steps that happen in your conversions. For the past 10 years, we have been looking at last click conversion in the industry, but a large percentage of conversions involve multiple clicks. It is important to understand the whole cycle for your site.

You might see really generic searches in the beginning, and then as people start to figure out exactly what they want, they get to a very specific search, and maybe the last search they do is a branded search. But all of the searches before that are often really important in convincing that customer that your company is a player in this space, someone they could trust to do business with.

Eric Enge: Can you talk about that a little bit about the problem of attribution?

Frederick Vallaeys: Google Analytics offers multichannel funnels, and what these allow you to do is see what touch points people have with your online campaigns before a conversion happens. Before we had this, we could tell you which keywords assisted in terms of search campaigns. This takes that one step further and tracks display campaigns and social media, so you can follow customers as they go through the funnel of conversion. Maybe you have three touch points through the display network and then you have two different searches happening, and then they bought something.

Where it becomes challenging is you have to figure out how to assign value to each of these actions, as they are all involved in the conversion. You have to start modeling that for yourself, and you have to experiment with it to see what makes sense.

Eric Enge: For display ads you have this concept of a view through conversion, right?

Frederick Vallaeys: Yes, but what is more powerful here is we can start showing you how your typical person who converts saw your email marketing campaign first, then maybe they saw a tweet, then they saw your display ad three times, and then they did seven searches. You can actually see how all these events contribute to lead to that conversion. Maybe there are 500 people who took a path that was similar to that, and then there are other people who go directly to search because they know exactly what they want.

In the past, if you just looked at last click conversion, you would eliminate these keywords because they had never given you a conversion.

Now you have the data and now you can start figuring out why. If you were to cut out this list of keywords would that have an impact on your campaign? In the past, if you just looked at last click conversion, you would eliminate these keywords because they had never given you a conversion. That could be a big mistake, because maybe that is the keyword everybody always ends up searching, one search before they do the final one that at leads to the conversion. If you got rid of these searches, then people might not even realize your company existed or had this service available, and you wouldn’t get these last click conversion anymore.

Eric Enge: Unfortunately, there really is no science to how you attribute value across multiple clicks or views.

Frederick Vallaeys: Exactly, at some point maybe we will have some more insight into that, but for now the point is to give advertisers the data, and then they can start making decisions off of that.

Eric Enge: Can you talk about the experiments segmentation?

Frederick Vallaeys: One tool that we have is the AdWords Campaign Experiments. That’s a great way for an advertiser to explore how to improve their ROI. They can send 10%, 20%, 30%, whatever percentage they want of their traffic to that experiment. This shows up in your campaign reports, so you can see how the experiment compares to the rest of your campaign. If the experiment is not working well then turn it off and try a different variation.

Eric Enge: It is an A/B test mode you can setup right within the interface.

Frederick Vallaeys: Exactly, in the past if you wanted to experiment, you would take two weeks of traffic and do one thing and then the next two weeks do something else. But the problem with that is, you are not comparing apples to apples because there might be outside factors during those two different periods that caused the numbers to change. With Campaign Experiments, you can actually split your traffic so all of the experiment is happening at the same time as the control and you get much more reliable data about how your changes impact your ROI.

Eric Enge: What about some of the new ad formats?

… we have seen tremendous success with advertisers who run Sitelinks.

Frederick Vallaeys: These were a big thing for us in 2011, and will continue to be a big thing in 2012. For example, we have seen tremendous success with advertisers who run Sitelinks. These are the additional portal links that you can have in addition to your headline in your ad. In the reports, you can segment on that so you can see how many clicks did you got from headline clicks and how many from your Sitelinks.

This will prove the value for the majority of advertisers and we have seen that these Sitelinks actually do work and have good click through rates and good conversion rates. You can start seeing how much of an impact this is causing and for those campaigns where you’re not using it, how much you are potentially losing as a result.

Eric Enge: Can you talk a bit about the bid simulator?

Frederick Vallaeys: It takes historical auction data and if you have bid x amount of dollars or y amount of dollars, where would you have come out in terms of the typical ad rank and what would that have done for your CTR and the number of clicks that you would’ve gotten. Instead of having to do an experiment and changing your bids around to get that data, we take whatever new number you put in and we run it against the past auction data, and model what would’ve happened in those cases. If you go from bidding a dollar for a click to a dollar fifty, is that going to give you a significant increase in the number of clicks.

What you can figure out from this is your incremental cost per click. Incremental cost per click is a number, by the way, that too few advertisers understand and leverage. And basically, the notion of incremental cost per click is simple. It is the cost of the incremental clicks I get by bidding higher. When you know this number, you can figure if the additional clicks that resulted from an increased bid cost more than what it was worth or does it cost me less. The problem is that most people when they look at an AdWords account only look at the big picture.

If you look at an average, what you are not seeing is how did that increase in my bids change the cost of individual clicks. So on average, you might still be under your desired cost per click to meet your ROI goal, but what you are not seeing in that average is the fact that your last ten clicks, the additional ten clicks that you got by bidding higher, actually cost you $2 per click, higher than the $1.50 average, and maybe $1.50 is the maximum you can afford to spend for a click to still be profitable.

Eric Enge: Can you talk about the Ad Preview Tool?

Frederick Vallaeys: The Ad Preview Tool is lets you find and click on your ads in a test mode without paying for them. For example, you can see if the ad you have for people in Milwaukee is going to the right page, and what would someone from Milwaukee see. You put in your keywords and the location you want to test, you can see if your ad would’ve shown up in that case.

Eric Enge: The diagnosis part also allows you to get more visibility into why it is not showing, right?

Frederick Vallaeys: Exactly, so if it is not showing up it will give you some ideas why that might be.

Eric Enge: Can you talk about top of bid page estimates?

What we do now is we also tell you how much you have to bid to show in the paid results above the organic results.

Frederick Vallaeys: In the past, we had first page bid estimates, which tell you how much you need to bid, on average, to be on the first page of search results. That’s the page where most people are going to click on ads, because most people do not go to the second page of results. What we do now is we also tell you how much you have to bid to show in the paid results above the organic results. We also offer segmentation in the reports between top ads and the side of the page ads.

Frederick Vallaeys: I did this in one of my test accounts yesterday and it was amazing. On the right hand side I was seeing a much lower click through rate than on the top of the page. That could be different for other people; but it tells you that this is a lot of potential clicks that I gave up by being on the right hand side as opposed to having bid a little bit more and showing up on the top of the page.

Eric Enge: What about impression share data?

You may find that you can get 30% more traffic just by tuning your bids because you only have 70% impression share.

Frederick Vallaeys: Impression share tells you what percentage of the available impressions your ads are being shown for. It tells you how many clicks you are missing out on by having bids too low, or by not having the right keywords. You may find that you can get 30% more traffic just by tuning your bids because you only have 70% impression share.

Eric Enge: I think few people realize that getting a hundred percent impression share is actually very hard, even for your brand terms. There are cases where people are leaving significant amount of the traffic on the table and that they are busily trying to add new keywords to diminishing returns when there is actually can be 20% and 30% gains by just going through and finding places where they are getting low impression share.

Frederick Vallaeys: That’s a great point. Where you should start is with your exact match impression share, because that is when somebody types in your exact keyword. You probably want to show up on a hundred percent of those. Sometimes your impression share could be lower because you are just not able to afford as high a bid. But even if you are in that situation, maybe it is a great time to go and work on your landing page. Somebody is apparently able to bid higher than you are in those instances and that is probably because they do a better job at converting the customer once they come to that site.

That’s where you can then connect on to Google Analytics and take a look at its flow visualization tools and see if there is some road block somewhere on your site that is causing a huge drop off in terms of conversions. If you can fix these types of things, you may be able to afford to spend more for that click and your 70% impression share goes up to a 100%.

Eric Enge: This is particularly powerful when you start with your high ROI keywords, as it can be easy money. Can you also tell us about the social platform integration in Google Analytics?

Frederick Vallaeys: What people on the web are starting to realize is that a lot of activity around your website, around your content is actually not happening on your own website anymore, and it is happening through social platforms. We are working right now to include some of that data such as likes, and +1s, and thumbs up, and votes and all that stuff that you get on third-party sites and bring it into Google Analytics so then you will have an even better view into how people engage with your brand and your site on the internet today.

Eric Enge: This is an expansion beyond what you talked about before with the social reporting

Frederick Vallaeys: Exactly, it is taking it beyond just the Google properties in these cases, so when it comes to +1s, we have all of that data, we can share it with our advertisers. There are a number of social properties that would be interesting to get some data about how people are interacting with your site and brand. We are building an API so that those other companies can plug into the Google tools and then hopefully they will be able to show the benefit of their platforms to advertisers, because those businesses will start seeing these metrics inside Google Analytics.

Eric Enge: Of the things we have discussed, what are the priorities, where do I start, what do I do first?

Frederick Vallaeys: I would definitely go to all of the segmentations that we have talked about, that is the number 1 thing, just look at those segmentations for your account and start looking for big differences. So, if you see there is a big discrepancy between your mobile performance and your desktop performance or your tablet performance, then that’s a good indicator that you need to focus on that.

Eric Enge: Great! Ryan, any extra thoughts from your side?

Ryan:Ryan Voccola: One minor thing I did want to touch on, we talked briefly about Ad Diagnosis, there is a bulk ad diagnosis feature in the account and that’s under the ‘More Actions’ button which will allow you to bulk diagnose a set of keywords and gain insights without having to go to the ad preview tool.

Eric Enge: Excellent. Thanks Fred and Ryan!

Frederick Vallaeys: Thank you!

Ryan:Ryan Voccola: Yes, thanks Eric!

Cindy also served as the co-chair of the SEMPO Emerging Technologies Mobile Web Task Force, and is an active member of the search community. She is passionate about bringing creative online marketing solutions to clients, and working with clients to develop high level, integrated mobile marketing strategies.

Mobile Search and Mobile SEO – Full Transcript

Eric Enge: Hi, this is Eric Enge. I am the president and founder of Stone Temple Consulting an online marketing firm, and I am here today with Cindy Krum the founder of MobileMoxie. Thanks for joining us today!

Cindy Krum: Thank you, happy to be here.

Eric Enge: Cindy, you have a lot of background knowledge on the world of mobile search and mobile SEO, so I am hoping to talk about that a bit today. Maybe you could share some of your thoughts on mobile market growth.

Cindy Krum: Sure, so there is obviously been loads of mobile market growth both in the US and internationally and especially in the past years some important things have happened, such as Android overtook iPhone in terms of Smartphone penetration. Then, beyond that Smartphone penetration overall has grown to a really healthy rate where, as much as half of the marketplace would be controlled by Smartphones.

It used to be, we were dealing with feature phones, or mid-level phones that had a Smartphone capability, without a true web rendering experience. So that’s all good. I don’t talk a whole lot about the statistics because it’s difficult in the mobile world to talk about statistics because they come from a lot of self-interested reporting bureaus who have an interest in showing particular kinds of growth.

That’s true of most statistics, but also just because in the world of mobile we don’t have a lot of clean definitions, with the midlevel Smartphones, or the Smartphones that don’t have true web browsing. You would market differently to a Smartphone that doesn’t have a true web browsing experience like a Sidekick or BlackBerry Curve or things like that where it says that it’s a Smartphone and it does email and it does lots of things, but it still isn’t very good at rendering the web.

So, there are three or four subcategories within mobile, you have feature phones, you have the midlevel Smartphones, you have the Smartphones that have true web rendering. And then, some people include tablets in the mobile world. When you are looking at statistics that say there is a massive growth in Smartphones or there is a massive growth in mobile, you have to ask those questions before you can really understand what those statistics mean for your marketing campaign.

Eric Enge: Understand. So, that makes it very challenging to understand where things are really at, especially when you try to compare one set of stats from one source with another set of stats from a different source.

Cindy Krum: Exactly, so if people try and drill me and question me about stats where one set says this is this and another set says this is that, but how could those things both be true? They could both be true depending on the methodology they used in their reporting and what they were considering as mobile and what they weren’t. So, there are a lot of questions to ask to get a meaningful set of statistics.

Eric Enge: Right, if we talk about the difference between mobile and desktop search, there are a number of interesting aspects to that, isn’t there?

Cindy Krum: The most obvious difference is the screen size, where you get a whole different experience between how much real estate you can get and the importance of that, and then also the interaction between organic and paid results. And then, of course there are differences in the results themselves because the search engines and especially Google, when you submit a query from your phone, they look at the phone that you are on in the user agent string that passes through their servers and so they can see what’s phone you are on, and they will adapt the results, sometimes it will make slightly to fit your phone.

So in some cases, you will get the results that are in a different order if you do the same search from two different phones, or results that are presented differently like with a map or without a map, or with a video thumbnail or without a video thumbnail. So those things are different as well. There are so many different things to consider that it can be very difficult to predict what the results will look like on all the different phones so I have actually build a tool to help people with that.

Then, on top of that, there is the inclusion of location in your queries. So if you have a phone that has a GPS or quasi GPS capabilities in it, they will try and add your location into the search query and use that to again change, or update, or inform how the query result should be presented to you.

Eric Enge: Right. And also, users because they are searching from a mobile device, there is evidence that they are closer on average to conversions, isn’t that so?

Cindy Krum: Yes, but a different conversion, one that’s harder to measure. If you are used to being an online marketer, you know what’s really easy to measure is traffic that converts online, but for the most part when you are doing mobile marketing, you are driving a lot of offline sales actually and you are driving foot-traffic which makes it difficult to measure. One of the things that a lot of companies are struggling with is to prove to management the value of mobile SEO for driving foot-traffic or for driving offline conversion because it’s hard to show that in your reporting.

Eric Enge: Right. I mean no better or worse than your average TV ad too?

Cindy Krum: Well, that’s not necessarily true. There are things that you can do that make it slightly better than a T.V., just an average T.V. or radio ad, but essentially it’s the same hurdles. Yes, it’s a bit more of a broadcast message that you push out, but it’s not as much, like in T.V. and radio I feel like a lot of the times you are closing your eyes and crossing your fingers, but there is more you can do with mobile to get slightly better measurements.

Eric Enge: If we are trying to rank well in mobile SEO, it seems to me that there is a few basic questions to start with and one of those is what platforms you intend to support where you know, at a very high level choices of things like feature phones, Smartphones and tablets. What are your thoughts on that decision making process?

Cindy Krum: Well, there are a lot of things you can do to have a good experience across all of them. It more depends on what your resources are in terms of development. When you are thinking about SEO, you can focus more on the tablets and the Smartphone than on the feature phones. Now, a lot of mobile marketers hate to hear me say that because the feature phones still make up a big portion of phone owners even in the US, but studies show that people with feature phones are less likely to search and less likely to be on the web even if they have web capabilities on their feature phone, they are just not as active because it’s such a bad experience. So, you want to focus your energy and focus your planning on the Smartphones because the better the phone the more likely the user is to really engage.

Eric Enge: Yes, understand and I guess you can just use your analytics to see whether you are getting feature phone visitors, right?

Cindy Krum: Sometimes, but sometimes no. There is this big trap in analytics where the older feature phone can’t execute JavaScript and thus can’t do cookies. So in some cases, you will be in a situation where you look at your analytics and you say oh, we have no feature phones at are all or we don’t have any of us x, y, z phone so we are not going to worry about that at all. Well, it could be that you have none of those or it could be that they just aren’t setup to track, they can’t execute the JavaScript necessary to be tracked in your analytics program.

Eric Enge: Right.

Cindy Krum: So you don’t, I never say write them off entirely, but you can definitely prioritize your efforts towards the Smartphones and the true web browsing experiences and true web browsing search.

Eric Enge: Right. And, Google has a separate bot called Googlebot Mobile, right?

Cindy Krum: That’s true. It’s interesting that they don’t, it’s hard to tell exactly what the point of Googlebot Mobile is anymore except that Googlebot Mobile does seem to focus on feature phones content or older text-based WAP sites. And so, for a while, the people in the SEO, the mobile SEO space figured that they would update Googlebot Mobile to address the Smartphones and do a better job indexing and organizing for Smartphones, and it doesn’t seem like that that’s happened.

It seems like Googlebot Mobile has really focused mostly on feature phone content, and that they are doing the mobile search queries that come from Smartphones from the traditional Google algorithm, and they are so doing slightly differently, but it seems like the indexing with Googlebot Mobile does not, as far as I can tell, have a super duper strong effect on your rankings in a mobile search from a Smartphone just from a feature phone.

Eric Enge: Yes, it’s interesting and that leads to the next big question that people face, do they implement the mobile sub-domain or do they implement something on the same URLs. Essentially so, if you are on the mobile device you are seeing the content and exactly the same URLs as you do on your desktop, but you see mobile phone version when for the content. What are your thoughts on that discussion?

Cindy Krum: So, there is a couple different ways you can go. For a very long time, I have said your best option is always to use the same URL on mobile as you do for your desktop searchers. The reason is because you get to leverage all of the links, and history, and good stuff, good SEO efforts that you have been doing on those URLs. You don’t have to start over. If you wanted to do an “m.” mobile subdomain, which is very common, that’s fine, but you have to acknowledge, in terms of SEO, you might be starting over to get those “m.” pages to rank.

Now, even if that’s the case, it’s not the end of the world because what we are seeing now is that if you have page-A on your desktop site and you have same page, but a mobile version of page-A on an “m.”, and they both have the same keyword saturation and you know, essentially the same SEO indicators. When you do a search from an iPhone or an Android phone, anything with a true web browser, what’s going to happen is that traditional page (the desktop page) is still going to outrank the mobile version of the page.

Possibly, because it has more history and links or possibly because the Google algorithm just isn’t that good or isn’t sophisticated enough to say hey, this is a mobile page and that is traditional page, these are the same thing and they are on the mobile device so we should rank the “m.” one better. So, you see lots of problems with that where people will build an “m.” site and they will say look we did everything you said we have these light, grey, awesome pages that work really well on mobile phones, but Google is still ranking the traditional versions better.

I don’t expect that that’s going to happen forever, but the best solution or the best way to share the values that you have created on your desktop site with your mobile pages is to setup what’s called user agent detection and redirection on all of your desktop pages and you can even do it on all of your mobile pages too. And, the idea is that it detects if you are on a mobile phone or not and redirects you to the right version of the page.

So even if your desktop site outranks your mobile site in a mobile search, when people click on the desktop version of the page, it will get them to the mobile version of the page. So, from the search results, even if the desktop one is there, it gets sent to the mobile version of the page. So, that’s a really good, strong, solid workaround; it’s not considered cloaking or anything like that. Google has come out and said that this is okay because it’s for the benefit of your users, as long as the pages, as long as you are not trying to do anything sneaky between the two pages.

Eric Enge: Right. And, I believe Google has also said that it isn’t cloaking provided that you are also bringing Googlebot Mobile to those mobile pages, right?

Cindy Krum: Yes.

Eric Enge: That’s one of their requirements. So, there is a version of Googlebot that is doing the exactly the same as what’s the users see.

Cindy Krum: And, you can even take it a step further and I always offer this recommendation with lots of caution, be careful when you do this, but you can canonicalize the mobile versions of your pages up to the traditional desktop ones, so that you consolidate the SEO value. As long as your user agent detection and redirection is really strong and reliable, you can use that rel=canonical tag.

Eric Enge: Right, yes because otherwise if you have a mobile sub domain the mobile sub domain might accumulate some links which aren’t really occurring to the benefit of the overall site.

Cindy Krum: Right.

Eric Enge: So, I guess one of the reasons why someone would do a mobile sub domain rather than the same URL strategy, if I understand this right, isn’t the complexity of the development much higher when you are dealing with feature phones support plus Smartphone support, especially giving that there are so many different form factors with feature phones. In that case it is easier to deal with an “m.” approach from a technical perspective?

Cindy Krum: It does, and what we find is if you do a mobile site that’s targeted at midlevel Smartphones, but not your, not necessarily the true web rendering Smartphones, then you do a good job for both worlds so the real older feature phones and it should still look fine and you know, work well on the true web rendering phones. And then, it will also force you to keep your page size and load time low, which is a good indicator for Googlebot Mobile and just better for your users in general.

Eric Enge: Right, so that way you are sending positive signals in terms of bounce rates and things like that.

Cindy Krum: And, mobility. Remember that Google is rearranging the rankings based on what’s going to work best on the phone and in general what’s going to work the best on the most mobile phones are clean fast loading pages.

Eric Enge: Yes. So I guess it would be fair for you summarize what we have talking about, it would say that if we are doing feature phone and other mobile device support then arguments for going with the mobile sub domain are stronger. If you are just in Smartphone on up, we would lean towards the same URL implementation, but mobile sub domain with good user agent detection and rel=canonical is okay too.

Cindy Krum: Yes, and there is trade offs all over the place. So if you go with the same exact URL, that’s fine, but that doesn’t mean that you should go with the same exact design. You can do things on the server or with the style sheets to rearrange how that page lays itself out on the mobile phone, because what you want to avoid in terms of in terms of user experience is you don’t want to send people to, people on an iPhone to a page that’s formatted for their desktop, because it’s like 1995 all over again.

I mean with the left to right scrolling, that’s miserable. You don’t want to give people that experience. That’s one of my easy to determine if this is a good mobile design is if there is left to right scrolling or if you have to pinch and zoom to interact with the page or to even understand the page, that’s also not a good user experience.

Eric Enge: Right, and the other thing you want to do even in the same URL is do things to make the page a little lighter weight without fundamentally changing content of course, but you don’t want to be loading a 150 K.

Cindy Krum: Yes, so what you have to do, one of important tests is that you want to make sure that user agent detection is detecting the mobile bot in the same way as it would detect the mobile phone, and that’s the separate test that you want to make sure that you have done to make sure that the mobile bot is triggering the mobile version of the page.

Eric Enge: Yes.

Cindy Krum: Even if it’s on the same URL, so that it can index that information rather than trying to index the desktop site.

Eric Enge: Thanks so much for joining us today!

Cindy Krum: Thank you!