Archive for the 'search' Category

The Impact of Free in Local

Techcruch reports:

Wired editor-in chief-Chris Anderson kicked off his magazine’s Disruptive By Design conference today in New York City with a speech about how the Internet makes everything free, which is the topic of his latest book, Free: The Future of A Radical Price He articulated something that is now increasingly becoming obvious: As products go digital, their marginal cost goes to zero.


In one slide, Anderson comes up with the following rules for media companies trying to figure out how to make money online:

  1. The best model is a mix of free and paid
  2. You can’t charge for an exclusive that will be repeated elsewhere,
  3. Don’t charge for the most popular content on your site,
  4. Content behind a pay wall should appeal to niches, the narrower the niche the better

Applied to Local Search and Advertising

Local search and advertising have seen the ‘free effect’ in action:

  1. In the Yellow Pages, merchants pay for Enhanced Listings.  But on Google listings are free.
  2. GOOG411 (and similar services) provide free directory assistance (albeit without human intervention).
  3. Maps are freely available online (we used to purchase printed maps).
  4. User’s freely contribute reviews.

So, what’s the equivalent of ‘paid niche content’?  Well, I suppose, editorial reviews of things like restaurants might still fall in to this category.  Are there other examples?  But at the consumer end, it seems likely that the vast majority of services provided to consumers will be free.

So I wonder if local ‘advertising’ is really going to morph in to a services business as I suggested in my previous blog post – The Biggest Opportunity in Local Advertising?


Microsoft and Google Declare War

On Thursday, May 28, 2009 Microsoft and Google officially declared war.

Microsoft announced Bing their new search initiative competing directly with Google’s core business.

Google announced Wave: a new product, platform and protocol that re-imagine communication and collaboration in the cloud.  It has grand ambitions that includes a direct assault on Microsoft’s core business of office communication and collaboration.

Initial reviews of Bing by industry insiders suggest it is competitive with Google’s search and offers some interesting features.  Most also believe these features and function won’t be sufficient by themselves to overcome people’s entrenched familiarity with Google.  Microsoft has anticipated this challenge by also announcing a massive advertising campaign to get people to try their new offering.

Microsoft is betting that search has matured, even becoming something of a commodity.  As such, by offering a comparable product they are able to shift the battle-field to a marketing and branding effort.  This MO is consistent with Microsoft history.  They have never been a first mover or an innovator.  They are an exploiter – a very determined one with deep pockets and patience.  And arguably, Bing is their ‘3rd generation’ of search engine (MSN and Live being the prior 2) — and it took them three tries to ‘get it right’ with Windows.  It will be interesting to see if they can finally grow their market share in search.

Google is not standing still on search – they continue to announce new search offerings at a heady pace.  It’s clear they intend to seriously defend their core business of search.  This highly profitable business is what allows them to make all their other big bets.

And Wave feels like a big bet.  It redefines how people communicate and collaborate.  This directly challenge’s Microsofts traditional paradigm of a ‘computer on every desktop’ in which that desktop computer is the primary repository of one’s information.  In Google’s cloud based future the desktop computer becomes irrelevant.  It people shift to the cloud, it represents a huge threat to Microsoft’s ability to license software stacks running on each of those computers.

Google has made many previous guerrilla attacks with products like Gmail and Google Docs.  But these are really just cloud based implementations of traditional paradigms.  Wave on the other hand is a full frontal assault because it encompasses not only these traditional means of communication and collaboration but also extends to include blogging, micro-blogging (Twitter) and activities currently associated with social networks.

It’s unusual for Google to announce such a grand product in such a relatively immature state.  The timing seems chosen to steal some of Microsoft’s Bing noise.  But it is a grand enough vision that Google will need help from legions of developers to make it happen   It is those legions who are the foot-soldiers in this battle – and they are mercenaries who will go where they see the biggest opportunity.

This battle is going to be played out over many years.  But we’ll likely look back and see these two announcements as a significant milestone in the struggle.

Microsoft Researchers Increase CTR 670% Using Behavioral Targeting

Behavioral targeting has been around for a while in various commercial products.  But now, for the first time, researchers at Microsoft Research Asia have completed an important empirical study demonstrating that it works.  In fact, it works extremely well increasing the click-through rate (CTR) up to 670% with the potential to achieve improvements in excess of 1000%.  Wow.  And kudos to Microsoft for undertaking this as an academic research program and making the results available to all.

They cover a lot of important ground in an academically rigirous way.  There are three key conlusions.

The Basic Assumption Holds

The basic assumption of behavioral targeting is that all the people who click on an ad are more similar to each other than they are to all those people who clicked on other ads.  If you can’t prove this assumption, it’s back to the lab to whip up something new.

But, not to worry, the researchers found that the people who clicked on the same ad are up to 90 times more similar to each other than users who clicked on another add.  Whew!  I guess that’s good news for anyone who has been touting the merits of behavioral targeting.  It’s also intuitively satisfying.  Still, it’s great to have it proven by research.

CTR Can Be Increased by Up to 670%

They used click-through rates as their measure of performance (because it is a readily available measure).  They implemented behavioral targeting by segmenting the users with various strategies and then compared what the CTR would have been with and without the segmentation strategy:

Through studying ads CTR before and after user segmentation for ads delivery, we observe that ads CTR can be improved by as much as 670% over all the ads we collected.


In addition, we notice that if we can further design more advanced BT strategies, such as novel user representation approaches and novel user segmentation algorithms, ads CTR can be further improved beyond 1,000%.

Short Term Search Behavior Gives the Best Results

Finally, the researchers examined several different approaches to implementing behavioral targeting:

Through comparing different user representation strategies for BT, we draw the conclusion that the user search behavior, i.e. user search queries, can perform several times better than user browsing behavior, i.e., user clicked pages. Moreover, only tracking the short term user behaviors are more effective than tracking the long term user behaviors, for targeted ads delivery.

What it Means: A Mobile, Local Perspective

This study was done using logs from users searching, browsing and clicking on the web.  Local and mobile bring additional nuances to the equation.

This report is exciting for us here at Predictabuy because it confirms a lot of our own research which is specifically aimed at understanding user behavior in a mobile, local context.  Our research shows:

  1. short term behavior is also a stronger predictor than long term behavior in a mobile, local context;
  2. situational factors such as location, time of day, day of week and weather are very useful in user segmentation; and
  3. advertising performance benefits from dividing users in to  more segments than the number used in the Microsoft study.

via Greg Linden.

Read the full paper for yourself:”How much can Behavioral Targeting Help Online Advertising?” (PDF)

Random Ranking: Why Are Google Maps Results so Arbitrary?

I started with this search for “restaurants, Calgary” which returns something like this:

Google Maps Search for Restaurant in Calgary

Is there any rhyme or reason to the choices in the tiny subset that are highlighted for my viewing pleasure?  Oh, I realize many people have spent a lot of time reverse engineering the algorithms and that understanding these algorithms is important for Search Engine Optimization (SEO) if you want to find your business in that anointed list.  But, I mean from a USER’s point of view – it just seems arbitrary doesn’t it?

(The only that actually makes any sense is the Earl’s e – I know they are there because they paid to be there.)

Then Sebastian Provencher suggested changing the query to “category:restaurant, loc:calgary”.  Nominally, the same thing right?  Uh, no:

Another search on Google Maps for restaurant in Calgary

A different, equally arbitrary, set of results.

Now, in fact, there really isn’t enough information in a broad query like ‘restaurant, calgary’ to give me anything very meaningful.  The answer almost has to be arbitrary.  So, here’s my beefs:

  1. The results are presented AS IF they have some sort of authority or relevant ranking.  Why not provide the user with an indication or explanation on how the results have been ranked?
  2. If they are essentially arbitrary, why not make them truly random or semi-random?  Change them up.  This would drive the SEO guys crazy but seems like it would be fairer.  Why not just give me the ability to shuffle the results?

Now, the folks at Google are pretty smart.  They certainly know a thing or two about ranking things.  So, what’s up?

(Aside: the results today seem different from the results yesterday – at least somewhat.  So, I’m thinking they might actually be randomizing the ranking somewhat.  Does anybody know?)

Thinking holistically about local search

Emerging mobile and social applications are changing the way we find local information from a search paradigm to a recommendation paradigm.  Just this week we saw the announcement of several new products promoting this shift – which Greg Sterling reflects on in this post.   And I agree with Greg that in some ways we have almost come full circle:

The underlying consumer behavior is simply asking for word of mouth recommendations and is as “old as the hills.” But the ability to efficiently ask many people for advice or a local business referral at once online is new. Reviews were step one; the combination of quasi-real time answers and social networks is an evolution of that phenomenon.

We’re seeing many different approaches to capturing and sharing opinions — and people vigorously debating the merits of these approaches.  Is it better to have lengthy, insightful reviews or should you just have a simple rating or voting system so you get more participation?  Can you just ask your friends?  Is an answer format better than a review format?

It’s going to be great to see how it all evolves – exciting times!

I believe a holistic and inclusive approach will be needed.  Perhaps the greatest challenge in local information is to achieve sufficient depth and breadth to provide truly meaningful recommendations at the local level.  A modest sized city has tens of thousands of businesses.  This means you need millions of points of view in order to fairly represent the different needs and preferences of consumers.  Simply put:  you need active participation from a large population of local users.

This has several practical implications:

  1. You need to accommodate the different ways users want to interact with local information, but still be able to aggregate this information in meaningful ways.
  2. We can’t afford to ignore the implicit signals provided by all users.  These signals include the things they search for, the maps they request and the businesses they call.  Research on movie recommendations published by participants in the Netflix Prize clearly shows that this kind of implicit data is critical to creating high quality recommendations.
  3. A small percentage of participants will create the majority of the explicit opinions – the silent majority still needs a way to find and evaluate opinions that are consistent with their preferences.  We won’t all have 1000+ friends to ask.


Netflix Prize has lessons for local search.
Forget search: local is a recommendation problem.

Netflix Prize has lessons for local search

The Netflix Prize seeks to substantially improve the accuracy of predictions about how much someone is going to love a movie based on their movie preferences.

So what does this have to do with local search? Researchers working on this problem have found that you should ignore everything you know about the movies (the genres, the actors, etc.) and base your predictions on how people have rated them.  For local search this means we should base recommendations on people’s preferences – the businesses they like and have used – rather than categorical data about businesses.

Lessons from the Netflix Prize

From a New York Times article on the Netflix Prize:

“You can find things like ‘People who like action movies, but only if there’s a lot of explosions, and not if there’s a lot of blood. And maybe they don’t like profanity,’ ” Volinsky told me when we spoke recently. “Or it’s like ‘I like action movies, but not if they have Keanu Reeves and not if there’s a bus involved.’ ”

So, you can’t base movie recommendations on a simple categorization like ‘action movie’.  Its difficult for most people to articulate what they like or dislike about a movie. So, its better to base predictions on what you know about which movies the person likes or dislikes. In fact, researchers have consistently found that adding in categorical information about the movies doesn’t help with making predictions at all. (And there have been numerous attempts.)

Applying it to Local

The research from the Netflix Prize shows us that you shouldn’t recommend a pizza place to someone just because it’s a pizzeria (i.e. it’s category) and it’s ‘close enough’ to you.  That’s because the best choice can be influenced by many factors:

  • Am I a person who likes my pizza ’straight-up’ or exotic?
  • Am I in a rush and looking for the quickest option?
  • Am I bored and looking for something new?
  • Have I just come back from an exotic location and looking for an old favorite?

Today,  local search engines continue to rank results based primarily on  factors like category and location.  Even sites such as Yelp with extensive reviews rank and present results to users without considering the preferences of the searcher.  This is akin to simply presenting movies ranked by popularity alone.

Research coming out of the Netflix Prize shows the way towards a different way of thinking about local.


Forget search: local is a recommendation problem

Forget search: local is a recommendation problem

If I’m looking for local facts – an address, directions, a phone number – then it’s appropriate to frame the problem as a search or information retrieval one.

But otherwise, I need recommendations based on my preferences, situation and current need.  And here, the search paradigm fails spectacularly.

The Random Ranker: at Best Arbitrary…

The search paradigm starts by trying to match the words in my query with the words in the listings — and maybe with the words in reviews for the listings and maybe even the website of the listings.  It then retrieves the ones where there is some kind of match and ranks the results.  Often this default ranking is called something like ‘relevance’.  But there is no explanation of how this so-called ‘relevance’ is determined — and it certainly isn’t obvious by looking at the results.

Do you really have any idea how Google decides which 10 listings to include in the ‘featured box’ on a search results page?  From the point of view of the user, such an ordering is at best arbitrary and at worst suspicious.  This undermines user trust and confidence.

In web search, the path to relevancy usually involves a number of incremental refinements to my query.  After each query, I quickly scan the page and if the results are clearly not what I’m looking for, I re-formulate it and try again.

This approach isn’t applicable in local.  Quite often I get to a more or less reasonable set of results in short order.  In fact, the basic search problem – getting some reasonable listings – just isn’t that hard.

But my real problem, is finding a way to differentiate between the options.

Manual Filtering: Too Complicated, Doesn’t Work…

Now, it’s true that most services provide me with various tools to filter and re-order the results.  But the results are frequently less than satisfying:

  1. The tools are complex and time-consuming.  You’re making me – the user – do all the work.  So adoption of evaluation tools tends to be low.
  2. The information is often incomplete and inaccurate – so the filtering doesn’t work properly.
  3. There is too much information — too many reviews, too many conflicting opinions — and I have no basis on which to evaluate the alternatives – because I don’t know the reviewers or I don’t know the area.
  4. And often, I can’t actually do the evaluation on the criteria that matter to me — and sometimes I can’t even articulate those criteria.  I just want someone I’m going to like to cut my hair — is that too much to ask?


I need help in the form of meaningful and transparent recommendations.  Meaning — you need to explain to me how you arrived at the recommendations.  Are they based on other people’s opinions – how do I know I should trust these people?  If it’s based on expert assessment – what are the credentials of those experts?

And you need to learn my preferences and understand my situation in a painless way.  Don’t make me fill out a bunch of questionaires – they don’t work, I don’t know what I want and I’m not going to fill them out anyway.  Instead, learn what I need form what I do and what I’ve done and what I’ve told you I like.

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