Category Archives: Local Search

The Future of Local Search: Back to Basics

The following post was originally published on BostInnovation, a blog that covers the Boston’s technology and innovation scene.


As a guy who’s title is Chief Innovation Officer, I often get asked questions like, “What’s the Future of Local?” It’s a very tough, interesting, and challenging question to (attempt to) answer.

Local (with a capital ‘L’) is a complex ecosystem with lots of fast moving and changing players – from consumers, merchants, ads, maps, LBS, rich media, daily deals, payment systems, social, local search engines, recommendation engines, big data, analytics, mobile, carriers, to OEMs — just to name a few. (In fact, I ended up drafting about six different topics before stepping back and writing this one, each of which was about a deeper dive vertically in the context of SMBs and Local.) There certainly is some pressure to provide some “innovative” answer as well. But more I eat, drink, and sleep on this question, more I think we need to get back to basics.

Before we go any further about going back to basics, let’s step back and define what the problem of Local — with a capital ‘L’ — is. I would frame it this way: The name of the game is to effectively connect local merchants and consumers for commerce. It’s an old game. There are merchants who want to sell X, and there are consumers who want to buy X. If you can “effectively connect” the two parties and enable commerce, then you can win. Cool?

So now, let’s go back to what I mean by “back to basics.” With the advent of computers, internet, mobility, and the cloud to name a few, the world of Local became amazingly fast paced and ever changing. I feel like every morning a new player is popping into the ecosystem with a new “game changer” for Local. But, regardless of check-ins or group buying or RTBs, if we stick to the basics of effectively connecting local merchants to consumers for commerce, we can better understand the big picture and even better qualify and quantify how important and significant certain players are. I think this framework is a decent attempt at going back to the basics for the game of Local.

Within this framework, let’s discuss about a specific example within the Local ecosystem: recommendation or personalization engine. I think recommendation engines are one of the most direct ways of effectively connecting local merchants and consumers. Simply put, a recommendation engine is a way to connect consumers to merchants by attempting to understand what consumers may want to buy in the future determined from their past behavior. By understanding what consumers have done in the past — what they liked, disliked, bought, browsed, etc. — it is possible to project their behavior into the future using some computer algorithm(s), then suggest new things. Amazon and Netflix recommendation engines typically tell you what “you may also like” based on your past behavior.

Now, going back to basics, I think recommendation engines can significantly impact the game of Local — if you know what merchants want to sell, and if you know what consumers want to buy, then this could be the most effective way to connect them for commerce. Of course, for recommendation engines to work effectively for Local, they need to understand what millions of merchants want to sell, have enough past information to understand hundreds of millions of consumers to project into their future behavior, have the right algorithm and/or heuristics to compute the correct connection, and accomplish all this in real time. Yeah, it’s a tough problem. I think only a few companies can really pull this off (Where Inc. being one of them).

There are many other topics we can frame by going back to basics. How can mobility be used to effectively connect merchants and consumers? How about check-ins? How about group buying? LBS? Maps? Rich media?

This is just scratching the surface. Love to hear what you think in the comments.


What’s Next — Internet 3.0

Xconomy’s Greg Huang wrote a great article about what may be next for me (Greg’s a pretty amazing story teller).  Let me continue that line of thought and provide some snippets of evolving idea(s).

We certainly live in interesting times (blessing or a curse?).  Many of the technologies have become mature (v 1.0) and now are crossing over to the next version (v 2.0).  Mobile, cloud, social, smart phones, etc. are no longer pieces of technologies that cater only to the early adopters but has very much crossed the chasm to the rest of the world.

The mapping of the physical world (the “offline”) to the cyber world (the “online”) has begun in full force, from games (a la Foursquare) to servicing local merchants (a la Groupon).  Let me be provokative — I would venture to say that Internet 1.0 was search (e.g. Google), Internet 2.0 was social (e.g. Facebook), and Internet 3.0 is mapping of the offline to online — and NOT “semantic web.”  (Sorta ironic that no one really seems to know the semantics of “web 3.0″ is..)

Internet 3.0, offline to online mapping, is only showing just the tip of its iceberg.  Let the land rush begin!


The Indoor Mall Local Search Problem

I’m sitting in a pretty sizable mall (Natick Mall in MA near Boston) and thinking about how a mirror-world local search would work. In other words, think about how a use can browse to the Natick Mall, walk around this space immersively, walk into stores, and shop like in real life?  How would EveryScape implement this?  How would Bing or Google do this?

Natick Mall Partial Panorama

I snapped a few photos and created a partial panorama above within a few minutes using my iPhone 4 and AutoStitch.  You can see more than a dozen shops from a simple image like this, and it can give you a pretty good insight into what the space is like.

How can I get the “crowd” to do this for me?  Will Foursquare/Gowalla-like approach work?  How well is the photo-crowd sourcing working?  How do we solve the GPS problem for indoors?


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