Future of Local and Social Discovery

This post grew out of a response to an article on BostInnovation.com on the Future of Local Search by Mok Oh, Chief Innovation Officer at WHERE, Inc., as well as some further reflection on the guest posts by Adam Medros of TripAdvisor and others.

Last week, BostInnovation asked a few local thought leaders to discuss the Future of Local Search. This is a topic that is near and dear to my heart, having spent over a decade working on location based systems and services, and now working on a company that aims to take some of what we've learned and apply it to the next generation of applications.

Getting back to Basics

Mok posited that we collectively need to take a deep breath, and step back from the technology and mechanics of what is currently being done in the Local applications space, and focus on the primary problem of "effectively connecting local merchants and consumers for commerce". This is a great sentiment, and I think frames the rest of the discussion quite well.

If we take a look at the current landscape of what's being done in Local, we see an overwhelming trend towards identifying Deals at local merchants. While deals are certainly one way to connect merchants and consumers, I tend to think that they are missing a more fundamental part of the process of consumer purchasing habits.

I believe the next generation of Local applications should be focusing on identifying VALUE for consumers at local merchants, which may or may not include a DEALS component.

Identifying Value: WHO/WHAT/WHEN/WHERE/WHY

Traditionally, local business falls into the school of thought of the 8Ps of Service Marketing:

  • Product
  • People
  • Process
  • Place & Time
  • Physical Environment
  • Price
  • Promotion
  • Productivity & Quality

These give us a decent foundation for looking at the problem of identifying value for consumers with Local applications -- but I think we can still simplify it a bit more. My theory is that for a Local consumer, value falls into the following 5 components of the decision making process:

WHO: Who am I? Local IS Social by nature. Local consumers are not the anonymous buyers of the internet. By interacting one-on-one with a merchant, a personalized relationship is formed immediately (for better or worse). A consumer's traits and preferences become visible quickly, and good salespeople pick up on these identifiers. Local applications should be able to do at least the same, if not better, by using personal information to understand what a consumer's personal interests and particular affinities are before presenting recommendations. This gets even more interesting when we look at what happens when there is a group of people -- i.e. trips to the mall or dinner with friends.

WHAT: What am I looking for? Understanding the context of what a consumer is looking for, either consciously or subconsciously, is important to future local applications. This is where product comes into play: If I'm looking for dinner, present me with dishes I might be interested in -- just telling me about the restaurant is not as compelling, it leaves me guessing about what I might find when I get there. If I'm looking to buy a particular brand of shoes, don't take me to a store that doesn't sell that brand. The next generation of Local applications needs to know about the inventory of a particular business, and the context of what a consumer is looking for.

WHEN: When do I want it? Understanding whether a consumer has either an immediate need, or is researching for a future purchase or visit has a very real impact on what and where options should be presented to a user. If it's 5:30pm, and I'm searching for a place to eat, I'm probably looking for something very close by. But if I'm looking for that new pair of shoes during the middle of the day, I may want to be presented with places that are somewhere along my route home from work.

WHERE: Where am I relative to the things I want? When and Where are closely correlated to each other in determining proximity and inherent convenience in the results shown to the user. This is pretty simple today for the single consumer case -- applications look at where I am now and present me the closest options. The problem gets much more interesting when I start looking at planning a dinner with friends a few days from now. Where I am now doesn't necessarily mean that's where my friends and I will be next week. Applications today make us do the heavy lifting of figuring this out on our own, but most of the data needed to make these decisions is within reach, and doing predictive analysis on it would yield some fairly accurate results.

WHY: Why would I want to spend my $$$ at this place? Going back to the statement that Local IS Social, personalized recommendations, especially from my friends, carry some of the largest import in a decision making process. Word of mouth and reputation are critically important in the WHY or WHY NOT of a local search. Price and Promotion are certainly also a large component, but a 50% off deal is only a value if the product and service behind it are worth the 100% price to me -- so if someone I know says the service is much better down the street, I'll probably skip that 2 dinners for $20 promotion being offered.

My prediction is that deals will eventually take a backseat to value in coming years, as long as we can build systems that better understand why a consumer will find a local product valuable. As we start to combine knowledge of Local with the understanding of a consumer's social graph, we'll be able to build applications that are much more intelligent and truly present valuable suggestions to a user -- instead of making them search through a list of local promotions or recommendations from unknown third parties.


How do we get there from here?

Discussions like the series of posts on BostInnovation last week are helping to drive a greater understanding of what may be next. Here in Boston alone, we have a long history of companies working in the location based services space, we have dozens of startups working on individual facets of the problem, and we have folks like Adam and Mok, who are pushing some of our bigger local companies like TripAdvisor and Where into this new territory of contextual discovery. Certainly, many others elsewhere are also hard at work on these problems -- Facebook is building out the world's largest social graph, and Google was one of the first to use the Contextual Discovery label in talking about what's next.

No matter what paths we may take to get there, it is sure to be an exciting trip!


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