Access to information has curbed the value of brands, but they still matter, often as a proxy search. To wit: I travel often, and I typically end up searching for “Starbucks” when I’m looking for a place where I can get coffee and a snack, work for a few hours, or a place that simply has a bathroom. But in many ways, I do not care if it’s a Starbucks: I’m simply looking for a place that meets those criteria.
That behavior is a testament to the power of the Starbucks brand (and ubiquity of location) that in every city. If I don’t know where to go for any of the three above things I run a search for Starbucks. Search applications today often do a good job of finding a place to grab a bite, often through a keyword such as cafe or coffee shop. However, brands still beat local search applications for those less formalized attributes — things like whether a place has a bathroom or if it offers wi-fi. I haven’t yet found the ideal search term that gives me an adequate list of results — other than simply “Starbucks.”
Fast forward a few years to when the dream of personalized search is closer to reality. When that is the case, a local search app should be much better about disaggregating the use cases and giving me alternatives. By tracking my location (and subsequently inferring dwell time), and data integrations with mobile payments solutions, an intelligent app should be able to deduce that my usage pattern varies within a given type of location (coffee shop, Starbucks). Furthermore, by analyzing the behaviors of everyone else it should be able to associate and rank locations based on intended use, giving it the ability to better predict what coffee shops (or other types of places) are fits for my behavior.
With calendar integration, a generalized understanding of me generated from analyzing my geographic movements over time, and (perhaps) biosensor feedback from wearables, the app may be able to predict my actual need, and suggest places that may be closer, higher rated, or otherwise more convenient than a Starbucks. The first step may be a variation of the “did you mean” prompt we’re all used to but instead of correcting spelling errors actually disambiguating intent.
These effects will not cut across all brick-and-mortar categories equally. Quick-serve restaurants seems like the category most ripe for disruption in this manner. Within retail, the more undifferentiated and cross-category the product set, the more it seems prone to this type of effect. And while my particular use case is focused on travel, this is not a niche behavior.
In April, Google published an article that said that “near me” searches increased by 34x since 2011 and nearly doubled over the last year, with 80% coming on mobile. Basically, brand equity tied to ubiquity, consistency, or more generally, meeting a variety of needs loses value proportionally to the ability of an intelligent app to predict consumer intent.
Vikas Gupta is director of marketing and operations at Factual.