Why We Still Search for ‘Starbucks’ on Google Maps


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.

  1. Position_Technologies
    May 28, 2015

    Ironic you should use Starbucks as an example for local search. I often do the same type of local search because Starbucks represents a quality consumer experience that meets my needs, both as a coffee lover and mobile professional. Yet, I wish they would do a better job of managing their store location information across maps on mobile (which is often how I search for the nearest locations). Multiple times I’ve searched and drove to a location only to find it was an old location that closed, has poor geo-cordinates making it impossible to find or was a future location (literally a dirt field where a shopping center wasn’t build yet). That last one occurred two months ago and was particularly frustrating because I shared the location with someone ahead of time as a place to meet. Ironically the company I work for addresses this for brands, specifically managing store location information in maps. As a consumer, I wish they would use our services or someone elses, but for whatever reason these poor consumer experiences don’t appear to be on their radar.

  2. bolamike
    June 1, 2015

    Interesting. Is there any larger-sample data for this search behavior (branded starbucks query), beyond your anecdotal experience?

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