Ever wondered why there are four Starbucks stores within a one-mile radius of each other in Harvard Square? This is certainly not a phenomenon that is unique to Harvard Square. In many locations, from New York City to São Paulo, one will often find Starbucks stores clustered near each other. This seems counterintuitive, as logic would have you think that this strategy might actually lead to cannibalization and therefore negatively impact the profitability of the stores. However, Starbucks has been very smart in its use of data analytics to not only determine optimal store location, but to customize its menu offerings by location and therefore capture significant value.
In order to assess a potential new store location, Starbucks leverages data from Atlas, an in-house mapping and business intelligence platform developed by Esri, a geographic information system (GIS) company. Atlas provides Starbucks with data on consumer demographics, population density, income levels, auto traffic patterns, public transport stops and the types of stores / businesses in the location under evaluation. Through careful analysis of this data, Starbucks is able to project foot traffic and average customer spend of a given location, therefore helping Starbucks to determine the economic viability of opening a store in that spot. This also creates value for customers by providing convenient locations to grab that much-needed cup of java.
Once Starbucks determines the optimal location, Atlas is also useful for helping Starbucks determine tailored menu offerings. For example, Atlas helped Starbucks to determine areas that have the highest alcohol consumption. This information allowed Starbucks to determine stores that would pilot an initiative to serve alcohol as part of a special menu called “Starbucks Evenings”, which was launched in 2010 in Seattle and has since been expanded to a growing number of stores. In another example, Starbucks used Atlas to predict when a heat wave would occur in Memphis and launched a local Frappucino promotion to coincide with the hot weather. These data-driven menu offerings offer additional ways to capture value, beyond just charging a premium on coffee.
Starbucks’ competitors are also using data in a similar way. For example, Dunkin’ Donuts uses software developed by Tango Analytics, to similarly analyze demographics, presence of competitors and traffic trends in order to determine store locations. That said, Starbucks continues to outpace Dunkin’ in terms of number of stores (11,000 stores vs. 8,200 Dunkin’ stores in the US), as well as in terms of store growth (Starbucks is projected to open 600 net new stores in the US by end of 2015, while Dunkin’ is aiming at 440 net new stores).
Starbucks has done extremely well to become such a ubiquitous part of urban living. However, let’s not kid ourselves: Starbucks is overpriced and the quality of Starbucks’ coffee certainly does not warrant its premium price tag. Will the company be able to continue to charge $2.95 for a tall caffe latte? Probably. Objectively, consumers might balk at the relatively higher price but the convenience of Starbucks’ locations means that it will probably remain the go-to coffee chain.
One of the other challenges that Starbucks faces is the emergence of specialty coffee companies such as Stumptown Coffee Roasters and Blue Bottle Coffee. These chains are attracting the more sophisticated coffee drinker that is making a conscious choice to drink something other than the brew offered at the local Starbucks. While this probably will not make a huge dent in Starbucks’ soaring profits ($627M in June 2015), the shift towards coffee chains that are seen as better quality and therefore value for money may negatively affect Starbucks’ positioning as a more premium coffee brand than your local Dunkin’ coffee. Furthermore, as these specialty chains increase their presence, who is to say that Starbucks’ sophisticated use of data to optimize store location will win over consumers’ preferences for more sophisticated coffee?