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Starbucks / Thailand
Network Optimization, Trade Zone Analytics, Demographic Segmentation


Main Solutions  ⇂

Network Optimization
Trade Zone Analytics
Demographic Segmentation




Background: Starbucks Coffee Thailand, a wholly owned subsidiary of the Starbucks Corporation, with its first store open in July, 1998, is a relatively young presence in Thailand, nevertheless has seen rapid expansion throughout the nation in the past decade. Of the total 131 stores in Thailand, 94 are located in Bangkok.

The core Store Development team, made up of 3 people, has their vision to continue expanding within Bangkok, a market that looks saturated at first glance, but is still believe to have great potential. The strategy is to unearth hidden pockets in the city where a primary target audience of 16-44 year olds can be found in high concentrations. A single store generally serves an average of 200-300 customers a day. Malls with a rounded tenant mix are attractors and popular streets with heavy footfall seem like strong locations. Careful store planning is necessary to justify a minimum investment of 250k USD per store.

Challenge: With escalating capacity and competition, time and speed to market in untapped territories increase Starbucks’ effort to remain a leader in the Thai marketplace.

Neighborhood demographic data in emerging markets, including Thailand, retain at a macro level and surveys carried out to increase granularity bear high costs. Traditionally, Starbucks Coffee Thailand had deployed information from paper and digital maps, and assembled data with further support from site surveys to make siting decisions. With added local knowledge and gut feeling, short-listed locations are pretty much set for store execution.

With escalating capacity and competition, time and speed to market in untapped territories increase Starbucks’ effort to remain a leader in the Thai marketplace. Manual labor, a concerted effort, sees a new light as location intelligence with enhanced data and mapping capabilities added a new dimension to the development process. As a regional effort, getchee increased efficiency mainly by saving time on data collection and enhancing siting capabilities by validation of target demographics and location dynamics. “Instead of augmenting our best practices getchee gives me a quick glance of an area so I have a good sense of the kind of trade area so I have a good sense of the kind of trade area the potential stores is in”, says Supoj, Development Manager of Starbucks Thailand. Demographics distilled to a trade area size as small as 200m are made available via quick reports, easily printable or shared through getchee’s tool.


Main Solution Methods we used in this case:
  1. Network Optimization
  2. Trade Zone Analytics
  3. Demographic Segmentation 

Based on Starbucks’ specific target audiences, getchee is able to segment the different demographics of the population and estimate the sales potential for a given area based on customer visit frequency and expenditure per ticket. Furthermore, enriched proprietary data such as day-time and working population gives the team an even more accurate estimation of transactions during the day compared to night-time. Starbucks Thailand gained answers through hard numbers found conveniently via a few clicks on getchee through the Web.

Other advantages the team found were the heat maps which they deemed unprecedented in the market. The maps are customized to the relevance of the coffee business and help visually focus on areas that matter. “These intensity or heat maps save me a lot of time when I’m making a search”, Supoj says.

Even in the early stages Starbucks Thailand found getchee uncomplicated to use. With the suggestions and tweaks the team freely shares in the process not only reflect on the output, but improves future product enhancements. Overall, the Asia Pacific head office says that the benefits getchee brings are beyond data and numbers. It is in a standardized process they inculcate through the use of getchee that the regional managers can increase expansion and siting methods in the long terms via the same platform. “We cannot cut and paste U.S. standards of siting in these regions, but with getchee , a pattern can begin to form,” Supoj explains.

√  Grow business in an emerging economy where accurate data & location intelligent are hard to come by
√  Quick glance of area, having a good sense of the kind of trade area the potential stores 
√  Starbucks TH cannot cut and paste U.S. standards of siting in these region, but with getchee, a pattern can begin to form

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