#All #Retail

Hanshin / Taiwan
Revenue Estimation, Demographic Segmentation

Main Solutions  ⇂

Revenue Estimation
Demographic Segmentation

Background: Hanshin is a famous department store in Kaohsiung, and it opened in 1995.
Hanshin department store introduced technology and strategic management from Hanshin Umeda Main Store in Japan, and after preparing for 5 years, the first department store, Kaohsiung Hanshin, opened in 1995.

Challenge: cannot decide which locations is the best one.

In the past 20 years, Hanshin department store did their best to grow their business and now they wanted to reopen department store in Taichung. Hanshin opened a department store in Taichung in 1996, but it closed in 1998 because of the wrong strategy. So this time, Hanshin collected all information carefully, and did their best try to figure out the best location in Taichung for winning this time. They found out 3 target locations, but three of them are great. They cannot decide which one is the best. Therefore, they needed a precise suggestion to help them choose the right location.

Main Solution Methods we used in this case:
  1. Revenue estimation
  2. Demographic segmentation

For helping Hanshin to boost their decision, getchee used demographic segmentation and revenue estimation to evaluate 3 target locations that Hanshin preferred. First, we investigated the distribution of total population, working population, household number, age, and the range of key competitors. Second, measuring the distance and time from residential area to target locations by cars and public transportation. Our main project manager of this project also went to 3 locations for measuring by herself for minimizing the amount of error. Third, we contacted Taichung City Government to pre-realizing the future plans of this city. We needed to consider the future variation margin into this project because this department store will not open in 2018. Fifth, collected the data about per customer transaction in department store in Taichung, and separated all data into two groups: member and non-member. Moreover, collecting the average expense of membership in Kaohsiung Hanshin, and assuming the expanse of membership in future Taichung Hanshin according to different places. Final, getchee combined all data and different key factors together, and presented an estimated report to Hanshin for helping them make a decision.

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