#All #Retail

NTUC Fairprice / Singapore
CRM Analysis, Revenue Estimation, Demographic Segmentation, Kiwi

Main Solutions  ⇂

CRM Analysis
Revenue Estimation
Demographic SegmentationKiwi, a GIS platform

Background: NTUC Fairprice, the biggest supermarket chain in Singapore
NTUC Fairprice is the largest supermarket chain based in Singapore, and is a co-operative of the National Trade Union Congress, NTUC. The group has over 200 supermarkets across the island, with over 50 outlets of Cheers convenience stores island-wide.

Challenge: Stores overlapping

The method of evaluating a new site in the past:
  1. Choose the locations next to their competitors
  2. Find a location they preferred and use a traditional method to count population flow around that place

In the past, NTUC Fairprice used two traditional methods to decide a site for opening a new store. First, choose the locations next to their competitors. The majority of brand owners in Singapore use this traditional method to decide a site for a new store. Singapore is not a big country, and the population density is high. So store owners do not consider many effective factors for opening a new store, they just follow their competitors. Second, count a ball-park population flow, and decide their next location. First, Fairprice would find a location that looks like a good location for their new store. Second, hiring people to that place for counting the number of people who passed by that place. If the population flow is fit their standard, the next location will be that place. These two methods really help Fairprice open stores in a short time, however, both of them have some flaws: high-risk investment, uncertain target customers, inevitable competing with rivals, and overlapping in the future. Fairprice need to adjust their methods and avoid potential risks.

Main Solution Methods we used in this case:
  1. CRM Analysis
  2. Kiwi, a location intelligence platform
  3. Revenue Estimation
  4. Demographic Segmentation
The priority thing that getchee decided to do was solving the hot potato: overlapping. For solving this problem, we customized a systematical score card for helping Fairprice to redefine each stores.

First, evaluation all stores by important factors such as store visibility, metro system etc. After evaluating, all stores would be segment by their score. getchee gave some advices for the stores that under standard score to help Fairprice make up their business strategies especially for the stores that were under standard. For example, we compiled customers historical purchase behavior for finding out the purchase trend in each different areas, and used this data result to redefine marketing strategies especially focusing on the demographic structure because Singapore is a multiracial and multicultural country with ethnic Chinese (76.2% of the citizen population), Malays (15%), and ethnic Indians (7.4%) making up the majority of the population. Different races have different consumption patterns. Knowing the population patterns of each area will help Fairprice increase their store performance.

Historical purchase behavior → find out purchase trend → redefine marketing strategies

After solving the stores overlapping, we applied a customized potential heap map based on Fairprice’s point of interest and other market related factors to our GIS platform, Kiwi, for helping Fairprice can identify target areas and preview the market for channel planning. During this process, we found out that there are still many places are suitable for Fairprice, furthermore, some places even do not have any competitors. This data result was really interesting and also surprised Fairprice because they also did not know that there are still having potential sites without competitors for new stores opening. As a result, we suggest Fairprice can use customized score card that we made for them as a sample for evaluating their future stores for avoiding reopen the stores that have high-risk and uncertain target customers and opening new stores most similar to the top performers.

Apply data to kiwi study market competition and trends find out trade zone open new stores most similar to the top performers

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