- What Store of the Community is
- How to interpret Store of the Community data
- Identifying key demographics and opportunities
In our article, How Do I Do Segmentation Analysis in Retail Link? we discussed how to use a segmentation analysis to identify top- and bottom-performing segments, or groups, of stores. Those segments can help identify store groups with an inventory imbalance or higher-than-average sales performance, allowing the analysts to perform corrective actions on a smaller, more accurate scale.
Building on that lesson, we take one of those segments and explore how the analyst can use store and community demographics to identify commonalities between the stores within a particular segment. Walmart has aptly named the application in Retail Link that pertains to these demographics Store of the Community.
What is Store of the Community?
Store of the Community, or SOTC, is an application in Retail Link that displays store and community demographics. This information is especially useful in determining commonalities in store groups, identifying specific demographics that those stores might share, and possibly highlighting additional stores to add to your item’s traited store list.
The application is straightforward to navigate, with no need to build reports like in Decision Support. There are several sections in the SOTC application. We are going to explore one of these as it relates to the discoveries in the Segmentation Analysis.
To find the SOTC application, log in to Retail Link and search for Store of the Community in the search bar at the top of the home page, shown below.
From the search results page, click on the link named “Store of the Community – Supplier View.” This link will take you to the SOTC homepage, shown below. As you can see, the application is straightforward, with only four links from which to choose. For this lesson, we are only interested in the first link, “Display All Store Demographics.”
Clicking on this link will open a spreadsheet that lists all active Walmart stores in the United States, along with a wealth of community demographic information. We will explore this spreadsheet in detail and then see how to use the data contained herein to help identify commonalities in specific store segments from the previous Segmentation Analysis article.
Display All Store Demographics
Below is a partial screenshot of the data contained in the Display All Store Demographics spreadsheet. As you can see, the report lists each store by row, some basic store descriptors and identifiers, and several categories of community demographics.
As you will see, the report lists each store by row, along with the demographic breakdown by category for that individual store. Above this list, there is a national average representing the average of all communities for each category segment.
The community demographic categories are:
- Education Level
- Household Size
- Income Level
- Children Age Groups
- Housing (Rent vs. Owned)
Each of these categories has specific divisions. For example, the report divides the Ethnicity category into Caucasian, African American, Latin, and Other. The percentages displayed under each category represent the percentage of the whole. In other words, a value of 31.1% under the Caucasian heading means that 31.1% of that store’s surrounding community population identifies as Caucasian ethnicity. The sum of all four ethnicities equals 100%.
How to interpret Store of the Community data
So, we have all of this data. Now how do we use it to produce actionable insights? The answer, as with any analysis, is to follow the breadcrumbs.
For example, look at Store 11 in Mountain Home, Arkansas. Let’s say that we target our item at the younger population, college kids, and young professionals. Looking at the Store 11 age breakdown, you can see the following:
- Age 18-34: 18.5%
- Age 35-54: 25.6%
- Age 55-64: 18.5%
- Age 65+: 37.2%
So the population around Store 11 only has 18.5% in the 18-34 age range, which is our target demographic. Compare this to a national average of 30.14% in this same age range, and it is clear that this community consists primarily of older people. It would not be beneficial to include our item in this store’s modular in such a situation.
But what if we are selling our item in 3,000+ Walmart stores across the country? It is not practical to search line by line for demographic commonalities. This is where our store segments that we identified in the Segmentation Analysis come into play.
Identifying community demographics by store segment
In the Segmentation Analysis, we divided our entire traited store list into distinct groups, each segment detailing 10% of the total store mix. This analysis identified top- and bottom-selling store segments and any inventory imbalances.
Below is a recap of an example segment from the Segmentation Analysis.
Let’s dive further into Segment 1. The highlighted columns tell us that Segment 1, representing 10% of the overall traited stores, produced 26.6% of the total sales. However, this same segment only has 11.8% of the total store inventory. This result represents an inventory imbalance that we need to research further.
But what if we were able to identify who the consumers were in this segment? Doing so could help us focus our growth efforts on this specific segment rather than applying actions across all our traited stores. Focusing our efforts will help drive sales up in our top-performing stores without accidentally forcing additional inventory into stores that do not need it.
As we saw in the “Display All Store Demographics” spreadsheet above, we have a list of demographics by store. So, what if we can grab our list of stores in Segment 1 of the Segmentation Analysis results and cross-reference that against the community demographics? The results of this comparison might identify a demographic commonality for this segment, which we could then consider for further promotions, expanded modular representation, or inclusion of additional items with a strong affinity.
To accomplish this task, we are going to have to use an Excel formula called VLOOKUP. The specific syntax for this formula is beyond the scope of this article. However, a broad explanation of the formula is that it allows the user to look up a reference cell in a larger array and return relevant information for that row of data. In our exercise, we want to look up the stores in Segment 1 in the community demographics spreadsheet, returning the demographics for those specific stores.
Below is a visual representation of what VLOOKUP accomplishes. On the left is our list of the stores contained in Segment 1. On the right is a partial screenshot of the demographics spreadsheet. We have highlighted Store 11 for our explanation.
As you can see, the VLOOKUP formula looked up Store 11 in the broader array of community demographics and returned the demographic values for that store. If we expand on this calculation for all of the stores in Segment 1, we will produce a subset of community demographics for just Segment 1. We could then take the average of each demographic in Segment 1 and compare it to the national average.
As an example, let’s say we applied the VLOOKUP formula to all Segment 1 stores, took the average for each demographic in that store segment, and discovered the following:
- 18-34 Age Group:
- National Average: 30.14%
- Segment 1 Average: 19.32%
- 65+ Age Group:
- National Average: 16.32%
- Segment 1 Average: 35.64%
Since the segment average breaks significantly from that national average, these results suggest that the age demographic of our top selling stores is 65+. This number might be a surprising result if your internal market research suggested something different and could be an actionable insight into further store expansion.
Similarly, you might discover commonalities in ethnicity, education level, income level, or more. Suppose you see a segmentation demographic that differs significantly from the national average, whether higher or lower than the average. In that case, the report indicates a commonality of that segment to the specific demographic in the investigation.
As you can see, there is a bit of analysis involved in comparing the Segmentation Analysis results to the Store of the Community demographics results. However, the impact of these discoveries could significantly improve your account performance beyond simply increasing your sales. More efficient allocation of inventory, tighter forecast accuracy, and decreased store markdowns are all significant metrics in identifying a successful Walmart business.
Remember the adage, “right product, at the right price, at the right place, at the right time”? Identifying community demographics across your traited store list can go a long way to complying with this guidance and ensure that your relationship with Walmart remains strong.
Find store opportunities
Using SupplyPike’s Retail Intelligence app, you can instantly take data from your Store of Community analysis and find new store opportunities. This metric identifies stores you are not currently selling to but may be high performers.
Take a tour today to see your data in action!