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Retail Data Warehouse

Use your retail data warehouse to get a view of your business from the perspective of your customers.

This article originally appeared on the BeyeNETWORK.

In this month’s article, we flip an industry cliché on its head. Most data warehousing practitioners have heard the phrase, “I would like to get a 360-degree view of my customers.” Translated, this usually means, “I don’t know much about my customers.” The reverse is not nearly as accurate. Customers know a great deal about the places they shop, and they certainly have a 360-degree view of a retailer, whatever your interpretation of that may be. Your retail data warehouse can be used to put yourself in your customers’ shoes and get a 360-degree view of your business from their perspective.

Influencing the Customers’ Views
Many factors can influence whether or not customers choose to shop at your stores. There is certainly a question of needs versus wants, which can influence the likelihood of customers going out of their way from a convenience or price perspective to buy a product. Most retailing does not work this way. Assuming, though, that customers have decided they need a certain product or type of product, their shopping behavior is affected by the following:

  • What stores are nearby that carry what the product? With traffic and gas price influences in many major metropolitan areas, site location is clearly important.

  • What is the price image of the store? Customers, of course, care about price, and they will consider whether the nearby store is going to give them a good deal or if the competitor a mile further away will be less expensive. A more discretionary purchase may cause customers to time their purchases to coincide with a promotion that will save them money.

  • Can I get other things there? Now more than ever, consumers are pressed for time in their personal and business lives, and the ability to accomplish multiple errands in a single trip is important.

  • How do customers feel about the shopping experience? Some stores have a certain cachet that makes the shopping experience almost a treat. This certainly satisfies customers’ “wants,” but not necessarily their needs. Simpler impressions of a shopping experience are the “newness” of the store, the amount of assistance they get in the store and the time it takes to check out once their baskets contain everything they have chosen.

  • Is the store likely to have the product in stock, or is it available online? Nothing is more frustrating to customers than going to the store to buy something and not being able to do so. The ability of a retailer to integrate the online and in-store experience plays a role here.

Getting complete insight into all of the questions in customers’ heads is, of course, impractical to do with a data warehouse. However, several common metrics found in the data warehouse enable a retailer to infer the answers to their questions.

 

Data Can Infer the Customers’ Views
A number of data elements should be stored and/or derived from your comprehensive retail data model to analyze how customers view your store. For a list of the basics that should be in your data warehouse, see my recent article, Sizing Your Retail Data Warehouse.

Given that we are taking an individual customer’s view of your business, we should conduct an analysis at an individual store level – a “store-card” – that can be rolled up into local, regional and national numbers. Your analysis should include the following:

  • Distance to closest “competitive” store

  • Proximity to customers with the desired income demographics (census data and store siting)

  • Comparison shopping factor (price index of comparable products from local competitive stores)

  • Response to promotions at the store level (tracking variations by store)

  • Foot traffic details (if available) analyzing foot traffic converted to transactions

  • Visit/basket metrics (item counts, selling prices, revenue/margin)

  • Returns frequency

  • Renovation recency

  • In-stock percentages

  • Transaction length at checkout

While the list could include more metrics, it does address the broad range of factors that influence the customer behavior. A simple approach would be to rank your stores from best to worst in each metric, sum up the ranks and produce a single factor that can demonstrate the relative “customer-friendliness” of every store in a retail enterprise. The “store-card” data should be provided to store managers, merchants, marketing and finance staff so action can be taken to improve these factors.

Improving the Customer’s View
Low scores in any of the areas can dictate particular actions to remedy the situation. The biggest single change a retailer can make is to move away from an “average store” approach to operations. Many retailers use the same floor layout, pricing, merchandise plan and allocation, and promotion approach across all stores or have few variations. The process we have outlined will naturally encourage a more detailed view of what needs to be improved on a store-by-store basis. In short, no store is an average store, and treating each store like an average store undoubtedly means that you have not adequately addressed what each particular store needs in order to satisfy its customers.

For example, local price comparisons combined with information about the distance to the nearest competitive store should influence or encourage specialized pricing that is tuned to the local market. Used in conjunction with demographic information, this information could also influence future site expansions or relocations.

Seeing a store or set of stores that are not responding well to promotions should encourage an attempt to understand what makes promotions successful. Other factors in our list could provide hints as to why promotions may not be working well, such as in-stock percentages and transaction length at checkout. Customers may view a particular store as too busy or too slow. Poor results in these areas could also indicate labor productivity or scheduling problems.

A final piece of our analysis of customer views of the business should be to conduct some rudimentary correlation analysis between the scores in each area and the store’s performance from a sales and margin perspective. Once this is complete, you should draw some conclusions about which factors are most important, add new factors that could be important and delete factors that are not important. Over time, you can then track how the changes made in operations improve a store’s relative performance.

While it is vitally important for retailers to analyze their customer base, it is also equally critical that they understand how the store image they present to their local customers influences each customer’s view of their business. Through methods described in this article, a retailer can begin thinking more like its customers think and move toward improving its relationship with them.

  • Dan RossDan Ross 
    Dan is the Managing Partner of the Retail Practice at Claraview, a strategy and technology consultancy that helps leading companies and government agencies use business intelligence to achieve competitive advantage and operational excellence. Claraview clients realize measurable results: faster time to decision, improved information quality and greater strategic insight. Dan is a frequent contributor to business intelligence literature, writing on topics spanning technical approaches and business impact, and the Claraview Retail Practice serves some of the world's most advanced users of retail data warehouses.

 

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