From supermarkets to electronics stores, gas stations to coffee shops, loyalty programs are abound in the retail sector, and flourishing more than ever. But it’s the rare retailer that has truly tapped into the value lying within the program’s customer-related data…
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Loyalty programs in the retail sector are nothing new. One of the best-in-class programs – luxury department store Neiman Marcus’ InCircle – last year celebrated its 25th anniversary. Supermarket powerhouse Tesco also recently marked an anniversary (the 15th) for its Clubcard program.
Albertson’s, the Southwestern United States supermarket chain, also recently celebrated an anniversary, of a much different kind though – the 3rd since the demise of its Preferred Card. The reason behind the program’s failure, according to an anonymous company source (who spoke with eWeek after the program was discontinued), was that the chain was not using the data it collected about its customers.
Regardless of sector, loyalty programs that are considered best-in-class have one trait that is common across all of them – the companies actively use the data coming out of them in their day-to-day decision making process, be it to improve the way they serve their customers, capture more share-of-wallet, keep customers, or win back customers.
In its simplest form, loyalty program data can be analyzed to take the following actions:
1. Identify customers who are spending less than in the past, indicating their potential to churn – companies can intervene and take specific actions to ensure not to lose such customers.
2. Identify customers who can spend more, based on the fact they are not giving full share of wallet – companies can likewise incentivize such customers to shift all category spend to themselves.
3. Identify high value customers, based on their relative spend across all loyalty program members – companies can provide an added level of service to such customers to ensure longevity in terms of relationship.
4. Identify customers who have churned, through the fact spend on the loyalty card has completely stopped – companies can offer incentives to bring such customers back into the fold.
The above is the tip of the iceberg, though, in terms of how loyalty program data can be used by retailers to impact their bottom line. By digging deeper into the data and identifying ways in which data can be used relative to specific store locations, we believe companies can add to the value their loyalty programs generate for their company.
Driving this opportunity is the fact that there are vast differences in retailers’ retail points – each specific location has a different set of customers, with different demographic profiles, spend patterns, behaviors, preferences, needs, etc. To most effectively serve such different customer groups, companies need to take such different factors into consideration and reflect this through customization of each retail point – but they rarely do.
For the most part, companies have a cookie cutter approach to their retail operations, having the same layout, products, messages, selling points, stocks, etc., across all of them. Loyalty program data can help this change, helping retailers customize each and every location so as to best serve their customer base, one location at a time.
Three specific value-added location-related reports can be prepared through analyzing loyalty program data, reports we believe are not being widely used by retailers today:
1. Store-by-Store Customer Mix Profile Report – Using loyalty program data, a report can be prepared for each store that profiles its own personal customer mix – the number of customers, their age, nationalities, gender, marital status, where they live, how far they travel, the top spending demographic groups, etc.
Through conducting such a profiling effort, each store can immediately begin considering what it does in terms of servicing its most critical demographic groups. For example, one store may find that their most valuable customers are younger Asian men, whereas another may find that it’s older Arabs that are critical to their bottom line. Accordingly, each one would need to address these groups differently, ensuring that the products they buy are in stock, that such products are put out in the front of the store or on eye-level shelves, or even that a native speaker is available among the employees to assist them should they need help.
Knowing critical demographic groups can even assist in the way messages are designed and communicated – based on the group’s profile, the medium used for advertising (the group watches certain television channels in higher proportion), the key words in the communication (to appeal to the group specifically), the location and timing of the communication (billboards in the neighborhoods the group lives in), and even the language of the communication (based on nationality) can be customized.
2. Store-by-Store Spending Profile Report – Layered on top of the Customer Mix Profile Report, this report profiles the spending behaviors and patterns of the various demographic groups – when they spend, how much they spend, what categories they spend it on, how frequently they spend, how profitably they spend, etc. This report would also highlight the actual most valuable individual customers for each store.
Through such a report, a store would be able to understand when they need to keep higher levels of staff on-site (based on time-of-day spend patterns), what categories of products need to have high safety stock levels and should be located optimally in the store (based on category spend observations), what is considered a valuable customer (based on the mean and median in terms of monthly or annual spend per customer), and who specifically is a valuable customer and thus should be treated differently (based on each customer’s actual profitability, based on per-product and basket margins).
Empowering store managers to take actions on their most valuable customers is a must in such a case; loyalty program managers should consider how to incorporate such micro-level localized customer treatment into their marketing mix.
3. Store-by-Store Product Mix Profile Report – This report would profile what specific products are purchased by each demographic group in what pattern – what they buy the most, what they buy the most uniquely, what they buy together, what products in their baskets are high-margin, what products in their baskets are low-margin, etc.
Used together with the prior two reports, the Product Mix Profile Report can identify tactical level actions that should be taken on a product-by-product level – where products should be located throughout the store, which products should have high safety stock levels, which products can be bundled together or located near each other, which products should be considered in campaigns, and which products can be removed.
Each of these decisions need to be taken in light of the value specific products play for different demographic groups, and how they are or are not purchased together in baskets. Products critical for high value customers need to be played up, those critical for low value clients can be considered secondary in tactical decisions.
The reports mentioned above need to be produced on a monthly basis so as to track changes in patterns over time. What matters to different demographic groups at a given moment in time will not down the road, and thus, trends must be identified through observing the changes in spending patterns, products purchased, time of purchase, frequency of purchase, etc. Accordingly, tactics taken must accordingly change to address customers in the most effective manner possible.
To learn more about creating value-added loyalty program reports, please contact email@example.com.