Merging POS and Loyalty Card Data to Increase Sales, Customer Insights for Retailers
By Kent Smith, Kent.Smith@galleria-rts.com
Virtually all grocery stores, especially large chain grocery operators, are dealing with the same dilemma when it comes to clustering customers by linking point-of-sale data with loyalty card program information.
And, based on the two very different types of data generated by POS and loyalty cards, it’s obvious to see why grocers are in a quandary on how to approach the issue.
POS data tracks customer sales on a store-specific basis, by category or by item. Loyalty card data is information about the customers themselves. In short, the data is not oriented the same way.
But there are practical ways to get value out of loyalty card data to develop POS opportunities that work – giving retailers the ability to use loyalty information to micro-market to individual customers.
The first step is manipulating the loyalty data to develop customer segments or groups of customer types. A retailer may have data on millions of customer types, and these large groups have to be boiled down to smaller, manageable groups identified with qualitative descriptors. Identified groups could include ‘comfortable country,’ ‘urban elite’ and ‘grass roots green’ consumers. To be practical using the information, retailers must identify a finite number of easy-to-describe groups of customers.
From there, retailers can develop an incidence of customers by store, creating an index to find out where each customer group is shopping, which gives retailers insight into which stores index high in one customer group or low in another.
Retailers will discover the purchasing patterns, along with lifestyle and qualitative features, for each group. Depending on how rich the data is, you know what magazines the people in each group are reading, how much they spend on cable television and many other preferences among the group. Retailers can take that information and color their assortments and their overall merchandising programs with a lot more depth compared to looking at POS data alone.
Merging the data also allows you to understand how customer groups cluster together.
So, if VW car loyalists always cluster together with 65-year-plus ‘urban elite’ clusters or ‘grass roots green’ clusters, retailers can group those clusters of customers together. This can drive what particular assortments of products appeal to divergent groups of customers. It comes down to identifying specific categories of shoppers and looking for overlaps among them – and the synergy around marketing to these overlapping groups.
If used correctly, the loyalty card data, when coupled with the qualitative descriptors of the customers, gives retailers the ability to make predictive analysis about customer behavior in different categories and within the store.
When you have demand clusters and a way to gauge those demand clusters, retailers can also see if there are gaps.
For example, if a retailer expects that the grass-roots green movement customers tend to buy a lot of the naturally green organic products or less processed foods, but finds there are no spikes in the sales of those sub-categories of products, they need to ask themselves two things: Did I go wrong with my prediction or am I not marketing enough to sell to those people in that store? The retailer may find they don’t have enough signage, the correct assortment of products, or that the group is shopping for those items at the health food store down the street.
In addition to the gaps, retailers can also identify sales consistencies.
For example, if the ‘grass roots green’ group does tend to buy organic products, how consistently do they purchase them? If sales of organic products are low across the board, maybe it’s a national image issue, chain-wide issue or just a store-by-store issue, or perhaps a nearby competitor is winning the business.
Finally, retailers can use the information to understand how the incidence of loyalty card customer groups in each cluster relates to cross-merchandising opportunities.
For example, if you know that within the VW-lovers group customers tend to buy certain things, the retailer can identify both related and unrelated products that they purchase. This can help guide the retailer on the types of products to place adjacent to displayed items.
Retailers also can determine a shopper’s propensity to buy sale and full-price items. Retailers may find customer clusters that always buy a premium coffee when it’s on sale and another variety that they never purchase at a discount. This can help guide a retailer on discounting or when combo-purchases should be employed. A retailer may find it is smarter to cross-promote the full-price coffee with an organic creamer, orange juice or some related product the cluster has a tendency to purchase.
The ability to use the POS and loyalty card data to guide product displays and assortments on the shelves may be the most practical use of the information.
Retailers who want to explore marketing opportunities that merge POS and loyalty data may want to do so sooner rather than later, given consumer trends emerging in this weak economy and subtle moves by major retailers in other countries.
Having invested heavily in loyalty club card programs, some major retailers in the United Kingdom are backing down loyalty promotions. The reason: with the recession and the pressures on the household budget that have come with it, people are shopping more on price than product loyalty.
In response, retailers are instead putting loyalty card-dollars into product price reduction.
If a retailer wants to put loyalty card and POS data to good use in driving sales, now is the time to act. With the right approach, it gives retailers an opportunity to garner more sales by identifying customer clusters to market to, guiding aisle assortments and displays, and enabling other promotions that put two very divergent types of customer data to its best use.
With more than 20 years retail industry experience, Kent Smith leads consultancy and business development in the Americas for Galleria Retail Technology Solutions, a provider of retail and category optimization solutions and services. He can be reached at Kent.Smith@galleria-rts.com.