Delving into Data Sets: Where Does Supermarket Customer Card Data Fit?

Delving into Data Sets: Where Does Supermarket Customer Card Data Fit?

The desire and need to understand consumer behaviour is on the rise, and the imminent release of customer card (loyalty) data is a welcome sight for the FMCG industry. 

Many inroads have been made in recent years into the use of big data sources. Supermarket customer card data offers a view of an individual retailer’s customers at a very granular level. This means both retailers and suppliers can understand things like customer responses to short term promotions, and the composition of items in a basket, right down to item level. It’s a unique opportunity for sales, category and key account executives to work more collaboratively with their retailer partners by putting the customer at the centre of their discussions.

Yet while customer card data can help us understand shopping behaviour and to some degree who these people are, it should not be used as a proxy for the market. It’s important to recognise that the data only represents customers of an individual retail banner, not the market. And the market in New Zealand, as we know, consists of banners with very different characteristics and in store behaviours.

Taking the three key metrics that help explain sales (the number of households that shop, how often they shop and how much they spend each visit) we see distinct differences across each banner. Consider the fact that the average number of households that shop at a PAK’nSAVE store is more than double (2.5 times) that of New World, and the average spend per trip is 30% higher in PAK’nSAVE compared to Countdown. This means the composition of items in a trolley or basket purchased at PAK’nSAVE will differ significantly from a basket purchased at Countdown.

Do the same types of households typically shop at each banner? Far from it. While Countdown shoppers skew a little with respect to affluence (a combination of household size and household income), and household composition, the profiles of New World and PAK’nSAVE shoppers are far more pronounced. Large families and low affluent households are synonymous with PAK’nSAVE, while higher affluent, one-two member households tend to be more prevalent at New World.

Very few people shop at a single banner; half of all shoppers shop at more than one supermarket in a week. If we look at this from a spend perspective, over a month, shoppers at Countdown spent 60% of their total grocery spend outside of Countdown. Shopper ‘promiscuity’ is rife, illustrating the risk in using a single banner as a proxy for the market.

Finding opportunities for growth by understanding shopper’s’ path to purchase requires an in depth measurement of the retail ecosystem using scientific rigor. This is achieved when relevant data sets are used in conjunction with each other.  No one data set has all the answers, it is important to understand which data source to use. 

Customer card data will give you the depth you need to optimise the impact of in store activity for key items through smarter pricing,  promotions and merchandising. Market level data will give you the breadth you need to understand the relative performance of your brand across the entire retail landscape so you can decide whether your plan requires tweaking or remedial action.