Loyalty Cards and Personal Information

Dr Christos Themistocleous, of the University of Nottingham Business School, shares his research and expertise on customer loyalty schemes. 

Loyalty schemes are considered to be integral parts in the development of customer loyalty for companies within different sectors. For example, supermarkets within the Cypriot industry like Carrefour, Metro and Alfamega have in place loyalty cards while bonus or club cards are also present in retail industries like Stephanis – or even within the hospitality sector with Starbucks and Costa coffee rewards cards. Even though the name of these schemes might differ from case to case – from loyalty, rewards or membership cards – they are all examples of loyalty schemes that seek to maximise the buy-rebuy behaviour of customers through a strategy of “grow and retain”, also known as customer loyalty.

Previous academic research identified customer loyalty to be linked not only to profit maximisation for businesses, but also to brand equity enhancement, meaning that the business as a brand is positively perceived by consumers within the market.

Nevertheless, at the time of their introduction, loyalty schemes had a secondary objective, which is now becoming more and more apparent. This secondary objective is the gathering of customer information. With the introduction of loyalty schemes revolving around electronic cards in the 80s and 90s, organisations were provided with the capability of recording the consumption volumes of specific products by customers as a whole. This enhanced the inventory management of organisations and improved efficiency by providing them with the ability to more effectively identify when to order in new volumes of a product. Additionally, this improved the efficiency of organisations by allowing them to identify which products performed well in terms of sales and which ones didn’t, and to see which products would lead to increase or to decrease of the volumes being ordered. This knowledge allowed companies cancel or terminate orders for unpopular products.

As technology progressed, new analyses of patterns could be calculated from the data acquired through the loyalty cards. From seeing customers as a whole, larger organisations with the appropriate resources and devoted departments could now identify patterns to the singular level, meaning patterns for each customer individually.  

By accumulating data of the shopping pattern of the individual over a period (say for one month) through scanning of loyalty cards, organisations could now identify products to which that individual is loyal to and accordingly provide him/her with offers for particular brands to reinforce their loyalty to the company. See, for example, the table below.

Customer A
Product typeBrandQuantity (over a 1 month period)
 Lion (Nestle)19
ChocolateMars (Mars inc)6
 Kinder (Ferrero SpA)3

From this table, we can see that consumer A has a clear tendency to like Nestle’s Lion chocolate, meaning that he/she is strongly preferring this brand over competitive ones. Large organisations with the capabilities of identifying patterns like these on an individual level can target customer A with tailored discounting schemes that involve his/her brand of choice, in this case Lion bar. This ensures that buy-rebuy behaviour is in place which, by extension, leads to customer loyalty.

Now the newest generations of customer analysis techniques seek to explain customer behaviour through shopping patterns. Already some time ago, Smith and Sparks (2004), in their paper “All about Eve?”, demonstrated how customer information can shed light to demographic, lifestyle and up to a degree psychographic characteristics of individuals by simply analysing their shopping pattern and without any prior knowledge of that individual. See, for example, the table below.

Consumer A
Product typeQuantity (over a 1 month period)
High-calorie snack18
Soft drink19
Condoms (pack of 6)1
Pampers for babies (pack of 12)3

The table summarises the purchasing pattern, by product type, of Consumer A, whose pattern was also previously analysed to see his/her strongest preference of chocolate. Through the table above, specialists of data analytics can make inferences of what characterises this individual. For example, by identifying the high number of unhealthy products being purchased, namely high-calorie snacks, soft drinks and chocolates, arguably and up to a degree it can be inferred that this individual must be following a relatively unhealthy lifestyle with a good chance to have some excess weight. Furthermore, the purchase of condoms can lead to the inference that this individual is sexually-active with a high probability to be a man, while the pack of pampers hints that he must be a father of a young baby. Therefore, the previously unknown Consumer A now is being translated to a relatively young father with a baby who has some excess weight and is sexually active. Arguably, the above description can also be relevant to a young mother who happened to buy condoms for her partner. Still, the precision of the description is increased when examining the shopping pattern over a longer period (2 years for example) and by including other types of products to confirm gender (for example, tampons). Through these, one can achieve statistical significance when describing customer A.

Now, the uses of the information demonstrated above can arguably follow two paths. First, organisations, through analyses like these, have the ability to characterise and understand their customers and, accordingly, to provide them with more related offers. For example, for Customer A, offers on healthier food and on baby products.

It is important, however, to consider ethical questions – especially when dealing with personal data and information. Companies may sell the characteristics of a customer to third parties, for example, sell the characteristics of Customer A to a local gym, who would in turn target this young father with offers to lose weight. Similarly, Customer A may be targeted by email campaigns for baby carriages. The possibilities are limitless and organisations in the past have shared information without the knowledge and consent of the individual. Previous research by Themistocleous et al (2014) sought to examine voluntary and ethical ways to facilitate information accumulation by consumers, while attempts to regulate the non-consensual selling of information have been made in the UK (Midata) and Central Europe (Data Protection Directive). A definite sustainable result is yet to be achieved, however. Even though this paper does not make insinuation of the fact that the latter practices are present in the Cypriot market, knowledge by consumers as to how their information could be used by organisations is key.

Based on the above, we can see that loyalty cards are not devised to solely provide discounts and benefits to customers but mostly to return customer information to organisations. The decision to subscribe to a loyalty card scheme must be an informed one.

Smith A., and Sparks L., (2004), ‘’All About Eve?’’, Journal of Marketing Management, vol. 20 (3-4), pp. 363-385.

Themistocleous C., Wagner C., and Smith A., (2014), “The Ethical Dilemma of Implicit vs Explicit Data Collection: Examining the Factors that Influence the Voluntary Disclosure of information by Consumers to Commercial Organizations”, IEEE in Ethics, Science and Technology book of proceedings, Chicago, US.

Like this article?

Scroll to Top