PRICING NEWS
How to Use Customer Cards for Personalized Pricing
by Prof. Dr. Hans-Christian Riekhof
Customer cards: they bring (today) little for the consumer
We have known customer cards for many decades, and some consumers still carry plastic cards from various companies in their wallets. Under the headline “Bonus programs promise a lot – and save little” the Handelsblatt (9.-11.2.24, S. 26 f.) reports about these customer cards and the bonus programs associated with them. The conclusion is quite clear: the savings for customers are less than one percent.
Usage rates of customer cards are declining
With a purchase of 30 €, that’s 30 cents that the customer can then be happy about. Is it worth it? Do customers calculate that? You can’t even get a single Nespresso capsule for that.
On the way to location-driven pricing
That there are question marks regarding customer cards can also be seen from the declining usage rates of these cards. But one should not too quickly abandon the idea of the customer card. Used correctly, it can become a tool for personalized and location-driven pricing.
Digital customer cards as an app: a tool for individualizing pricing
From our UNICconsult and locandis projects we know the success factors of these app-based customer cards. At www.unicconsult.com you can also find our empirical studies on this topic. I would like to present some important results of our projects.
1. Evaluate transaction data and customer data using AI:
Customer cards only make sense today if they are available in digital form as an app. This allows the transaction data to be evaluated in connection with the customer data and used for pricing (in the form of discounts or points). AI-based analyses of purchasing behavior and corresponding strategic derivations are useful here.
Digital customer cards: Individualized and location-driven discounts
Source: © iStockphoto
2. Rely on your own customer cards instead of sharing data with others
We are skeptical when companies share their customer data with Payback or the Deutschlandcard. Most large retailers have the potential to introduce their own digital customer cards. And there are signals that large retailers like Lidl, Edeka, Kaufland, REWE are increasingly relying on their own customer cards. Aral should also be mentioned here.
3. Differentiate discounts by categories and customer groups
In our experience, the potential to differentiate discounts or points very strongly, for example by categories / product groups or by customer groups, is largely untapped. Some product group is calculated so tightly that a discount is not economically justifiable. Other items, on the other hand, can also withstand a discount of 10% and more (this sets a real buying impulse). The previous buying behavior is the best predictor for the future buying behavior, so that the effectiveness of discounts can be predicted based on data.
4. Send situational buying impulses directly at the shelf: Location Based Marketing
An important and largely underestimated dimension of the buying decision are the situational factors. The final buying decisions in retail are made to a high percentage directly at the POS. And here digital customer cards can set buying impulses by playing out advertising messages or discounts for certain product groups directly in the store, at the shelf or in the checkout area through Location Based Marketing. Very few retailers use this type of digital situational pricing today. At www.locandis.de you can see projects that we have implemented in this area.
5. Get the brand manufacturers on board
We have had very interesting experiences in two pilot projects with the inclusion of suppliers in these bonus programs. The suppliers – mostly brand manufacturers with very limited access to end customer data – learn a lot about the buying behavior of customers in their category in these projects. And for this they are willing to participate in the costs of these programs. Given the profit margin of many retailers, a fair deal.
So if bonus programs promise a lot and deliver little, as the Handelsblatt expresses it, then it is also because these programs could be designed much more intelligently.
Best regards
Your
Prof. Hans-Christian Riekhof
