For many businesses, one of the most significant benefits provided by a loyalty program is tracking the purchases made by individual customers over time. This generates what you might call “longitudinal customer insight.” All marketers need longitudinal insight to see how their business is actually affected by the experiences their customers have with their products, but some business models won't naturally provide that insight without a tool like a loyalty program.
If you operate a chain of retail stores, for instance, your customers come in and buy your products, pay for them at the cash register, and leave. Without a loyalty program or some other customer-specific mechanism (for instance, a membership requirement), you have no way to connect a customer's purchases today with what that particular customer purchased yesterday or last week.
So let’s say your merchandising manager wants to manage the shelf space you allocate to various products carried in the store. She’d like to reduce the space allocated to the least profitable products, while increasing the amount of space allocated to more heavily bought products. To make this decision, she relies on your point-of-sale (POS) data. The problem is, while POS data will give her an accurate snapshot of sales at any one point in time, the data won't show how any individual customers' purchases have varied over time.
To see how crippling this is, suppose the POS data your merchandising manager sees for three different products stocked in one category show this:
From this table of data, it certainly appears as if Product A is your best seller. At $99 per store, each week it generates 50% more revenue than Product C, while Product B is somewhere in between. Based on this "snapshot" of each week’s sales, if any product ought to be de-emphasized or even discontinued, it would be Product C.
The problem with a data shapshot like this, however, is that it gives you no insight into how many of your customers have actually tried each of these products, or how happy they have been with them. To see this, you need to be able to view a "movie" of how your customers' behaviors are changing, over time. You need longitudinal insight.
So now let’s suppose that, in addition to POS data, your merchandising manager also has access to individual customer data, as provided by a loyalty card linking every customer’s transactions from week to week and month to month. This new data would give her a longitudinal view of your customers' behaviors, so she can see the number of customers who have tried each of these products at least once, as well as the number who have re-purchased each one. The new table of data, based on customer-specific tracking, might look like this:
From this new data, providing a longitudinal view, she can see that Product C is actually your stores' star performer in this category, while Product A is the dog. Product C is repurchased by consumers almost four times as frequently as Product A, but revenue per store is low because less than one in 200 of your customers has even sampled it yet.
So rather than discontinuing Product C, your merchandising manager might consider promoting trial with some coupons or two-for-one deals (offered through the loyalty program to those customers who haven’t yet bought it). After all, the more customers buy this product, the more loyalty and revenue it will generate.
If you don’t have customer-specific data you can’t get a realistic view of how your customers buy from you. And if your business model is one where customers have traditionally bought without providing any form of identification, then a loyalty program is a great way to acquire this kind of insight.