Got a Loyalty Program? Here's How To Monetise It.
Everyone wants loyal customers and loyalty programs are designed to encourage and reward loyalty. But more than encouraging customers to return, they also allow you to track customer behaviour which is critical to understanding your customers better.
So, by their very design, loyalty programs are a treasure trove of valuable data. An analysis should yield insights that directly grow revenue and enhance customer experience.
As a start, an analysis of your loyalty program will reveal:
- What kinds of customers are attracted by your product?
- What drives customer value for your business?
- What characteristics do your most valuable customers share and what common behaviours do they exhibit?
- How your multi-basket, single basket, and most valuable customers differ from one another in who they are and how they shop?
This basic information can be used to:
- Inform product bundling, placement, cross-selling strategies.
- Optimise your messaging and your marketing to make sure you’re getting more of your best kind of customers.
- Resurrect your single-basket members with offers that are based on what they are most likely to return for.
- Predictively identify behaviours that precipitate abandonment to allow you to intervene before it happens.
Amazing, right? All of the above is eminently doable with some well thought through analytics and data strategy know-how.
So you’ve got customer loyalty data and you want all of the above. Excellent. Where do you start?
Here are the two most important questions to answer to supercharge your data monetisation and measurably grow data ROI.
Who are your most valuable customers?
The ‘most valuable’ customer segment is the first and most important segment to identify in order to monetise loyalty data.
It also seems like a question everyone in the business should know the answer to but it’s not as straightforward as it sounds. There are many ways to cut the cake.
The answer to who the most valuable customers are depends on what drives customer value for your business. For example, does the frequency of purchases or the value of each basket contribute most to high aggregate customer spend?
An analysis of loyalty data will reveal your unique value drivers. Once they’re known, it becomes easier to define what value really means.
For instance, if frequency of purchases is the value driver, then in defining this segment you will rank your loyalty members based on the number of times they have transacted. If, on the other hand, higher average item value purchased is the value driver, then you would use this metric to rank your loyalty members instead.
Step two involves determining the cut-off value for members that make and don’t make this most valuable segment.
How you decide to define the top tier will depend on your objective as well as signals in the data. It may be that a logical delineation is apparent from the data. For instance, when you rank your customers the top five percent may represent 50 per cent of all transactions and then the allocation per incremental percentage drops precipitously. In that case, you would probably consider the top five percent as your most valuable segment. Alternatively, you could use segment information to offer VIP status to a limited number of members. If you are constrained to offering VIP status to only 1,000 loyalty members, you would cut off the segment there.
Whatever your unique objective, the first step to monetising your loyalty database is having a clear answer to the question of what customer value means to you and to identify exactly who these customers are.
Analysing most valuable customer data is fascinating and sometimes the findings are stark. More often than not, however, the data requires some expert splicing to locate the signal in amongst the noise.
The key point here is that your most valuable customer segment data is also valuable as a point of comparison to other segments. How does your most valuable customer segment differ from other customer segments you have identified?
That brings us to the second most important question.
Who are the customers in your loyalty program that never came back?
The single basket segment is more important that an analysis of customers who do not buy high value items or have lower than average basket values. Not purchasing high value items may be a factor of what is on sale, the size of the price markdown, the time of year, the purpose of the purchase, and many other factors.
However, the critically important characteristic that single basket customers have in common is far more important: these customers never came back.
This group is always of particular interest when analysing loyalty databases because this ‘single basket’ customer segment is so undesirable. Single basket loyalty members are the antithesis of your most valuable members and it’s critical that you understand how and why they differ in how they buy and what they buy.
And that's where you start to monetise your customer loyalty data.
Running all your analyses including product bundling and subsequent purchasing propensities for these two segments at a minimum should yield information you can immediately use about how your most and least desirable segments differ from one another.
The time taken to read this far into the blog? Five minutes.
The insights you’ll gain by running your data analytics based on these two customer segments? Priceless.