Investments in customer loyalty are booming… but do program managers and partners really know the true ROI from these investments? Marketing teams are spending big on rewards, partnerships, agencies, data capabilities and marketing technology. Loyalty has become table stakes in most consumer facing sectors. Having a loyalty program is becoming a cost of doing business, a hygiene factor. If you are a typical consumer you have dozens of plastic cards in your purse/wallet/drawer at home.
American companies are spending over $52 Billion1 in loyalty points and rewards. It’s clear that companies are investing significant amounts of money into customer facing programs and activities, but what are we trying to achieve? The most loyal customers, who are sometimes the largest, are not always the most profitable. For this reason, it is critically important that we measure the profit returned from customer loyalty and retention marketing investments before potentially sending more good money after bad.
There are some real challenges in doing this accurately
- Loyalty is long term: How do we apply campaign disciplines to multi-year customer retention targets? Customers do not react well to being told “you can’t join this terrific reward program because you’re in our control group”.
- Self-selection bias: Customers most likely to enroll in your program are those already most engaged with your brand. So any comparisons of members versus non-members do not necessarily measure program effectiveness.
- Myopia: some (most?) of your best customers are also shopping frequently with your competitors.
An example to consider… A large retailer sees an increase in spend size and frequency in a segment of mid-value customers over a given period…this is good, right? Well what if your share of wallet with these customers went backwards in that same period as they ramped up their involvement with Prime? Not so good now, they are spending a bit more with you, but potentially spending more with your competitors. How is it possible to see this?
A multi-discipline approach provides measurement solutions. A sample:
- Before and After: Compares customer sales behaviour, at the individual customer level for identical periods before and after enrollment to avoid any seasonal distortions and highlight the immediate impact of the customer program.
- Statistical Pairing: Matches customers on every available attribute, producing a set of paired customers large enough to extrapolate differences in value and churn that can be attributed to the loyalty investment.
- Redeemer versus Non-Redeemer: For programs with a loyalty currency (e.g. points) contrasting the behaviour of Redeemers with Non-Redeemers provides a measure of program effectiveness as it reveals customer engagement with the program (or not).
- Market Share of Category: Being able to accurately measure current levels of loyalty (internal view), with the additional share of wallet perspective (external view), provides critical insight for marketers to best direct customer investments. What happened to your share of the market, in your category when you increased/decreased customer loyalty investments?
The data sharing economy (and coalition loyalty programs) are now unlocking new insights for brands – enabling you to look beyond your own view of the customer. Credit card companies are now opening their data to help brands build a market-wide view of their customers’ behaviour, including (in certain circumstances) the ability to append external data at an individual customer level.
We are firmly in the ‘Age of the Customer’ so managing customer engagement should attract as much rigour as managing cashflow. There is after all, one source of profit; a customer who pays their bills.
Ellipsis understand that measuring loyalty and retention program ROI can be difficult. Our Return on Loyalty® offering is a powerful new way to accurately measure your customer investments and guide better decision making, helped by access to expert consultants to interpret insights for actionable recommendations.
But the really high returns from the effort required to measure ROL come from insights that allow you to make adjustments for a better return in the future, before it is too late.