This is a good time to take a small detour into the world of customer loyalty programs and the data that goes into them. Our guide is Ajay Row. In a series of posts on LinkedIn a couple years ago, Ajay Row dug deep into customer loyalty programs.
To my mind, there are two purposes to a loyalty program:
- Reliably and consistently deliver profitable revenues in good times and bad. Revenues include both core revenues (company’s main business via retention, cross-sell, up-sell and new business via Member-Get-Member type programs and sharing positive reviews) and ancillary (typically membership fees, profits at redemption and money made via external partners who value the member base).
- To consistently deliver incremental actionable (and actioned!) insight. All the way from the overall base, through effective segments down to what could be millions of “audiences of one”.
The loyalty program is the price you pay to get these two. How does it work?
Loyalty programs deliver four levers:
- Data: at an individual member level includes profile, transaction, interaction, stated and inferred/observed preferences and finally derived data (i.e. data you build from data goes all the way from H / M / L value members through Cohorts to price sensitivity and basket type). This data, which is of course “internal” to the loyalty program, can be overlaid with data external to the program and even external to the company to build more value. Example, building geographical or seasonal models by individual members.
- Communication: the program has the opportunity, even the responsibility, after all the member has put up a hand and said they want to be a friend, to communicate respectfully and effectively. Communication keeps members engaged and typically covers one of three broad areas: brand/relationship building with the individual member, incremental reasons for a profitable behavior (e.g. point-based promotions that aren’t available to non-members) and program-related information (new status, number of points). Sometimes, a single point statement is enough to achieve all three ends.
- Rewards / Currency: the program, if well designed can deliver a currency other than money, one that can be even more valuable than cash depending on how effectively the redemption works, and the company controls the value! Using what we call the Value Per Point and Cost Per Point difference is one of the key levers by which loyalty programs make money. Point liability is fascinating game to play, but that will have to wait for another article.
- Recognition: the program can make your most valuable members feel exceptional by recognizing them as being special to you. And the program if designed with a little imagination can do this at an individual member level. Based on their value but also based on what is valuable to them.
Armed with these four levers, the loyalty program can deliver both reliably profitable revenues and incremental insights.
One of the key facets of any loyalty program is data. Here is how Ajay Row explains the different types of data:
Profile data: Member-given (e.g name, address, preferences, interests, etc.) — members puts up their hands and offer friendship, investing time to teach the organization about themselves. It the organization’s responsibility to think this one through carefully, what will we ask? How will we use it? Is it overly intrusive given our brand values?
Transaction data: System-generated (e.g. typically spends and related categorization data etc.)
Interaction data: System-collected (e.g. visits, social media related, eDM responses etc.)
Point related data: System-calculated(e.g. points earned, redeemed, balance, expired, bonus etc.)
Financial data: System-generated (e.g. transaction and customer profitability, point liability etc.)
Environmental data: Variously-collected, as far as possible, systematically (e.g. season, TOD/DOW, advertising running at the time)
Derived data: System-calculated (i.e. data derived, usually totaled, from some permutation or combination of the data sources above e.g. customer value growth, customer life-time value, cohort analysis, clusters and segments, correlations and causation etc.)
As Ajay Row summarises it: The objective of any loyalty program and hence analytics project is to: [Maximize the Sum of Customer Life Time Values across the Member Base].
As we will see going forward, calculating customer lifetime value (CLV) right to then correctly identify the Best Customers is the key to winning in customer loyalty.
Tomorrow: Part 7