Our members are worth more than non-members. Does that prove memberships work?

Selection bias in one paragraph

The customers who agree to a recurring service plan tend to be homeowners rather than renters, tend to have owned the property longer, tend to have had a good experience on the visit where it was offered, and tend to be the kind of people who plan maintenance. All of those traits independently predict higher future revenue.

So a member-versus-non-member comparison is measuring the traits, the program, and the technicians' judgment about who to pitch, all mixed together. The gap is real; the causal claim inside it is not established.

Comparisons that get closer to the truth

• Match on pre-enrollment characteristics. Compare members to non-members with the same job type, similar first-visit ticket, similar property tenure and similar time as a customer. The gap usually shrinks, sometimes a lot.
• Measure only post-enrollment behavior. Revenue that happened before someone joined tells you who joins, not what joining does. Include it and the program looks better than it is.
• Randomize the offer where you can. If enrollment offers are not made to everyone anyway, making the choice random for a slice of eligible customers turns the comparison into an actual test.
• Watch who was offered and declined. Customers who were pitched and said no are a far better comparison group than customers who were never pitched at all.

Measure what the program is supposed to change

A membership is meant to do specific things: create a scheduled reason to be in the home, catch problems earlier, and keep you in position when the equipment finally needs replacing.

So measure those directly. Visits per year, share of repairs found on maintenance visits rather than emergency calls, and the share of members who replace their system with you rather than someone else. Those are narrower questions than lifetime value and much harder to fool with selection effects. They also require membership records joined to job history, which is a standard part of connecting field service data to reporting.

This matters even if the program is good

The point of getting the measurement right is not to talk yourself out of memberships. It is to learn which customers the program actually changes, because that determines who your technicians should be pitching and what the offer should look like.

If the lift is concentrated in one segment and near zero in another, that is a direct instruction about where to spend selling time. You cannot see that in an aggregate comparison, and it is exactly the kind of thing a proper analysis layer is worth building for.