The Membership Targets One Customer Segment and Ignores the Others

Most membership programs were designed around a behavioral archetype: the loyal, frequent buyer who orders regularly, cares about the brand, and would join a membership because the economics made obvious sense. That customer exists. The program was built for them.

The membership then launched to a customer base that is much more varied. Some members order intensely for a few months after joining and then quiet down. Some buy in large infrequent orders rather than regular small ones. Some came through the checkout because the offer appeared at the right moment and not because they had an established ordering habit. Some buy exclusively during one category's peak season.

None of those customers are poorly served by the membership because the program is bad. They are poorly served because a program designed for a specific behavioral profile cannot optimally serve every profile it acquires, and most membership programs have only one design.

The Standard Credit Model Fits One Behavior Pattern

A monthly credit that resets at the end of the billing cycle works perfectly for a member who places at least one order every month. For a member who orders quarterly in larger baskets, the credit expires unused in the months between orders unless rollover is available. For a seasonal buyer, a monthly credit that reloads twelve times but is only relevant during three of those months produces nine months of breakage that builds the cancellation case without anyone noticing.

Research from Recurly on subscription churn patterns found that 52% of consumers canceled at least one subscription in the past year due to lack of use. In a membership with a monthly credit model, the members generating that lack-of-use pattern are often not disengaged members. They are members whose actual purchase behavior does not fit the monthly cadence the credit was designed around.

Identifying which segments of the member base are generating systematic breakage because of a cadence mismatch requires a simple query: which members are letting credit expire unused, and what does their order frequency look like relative to the billing cycle? Most brands have never run it.

High-AOV Infrequent Buyers Need Different Perk Proportionality

A member who places two orders per year at $200 each has a very different relationship with a $15 monthly credit than a member who places one order per month at $50. Over twelve months, the first member has received $180 in credit while paying $180 in fees and placed orders totaling $400. The credit covers the fee but barely makes a dent in the order total, which makes the mathematical case for the membership weak compared to the experience of a higher-frequency member.

McKinsey's research on integrating loyalty and pricing found that the most effective loyalty programs are calibrated to where specific customers sit in their journey. A high-AOV infrequent buyer is in a different journey than a mid-AOV frequent buyer, and a single perk structure serves both poorly by trying to serve both equally.

The Power User Is Also Underserved by a Middle-Market Design

The inverse problem exists at the other end of the frequency spectrum. A member who orders every two weeks and uses every perk the program offers has reached the ceiling of what the standard tier delivers within months of joining. There is nothing left to unlock, no tier above to reach, and no incremental value in being a more active member than the program was designed to acknowledge.

That member is not at cancellation risk in the short term. They are at risk of plateauing in their relationship with the program, finding nothing new to engage with, and eventually drifting toward a competitor who has built something specifically for a customer at their level of commitment.

Subscribfy's own merchant data shows member LTV running 115% higher than non-members at twelve months. That premium reflects programs where the membership has been able to serve members at different behavioral levels rather than optimizing for one profile and underserving the rest.

If your membership program was designed with one customer archetype in mind and has never been segmented to identify which member populations it is systematically underserving, the program is performing well for some members and explaining nothing about why others are leaving.

Subscribfy helps Shopify Plus brands identify which customer segments their membership is serving well and which are generating systematic breakage or churn due to a structural mismatch between their behavior and the program's design. See how at subscribfy.ai.