Pilot thesis

One-line thesis

Taango Signal can improve gym retention decision-making by capturing reliable exit events, turning access data into completed visits, and showing operators when member behaviour changes before cancellation.

Problem

Most gyms can see entries but do not consistently capture exits. Entry-only data confirms attendance, but it cannot reliably show session duration, open visits, or whether a member's pattern is changing in a way that deserves attention.

Operators also lack a closed measurement loop. They may contact members, but often cannot connect the original signal, the action taken, and the later retention outcome in one system.

Pilot proposition

Taango installs a passive exit reader alongside the gym's existing access system. Each valid entry and exit pair creates a completed visit. Taango then presents two early signals:

1. Visit frequency decline: A member is attending less often than their established baseline.
2. Session duration decline: A member's completed visits are becoming shorter than their established baseline.

Frequency decline is the primary operational signal. Session duration decline is a pilot hypothesis and must not be presented as a proven predictor until the data supports it.

Closed-loop workflow

1. Capture entry through the existing gym credential.
2. Capture exit through the Taango reader.
3. Match events into a completed visit.
4. Compare recent behaviour with the member's baseline.
5. Flag meaningful decline with a clear reason.
6. Deliver a weekly operator report and action list.
7. Record contact, intervention and outcome.
8. Compare retention and return behaviour with a control group.

Primary pilot questions

• Can Taango capture and match exits with enough accuracy for operational use?
• What proportion of valid entries become completed visits?
• Does falling visit frequency precede cancellation or membership inactivity?
• Does shorter session duration add predictive value beyond frequency decline?
• Will gym teams open reports, take action and record outcomes consistently?
• Are flagged members more likely to return after an intervention than comparable members who do not receive one?
• Can the measured effect support a credible retained revenue estimate?

What Taango is not proving in this phase

• A full member app
• A CRM replacement
• Automated marketing journeys
• Workout, class or community features
• A complex AI churn score
• Network-wide benchmarking

Evidence required at the end of six months

The pilot should end with a defensible dataset linking member access behaviour, completed visits, detected changes, operator actions, return behaviour, cancellation outcomes and estimated retained revenue. This evidence becomes the basis for the next product and funding decision.