Who Stops Scanning: How a Continuous Glucose Monitor Manufacturer Used Consulting and Analytics to Identify Which Patient Segments Were Abandoning Device Use and Why
Executive Snapshot
Client
Situation/Challenge
Objective
Constancy Researchers Solution
Impact
Client Outcome
The Situation / Challenge
Continuous glucose monitoring works best when patients understand how to use the data it generates. A device that produces a glucose trend graph every fifteen minutes is clinically useful only if the patient knows what the graph means, what actions they should take in response to different patterns, and how their dietary and activity choices are translating into the glucose curves they are seeing.
The client’s commercial team had been tracking renewal rates as the primary commercial metric and had noticed they were below projection, but had not disaggregated the cancellation pattern by patient characteristics to understand whether specific segments were driving the shortfall. The clinical data team had begun to notice the scan frequency decline pattern independently but had not connected it to the renewal data in a way that produced an actionable diagnosis.
Understanding who was disengaging before they cancelled, and what the disengagement pattern looked like in the data, was the diagnostic step that could convert a renewal rate problem into a specific clinical and commercial intervention.
Key Challenges
- No analytical framework connecting the clinical data team’s scan frequency observations to the commercial team’s renewal rate shortfall.
- No patient segment analysis distinguishing which groups were driving the renewal shortfall from those showing normal engagement and renewal patterns.
- A broad reimbursement expansion that had enrolled many type two patients with no prior glucose monitoring experience, a segment potentially underserved by the standard onboarding provided.
- No early warning system identifying patients showing engagement decline before they reached the cancellation decision.
- Clinical education gaps in the standard reimbursement pathway that the client had not designed its onboarding to address.
- Commercial team pressure to improve renewal rates before the next reimbursement contract review relied on the subscriber base numbers projected from current renewal trajectories.
A continuous glucose monitor subscription that a patient stops engaging with before cancelling is sending an early warning signal weeks before the commercial team sees a cancellation. The patient who drops from twelve scans a day to two is not yet a cancelled subscription, but they are almost certainly a cancelled subscription in waiting. Catching that signal and responding to it before the decision is made is the difference between a retention programme and a win-back programme, and win-back is considerably harder.
Constancy Researchers Solution
Constancy Researchers built an engagement diagnostic framework that connected the scan frequency data the clinical team was watching to the renewal outcome the commercial team was tracking, and then applied it to the full subscriber dataset to identify which segments, timings, and patterns were most strongly predictive of eventual cancellation.
Patient Engagement Diagnostic Framework Design
- Designed a consulting-led engagement diagnostic framework defining scan frequency decline thresholds at specific post-enrolment week intervals.
- Established two diagnostic decline pattern categories: a rapid early decline in the first eight weeks indicating possible onboarding failure.
Patient Segment Engagement Trajectory Analytics
- Applied the diagnostic framework to the client’s anonymised subscriber dataset.
- Found that rapid early decline was disproportionately concentrated in patients with no prior glucose monitoring experience and in patients referred through a high-volume primary care pathway that included minimal structured education, while mid-period decline was more broadly distributed but strongest in patients who had not accessed the client’s available digital education resources.
Clinical & Behavioural Correlate Analysis
- Analysed the clinical and behavioural correlates of each decline pattern, examining whether HbA1c level at enrolment.
- Found that a low initial scan frequency in the first two weeks was the strongest early predictor of rapid decline.
Early Intervention Programme Design
- Recommended a digital education module delivered at week six for all users showing the early decline pattern.
- Designed an automated engagement alert for the commercial and clinical teams that flagged individual subscribers crossing the decline thresholds.
Renewal Rate Impact Modelling
- Modelled the expected renewal rate improvement from the early intervention programme under conservative and base case assumptions about intervention effectiveness.
The engagement gave both the clinical and commercial teams a shared, analytically grounded view of the same problem, producing an intervention design that addressed the actual discontinuation drivers rather than a generic re-engagement campaign.
Impact
- The diagnostic framework identified two distinct scan frequency decline patterns with different timing and likely causal profiles.
- Rapid early decline was concentrated in patients with no prior monitoring experience referred through high-volume primary care pathways.
- Mid-period decline was most common in patients who had not accessed available digital education resources.
- Low initial scan frequency in the first two weeks was the strongest early predictor of rapid decline.
- A digital education module at week six was introduced for users showing early decline pattern.
- A clinical support outreach at month four was introduced for mid-period decline patients.
- An automated engagement alert was deployed for both the clinical and commercial teams.
- Twelve-month renewal rates improved measurably within three cohort cycles of the intervention programme.
Client Outcome
Renewal Rate Improvement
Twelve-month renewal rates improved measurably within three cohort cycles of the targeted intervention programme.
Early Intervention Capability
An automated engagement alert gave the clinical and commercial teams early warning of declining patients before cancellation.
Digital Education Deployed
A week-six education module addressed the data interpretation gap that was driving early decline in the no-prior-experience segment.
Clinical Outreach Added
A month-four support outreach addressed mid-period decline in patients not engaging with available educational resources.
Segment Visibility
Analytics identified the specific patient segments driving the renewal shortfall, replacing a network-average view with a segment-specific intervention plan.
Teams Aligned
The clinical and commercial teams gained a shared analytical framework connecting their separate observations of the same underlying problem.
Onboarding Redesign
Early decline pattern analysis informed a redesign of the standard onboarding for the no-prior-experience patient segment.
Financial Validation
Renewal rate impact modelling confirmed the intervention programme generated net positive return at both conservative and base case scenarios.
Market Positioning
The manufacturer was repositioned as a diabetes care company that manages patient engagement analytically through the subscription lifecycle rather than treating renewal as a passive outcome.
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