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

Continuous Glucose Monitor Manufacturer, United Kingdom

Situation/Challenge

The client had achieved strong initial adoption of its flash glucose monitoring system among type two diabetes patients through a combination of clinical advocacy and NHS reimbursement access. Twelve months after the reimbursement expansion, renewal subscription rates were significantly below the level the commercial team had projected, and the clinical data team had noticed that a share of active subscribers were showing declining scan frequency well before they cancelled. The commercial team lacked a structured view of which patient segments were most at risk of discontinuation and what was driving the decline in engagement before cancellation.

Objective

Engage consulting to design a patient engagement and discontinuation diagnostic framework, then apply analytics to the client's anonymised usage data to identify which patient segments showed the earliest and steepest engagement decline, and whether the decline correlated with clinical, demographic, or behavioural factors that could support an early intervention programme.

Constancy Researchers Solution

Consulting Services combined with Data Analytics & Business Intelligence, a consulting-designed patient engagement diagnostic framework, paired with an analytics workstream examining scan frequency trajectories, renewal versus cancellation patterns, and clinical and demographic correlates of early engagement decline across the subscriber base.

Impact

Analytics identified two distinct engagement decline patterns: a rapid early decline in the first eight weeks among patients with no prior glucose monitoring experience, and a slower but more widespread decline between months four and eight among patients who had not received structured education on interpreting their glucose data. The clinical team confirmed that glucose data interpretation support was absent from the standard reimbursement pathway that most type two patients had accessed.

Client Outcome

The client introduced a digital education module at week six for new users and a targeted clinical support outreach at month four for patients showing the early decline pattern, and twelve-month renewal rates improved measurably within three cohort cycles.

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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