Predict churn with the signals each location actually generates — clinic staff turnover, scheduling friction, insurance shift, competitor opening
Per-subscriber, per-location churn-risk scoring built from the signals that actually move retention in your business.
The problem
You run an 80-location dental DSO with a $79-per-month membership program — 12,000 active members. Mixpanel scores each member on a 0 to 100 churn-risk index using generic engagement signals. The model does not know which of your locations the member belongs to, which means it cannot see that Phoenix members churn faster than Denver members. Your analyst suspects it has something to do with clinic staff turnover or scheduling friction in Phoenix, but the model offers no way to confirm. ChurnZero gives you a retention dashboard. Gainsight predicts churn at the account level for B2B. Last quarter your team ran a save-offer campaign on the 200 highest-risk members brand-wide and it misfired in Phoenix — the offer included a discount on a service that the Phoenix clinic does not even provide. The default outcome is a quarterly cohort report that surfaces vague 'engagement drop' patterns two months after the cohort has finished churning.
What success looks like
Every subscriber's churn risk is scored using the signals that actually predict it in your business — and those signals are location-aware. Clinic staff turnover at that location (a lost-trust signal). Insurance changes (payor shift). Local-competitor pressure (a new dental practice opening within two miles). Scheduling friction at that clinic (wait-time increase). Payment-method issues (expired-card patterns). Visit-frequency drift compared to the cohort cadence for that location. Appointment no-show velocity. Customer-service ticket sentiment from that location. The score is weighted by LTV cohort and by the conversion patterns of the acquisition source that brought that member in. State-by-state and federal rules apply automatically — no PHI flows into the features. Multi-banner operators see one consolidated view. Every score is preserved with the feature inputs, the output, the confidence, the compliance attestation, and the intervention trigger.
How most operators solve this today
Five categories of tools touch churn prediction today. None of them score per-subscriber risk with per-location signals:
Churn-prediction and retention platforms (Gainsight, ChurnZero, Totango, Pendo, Mixpanel, Amplitude)
$25 to $833+ per month or $25,000 to $250,000+ per year enterprise tiers
Generic engagement-based churn. Not multi-location-aware. The Phoenix-versus-Denver delta disappears into the brand-wide average.
Customer data platforms with churn modeling (Segment, mParticle, Tealium AudienceStream, RudderStack, Hightouch, Census)
$120 to $200,000+ per month or year, plus enterprise tiers
Generic CDP-based modeling. Not per-location.
Subscription analytics platforms (ChartMogul, Baremetrics, ProfitWell, Recharge Analytics, Subscript, Maxio)
Free to $1,499+ per month, plus enterprise tiers
Generic subscription metrics. Not multi-location, not predictive at the subscriber level.
In-house data science with custom models
$150,000 to $240,000 per year per data scientist, plus four to sixteen weeks per model
Custom models. The model ages quickly as new feature sources come online or HIPAA boundaries shift.
Build it in-house
Engineering plus data science work, plus ongoing maintenance
The signal sourcing, the HIPAA-safe feature engineering, the per-location weighting, and the LTV-cohort joining all have to stay in sync.
What changes when this is an agent skill
Every subscriber's churn risk is scored from the signals that actually move retention in your business — and those signals are tied to the specific location the member belongs to. Clinic staff turnover at that location surfaces as a lost-trust signal. Insurance changes in that market surface as a payor-shift signal. A new dental practice opening within two miles of that clinic surfaces as competitor pressure. Scheduling friction at that location surfaces from the wait-time series for that clinic. Payment-method issues surface from expired-card patterns. Visit-frequency drift is compared to the cadence cohort for that location, not for the brand average. Appointment no-show velocity and CS ticket sentiment add the rest. The score is weighted by the LTV cohort the member matches and by the conversion patterns of the acquisition source that brought them in. PHI never flows into the features — the boundary is built in. The score works alongside your cancellation-reason clustering, save-flow scoring, pre-emptive intervention, and save-offer library because they share the same customer record. Multi-banner operators see one consolidated view. Every score is preserved with the feature inputs, the output, the confidence, the compliance attestation, and the intervention trigger.
How this compares
gainsight.com
Generic engagement-based churn. Not multi-location-aware. The Phoenix-versus-Denver delta disappears into the brand-wide average.
mixpanel.com
Generic engagement-based churn. Not multi-location-aware. The Phoenix-versus-Denver delta disappears into the brand-wide average.
amplitude.com
Generic engagement-based churn. Not multi-location-aware. The Phoenix-versus-Denver delta disappears into the brand-wide average.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Subscription Lifecycle Orchestration Agent
Predicts churn, scores save-flow propensity at the cancellation surface, and triggers email + SMS interventions 7-21 days ahead.
Related skills
Cluster the real cancellation reasons by location — and stop calling 60% of them 'other'
Per-location cancellation-reason clustering from free-text exit surveys, weighted by the LTV cohort of who actually walked away.
Customer LTV per location, per channel, per brand — at runtime
LTV cut by location, channel, brand, and cohort — historical and predictive — exposed as a real-time signal your save-flow, your ads, and your loyalty program can all act on.
Customer cohorts every AI agent reads at runtime, not weekly
Cohorts like "high-LTV", "at-risk", and "lapsed" computed on your unified customer record and exposed as a real-time signal — so your save-flow, your email, your ads, and your loyalty program all act on the same up-to-date answer.
One customer record across every device, channel, and location
The same customer on desktop Tuesday, in-app Wednesday, in-store Friday, and on the phone Saturday gets unified into one record — so cohort, LTV, and decisioning all see one person, not five.
Behavioral signals from every channel feeding one customer record
Web, mobile, in-store, call center, ads, email, SMS, and chat behavior collected into one unified customer record — every AI agent reads engagement signals at runtime.
One attribution model for the whole brand misses what each market is actually doing
Per-location attribution that reflects how each market actually buys — Google Ads in Denver, walk-in foot traffic in Austin, direct mail in Tampa — joined to the same customer across every touchpoint.
One master record per location — reconciled across every source, propagated everywhere automatically
One trusted record per location, reconciled across POS, Google Business Profile, Yext, HR, and your CMS — and when anything changes, every marketing surface updates automatically.
FAQ
- What does per-subscriber churn prediction actually do?
- It scores each member's churn risk using signals tied to the specific location they belong to — clinic staff turnover, insurance changes, local-competitor pressure, scheduling friction, payment-method issues — weighted by LTV cohort and acquisition source. You see why the score is what it is.
- How is this different from Gainsight, ChurnZero, Totango, Pendo, Mixpanel, or Amplitude?
- Those score on generic engagement signals. They do not know which of your locations a member belongs to, so they cannot see why Phoenix churns differently from Denver.
- How is this different from Segment, mParticle, Tealium, RudderStack, Hightouch, or Census?
- Those are CDPs with generic churn modeling on top. Not per-location, not LTV-cohort-weighted.
- How is this different from ChartMogul, Baremetrics, ProfitWell, Recharge Analytics, or Maxio?
- Those report subscription metrics at the program level. This predicts at the subscriber level with per-location signals.
- Which location-specific signals are used?
- Clinic staff turnover at that location, insurance-payor shifts in that market, new local-competitor openings within two miles, scheduling-friction wait-time series at that clinic, payment-method issues, visit-frequency drift versus the cohort cadence for that location, appointment no-show velocity, customer-service ticket sentiment.
- How is HIPAA handled?
- PHI never flows into the features. The model can know 'appointment no-show velocity is up' without storing 'patient John Smith no-showed a root-canal follow-up.' The boundary is built in.
- How does the LTV-cohort weighting work?
- A high-LTV-cohort member with a high churn score routes to your strongest save intervention. A low-LTV-cohort member with the same score takes a lighter-touch route. The score is the same; the response reflects the value.
- Can a retention team review why a member was flagged?
- Yes. Every score is preserved with the feature inputs, the output, the confidence, the compliance attestation, and the intervention trigger.