Skip to content

Use Case · Prevent Churn

Catch at-risk customers before they leave

Churn is visible in the data before it shows up in your cancellation numbers. Sfere surfaces the behavioral signals that precede departure, triggers the right intervention in real time, and gives you the segmentation to win back customers who already went cold

Book a Demo Explore Sfere ID

One customer · twelve weeks · watch the signal

Sfere ID · Customer #48211
Consented profile · your region
Healthy
Lapse risk 8/100
Sessions / wk 5.4
Days since active 1
Threshold 60crossing fires the journey
Week 0.0 / 12 drag · or press play

Press play or drag the timeline to watch twelve weeks of one customer’s engagement

Simulated customer profile for illustration: production deployments stream your own first-party signals

Two ways this story ends

Option A

Act on the signal

Week 8: the threshold crossing fires a journey in real time. The customer engages again, and the cancellation moment never arrives

Customer retained, on the same profile

Option B

Act on the cancellation

Week 12: the first “signal” is the cancellation email itself. Now it is win-back: slower, costlier, and far from guaranteed

Win-back from cold, at a discount

Churn prevention

Act on the signal, not the cancellation

By the time a customer cancels, the churn has already happened. Sfere works upstream of that moment

See drift before it becomes departure

Real-time behavioral signals (declining session frequency, shorter dwell times, skipped interactions) surface in Sfere ID as lapse risk scores before a customer cancels or goes cold. You act on the trend, not the cancellation

Know what each at-risk customer looks like

Every customer’s churn risk is calculated on their individual profile history, not a population average. A lapsed season ticket holder looks different from a lapsed shopper, and the intervention should reflect that

Trigger reengagement at the right moment

When a customer’s profile crosses a lapse threshold, Sfere triggers a journey automatically (a personalized email, a reward top-up, a win-back offer) without waiting for a batch report or a manual export

Win back the ones who already left

Suppressed or lapsed customers are not lost forever. Audience Builder segments them by last active date, engagement history, and tier status, so win-back campaigns reach the right people with the right incentive, not a blanket discount

Real-time lapse detection

The drift is visible long before the departure

A customer who is about to churn does not go from fully engaged to canceled overnight. They drift: sessions get shorter, interactions get sparser, content gets ignored. Sfere tracks these signals in the customer profile in real time, so a lapse risk score is available the moment the pattern starts, not after the cancellation email arrives

See how real-time profiles work
Signal anatomy · Customer #48211 Real time
Session frequency 5.4 → 1.2 /wk
Dwell time 6 min → 40 sec
Interactions daily → skipped
68 LAPSE RISK · THRESHOLD 60

One score per customer, not per cohort

Lapse risk is computed on this customer’s own profile history, not a population average, and surfaced in Sfere ID the moment the pattern starts. Crossing the threshold fires the journey automatically

How it works

Detect, understand, intervene, win back

Four steps turn a churn signal into a recovered customer

  1. 01

    Detect lapse risk early

    Declining signals surface as at-risk flags before the customer formally churns

  2. 02

    Understand each customer’s risk individually

    Risk is computed on the individual profile, not a cohort average

  3. 03

    Trigger the right intervention automatically

    Crossing a lapse threshold fires a real-time journey, with no manual exports

  4. 04

    Win back those who have already lapsed

    Win-back audiences segment former customers by history, tier, and last active date

Win-back segmentation

Gone cold is not gone forever

Suppressed or lapsed customers are not lost forever: Audience Builder segments them by last active date, engagement history, and tier status, so a lapsed season ticket holder and a lapsed shopper get different offers on the channels they actually answer

Because segmentation runs on the same first-party customer graph that flagged the drift, win-back campaigns reach the right people with the right incentive, not a blanket discount sent to everyone who ever went quiet

Audience Builder Win-back segment
Last active 30–90 days ago
Engagement history Previously weekly
Tier status Gold
Win-back audience Ready to activate
Email Push WhatsApp

Segmented on the first-party customer graph you own: the right people, the right incentive, not a blanket discount

Stop churn before it starts

See how Sfere surfaces at-risk customers from behavioral signals, triggers real-time reengagement, and segments lapsed customers for win-back campaigns, all on a first-party customer graph you own

Book a Demo