AI churn prediction for SaaS, subscription, telecom and fitness businesses. Every account gets a risk score 60–90 days out, the three reasons behind it, and a save-play assigned to an owner in Salesforce or HubSpot.
AI churn prediction is a machine learning model that reads usage, billing, support and contract data to estimate how likely each customer is to cancel or not renew in a set window. It outputs a ranked risk score per account, the signals driving that score, and a recommended retention action that your team can work from inside the CRM.
Most teams see churn in a monthly report, after it has happened. The early signals were there: fewer logins, a stuck ticket, a missed payment, a champion who changed jobs. Nobody joined them up in time.
Winning a new customer costs 5 to 25 times more than keeping an existing one, per research summarised in Harvard Business Review.[1]
A 5% lift in retention raised profits 25% to 95% in Bain & Company research by Frederick Reichheld.[1]
Analytics-driven base management can cut churn by up to 15% in telecom, according to McKinsey.[2]
63% of consumers would switch after one bad experience in Zendesk's 2025 survey across 22 countries.[3]
We join the data that already predicts churn but sits in different tools, keyed to one account ID.
A gradient-boosted model trained on your past churned and renewed accounts, retrained monthly and checked against a holdout.
High-risk accounts become tasks with a playbook attached, and every outcome feeds back into the model.
We define churn for your business, pick the prediction window (60 or 90 days) and agree one success metric, usually precision in the top-risk decile.
Read-only connections to billing, product and support data. We train on history and show how the model would have ranked last year's churned accounts.
Every active account is scored nightly with reasons. Your CS leads review the top 50 and tell us where the model is wrong.
Scores and save-play tasks go live in the CRM. A random control group is held back so the retained revenue can be proven.
| Criterion | Health score spreadsheet | Off-the-shelf CS platform | Stratgik build + run |
|---|---|---|---|
| Data used | A few manual fields | Platform data plus some integrations | Product, billing, support, contract and CRM joined |
| Weights | Set by opinion | Rules or a generic model | Trained on your own churn history |
| Explains each score | No | Partly | Top reasons per account in plain language |
| Proves saved revenue | No | Rarely | Holdout control group built in |
| Best fit | Under 100 accounts | Standard SaaS with clean data | Multi-source data, telecom, fitness, hybrid B2B/B2C |
| Ownership | Your team | Vendor | Your cloud, your model, run by us |
Acquisition costs keep rising and buyers switch faster. The companies that hold revenue are the ones that see risk early and act on it account by account.
Enter your base and churn rate. The save rate is an assumption; the pilot measures it against a control group.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
21-day pilot, one product line or segment
per month, production scoring and retraining
per month, multi-segment or multi-brand
Cloud compute and any model API usage billed at cost with no markup; deployed in your cloud account; taxes excluded. GBP and AED prices are indicative conversions from USD.
AI churn prediction accuracy depends on your data, so we measure it on your own history before going live. In the pilot we backtest on 12–24 months of cancellations and report precision in the top-risk group: of the accounts flagged, how many actually churned. Calibrated scores let your team read a score of 80 as roughly an 8-in-10 historical chance at that level.
You need at least a customer list with start and cancel dates, plus some behaviour data. The strongest signals usually come from product usage, payment failures, support tickets and contract dates. We connect read-only to tools like Stripe, Chargebee, Salesforce, HubSpot, Zendesk and your data warehouse. Twelve months of history with a few hundred churn events is a practical minimum.
Most businesses get useful warning 60 to 90 days ahead. Monthly consumer subscriptions such as fitness often suit a 30–60 day window, while annual B2B contracts suit 90 days before renewal. We test several windows in the backtest and pick the one where the model is accurate enough and your team still has time to run a save-play.
A Stratgik churn prediction pilot costs $14,000 for 21 days, covering backtest, live scoring and CRM save-plays for one segment. Production runs at $3,500 a month including retraining and reporting. Cloud and model usage is billed at cost. Compare that with the revenue from the handful of accounts the model helps you keep each quarter.
A trained churn model is usually more reliable than a hand-built health score because its weights come from your real cancellations, not opinion. Health scores are fine for small books of business. Once you have hundreds of accounts and data in several tools, a model catches combinations of signals, such as falling usage plus an open ticket, that manual scores miss.
We hold back a random control group of high-risk accounts that receive no save-play. After the window closes we compare churn in the treated group against the control. The difference, multiplied by revenue per account, is the retained revenue. This is the one metric we agree before the pilot starts, so the result is not open to interpretation.
Send us a sample export. In a 30-minute call we will tell you whether your data can support a reliable churn model and what the pilot would measure.