← All use cases
retention · ml

See churn coming while it's still preventable.

The signals that predict churn — usage decay, billing friction, support sentiment — live in three different systems. Your agent joins them, trains a model in the runtime, and returns scored accounts with the reasons why.

Book a demo →
Why it works here

Churn prediction, the Contextually way.

Features from every silo

Product events, invoices, and ticket history become one training frame inside the runtime — no ETL project, no feature store to stand up first.

The model never leaves

Training and scoring happen next to the data. What returns to context is the output: ranked accounts and attributions, not the dataset or the model.

Reasons, not just scores

Every score carries feature attributions, so your success team gets "seat usage fell 60% after the admin left" — something they can act on.

The flow of data
snowflake.usage_rollup_90d 1,684,112 rows postgres.accounts 214,188 rows contextually runtime train + score · gradient boosting model stays in-runtime 25 at-risk accounts 886 tokens → context
The program, case by case
goal → churn-risk scores for active accounts, 90-day window
df = join(sql("snowflake", "SELECT * FROM usage_rollup_90d"), sql("postgres", "SELECT account_id, plan, seats FROM accounts"), on="account_id") model = GradientBoosting().fit(df[FEATURES], df["churned"]) scores = model.predict_proba(df_active[FEATURES]) return top(scores, n=25, explain=true)
trained on 1,684,112 rows · model stays in-runtime returned 25 at-risk accounts + drivers · 886 tokens → context
Get started

See it on your data.

Book a demo and we'll run churn prediction against systems like yours — your stores, your policies, live — and map the path to a deployment in your VPC.