Use case
Companies wanting predictive visibility
Churn, value, conversion and revenue, ahead of time, with confidence stated.
The problem and what DCS-PIS does
The problem
Which accounts are likely to churn, what each is worth, and what to do about it.
What DCS-PIS does
Churn, value, conversion and revenue, ahead of time, with confidence stated.
Predictions over thirty, sixty and ninety days, each with calibrated confidence and its drivers shown. When completeness or freshness falls below threshold the prediction is withheld and the reason is shown — a prediction on bad data is worse than none.
Where it happens Customers · Insights
- 01
Churn, lifetime value, conversion, deal win and revenue forecast
- 02
Calibrated confidence on every prediction
- 03
Withheld, with the reason, when data health is low
Use cases
Other use cases
- 01
Businesses drowning in disconnected data
The CRM, the books, the ad accounts and the support inbox describe the same customers differently.
One graph, one definition per metric, one place to ask.
Customers · Metrics - 02
Teams spending hours turning data into decisions
A dip in pipeline used to mean a week of exports.
Recommendations arrive ranked, costed, and with the action attached.
Sales · Actions - 03
Organisations exploring AI agents
Agents sound useful and feel risky.
Sixteen catalogued agents, each inside a permission grant you set.
Agents · Governance