Predictive analytics to anticipate demand, risk and churn
We train models on your sales, inventory or collections history and connect them to your operation: forecasts and alerts your team understands and can act on.
- In
- Sales, inventory, collections
- Out
- Forecasts and alerts
- Explanation
- In business language
Decisions backed by data, before it happens.
- Problem_01
Gut-feel decisions
Purchasing, production and collections are planned on the experience of a few people.
- Problem_02
Stockouts and overstock
What sells runs out and what doesn’t piles up, with capital stuck in the warehouse.
- Problem_03
Risk seen too late
Customers who will stop paying or buying are spotted after the fact.
- Problem_04
Reports that look back
Dashboards explain last month, but not what comes next.
Models you can understand and measure.
- 01 · Data
We review what history exists and what shape it is in.
- 02 · Baseline
We measure how well things are predicted today, to have a comparison.
- 03 · Model
We train and validate on data the model hasn’t seen.
- 04 · Integration
Forecasts reach your ERP, dashboard or inbox.
- 05 · Monitoring
We compare forecasts with actuals and retrain when needed.
Every forecast comes with the factors that weighed most. When the model has low confidence, it flags it for a person to decide.
What it usually predicts.
We start with the decision that moves the most money and already has history to learn from.
- Demand by product and branch
- Reorder points
- Payment default risk
- Customer churn
- Collections priority
- Ticket or call volume
- Delivery times
- Anomaly detection
- + whatever your operation needs
A solution in production, not an endless pilot.
- Models in production connected to your data.
- Forecasts and alerts in your ERP, dashboard or inbox.
- An explanation for each prediction and ongoing accuracy tracking.
- Code, models and documentation: 100% yours.
- Deployed in your cloud or on private infrastructure.
- Metrics agreed in the assessment and periodic reports.
Where it usually starts.
- Example_01Sales
Demand forecasting for restocking
A model anticipates stockouts and suggests when and how much to reorder.
Measured →idle inventory and stockouts
- Example_02Finance
Collections priority
It ranks accounts by default risk so the team calls the ones that matter most first.
Measured →receivables over 60 days
- Example_03Sales
Customers at risk of churning
It spots changes in buying patterns and alerts the account manager before the account is lost.
Measured →customers recovered after an alert
Connects to what you already use
- ERP
- CRM
- SQL databases
- Spreadsheets
- BI dashboards
- Custom APIs
About predictive analytics.
What would you like to know before it happens?
In the free assessment we review your current process and tell you what to automate first. We reply within 24 h.