Models built from your history, not off-the-shelf guesses
Predictive analytics only works when a model learns from the way your business actually behaves — your seasonality, your customers, your product mix. We start with the data you already have (sales, CRM, support tickets, usage logs, transactions) and figure out what can honestly be predicted from it and what can't.
You get a model that outputs a probability or a number you can act on: which accounts are likely to churn next quarter, how much demand to expect by SKU, which orders carry more risk. We are clear about confidence and about where the data is too thin to trust.
- Churn and retention scoring from CRM and product-usage data
- Demand and sales forecasting by product, location or channel
- Risk and fraud likelihood scoring on transactions or applications
- Lead scoring so sales works the accounts most likely to close
- Anomaly detection on operational or financial time series
How we keep the results honest
A model that looks great in a slide and fails in production helps no one. We hold out data the model never sees, test against it, and report accuracy in plain terms — including the cases it gets wrong.
We fit any scaling or feature engineering on the training split only, so the numbers you see aren't inflated by leakage. If the honest answer is that your data can't support a reliable prediction yet, we tell you that instead of shipping something that will quietly mislead you.
- Train/validation split done before any tuning to avoid leakage
- Backtesting against held-out historical periods
- Plain-language accuracy, error ranges and known failure modes
- Feature importance so you can see what's driving a prediction
- A defined retraining cadence as new data arrives
From model to something your team actually uses
A prediction is only useful when it reaches the person who acts on it. We deliver the model behind an API, a scheduled batch job, a dashboard, or pushed straight into the tools your team already lives in.
We can start with a focused proof of concept on one prediction, prove it holds up, then expand. That keeps your investment tied to results you can see.
- Delivery as API, scheduled batch scoring, or dashboard
- Integration with your CRM, ERP, warehouse or spreadsheets
- Monitoring so you're alerted when accuracy drifts over time
- Documentation your team can maintain after handoff
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Frequently asked questions
How much historical data do I need before this is worth doing?
It depends on the problem, but generally you want enough history to cover the patterns you're trying to predict — for anything seasonal, that usually means a couple of full cycles. In a free consultation we'll look at what you have and tell you honestly whether it's enough, whether we should start collecting more first, or whether a simpler approach fits better right now.
Do I need a data scientist on staff to keep this running?
No. We build it so your existing team can operate it, and we hand over documentation plus a retraining plan. If you'd rather we handle ongoing monitoring and retraining, that can be a separate arrangement — but you're never locked in.
What does a predictive analytics project cost?
Cost is driven by how clean and accessible your data is, how many predictions you need, and how deeply it integrates with your systems. Typically we scope a focused first model, price that, and expand from there. Book a free consultation and we'll give you a realistic range for your specific case.