What this covers
From raw records to a defensible call.
Forecasting is the easy part. The work is in the data you already have, the definitions you have not agreed, and the decision rights nobody wrote down.
Demand and sales prediction
Models built on your own history, for the Surinamese market, with error bands stated plainly rather than a single confident line.
Behaviour and churn
Segmentation, purchase rhythm, churn risk and lifetime value — defined once, in writing, so marketing and finance stop arguing about the numbers.
Dashboards people open
A small number of live views tied to weekly decisions, built on a warehouse that refreshes on schedule rather than on request.
Regional intelligence
Trend and competitor analysis for the Caribbean, assembled from sources you can cite — not a scraped chart with no provenance.
Price and margin analysis
Where margin actually comes from across SKUs, channels and currencies, including the SRD/USD exposure most reports quietly ignore.
Data quality first
Duplicate records, broken joins and undefined fields fixed before a model is trained on them — otherwise the model learns the mess.
What changes
Targets we set before we start.
We measure against the state of your reporting before we arrive. If the baseline cannot be established, that becomes the first deliverable.
- Forecast error reduced against your current method, measured on held-back history rather than the training set.
- Questions answered in hours, not in a request queue that takes a week.
- One agreed definition per metric — documented, versioned, and the only number used in meetings.
How we deliver
One decision at a time.
Analytics projects fail by scope. The first cycle is one decision, one owner, one number — and a written definition everyone signed off on.
Name the decision
What will be decided differently, by whom, and what would count as evidence? If the answer is vague, the project is not ready.
Fix the data
Minimum viable warehouse, deduplicated, with definitions written down. This stage is unglamorous and decides whether the rest works.
Ship and review
A forecast or dashboard with its assumptions visible, reviewed after one cycle against what actually happened.
What decision keeps getting made on instinct?
Name it, and the data you already have around it. We will tell you whether there is enough signal to model, or whether the honest answer is to collect better data first.
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