Explain concentration
Separate total opportunity from your realised business. Test local drivers and segment differences. Correlation between two map layers is not proof of a causal driver.
Evidence in.
Accountable action out.
Machine learning can inform a decision. Business constraints, local context and validation determine whether it is worth acting on.
MODEL DESIGN & VALIDATIONEvery recommendation
Every recommendation
needs an evidence trail.
The intended architecture combines statistical estimation, machine learning and operational constraints. A useful decision must show its inputs, assumptions and limits.
Measured ≠ estimated.
District proxies should never look more certain than the data behind them.
The product preview uses transparent rules. It does not run a trained ML model or connect to customer systems.
01 Connect the inputs +
Combine licensed market and household-panel data, secondary sales, population, outlet masters and geographic context. Align periods, categories, boundaries and units before scoring.
Refresh honestly: preserve each source’s actual cadence. A monthly sales update does not turn a quarterly household panel into monthly observations.
02 Estimate without overclaiming +
Per capita sales: period sales ÷ aligned district population, with the sales units and period shown.
Penetration: an urban/rural household-weighted blend can be a starting proxy. It is not measured district penetration and needs calibration.
Market share: estimate a compatible district category denominator, reconcile to state totals and compare candidate methods. Secondary sales are not automatically equivalent to retail offtake.
03 Validate the recommendation +
Check missing data, duplicate outlets, stale periods, boundary changes, denominator quality and cross-district sales flows. Withhold district estimates where evidence is insufficient.
Benchmark models against a simple baseline, test on held-out periods and geographies, assess leakage, and track errors by segment. A high score is a field-check hypothesis—not proof of incremental growth.
04 Close the loop in the field +
Give each action an owner, capacity requirement and measurable outcome. Capture field feedback and compare outcomes with the original hypothesis.
For beat planning, validate road access, service time, working hours, visit frequency and territory rules. Demonstrate business lift through a pilot comparison before scaling.
From a promising signal
to a testable decision.
Define right to win
Agree how competitive strength, access, penetration and execution contribute. Avoid treating market share alone as an opportunity or a guarantee of success.
Validate readiness
Define a productive order, prevent leakage and compare the probability model with a simple baseline. Check calibration and error by outlet segment.
Measure incremental value
Compare a pilot with a credible baseline or control. Include cost to serve and service constraints. Before/after sales growth alone does not establish causal uplift.
A clear boundary
around your data.
The demonstration runs locally in your browser using synthetic inputs. The page code does not upload selections, accept customer files, save submissions or use third-party analytics. Contact links open your email application; messages are handled by the relevant email providers.
The hosting service may process technical access logs under its own policies. A future pilot requires separate agreement on data access, retention, licensing and permitted use.
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