Retail · demand forecasting
ProfessionalReal dataMulti-Horizon Demand Forecasting
Data Scientist · Manor Park Trading Company · 2024
What this is
Demand forecasting across ~7,000 SKUs over Shopify, Amazon, eBay and The Range at three horizons, paired with a seven-cluster customer and product segmentation.
Metrics
Accuracy
+28% weekly · +19% monthly · +37% quarterly
Segmentation
Seven clusters for prioritisation
Campaign test
~7% ROAS
vs a randomly assigned holdout group
Associated revenue
~£78k, association only
Reporting
Automation cut per-draft time ~50%
Stack
Provenance
Two separate claims, deliberately kept apart. The ~7% ROAS improvement was measured against a randomly assigned control group held back while the new segment-based targeting ran concurrently, so it is a controlled comparison. The ~£78k is not: it is revenue associated with the analysis with no holdout behind it, and is never a causal claim.
What I would not claim
- The holdout covered the campaign targeting test only. It does not make the ~£78k a causal figure.
- No sample size, split ratio, test duration, statistical power, MDE, p-value or number of variants is published, because none is on file.
Explore the deltas
The same before/after figures, interactive. Toggle between the three roles.
Residual-load framework improved forward-curve accuracy on both near and far seasons; the Christmas-period fix followed a holiday-specific error diagnosis. Relative improvements only, against an internal baseline.