Case Study
Bouqs — Predictive Demand Forecasting
ML-driven demand forecasting across SKUs cut buffer-stock costs for Mother's Day and Valentine's Day fulfillment.
- Industry:
- D2C / Subscription Flowers
- Service:
- Applied AI, Data Engineering
- Engagement:
- Multi-year, ongoing
- ~90%
- Peak-season forecast accuracy
- +10%
- NPS lift
- SKU-level
- Inventory granularity
Challenge
Bouqs needed forecast accuracy on demand peaks - Mother's Day, Valentine's Day - where stockouts hurt NPS and overstock hurt unit economics. Manual heuristics could not capture SKU-level substitution patterns or the interaction between marketing initiatives and seasonal demand.
Approach
We built an ML-driven demand forecasting model integrating historic sales, marketing initiatives, seasonality, growth projections, and SKU-level inventory. The model layered classical time-series with feature engineering on marketing windows and holiday peaks, validated against multiple peak seasons before promotion to operational use.
Outcome
Forecast accuracy ~90% across peak windows. Reduced buffer-stock cost without sacrificing availability. NPS rose 10% attributable to product availability and on-time delivery. The model continues to retrain and refine as new peak seasons add data.
The numbers that moved
| Metric | Before | After |
|---|---|---|
| Forecast accuracy | Manual heuristics | ~90% across peak windows |
| Buffer-stock cost | Conservative overstock to avoid stockout | Reduced - tighter, model-driven planning |
| NPS | Baseline | +10% attributable to product availability and on-time delivery |
How we did it
- 01 Step
Data audit
Mapped historic sales, marketing initiatives, seasonality factors, growth projections, and SKU-level inventory across the warehouse network.
- 02 Step
Model design
Layered classical time-series with feature inputs for marketing windows, holiday peaks, and SKU substitution patterns.
- 03 Step
Validation
Ran offline backtests across multiple peak seasons. Tuned for peak accuracy without sacrificing baseline accuracy.
- 04 Step
Operationalize
Forecast feeds into procurement and warehouse fulfillment planning. Continuous re-training and signal monitoring.
The model paid for itself in one peak season.
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