Should the mulch promotion continue?
Company: Homedepot
Role: Data Analyst
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
A home-improvement retailer is running a seasonal promotion on bagged mulch.
### Business context
- Regular price: **$3.50 per bag**
- Promo price: **$2.00 per bag**
- Variable cost: **$2.40 per bag**
- Therefore, the promoted item currently has **negative item-level contribution margin**.
- The company has **6 weeks left** in the spring selling season.
Over the last 4 weeks:
- Weekly mulch unit sales increased from **10,000** to **26,000** bags.
- Management believes the promo may also drive larger baskets and more store traffic.
- However, some of the observed lift may be due to **seasonality, weather, competitor pricing, stock-up behavior, or cannibalization** of other landscaping products.
You have access to:
- store-day sales,
- transaction baskets,
- loyalty/customer identifiers for some shoppers,
- ad spend,
- inventory and stockout data,
- competitor price checks,
- store region and weather data.
### Questions
1. Should the retailer continue the mulch promotion for the rest of the season?
2. What metrics would you use to make the decision, and why is item-level margin alone insufficient?
3. How would you estimate the **incremental** impact of the promotion rather than relying on raw observed sales lift?
4. If leadership still decides to continue the promotion despite negative item-level margin, what business reasons could justify that decision?
Be explicit about causal inference issues, segmentation, tradeoffs between revenue and profit, and how you would present a persuasive recommendation to a business leader.
Quick Answer: This question evaluates data-analytic competencies including causal inference, experimentation, segmentation, and retail promotion economics within the Analytics & Experimentation domain, emphasizing understanding of incremental impact and trade-offs between revenue and profit.