Industry
Company Profile
- €300m annual revenue
- Home accessories and furniture retail chain operating in several countries across Europe
- 5,000 SKUs
Situation and Objectives
- Develop concept with an analytical algorithm to identify optimal mark-down price
- Reduce total cost of ownership by minimizing mark-downs and other follow-up cost
Approach
- Build up a database covering all marked-down items across all categories for the last four years
- Define mark-down guidelines based on the company strategy as side conditions for an optimal mark-down algorithm
- Calculate mark-down elasticities based on time-series regressions accounting for internal (e.g. store size, price level) and external factors (e.g. weather, season, holiday, payday)
- Code algorithm that recommends category manager optimal mark-down price for items with a sales ratio below target
- Test concept to fine-tune mark-down algorithm
System Based Mark-Down Pricing to End-Up with Profit-Optimal Closing Stocks

Results
- Apply pricing grid with psychological optimal price points
- Stop "scattergun approach" to manage mark-downs (i.e. item identification and mark-down level)
- Set mark-down price based on forecasted sales volume and related total cost of ownership
- Use expert judgment in a structured way if data is not sufficient to mathematically forecast sales volume
Impact
- Reduced discount level for mark-downs by 6%
- Reduced costs for eliminating remaining stock by 13%
- Reduced total cost of ownership by 8%
- Improved revenue by 3%