Retail | Merchandising & Category Management
Pricing Optimization
Objective
The client needed a robust pricing solution to make optimal pricing decisions (promo, markdown, clearance) across their product portfolio to maximize revenue and improve sales velocity.
Challenges
- High complexity in managing diverse product categories and sub-categories.
- Variability owing to factors like seasonality, cannibalization, and competition.
- Requirement for transparency and accountability through BI reports.
- Seamless and integrated workflow.
Solution Proposed
Integrated ML algorithms with BI tools to provide scenario-based pricing recommendations, enhancing decision-making capabilities using:
- Data Sources: Sales data, promo history, pricing history, competitor pricing, and market research data.
- Analytical Engine: Feature engineering assessed the impact of cannibalization, seasonality, and competition.
- ML Techniques: Price elasticity models were used to analyze price’s impact on demand and create pricing optimization models.
- BI Tools: Scenario-based evaluation and optimization.
Outcome
- Enhanced SKU velocity of slow-moving products with a 6% increase in gross margin.
- Improved capture rate optimizing pricing decisions across the product portfolio.
Design & Thinking Wins
- Scenario-based price optimization strategy that aims to maximize revenues and quantity sales across brands by product category-wise planning.
- Demand and product-specific factors (cannibalization, seasonality) are considered for precise pricing recommendations.
- Consolidated reporting framework for historical assessment and tracking KPIs.
Disclaimer: The outline showcases the typical challenges, solutions, designs, and outcomes for industries and functions, in general, based on Affine’s prowess in the Industry. The outcomes would be much higher for specific clients as they would be based on their data and specific problems to be solved.
