Product Assortment and Upsell Optimization for a Leading Global Footwear Firm
Company Size
1,000+
Country
- Worldwide
Product
- Minerva
Tech Stack
- Automated bots
- Descriptive and prescriptive analytics
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Revenue Growth
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Apparel
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Inventory Management
- Predictive Replenishment
Services
- Data Science Services
About The Customer
The customer is a leading global footwear firm. They operate in the apparel industry and have a significant online presence through their ecommerce site. The firm is faced with the challenge of optimizing their product offerings and upsells in the face of stiff competition from marketplaces that are out-pricing them. They are keen on leveraging data to make informed decisions and improve their market position.
The Challenge
The client, a leading global footwear firm, was facing challenges in optimizing product offerings and upsells on their ecommerce site. The situation was further complicated by marketplaces that were out-pricing the brand store. The client wanted to leverage both competitive and internal data to address these issues. The client also wanted to identify promotion opportunities for not-so-popular products and recommend ideal upsells that would help promote high-margin, low-cost accessories.
The Solution
The solution involved two phases. In the first phase, Minerva was deployed to evaluate the most popular shoe sizes and colors based on the volume of sales and stock available in the marketplace over a period of time. Automated bots were built to capture data sets for product attributes, pricing, promotions, and reviews for different size and color combinations for the same product range offered by competitor sellers. The bots also captured upsells being offered by marketplaces and other retailers. In the second phase, a combination of descriptive and prescriptive analytics was run to determine the optimized inventory on the basis of shoe type, color, and size. Optimal upsells were recommended on the basis of statistical models. Reviews were analyzed to comprehend the credibility of other sellers and investigate other aspects, such as whether a pair of shoes might be old stock.
Operational Impact
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