Learn how we optimized a vast number of SKUs using competitive intelligence and machine learning analytics to provide our customer with a data science toolkit to make informed decisions at speed and scale.
Results:
40% revenue increase
11% increase in product demand
Challenge: Maximize margins by deciding what products to keep, remove, add, and improve.
RS Components carries a portfolio of over 500,000 industrial and electronic product SKUs across 30 categories and thousands of brands. With operations in 32 countries, they trade through multiple channels and ship over 50,000 parcels a day.
The massive sales volumes for these SKUs and market demand vary widely and change often, making it time-consuming to optimize the company’s product range.
How can we optimize their SKU rationalization process by surveying the competitive landscape to identify and take advantage of market opportunities to maximize margins? And to do it at speed and scale?
Solution: Extract insights on product performance and opportunities to automatically assess the optimal SKU range.
Our solution automatically assessed the optimal SKU range for a product based on a multitude of factors:
- Digital interaction
- Customer information
- Transactions
- Equivalent competitor ranges
- Marketplace & customer search/demand data
Indices used to measure SKU performance across five dimensions that impact category performance:
- Traffic index
- Sales index
- Content index
- Conversion index
- Demand transfer index
Technologies Used
Results & Key Metrics: Significantly increased revenue and product demand and a toolkit for future decision-making.
Our solution empowered RS Components’ category management stakeholders with an objective, provided a data science-based toolkit to make informed decisions at speed and scale, and offered recommendations.
After implementation of the solution, RS Components saw a 40% increase in revenue and an 11% increase in product demand in a top category of their products.
“We partnered with eClerx Digital to help us streamline our product offer, taking a data-driven approach to removing, adding, retaining, and improving the assortment of SKUs based on demand. Their ability to quickly formulate an analytics framework to address the problem at hand and leverage data science and technology to develop a scalable solution enabled our category teams to spend less time trawling through data and more time strategizing their range mix according to customer needs.”
RS Component, Product Manager
How We Did It: A holistic data-driven approach for speed and scalability.
The inputs from the data scraping were modeled using an ensemble of technologies and tools to bring out insights on product performance and opportunities for supplier brands and product changes.
- NLP & machine learning algorithms
- Text matching
- Substitutability
- Assortment intelligence
- Clustering
- Forecasting
Experts & advisory used:
6 subject matter experts
Technologies Used
eClerx Digital won ISG’s 2021 Digital Case Study Award for its proprietary SKU Range Optimization for RS Components. ISG showcased the year’s best provider and client partnerships.
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