Customer Data Analytics for Mojekolo.cz
Mojekolo.cz, a large bike retailer and e-tailer, wanted customer segmentation and market basket analysis for better customer insights and higher customer retention. Discover how Simplity helped them achieve these goals and improved their revenues.
How to use transaction data to offer upsells and cross-sells
Mojekolo.cz bike shop has been using personas for their marketing campaigns but wanted to support these campaigns with hard data in terms of the rules between the products. Such business insights would help them to understand their clients' behavior better, target the campaigns and product recommendations more precisely, and bring about an uplift in revenues. To achieve these objectives, Mojekolo required a comprehensive data approach to customers and needed to solve these challenges:
How to use their ERP data about orders and products for better customer segmentation in order to maximize the value of each customer to the business
How to perform market basket analysis to see relations among the product items and categories
How to use these insights to serve customers with offers and recommendations, targeted to their needs, and thus achieve quality upsells and cross-sells
Project in numbers
Orders with customer data
Association rules for cross-sell opportunities
Obtaining quality customer insights to enhance the business
Mojekolo.cz wanted to increase the insights into their customer base to improve their sales and marketing tactics, and to advance their maturity of working with customer data. Insights discovered through customer segmentation and market basket analysis are cornerstones for recommending the right products to their customers and consequently will increase customer satisfaction and sales revenues.
As a first step, an analysis of available data in the source systems (primarily from the ERP system) was conducted to evaluate the current state of the data and data systems. 69,000+ order items were examined and then shortlisted to approximately 15,000 orders with the customer data, as such information was a prerequisite for further analysis.
This was followed by the definition of similar groups, called segments, based on product categories. Mojekolo offers more than 9,800 unique products, from bikes and helmets to custom parts. The four main segments were defined by the selection of the most important product type and its subcategories for Mojekolo. These were mountain bikes with measurements of 29", 27.5", women’s bikes, and children’s bikes.
Next was the market basket analysis, finding strong rules in a large amount of transactional data to discover regularities. These are called association rules and describe what products are purchased together. We identified 54,000 association rules for Mojekolo. These rules were prioritized based on the potential uplift in revenues they would bring. Mojekolo will implement these rules within their e-shop’s recommender system to suggest complementary goods to their customers e.g., cross-sell a woman’s helmet with a woman’s bike, or offer upsells to more valuable goods e.g., a bike with more durable brakes. All of this would lead to higher revenue for Mojekolo and a better experience for the customer, not only during the purchase, but also in the actual usage of the product.
The market basket analysis completed for the Mojekolo project helped the company better understand the purchase behavior of their customers. This also allowed the business to identify customers who bought the expected goods versus buying the goods elsewhere.
These insights offer Mojekolo a fantastic opportunity to offer cross-sell and upsell products to relevant customers within their website recommender. Additional usage of these insights can help with influencing sales promotions, loyalty programs, store design, and discount plans.
The project spanned only 30 man-days but brought extremely valuable insights and benefits for Mojekolo's business in the long-term. And because the COVID-19 pandemic has accelerated the demand for online purchases, retailers need to keep up with in-depth customer insights to keep their market share and meet the expectations of their customers for targeted offers.