Market Basket Analysis Helped an Online Retailer Refocus Attention on Customers’ Needs and Enhance Quarterly Sales by 12% – Infiniti Research Success Story

April 2, 2019

Business Challenge 

Recent improvements in data analytics technology have opened up a world of possibility for online retailers to enhance their operational efficiency and delight their customers. The advancements have made it easy for decision-makers to determine product affinity and understand the reason behind products being purchased together. Also, as customer needs and preferences sway with the time, online retailers need to keep track of customer buying behavior to predict their purchasing patterns. This makes it vital for online retailers to leverage market basket analysis solution to develop new products without compromising on the market trends.

The client, an online retailer in the US was looking at adopting a technique that would help them track customer purchasing behavior and predict their purchasing patterns, given a contemplated purchase or previous purchase history. The client’s failure to utilize past data to identify relationships between products and customers was a major roadblock that prevented them from creating products and pricing models to generate revenue. Also, the lack of a cross-selling strategy for their products proved to be costly. Due to this, the client witnessed a depreciating profit margin for two consecutive years. Hence, they wanted to leverage market basket analysis to develop new cross-selling strategies.

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Solutions Offered and Value Delivered

The experts at Infiniti Research analyzed the client’s historical sales records, customers’ online browsing behavior, and common purchase patterns. Our experts also reached out to various stakeholders to gain a broader understanding of the retail market landscape.

Based on the data collected, our experts devised a market basket model, that combined detailed information on customers’ previous purchases, pairs of products purchased together, and discounting strategies followed by major players in the online retail sector. The market basket model helped the client to identify the common purchase patterns followed by customers and combinations of products or menu items that frequently co-occurred in transactions. In fact, with the data obtained from market basket analysis, the client was able to create new products or pricing models to generate revenue. Also, with Infiniti’s market basket analysis, the company witnessed a rise in their quarterly sales by 12%.

The insights gained from market basket solution further helped the client to:

  • Categorize products that customers bought together
  • Recommend to customers the associated products that other customers frequently purchased together
  • Create targeted marketing campaigns and entice customers towards complementary goods related to their recent purchase.
  • Enhance company’s cross-selling opportunity

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What is the purpose of market basket analysis?

Market basket analysis is one of the most commonly used retailing techniques to enhance sales. By leveraging market basket analysis, retailers can determine product affinity and understand the reason behind products being purchased together. Knowledge of the products frequently purchased as a group will empower retailers to efficiently plan their store layout. Retailers can also extract insights from market basket analysis and utilize it to develop new products without compromising on the market trends.

Market basket analysis also offers detailed insights into transactional datasets which turns out to be useful for product recommendations. This technique relies on the theory that if a customer purchases a certain product, they are more likely to buy another set of related items. By identifying product combinations that frequently co-occur in transactions, the retail industry players can uncover associations between customers and their purchase patterns.

Key Benefits of Market Basket Analysis

  • Helps companies especially retailers, to analyze customer buying behavior and predict their next purchase.
  • Improves cross-selling and in turn, increase customer’s lifetime value.
  • Considered as a basis for creating a recommendation engine.
  • Based on the insights from market basket analysis, companies can predict future purchases of customers over a period.
  • Helps predict which item would probably fall short and maintain stocks in optimal quality.

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