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Creating a productive shopping experience through data mining

D2C (direct-to-customer) brands today are beset by two major issues: economic instability and inefficient internal processes. The two issues are known to single-handedly affect over 72% of business in one way or another. But unlike economic woes, it is possible to solve and control process-related issues such as incomplete data collection, inability to meet market needs in time, ineffective inventory management, and dwindling customer satisfaction.

Irregularities in process flow are at the root of poor inventory management. Does the firm often struggle with overstocking or stockouts? Is inventory costing too much, but turning over too slowly? Unfortunately, Excel forecast calculations don’t always bring accurate results, especially for large datasets. Plus, customers are a key reason a business exists. How well does the firm understand customer behavior? What drives their purchasing decisions, and how can a prospective buyer become a recurring one?

Addressing some of these concerns – no matter how daunting – is made easier with the right tools, or tool, in our case. Spoggle’s market basket analysis is tailor-made for retailers who want to build customer affinity by creating an exciting and productive shopping experience. But what is market basket analysis?

The new MBA on the block

Anyone who has made a purchase on Amazon is probably familiar with the nudges ‘Frequently bought together’ and ‘Customers who bought this item also bought...’ These nudges are the result of the data mining technique that we commonly call ‘market basket analysis’, or MBA. As the name suggests, the technique helps retailers understand which goods customers frequently buy together. At Spoggle, we deploy this technique to enable retailers to bundle their products tactfully and organize their stores and websites consistently.

However, this data-mining method is not used in only the retail industry—false credit card transactions and insurance claims also employ it.

How is market basket analysis implemented?

Market basket analysis is usually accomplished by examining users’ prior purchasing behavior. With Spoggle, the starting point is often a set customer of transactions. Each transaction represents a group of items or products that have been purchased together. For example, one set of purchased items could include pasta, tomatoes, oregano, garlic, and olive oil. In this case, let’s assume all these items were bought in a single transaction.

These transactions are further analyzed to identify ‘rules’ of association. For example, one rule could be that if a customer transaction contains pasta and tomatoes, customers are likely to be interested in also buying garlic and oregano. This is how a ‘rule of association’ is created.

Once the rules are validated, Spoggle uses relevant data mining algorithms to identify the products that are most likely to be purchased together.

How D2C brands can leverage market basket analysis

  • With Spoggle, retailers can increase sales and customer satisfaction. By using data to determine the products that are often purchased together, retailers can optimize product placement, offer special deals, and create new product bundles to encourage further sales of these combinations.

  • These improvements can generate additional sales and optimize ROI for the retailer, while making the shopping experience more productive and valuable for customers.

  • Market basket analysis also gives insight into product relationships at your store that retailers may never have known existed, and reveals novel ways to cross-sell, up-sell, and promote products.


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