Since the introduction of electronic points of sale, retailers have been collecting an incredible amount of data. To leverage this data in to produce business value, they first developed a way to consolidate and aggregate the data to understand the basics of the business.

Recently, the focus shifted to the lowest level of granularity—the market basket transaction. At this level of detail, the retailers have direct visibility into the market basket of each customer who shopped at their store, understanding not only the quantity of the purchased items in that particular basket, but also how these items were bought in conjunction with one another. This can be used to drive decisions about how to differentiate store assortment and merchandise, as well as to effectively combine offers of multiple products, within and across categories, to drive higher sales and profits. These decisions can be implemented across an entire retail chain, by channel, at the local store level, and even for a specific customer, with so-called personalized marketing, where a unique product offering is made for each customer:

MBA covers a wide variety of analysis:

• Item affinity: This defines the likelihood of two (or more) items being purchased together.
• Identification of driver items: This enables the identification of the items that drive people to the store and always need to be in stock.
• Trip classification: This analyzes the content of the basket and classifies the shopping trip into a category: weekly grocery trip, special occasion, and so on.
• Store-to-store comparison: Understanding the number of baskets allows any metric to be divided by the total number of baskets, effectively creating a convenient and easy way to compare the stores to different characteristics (units sold per customer, revenue per transaction, number of items per basket, and so on).
• Revenue optimization: This helps in determining the magic price points for this store, increasing the size and the value of the market basket.
• Marketing: This helps in identifying more profitable advertising and promotions, targeting offers more precisely to improve ROI, generating better loyalty card promotions with longitudinal analysis and attracting more traffic to the store.
• Operations optimization: This helps in matching the inventory to the requirement by customizing the store and assortment to trade area demographics, and optimizing store layout.

Predictive models help retailers to direct the right offer to the right customer segments or profiles, as well as to gain an understanding of what is valid for which customer, predict the probability score of customers responding to this offer, and understand the customer value gain from the offer acceptance.