Market Basket Analysis (MBA) or affinity analysis is a data mining technique used to uncover meaningful correlations across products and customer purchase patterns.
At GoGoGuest, Market Basket Analysis is a module that is part of our Analytics and AI platform that helps brands predict customer behavior and find purchasing patterns.
Large and multi-location hospitality, quick service retailers, restaurants and retail brands use Market Basket Analysis to unlock information at the purchase level. Let’s dive in!
Market Basket Analysis helps businesses understand product correlations by predicting consumer purchasing behaviors. It positions your business to make recommendations, strategic pricing, cross-selling and for restaurants it can influence your menu design and purchase and inventory of ingredients.
As an Amazon.com shopper you may recall seeing product recommendations each time you browse a product? That right there is Market Basket Analysis in action.
Similar to marketing automations or triggered emails, Market Basket Analysis or affinity analysis is about understanding large sets of data and applying rule sets.
Think about purchase transactions on a daily, weekly, monthly or yearly. Market Basket Analysis takes that data into statistical calculations to find purchase patterns. Understanding purchase patterns of your customers ultimately helps you customize your offerings to what customers really want.
MBA delivers data that is focused on the frequency and likelihood that certain products are consistently bought together, by similar customer profiles.
What are keystone products? Keystone products, are the defined as the lead products in your menu or product catalogue. Keystone products are often paired with or bought together with other items in your menu. The absence of a keystone products could impact the volume of sales at any given point in time due to a high retail price or unavailability.
For example, a certain group of customers who are calorie conscious will be attracted to Starbucks’ Nitro Cold Brew? Why? A Nitro Cold Brew Grande is 5 calories. The unavailability of a Nitro Cold Brew could turn away calorie conscious customers.
Albeit, an alternative keystone product is the Starbucks Reserve Cold Brew. Not as smooth as the Nitro yet it meets the calorie count.
There is a science in merchandising design and display that is the result of Market Basket Analysis. When a restaurant, quick service or retail brand understands which products are frequently bought with a keystone product, then it is natural that a combination of add-on products are displayed in the line of sight of a customer.
For online menus or eCommerce sites, upselling and cross-selling is much easier with a recommendations engine that is powered by AI. When you visit Starbucks online, you’ll often see pop up recommendations of add-on products to go with your coffee drink.
Upselling and cross-selling in-store is often easier to do – since the merchandise is right in-front of the customer, while it is so much easier to execute and drive online, it is also easier for a customer to ignore. This is why data-driven merchandising and recommendations becomes essential for a brand.
When you know your customer purchase patterns and most probable products to be purchased throughout any given period of the day or week, maintaining optimal stocks in the inventory becomes super efficient.
When a combination of unique variables and rules including a customer’s purchase pattern and engagement is understood, then applying rules, specific product combinations and offerings could result in an increase of frequency and spend.
Customer engagement driven by deep analytics and market basket analysis can:
Analyzing customer’s response patterns from various ad sources or media, can remove the guesswork on which ad channels are driving engagement and conversion.
Don’t take our word for it. We encourage you to dive in and dig deeper. Have you read The Customer Engagement Playbook? Produced by the GoGoGuest team, it offers actionable next steps and best practices on how to implement a data-driven, full-cycle marketing strategy to grow you brand and business.
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