Market basket analysis explained: How retailers like Walmart and Target use it to boost sales

In modern retail, every transaction tells a story — not just about what customers buy, but why. 

Market basket analysis helps retailers uncover these hidden relationships between products, turning millions of receipts into actionable consumer insights. It’s especially powerful for big-box retailers like Walmart, Target and Costco, where scale and transaction data make it possible to map shopper behavior patterns at a granular level.

When retailers understand which products are frequently purchased together and under what circumstances, they can design more relevant promotions and recommendations as well as smarter store layouts that drive both sales and customer satisfaction.

In this article, I’ll cover what market basket analysis entails, how big-box retailers use it, some simple steps to help you get started and more. 

At its core, market basket analysis helps brands identify associations between products that typically appear together in the same shopping basket. 

Using statistical techniques like association rule mining — a machine learning technique that identifies relationships within large datasets to find "if-then" patterns — analysts are able to uncover relationships between products that might not be obvious at first.

Some of the common metrics used in market basket analysis include:

• Support: Measures how often items appear together.

• Confidence: Indicates the likelihood of buying Item B given Item A.

• Lift: Tells analysts how much more likely these two items are to be purchased together than separately.

Take the classic peanut butter and jelly combo for instance. When data shows these items appear in the same basket more often than chance would predict, retailers might place them near each other on shelves, run bundled discounts or recommend one when a shopper buys the other online.

Market basket analysis helps retailers uncover these trends and opportunities by looking at patterns in their consumer data. 

Let’s take a look at some real use cases from big retail brands.

Market basket analysis is not just a data science exercise — it’s a revenue opportunity. Major retailers use it to guide everything from promotions to store designs to in-app personalization and more. 

Here’s a few examples of how familiar retailers like Walmart and Target use it:

• Promotional bundling: Walmart uses co-purchase insights to design cross-category deals, like bundling grill tools with barbecue sauce during summer promotions. While Target uses similar logic in its Target Circle loyalty offers, where discounts often pair complementary items, for instance: “Buy diapers, get 10% off wipes.”

• In-store placement strategies: By analyzing basket combinations, retailers can also optimize aisle adjacencies and end-cap placements. If shoppers who buy pasta sauce also tend to buy salad dressing, those products might be displayed nearby to encourage cross-category purchases.

• Digital applications: Both Walmart+ and Target’s mobile apps use recommendation algorithms informed by basket analysis. These surface relevant add-ons (Think: “People also bought…”) that increase cart value and enhance customer convenience.

Most of these examples may seem familiar and something you see in your day-to-day life that wouldn't be possible without market basket analysis. Let’s take a deeper look into the business impact of using this method. 

Market basket analysis directly translates data into measurable business outcomes such as:

• Higher Average Order Value (AOV): When recommendations or displays successfully encourage add-on purchases, the total basket size increases.

• Optimized inventory and supply chain: Knowing which items drive each other’s demand helps retailers forecast inventory more accurately, reducing out-of-stocks and overstocks.

• Personalized customer experiences: When retailers use basket data to tailor offers, customers feel understood — improving loyalty and long-term retention.

In short, market basket analysis bridges the gap between operational efficiency and shopper-centric marketing. 

Now let’s cover a few simple steps to help you get started. 

Here’s a brief overview of how retailers and analysts can perform market basket analysis in a few structured steps:

1. Collect transactional data: Start with detailed point-of-sale or ecommerce transactions, ideally including product IDs, categories and timestamps.

2. Prepare and clean the data: Remove any noise (like single-item purchases) and standardize SKUs or category names.

3. Run association rule mining: Apply algorithms such as Apriori or FP-Growth (available in tools like R and Python) to uncover relationships between products.

4. Interpret the metrics:

   • Support = Frequency of item combinations

   • Confidence = Likelihood of co-purchase

   • Lift = Strength of the relationship between items

5. Visualize and act: Translate these findings into dashboards or visual maps, helping merchandisers and marketers act on insights quickly.

Market basket analysis may sound technical, but its power becomes clear when you look at how retailers and brands actually use it. 

From grocery aisles to apps, basket analysis helps companies make smarter decisions and anticipate seasonal needs that feel intuitive to shoppers.

Classic CPG pairings that drive sales

Some of the most iconic examples of basket analysis come from everyday grocery staples.

• Chips and soda: During summer months, analysts often see a strong lift between salty snacks and carbonated beverages — a reflection of seasonal occasions like summer parties or barbecues. Retailers respond with paired displays or cross-category promotions, such as Walmart’s “Game Day Essentials” bundles that feature Lay’s chips, Pepsi and dips.

• Diapers and baby wipes: This pairing underscores a consistent, needs-based relationship. Retailers and CPG brands like Pampers and Huggies use this data to power loyalty offers (“Buy diapers, save on wipes”) and forecast re-purchase intervals based on average diaper consumption rates.

• Peanut butter and jelly: Beyond being a classic textbook association rule, this combination demonstrates how cultural habits surface in data. Retailers use insights like this to refine store layout, ensuring complementary staples appear within a short walk of each other, and to inform meal bundle marketing like “Lunchbox Ready” promotions and offers.

These examples show how basket analysis captures the logic of daily life — and how looking at the consumer data simply reveals the patterns we already live by.

Retailer-specific campaigns and strategies

As we mentioned above, large retailers leverage these insights to create promotions and offerings for their shoppers. Here’s few examples: 

Target Circle promotions

Target’s loyalty program often builds bundles informed by co-purchase data. For instance, past promotions offered gift cards for buying combinations of cleaning products, not just within one brand, but across complementary ones like Clorox, Windex and Swiffer or savings when you spend a certain dollar amount in one category, like toys. This kind of cross-category deal encourages shoppers to complete “the full solution set” or to get all their holiday shopping in one place, instead of cherry-picking single items.

Walmart seasonal bundling

Walmart’s merchandising teams rely on predictive basket analysis to anticipate seasonal trends. In the weeks before Thanksgiving for instance, transactions often show clusters of turkey, stuffing mix, canned vegetables and cranberry sauce. By grouping these items into dedicated holiday meal displays and promotions, Walmart improves shopper convenience and lifts average order value.

Kroger’s loyalty personalization 

Kroger uses a data platform to combine transaction analysis with personalization. For example, if a household consistently buys taco kits and cheese, Kroger may recommend complementary ingredients like salsa or tortillas in its email or app offers, turning insights into incremental sales.

💡 Insight takeaway 

Across industries, market basket analysis transforms purchase data into consumer intelligence. Whether it’s Walmart using item co-occurrence to build meal bundles, Target tailoring cross-category deals or Amazon predicting next-best purchases, the underlying principle is the same: When brands understand what shoppers naturally buy together, they can design experiences that feel less like selling and more like helping.

For big-box stores competing on experience, price and convenience, understanding why and when consumers buy what they buy is a competitive advantage that touches every part of the business. And in a world where every transaction leaves a data trail, retailers who can interpret the patterns behind the purchases will always stay one basket ahead.