Market basket analysis tells us what products customers buy together, but why is that something we need to know? Figuring out hidden shopping patterns helps to increase sales, optimize marketing strategies and inventory management.
Let's learn more about the methods, advantages, and limitations, as well as the analysis process.
Key Takeaways
Basket analysis finds patterns that grow sales via smarter cross-sells.
Reveals product affinities.
Improves layout adjacencies.
Refines promos to lift baskets.
Informs staff upselling scripts.
Guides digital recommendations.
What is market basket analytics?
In short, shopping cart analysis is a tool that helps to identify which products customers buy at the same time. As a methodology, it helps optimize assortment and display to sell more and increase customer satisfaction.
How market basket analysis works
Market basket analysis is based on three methods:
- Descriptive market basket analysis. This is an assessment of the relationship between products using statistical methods without any forecasting. For example, the pattern that 25% of customers who buy milk add cereals to their basket could be the result of one such analysis.
- Differential Market Basket Analysis. This is a data mining technique for comparing purchases between different groups of customers or different time periods to identify differences in habits. For example, you notice that customers aged 21–25 buy energy drinks with snacks, while customers aged 65+ buy tea with cookies. This will help you to differentiate the habits of different groups and adapt the assortment.
- Predictive Market Basket Analysis. This is the use of historical data to create forecasts of future customer behavior. The analysis is based on associative rules and machine learning methods. For example, if a customer buys diapers and baby cream, you can predict that next time they will need more things from the baby product group.
What you need to conduct basket analysis
One of the advantages of this analysis is the ability to collect customer data that is not confidential. What you need is accessible information about purchases that needs to be interpreted correctly. This step-by-step guide will show you how to conduct market basket analysis retail.
#1 Collect data
Collect information about customer transactions, e.g., items purchased on a single receipt, time and date of the transaction, amounts spent, etc.
#2 Process the information
Perform preliminary processing and cleaning, remove any irrelevant data, and convert it to a format that is convenient for analysis.
#3 Identify patterns and create association rules
Find the sets of items that repeat most often within a single transaction and a group of such transactions. Association rule mining helps to determine the likelihood of which items will be purchased if other specific items are purchased.
#4 Interpret the results
Determine which products are typically bought together, the strength of the connection between these products, and find other useful characteristics of customer behavior and preferences. Use this information to recommend products, optimize shelf layout, and organize targeted marketing campaigns.
What are the benefits of shopping cart analytics?
The data obtained as a result of the market basket analysis can make a big difference in your retail business. According to MasterCard, optimization based on the results of shopping behavior research has helped retailers around the world earn an additional $3.4 million a year. Let's take a look at the key benefits of the analysis.
Increase in the average check
This is an obvious result of high-quality analysis and correct interpretation. You get high-quality data for strategic product placement or creating cross-selling kits.
Optimize your inventory
Knowing which products are bought together, you can predict demand for them and avoid situations when customers come for milk and cereal but only milk is available. Plus, you minimize the cost of storing accumulated balances.
Precision marketing
Customer behavior and shopping cart analysis help you segment customers based on their habits. As a result, you can create personalized marketing campaigns for each target group.
Identify trends
Shopping cart analysis in retail helps to notice trends promptly, e.g., the growing popularity of certain product combinations. Here's a market basket analysis example. You've discovered that customers have started to buy a carbonated drink, lime, and creamy ice cream on the same check. Additional analysis showed that this was a new summer trendy cocktail popularized on TikTok. As a result, a seasonal group of products could be created and placed side by side.
What are the limitations of performing market basket analysis
If you're going to implement market basket analysis, keep three factors that affect the results in mind.
Average performance
Essentially, you get data based on all the transactions of the entire store (or even the chain). For example, before implementing data science principles, one retailer discovered an unexpected pair of products: beer and diapers. It had already become a meme in professional circles. Sales from this combination actually increased, but because of a change in the location of the goods, not inherent connection. Even now, the analysis of the market basket shows a variety of connections, but some of them are the result of your influence (for example, merchandising features).
Lack of clear conclusions
Let's say you've discovered a relationship between products. For example, 40% of milk buyers buy bananas within a week. But what does that tell you? Should you place bananas closer to milk? Or maybe offer a discount on one of the products? To make decisions, you need more context: season, demographics, and customer behavior. It may even turn out that the correlation was purely coincidental.
The need for testing before implementation
Implementing changes is difficult and expensive. Therefore, you need to make sure that they make sense. Therefore, the correlations identified in the basket analysis should be verified, for example, by A/B testing, but this process can take several weeks or even months. You need to think about the location of goods and update planograms and instructions for staff.
Despite these limitations, understanding customer purchasing patterns can lead to effective changes and growth in business performance. That's why we recommend using special tools for your market basket analysis.
Achieve more with LEAFIO AI
The Assortment Performance tool from the LEAFIO AI Retail Platform simplifies basket analysis to optimize assortment and increase shelf efficiency. The system provides a detailed overview of the assortment, helps to identify underperforming products, evaluate new products, and determine which SKUs are missing in specific stores.
Based on this data, you can make informed decisions for each product category. In addition, the shelf optimization function helps to create planograms that take into account actual sales. This is the basis for maximizing profits and creating a customer-oriented assortment.
Key takeaways
Take the following steps to effectively integrate market basket analysis into your business model and gain actionable insights.
- Identify the products that your customers choose in groups and place them side by side or offer promotions.
- Conduct an analysis to forecast demand and optimize your assortment.
- Conduct testing before implementing the results.
- Segment customers by their purchasing habits and offer personalized marketing campaigns for each group.
- Integrate modern analytics tools based on artificial intelligence and machine learning into your processes.
Have a question?
Have inquiries about retail automation or optimization? Talk to our expert for solutions!
Helen Kom
Inventory Optimization Product Director
