Nowadays, machine learning is helping retailers in many ways. Market Basket Analysis is one of the best machine learning applications in retail. By analyzing the past buying behavior of customers, we can find out what products they often buy together.
In this article, we will take a detailed look at a practical guide to market basket analysis and its components and then delve into market basket analysis.
What is Market Basket Analysis
Systematic analysis of a set of elements leads to discovering associations between elements in huge datasets. Due to the constant collection and storage of huge amounts of data, many industries are interested in extracting such patterns from their databases. Uncovering “these relationships” across a vast array of transaction records can help in many decision-making processes, such as catalog development, cross-marketing, and consumer buying behavior analysis.
A popular example of frequent item analysis is market basket analysis . This process determines customers’ shopping habits by finding associations between the various items that customers put in their “shopping carts,” as you can see in the following figure. Finding this kind of association will be helpful for retailers or marketers when developing marketing strategies by gaining insight into what products customers often buy together.
For example, if customers are buying a table, what is the likelihood that they will buy a chair when ordering a product from an online store? This information can increase sales by helping retailers conduct selective marketing and plan their retail space.
To solve the problem of analyzing the market basket, associative rules of the form “if … then …” are used. For example, “if a customer bought milk, he will buy cheese.” Each purchase is called a “transaction,” based on a more extensive set of such transactions and building customer behavior studies.
Association rules are an elementary and convenient form of knowledge recording. Still, the resulting association rules are the knowledge that helps big supermarkets save a lot of money.
Some metrics are used to characterize the rule:
The rule X->Y has supports (support) if s transactions from D contain the intersection of sets X and Y. The confidence of the rule shows what the probability that X follows Y is. The rule X->Y is valid with certainty c (confidence) , if c transactions from D containing X also contain Y, conf(X-> Y) = supp(X->Y)/supp(X ).
For example: “75% of transactions containing bread also contain milk. 3% of the total number of all transactions contain both goods.” 75% is the confidence of the rule, 3% is support, or “Bread” -> “Milk” with a probability of 75% and support of 3%.
As a rule, clear rules have high support and reliability (60% or more) but are not de facto knowledge. The main attention should be paid to rules with 5-10% support. They can become the source of the idea of a promotion or service.
Advantages of Market Basket Analysis
Implementing market basket analysis in marketing has many benefits. Market Basket Analysis (MBA) can be applied to customer data from the Point of Sale (PoS) systems.
It helps retailers:
- Increases customer engagement;
- Increasing sales and increasing ROI;
- Improving the quality of customer service;
- Optimization of marketing strategies and campaigns;
- Help you better understand your customers;
- Defines customer behavior and patterns.
Let’s take the example of Walmart, an American multinational retail corporation that operates a chain of hypermarkets. From the shopper’s point of view, shopping cart analysis is like shopping in a supermarket. As a rule, he observes all the goods purchased by customers together in one purchase. It then shows the most related products together that customers would buy in a single purchase.
As for the benefits, we can highlight the following points:
1. Store layout
Based on the market basket analysis results, you can organize your store to increase revenue. Items that go together should be placed next to each other so that consumers can see them. It will determine how the store is organized for maximum profit. You can avoid guesswork when choosing the optimal store layout with this data.
2. Marketing messages
Whether it’s email, phone, social media, or a sales pitch, shopping cart analysis can improve the performance of all of them. Using MBA data, you can suggest the following best product a customer is most likely to buy. This way, you will help your customers with fruitful offers and not annoy them with marketing explosions.
3. Maintain inventory
You can also predict future customer purchases over a while based on MBA data. Using your initial sales data, you can expect which item is likely to run out and keep inventory at optimal quality. It will help you improve the distribution of resources across different inventory items.
4. Content placement
In the case of e-commerce, posting content on a website is very important. If the products are displayed in the correct order, it can help increase conversions. Online publishers and bloggers can also use the MBA to show content that a consumer will likely read next. It will reduce your bounce rate, improve engagement, and better performance in search results.
5. Recommendation mechanisms
Recommendation engines are already being used by some popular companies such as Netflix, Amazon, Facebook, etc. If you want to create an effective recommendation system for your company, you will also need market basket analysis to serve it effectively. The MBA can be the foundation for building a recommender engine.
As we have seen, market basket analysis can help companies, especially retailers, analyze the buying behavior and predict their next purchase. When used effectively, this can greatly improve cross-selling and, in turn, help you increase your customer’s lifetime value.
At Magecom, we’ve helped many companies successfully use their customer data to generate insights that have enabled them to reach new heights. If you need help using cart analysis for your company, feel free to contact us.
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