Data Mining - How Market Basket Analysis Can Help Increase Sales
Data mining is a set of techniques for the automated discovery of statistical dependencies, patterns, similarities or trends in very large databases. For those involved in sales techniques and correcting sales results, data mining is an extremely valuable tool. It can help increase profits and improve competitiveness. Read on to find out where to start and how to benefit from market basket analysis in your e-store.
Due to ease of obtaining large amounts of online data, data mining has become an interesting issue for e-commerce, especially for exploring purchasing trends by analyzing customer market baskets. This involves finding a relationship between purchased products by discovering links using simple rules, known as the rules of association. Market basket analysis can effectively present product offers, create more effective promotions and develop more effective marketing campaigns. Simply put, they help increase profits and improve competitiveness.
Construction of association rules
While analyzing a market basket, you are looking for associations. This is done by a special algorithm which combines elements together and finds the relationships between the products or behaviors, simultaneously discovering the shopping habits of customers. The result of the analysis are rules of association in the form of : A → B, ie. IF [body] then [head]. The body and head can be freely extended and consist of a conjunction of many conditions and create a more complicated rule.
But let’s start with a simple example:
This rule assumes that if a customer buys a pizza, there is a substantial likelihood that they will buy ketchup. Of course, it is not necessarily true one hundred percent of the time. The fact that these two products are strongly linked determine specific indicators which define quality rules.
Measures of quality rules
The quality of the rules, that is how much they are true and valuable from the business point of view, outlines several measures:
- Support for the rule is the percentage of transactions containing the rule in all transactions in a retail outlet. In our example support determines the probability of buying both frozen pizza and ketchup by a randomly selected customer in a particular store.
- Confidence is the probability that the B product will appear when the A product also will be present. In our case, it is likely that the customer will buy ketchup when they buy a pizza.
- Lift informs the impact of the sale of the product A on sale of the product B. If the lift of value is 1, then we say that the products do not affect each other, if it is less than 1, we consider the products to be antagonistic, and if is greater than 1 the products are complementary
Let’s take another example in which we highlight support and confidence. Suppose that the sales report in a supermarket shows that last Thursday, of 1,000 customers, 200 bought loaves of bread, and from those who bought loaves of bread, 50 bought butter as well. In this case, you can see a rule that if someone buys bread, they also buy butter, with support of 5% (50/1000) and confidence of 25% (50/200).
Market basket analysis in online stores
Initially, the analysis of market baskets included transactions in supermarkets. Now, with the increasingly growing importance of e-commerce, analysis of market baskets in online stores seems even more relevant, because the internet is the perfect place to get to know user habits. Transactional data collected in an automated way from online stores provides reliable information about customers and the products they buy. Additionally, the utility developed on the basis of the rules and the effectiveness of their actions can be easily estimated by, for example, calculating the ROI.
The most common use of association analysis is finding products which are complementary to each other and are therefore commonly consumed together. It positively affects sales – knowing that the customer buys product A, you can show him products B, C and D, which are usually purchased together with A. For the purpose of market basket analysis in e-commerce you can use also other types of association rules:
- Negative association rules discover links between the products added and not added to the basket together. On this basis, you can find significant customer buying behavior like favorite brands (buying Coca-Cola but not Pepsi) or product type preferences (if someone buys juice, they will not buy bottled water). Negative rules are equally useful in the preparation of an online store offer as positive rules (presenting products sold together), because it lets you dynamically modify the appearance of the page so that the customers do not see the content they do not want to buy.
- Cyclic association rules take into account the repeatability/cyclical nature of purchases over time and thereby customize the appearance of the website to the customer.
- Inter-transactional association rules do not look for dependencies in a single transaction but examine the relationship between purchases made in a certain period of time. These rules take into account the context of shopping (eg. time, place, customers), which is ignored in the intra-transactional rules. They aim to find sequences of events.
- Ratio association rules – in addition to information about purchased products they also contain information about the amount of money spent on various goods and services. As a result, it is possible to predict the value of the transaction and the purchase of specific items from the e-store’s offer.
Before you start mining...
The most important element in data mining is relevant data. Data mining is often characterized not as “the search for rules in the database”, but as “mining for dollars” because it helps to increase profits. It allows you to optimize marketing campaigns by identifying the probability of the purchase of individual products, develop effective methods for cross-selling (selling a product or service to the customer associated with the purchase of another) and up-selling (selling more expensive product versions). It is necessary to know customers’ buying habits if you want to construct an offer consistent with their expectations.
After finding high-quality rules, you can easily reach each customer with a suitable offer with dynamic content available on the site (in the form of banners and pop-ups) and email. You can also profile the message based on customer segments. And if, despite a personalized offer, your customers add products to the market basket but don’t buy them, you can automatically send them an email regarding the items in the abandoned basket and a discount to encourage them to complete the transaction.