Market Basket Analysis is a powerful technique that enables businesses to gain insights into consumer behavior by analyzing their purchase patterns. Each time a customer makes a purchase, valuable information is collected and stored, whether it’s through membership cards, receipts, or online platforms’ transaction databases.
The principle behind Market Basket Analysis involves comparing the contents of each shopping cart, consisting of multiple products. These carts are then analyzed to identify relationships between the items. The ultimate goal of this analysis is to understand customer shopping behavior and uncover connections between the items within each basket.
So, how is this principle of Market Basket Analysis applied?
Step 1: Analyzing Product Performance
The first step in Market Basket Analysis is determining which products sell well and which ones don’t. This step is known as “support.” For example, if we want to assess the popularity of t-shirts, we can analyze the number of baskets containing t-shirts divided by the total number of baskets. Using a simple example:
- Basket 1: T-shirt, pants, lipstick
- Basket 2: T-shirt, pants
- Basket 3: T-shirt, lipstick
- Basket 4: Lipstick, Phone Case
In this case, t-shirts appear in 3 out of 4 baskets, indicating a support value of 75%. Conversely, phone cases only appear in 1 out of 4 baskets, representing a support value of 25%. This information reveals that t-shirts are popular while phone cases are not performing well in our store.
Using this data, we can take appropriate actions. For instance, we may offer promotions, discounts, or giveaways to boost sales of phone cases. On the other hand, t-shirts and lipsticks, being popular items, may not require additional marketing efforts.
Step 2: Identifying Product Associations
The second step involves identifying which products are likely to be purchased together, known as “confidence.” Continuing from our previous example, let’s determine if customers who buy t-shirts are also likely to buy pants. This can be analyzed by dividing the number of baskets containing both t-shirts and pants by the number of baskets with t-shirts.
From the given baskets, we have 2 out of 3 instances where customers who bought t-shirts also bought pants, resulting in a confidence value of 67%. This information can be strategically used in various ways:
- Group related products close together to increase the likelihood of customers purchasing additional items.
- Create promotions or offers that incentivize customers to buy complementary products.
- In the case of e-commerce businesses, leverage this information for targeted advertising and personalized product recommendations.
However, it’s important to be cautious when interpreting this information alone. Let’s explore why.
Step 3: Validating Purchase Intent
In some cases, high confidence values can lead to inaccurate conclusions. To validate whether customers buying certain products together is intentional or coincidental, another step is required. This step is known as “lift validation.”
Lift value compares the probability of customers picking up a specific item after selecting another item, relative to the probability of them purchasing the second item independently. Using the same example, let’s examine the lift value for t-shirts and lipsticks:
- Basket 1: T-shirt, pants, lipstick
- Basket 2: T-shirt, pants
- Basket 3: T-shirt, lipstick
- Basket 4: Lipstick, Phone Case
The confidence value for t-shirts and lipsticks is 2/3 (67%). Now, we divide this confidence value by the support value of lipsticks (3/4) to obtain a lift value of 8/9 or 0.89.
If the lift value is greater than 1, it indicates that customers are more likely to purchase those two items together intentionally. If it’s less than 1, it suggests that the association between the items is weak. A lift value of 1 implies the items were bought together by chance.
In our example, customers buying t-shirts are not inclined to purchase lipsticks with them, as lipsticks are commonly found in many baskets regardless of t-shirts. This insight helps us avoid making incorrect assumptions.
By employing Market Basket Analysis, businesses can better understand customer preferences, optimize product placement, and design effective marketing strategies. It’s a valuable technique for uncovering patterns and driving sales.
Start leveraging Market Basket Analysis today to gain a competitive edge and enhance your business performance!