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Market Basket Analysis: Meaning, Benefits and Role of Big Data in MBA

Posted by Sales

Sep 20, 2017 11:30:00 PM

Let me introduce to the world of cross-selling. Cross-selling is the process through which marketers sell a number of products to their existing customer thus banking on their customer lifetime value. Cross-selling is one of the most used technique to increase revenues and generate ROI from marketing efforts. With the ever-increasing cost of acquiring new customers and increasing competition, leveraging existing customers is the best option at the disposal of businesses, especially retailers. But how do you figure out which products to offer for cross-selling? The solution to your problem is market basket analysis (MBA) using big data analysis.

What is Market Basket Analysis?

what is market basket analysis and how big data can be used

Market basket analysis (MBA) is a business intelligence technique to predict future purchase decisions of the customers. It studies customers' buying patterns and preferences to predict what they will prefer to purchase along with the existing items in their cart. For example, if 3 out of 5 times a customer purchases egg along with flour and sugar (probably for baking cake) then market basket analysis can predict the possibility of buying egg if it is offered along with these two items. Market basket analysis is described mostly in form of associations for example:

  • If flour is purchased then sugar is also purchased
  • If sugar is purchased then flour is also purchased
  • If both flour and sugar are purchased then egg is purchased 60% of the time.

Here we use the term “antecedent” for IF and “Consequent” for THEN part of the statement. Thus, market basket analysis helps in making decisions regarding placement of goods, marketing communications, inventory maintenance etc.

Read More: How to Optimize Pricing Decisions Using Big Data?

Some Essentials of Market Basket Analysis:

Support - The support showcases the probability in favor of the event under analysis. If it is less than 50% then the association is considered less fruitful.


Confidence - It expresses the operational efficiency of the rule. It calculated as the ratio of the probability of occurrence of the favorable event to the probability of the occurrence of the antecedent.


Lift Ratio - The lift ratio calculates the efficiency of the rule in finding consequences, compared to a random selection of transactions. Generally, a Lift ratio of greater than one suggests some applicability of the rule.

Read More: Impact of Cloud Computing in Retail

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Role of Big Data in Market Basket Analysis:

what is market basket analysis. Role of Big data in market basket analysis


While all of this sounded really easy when we took an example of 3 items but think how complicated it will get when you combine data sets from different items from grocery, personal hygiene, clothing, food and beverages, bathroom accessories, stationery, electronics, bags and wallets, and many other items found in a common retail store. According to Walmart’s official website, A Walmart Superstore has 142,000 different items in its store. These items can result in a tremendous number of possible subsets. If we start forming sets of 3 from a data small set of 100 items then 161,700 combinations are possible. Think how massive is the amount of data that is needed to be analyzed to figure out best combinations from 142,000 items. Additionally, there can be data sets from 2 items to 2000 items in this calculation. In e-commerce, this problem can increase to an even larger extent because of a wider range of items. According to export-x, as of 2015 Amazon had 488 million items in store.

In order to perform market basket analysis using big data, you need to use sophisticated analysis and modeling tools which are very hard to master. By ingesting the data from point of sale systems (offline stores) and carts (online stores) you can collect insights that can help you increase the efficiency of your cross-selling efforts. Big data analysis tools like Hadoop, Hive, Pig etc. make analysis of these huge data sets possible and data visualization tools like Tableau & Qlikview demonstrate the insights in the form of graphs that you can use to understand the data and take decisions accordingly.

Read More: 5 Uncommon Ways of Using Big Data in Retail

Benefits of Market Basket Analysis:


1. Store Layout:

Based on the insights from market basket analysis you can organize your store to increase revenues. Items that go along with each other should be placed near each other to help consumers notice them. This will guide the way a store should be organized to shoot for best revenues. With the help of this data you can eliminate the guesswork while determining the optimal store layout.

2. Marketing Messages:

Whether it is email, phone, social media or an offer by a direct salesman, market basket analysis can improve the efficiency of all of them. By using data from MBA you can suggest the next best product which a customer is likely to buy. Hence you will help your customers with fruitful suggestions instead of annoying them with marketing blasts.

3. Maintain Inventory:

Based on the inputs from MBA you can also predict future purchases of customers over a period of time. Using your initial sales data, you can predict which item would probably fall short and maintain stocks in optimal quality. This will help you improve the allocations of resources to different items of the inventory.

4. Content Placement:

In case of e-commerce businesses, website content placement is very important. If goods are displayed in right order than it can help boost conversions. MBA can also be used by online publishers and bloggers to display content which consumer is most likely to read next. This will reduce bounce rate, improve engagement and  result in better performance in search results.

5. Recommendation Engines:

Recommendation engines are already used by some popular companies like Netflix, Amazon, Facebook, etc. If you want to create an effective recommendation system for your company then you will also need market basket analysis to efficiently maintain one. MBA can be considered as the basis for creating a recommendation engine.

Read More: 5 ways augmented reality is changing the face of retail

As we have seen, market basket analysis can help companies especially retailers, to analyze buying behavior and predict their next purchase. If used effectively this can significantly improve cross-selling and in turn, help you increase your customer’s lifetime value. At NewGenApps, we have helped many companies successfully leverage their buyer’s data to generate insights that enabled them to reach new heights. If you need help in utilizing market basket analysis for your company then feel free to contact us:

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Topics: Big Data, eCommerce, Predictive Analytics, Retail

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