Market Basket Analysis

Know your customers & deliver what they desire

Market Basket Analysis

Identify products and content that sell better together

The retail industry has moved to a whole new level of superior customer experience where retailers are analyzing consumer behavior and selling what customers demand. Data science helps you attract and satisfy targeted customers by analyzing and forecasting demand based on their buying history. It’s about understanding the correlation between the products and marketing content to help retailers sell more.

Market Basket Analysis (MBA)

The science of identifying customer behavior, buying patterns, and finding the relation between products and content delivery by the retailer inside the store or on their online shop is known as market basket analysis. It helps in rectifying target markets, getting, keeping, and growing customers through creating, delivering, and communicating superior customer experience. Technically, it’s a combination of association rule mining techniques to identify frequent patterns, affinities, correlations or casual structure among different sets of items in the transactional database. Data scientists deal with a data set that consists of a number of transaction records, each containing a set of items purchased by a particular customer.

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Recommender systems – How they can help retailers deliver personalized shopping experiences

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  • What are recommender systems?
  • Different types of recommender systems
  • Use cases of recommender systems and how to create one
  • Difficulties of building the right system and how to overcome them
  • How a customized recommender system can benefit your business

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Understanding consumer behavior to boost your retail business

For getting better insights into the customer behavior inside the store, market basket analysis can be further implemented on individual datasets based on weekend vs weekday sales, month beginning vs month-end sales, sales pattern in different seasons of the year. Also, it is useful to perform MBA on different customer profiles to gain the bigger picture of your customer buying pattern. It helps retail store owners to drive business decision making, target marketing, and customer behavior analysis for optimizing profits.

The retailers can use the gathered information to structure store layout and their marketing strategy. MBA is very useful in driving recommendation engines while delivering targeted marketing.

“It’s not just enough to satisfy your customers but you need to delight them as well.”

Market basket analysis can be applied in the following different ways to boost your retail business:

  • To develop combo offers based on products sold together
  • To organize and place associated products/categories nearby inside the store
  • To determine the layout of the catalog of an eCommerce site
  • To control inventory based on product demands and what products sell better together
  • For customer segmentation and to create customer profiling based on their buying pattern
  • To classify different shopping trips for creating best shopping experience

Market basket analysis: How we do it.

Our data scientists help you in identifying the right point of sale to maximize your profits. They will find out the products/items association that has a good buying history to sell them together. They will create a customer profile based on their buying patterns to target market potential. Ultimately, this helps in predicting sales on the right time at the right place for the right customer. That’s how we calculate the customer’s lifetime value for maximizing profits of your business.

Transforming retail business: Are you ready?

Softweb Solutions can help you figure out how you can better serve your customer’s interests and create a value for the business. Our team of expert data scientists will assist you in optimizing per time period, per product, and per store. Are you ready to create a smart retail solution for your business to become a market leader or will you let your competitors lead the game?

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Our Market Basket Analysis Experiment on Walmart’s Dataset

The retail industry needs to become smart as the users have become smarter than ever. There is a great need to apply modern data science to identify and target customers appropriately.

Our team of data scientists took data from Kaggle to analyze different aspects of customer behavior inside the store. The team experimented with different sets of data to analyze customer behavior inside the Walmart store and further classify it for defining the right product association, trip types, point of sale, and marketing.

After analyzing consumer behavior inside the store, the next step was to apply modern data science techniques on different data sets. By applying association rule on gathered data our data scientists used different mathematical formulas to identify association between products/items, which is helpful in creating appropriate item sets. Overall, the practice helps in finding frequent patterns, associations, correlations or casual structures among the set of items or objects in a transactional database.

To get a better insight how market basket analysis (MBA) works just have a look at the below example:

Story of beer and diapers

Jane calls her husband John who is coming home from work. She tells him to buy diapers for their three-month old daughter Laurel. John stops by the supermarket and along with diapers, also decides to buy a six-pack of beer. This kind of correlation will not be obvious to the retailer but with insights gained through implementing market basket analysis, he will know of such rare occurrences. He can then take steps to position items of interest to young fathers in such a manner that the store’s sales increase.

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Walmart’s Inter-departmental Co-occurrence Graph

Walmart has categorized its entire range of products into 68 predefined department descriptions such as grocery, seafood, electronics, etc. The dataset contained details on the 95,674 unique visits to a Walmart store.

In our experiment, the Apriori algorithm was used which is the most popular algorithm when it comes to market basket analysis. It is used to create associations between the item sets.

Other popular algorithms that we have used in our other retail related experiments are Eclat and APrioriInverse.

With the help of market basket analysis, association rules are used to detect and extract useful information from the department products indicated in the figure given below. In the network graph the department products are indicated by green circles. Size of the orange circle indicates the support between the department products. The smaller the circle size, the lower the support, indicating low occurrence of item set (department products) from all the transactions.

Walmart-graph

Here are some of the item sets and how they are related to each other.

{CANDY/TOBACCO/COOKIES*, DAIRY, GROCERY DRY GOODS} -> {DSD GROCERY} having support of around 1% indicates that these items occur less frequently together in transaction.

*Walmart has classified candy, tobacco and cookies as one department

The other is {HOUSEHOLD CHEMICAL SUPPLEMNTS, DAIRY, PRODUCE} -> {GROCERY DRY GOODS} having only 3% out of 100 transactions support, indicating that these items occur less frequently together in transaction together

The item set product {PRE PACKED DELI, HOUSEHOLD PAPER GOODS} -> {GROCERY DRY GOODS} has a support of around 2% out of 100 transactions, which indicates that the items occur very rarely in the transactions.

{DSD GROCERY, DAIRY, PRODUCE} < – {GROCERY DRY GOODS} has a support of around 5% out of 100 transactions, which indicates that the items occur frequently together in transaction.

Based on customer classification and segmentation, it is easy for the store owner to assign the probabilities to each trip-type. It is helpful in defining the right point of sale (POS), classifying product sets, trip type, and right marketing strategy inside the store. Ultimately, the retailer will be able to optimize revenue and profit.

The overall data science experiment can help retailers in the following processes:

  • Finding the best product association
  • Classifying market trends
  • Creating more appealing product sets
  • Identifying sales seasons and items
  • Delivering superior customer experience
  • Increased sales volume and profits
  • Improve overall store performance
  • Profit optimization

If you want to take your retail business to a next level, hire our team of data scientists to tap the opportunities in the modern retail industry.

Let our data science team help you solve complex data and business problems