Market Basket Analysis

What is Market Basket Analysis ?

It is a mathematical modelling technique based upon the theory that if a customer buy a certain group of items, they are likely to buy another group of items.

What is the use of Market Basket Analysis ?

It is used to analyse the customer purchasing behavior & helps in increasing the sales and maintain inventory by focusing on the point of sales transaction data.

Market Basket Analysis

  • Allows us to identify patterns in customer purchases.
  • MBA uses the information to:
    • Identify who customers are (not by name)
    • Understand why they make certain purchases
    • Gain insight about its merchandise (products):
      • Fast and slow movers
      • Products which are purchased together
      • Products which might benefit from promotion
    • Take action:
      • Store layouts
      • Which products to put on specials, promote, coupons…
  • Ideally, we would like to answer questions like
    • What products tend to be bought together?
    • What products may benefit from promotion?
    • What are the best cross‐selling opportunities?

Example:

  • One basket tells about what one customer purchased at one time.

  • A loyalty card makes it possible to tie together purchases by a single customer (or household) over time.

Apriori Algorithm:

It is a level wise, breadth first algorithm which counts transaction to find frequent item sets & them derive association rules from them.

The following measures are used to evaluate the strength of association. Suppose, we are interested in the association between two events A and B:

  • Support = (Number of Rows having both A & B)/(Total Number of Rows)
  • Confidence = (Number of Rows having both A & B)/(Total Number of Rows with A)
  • Expected Confidence = (Number of Rows having B)/(Total Number of Rows)
  • Lift Confidence/(Expected Confidence)

Analysis done for given data using Market Basket Analysis:

We had a data containing the purchasing behavior of the customer in which we did the following analysis:

Analysis 1: Top 5 related product:

Here we can know customers buying lhs products may also buy rhs products for example customers buying Instant food products & soda also buy Hamburger meat, people buying flour & baking powder also buy sugar, etc.

 

Analysis 2: Top 25 most frequently purchased items:

Analysis 3: Rules that lead to buying specific product(Milk):

  • By this we can analyse that if a customer is buying Other Vegetables, rolls/buns, yogurt, root vegetables & tropical fruits then with that they prefer buying Milk mostly.

 

Analysis 4: Those who took particular product (‘milk’) also took:

  • By this we can analyse that if a customer is buying Milk with that they prefer Other Vegetables, rolls/buns, yogurt, root vegetables & tropical fruits.

 

 

For any queries or any services (in Analytics) please contact us:

Nikhil Analytics (Nikhilguru Consulting Analytics Service LLP)

Contact No.: +91-(080)-42124127, +91- 9741267715, +91-9945339324

Email: dyutilal@nikhilanalytics.com, alokranjan@nikhilanalytics.com

 

Subscribe for Data Analytics Edge Newsletter & Share..:-)

error: Content is protected !!