Drive high value Sales in Retail - A Data Science Case study

In a Retail store, many a times we have bad user experiences due to long queues at the billing register. But, even with efficient billing agents, we can only achieve a certain limit. So, if "N" is the total transactions and “t” represents time, the below formula signifies that we can accommodate maximum 2000 customer per billing register.

                                  R (Resultant transactions) =  0ò²⁰⁰⁰dN/dt

We can only accommodate 2000 transactions per day per register. So, it’s a bottleneck.

Now, we can add more registers, but that comes at a cost.

The alternative to increase the sales is to convert each transaction to be a high dollar purchase. So, we implement business intelligence algorithms to drive high value sales.

Even price sensitive users on exposure to better quality/service opt for higher value products.

For example:

A customer buys 2 items.

Average time for billing 2 items = 2*R = 4 mins

Case 1 :

Each item is $5 each. Total value of transaction = $10

Time taken = 4 mins. 

Hence, value generated in unit time = $10/4 = $2.5

Case 2 :

Each item is $9 each. Total value of the transaction = $18

Time taken = 4 mins. 

Hence, value generated in unit time = $18/4 = $4.5

Probability and Statistics can help answer numerous Business problems.

Think about it !!

Business Intelligence helps us to answer many such questions with ease. We will cover more such case-study later.