10 behavorial variables to segment Retail shopper

Here are some possible variables to segment customers using their behavorial profile

1) Customer Purchase Recency
The number of days which have elapsed since the customer last purchased from a store
Example : less than 30 days, less than 3 months, less than 6 months etc

2)Tenure
The number of months the customer has been a member of the loyalty card program
Some targeted campaigns could have increased loyalty subscriptions during certain periods

3)Average basket value
Average amount spent by the shopper during each visit to the store
Example : less than $ 50, 50à125 $, greater than 125 $

4)Average basket size
The number of items the shopper purchases during each visit

5)Spend dispersion
The % of spend dispersed across various categories of products like music, books, stationery items, perfumes etc
Example : 12 % on stationery, 35 % on books, 43 % on perfumes

6)Customer Purchase frequency
The number of times the customer purchases from the store in a year
Example : 10 times in a year

7)Overall spend dispersion profile
The % of deviation between spend of this customer and an average store shopper to benchmark the intensity of shopping on various categories relative to an average buyer
Example: An average Joe would spend lets say 20 % on stationery, 50 % on books and 30 % on perfumes. If David’s spend dispersion profile is 60 % on stationery, 35 % on books and 5 % on perfumes, his spend bias helps us understand his profile better relative to an average Joe.

8)Demographic spend dispersion
The % of deviation between spend of this customer and the demographic segment to which the customer belongs to

9)Range of products purchased
Out of the overall number of categories present in the store, what % of the categories has the customer purchased

10)Range of channels used
A product can be sold thru multiple channels – company owned store, franchisee store, web , phone/contact center

Store metric - Shelf alignment index

Definition of shelf alignment index

Shelf alignement index is a simple metric which stores can use to check if the merchandise displayed on their shelves is in alignment with shopper behavior at the store. For example lets say

Input data required to calculate shelf alignment index

1. POS transactions ( SKU, Store, date, um, qty, value )
2. Shelf space audit ( Category, date, shelf area in sq ft, relative share of shelf )

How does one calculate shelf alignment index

1. Rank all categories ( soaps , shampoos , vegetable, ice creams ) based on the relative number of units sold per month.

2. Rank all categories on share of shelf

3. Calculate difference in rank = Rank(Sales)-Rank(Share of shelf)

4.
Example of shelf alignment index


Application of shelf alignment index to optimize store operations