Using T test to understand shelf placement decisions

Lets consider a simple scenario.

Say ABC retail is setting up a new store in Frazer town area

Pre store launch catchment area/competitive store analysis helped in deciding store format and product mix to display

The store in Frazer town, the following categories are relevant for the consumer ( Mangloreans, Muslims,Goans )
Non veg food items
Hair care
Basmati Rice
Atta
Kerala Parottas

The store manager of Frazer town branch wants to optimize the 12000 sq ft of shelf space he has

He wants to know which “Hot” shelves and he has a few hypothesis based on experience which he wants to test

A/B Multivariate testing can come handy to discern which shelf combination maximises revenue and minimize inventory carrying cost Or if the product is moving slow ( it becomes a slow moving item and a promotion /advertising may be required to stimulate sales )

HOW DOES A STORE MANAGER DECIDE SHELF PLACEMENTS TO MAXIMIZE STORE REVENUE AND MINIMIZE SHELF SPACE INVENTORY ?

Shelf space optimisation framework

Product inventory and shelf space are a retailers most precious resource
If there is too much inventory it affects store profitability
If there is too less inventory it affects store sales
If the product is not kept in the right shelf it could affect consumers ability to find it
It is estimated that assortment and shelf optimization can lead to 7-15 % improvement in store sales and gross margin
Non aligned shelves could result in
Sub optimal customer experience ( as he could not find the product he/she is looking for )
Increased inventory carrying cost ( as the yield per square feet of retail space is low )

There needs to be a balance between breadth of a category kept and depth of a product category kept
Ex: In retail outlets do they stock breadth : Rice, Soap,Shampoo,Electronics,Stationary or breadth ( Anapoorna rice, taaza rice et )
In a jewellery store do they stock breadth : Diamonds, Gold,Silver or depth : Diamond ring, Gold chains etc

The questions defining the shelf space optimisation problem are as follows

1.Which are the top 5 products which need an increase in share of shelf ?
2. Which are the top 5 products whose shelf space has to shrink ?
3. How much additional shelf should I need for products which need to expand ?
4. How much shelf space should we shrink the space of the products occupying too much shelf space ?
5. Is their a number which quantifies the degree of misalignment ? How does one interpret this KPI ?

Using structural equations modeling to understand store performance drivers

Structural equations modeling is an extremely good technique to model multiple cause and effect simultaneously. Take for example we want to trace the causal chain from foot fall to conversion to spend dispersion to monthly sales turnover to store profitability. How do we get to see the complete cause and effect chain. Techniques like regression etc cannot handle one outcome variable recursively being a causal variable

Co-relating shopper sentiments to footfall and basket size

Statistics collected by Media agencies suggest that Teenagers are spending more time on the Web than watching TV. This is a huge inflection point as web has replaced TV as a more engaging channel. And within Online channel , Blogging and Online videos ( youtube etc ) seem to be most engaging activity. What that means is that it is important for retailers to track if shoppers express sentiment about the instore experience or product attributes online ? There are 2 kinds of scenarios which can be envisioned here.

Scenario-1 : When shoppers are expressing about their instore experience on http://www.yelp.com/ or http://www.mouthshut.com/ or http://www.eopinions.com/. But the sentiment volume has not reached a threshold where it has started influencing footfall, basket size and revenue per shopper.

Scenario-2 : The volume of sentiment expressed on online platform has reached a critical stage where more shoppers are coming to the store or the number of shoppers / basket size has decreased.

What this means is to that the retailer needs to have a framework which can keep track of the buzz velocity online and track in real time the effect of buzz velocity on instore footfall and basket size.

Retail Decision Landscape to Optimize using Analytics

Clustering stores

Stores can be clustered on the basis of various store performance metrics and differential treatment strategies can be adopted. This could be with respect to the

1) Store format to be adopted for each store cluster

2) Mix of merchandize to be stocked for each store cluster

3) Pricing strategy for each store cluster

4) Promotion mix strategy for each store cluster. Coupons vs Temporary price reduction vs Gift off

5) Instore experience strategy . Lighting vs staff per square feet vs Displays vs sampling

6) Shelf placement strategy

Stores can be clustered on a variety of attributes like

1) Price sensitivity to strategic products

2) Footfall in store

3) Spend dispersion observed in basket across categories like grocery, hair care, electronics, juices etc

4) % revenue accrued from loyalty card holders vs anonymous buyers

Retail analytics roadmap

How does one go about isolating the retail scenarios which are relevant and the order in which they must be deployed ? A re

Customer sentiment analysis using Unstructured Text mining

There are 6 steps to mining consumer sentiments from the blog they. They are

1. Sourcing the comments from http://www.mouthshut.com/ or http://www.yelp.com/ or http://www.eopinions.com/

2. Indexing

3. Filtering 'noise' words

4. Stemming

5. Geneating themes and summary gists

6. Analysis of sentiments

Product Cannibilisation analysis

Product cannibilisation is said to occur when one product starts 'eating' into the sales of the other product. For example when a 1 litre bottle of juice is offered at a 8 % discount its conceivable that a consumer purchases that instead of a 1.5 litre bottle juice or a 0.5 litre bottle juice. There are 2 types of cannibilisation
1) Internal cannibilisation
2) External cannibilisation

Internal product cannibilisation can happen when a companys own product eats into the sales of the other product. External cannibilisation occurs when a competitive product eats into the sales of the companys product

Another way to classify cannibilisation is on the trigger which was responsible for the cannibilisation to begin. It could have been triggered by 2 events
1) New product launch triggered cannibilization.
For Ex : A new lemon flavored juice is introduced which cannibalized apple juice
2) Promotion triggered cannibilisation
For Ex : A temporary price reduction scheme has been introduced on a particular soap which induces price sensitive customers to switch to the new soap

In subsequent postings we can discuss a quantitative framework to measure cannibilisation. How do we quantify cannibilisation ? What data is required to quantify cannibilisation

Overall Retail outlet/store analysis framework

10 overall store data sets

9 surgical store actions

Shopper behavior segmentation - 9 Shopper questions

Segmentation is a basic first step a retail organization can undertake to understand the behavioral characteristics exhibited by the shoppers and to build a comprehensive behavioral portrait of the customers shopping at the store. Segmentation is basically the process of dividing shoppers into meaningfully distinct groups.

Once shoppers are grouped into distinctive segments each group can be offered a different marketing mix plan depending on behavioral characteristics they exhibit.

Segmentation is both an art and a science where behavioral niches are identified and pin pointed marketing actions are initiated as opposed to “carpet bombing” the entire customer base. It is a very selective demand stimulation strategy which the retailer can adopt.

Before getting into the actual process of segmenting shoppers in your store it makes sense to get an understanding of the flavor of business questions which can be answered using a loyalty based segmentation framework based on raw POS, Store and Loyalty card data.

What are some interesting business questions which are available in the raw data but remains unanswered in most organizations

1) Do you know the behavioral portraits of your customer? Are they price sensitive? Brand conscious? Convenience shoppers? “Once a fortnight” weekend grocery shopper?

2)Which customer segments drive repeat purchase behavior?

3)Which customer segments exhibit propensities to you strategic categories and brands?

4)Do you know what the drivers of behavior are for each of your customer segments?

5)Do you know what behavior discriminates one customer segment from another?

6)How are segment memberships changing over time ? What does it tell us about our product mix, price and any competitive activity in the market where the store is located?

7)How can customer behavior portraits be used to drive customer treatment strategies and targeted outbound campaigns?

8)How can customer behavior portraits be used to drive in store experience and merchandise mix?

9)How can you use customer behavior portraits to increase the intimacy level with the customer?

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

How can statistics be used to optimize store operations ? 12 scenarios

How can one use sophisticated mathematics / statistical techniques to get competitive differentiation while running store optimally. Here are 12 areas where statistics has been found to add dispropotionate value to the store related decision making process and thereby bringing game changing opportunities for the organisation
1. Promotion uplift modeling using regression

2 Sales forecasting using multivariate analysis, holt winters model,ARIMA, exponential smoothing etc

3. Store segmentation using K means clustering

4. Life time value modeling for loyalty card holders using Survival analysis, regression etc

5. Store experience sentiment analysis using unstructured text data mining

6. Survey analysis using discrete choice modeling, factor analysis etc

7. Pricing analysis using constraint based optimisation techniques

8. Understanding drivers of store performance using structural equations modeling

9. Shelf visibility analysis using A/B testing, design of experiments and multivariate analysis, chi square hypothesis testing

10.Cross sell recommendation engines using collaborative filtering and MB analysis

11. Shopper behavior based segmentation using K means clustering

12. New product launch analysis using engagement segmentor

Each of the above techniques will be ellaborated one by one in a separate blog

What are 5 important levers a retailer activate to stimulate growth to shoppers basket item count ?

One of the basick checks in Retail analytics one can do by processing POS Transactional data is to verify if the size of the basket in terms of number of items shopped for in one visit to the store is increasing or decreasing. Once we find that, the trend in basket count is coming down when benchmarked with no of items in an average shoppers basket size one year back or even a quater back then we can activate 5 possible levers.

Action trigger-1 : ACTIVATE STORE LEVEL BUNDLED PROMOTIONS
Can we create a new store promotion to entice shoppers to buy more ?Can we have bundled promotions. Example if you buy a soap and a shampoo you get 2 % off the shampoo. It could be a 'gift off' ( where a consumer gets an item free with the purchase of another item ) or temporary price reduction scheme or weekend coupon promotion scheme is working well . In case of food items, can we have live sampling for food items in store which consumer normally does not purchase ? How about an SMS blast to people living within the store vicinity ?
Action trigger-2 : CHANGE MERCHANDISE MIX AFTER SURVEYING SHOPPERS
It is conceivable that consumers come to our store and do not find specific items which they would like to purchase. A simple survey can be administered to shoppers to find out "What is it that you would like to see in our store ?". If there are common items which are occuring frequently in the response then the merchandise mix can be changed in the store to include the most frequently requested item by shoppers which is currently not displayed in our shelves.
Action trigger-3 : CHANGE SHELF PLACEMENT AND VISIBILITY STRATEGY
In some cases it has been found that even though the SKU ( Stock keeping unit) is present in the store, shoppers do not purchase it because it is 'buried' deep in some corner shelf and not easily visible to the eye. A simple planogram multivariate data analysis can reveal co-relations between shelf placement ( At eye level, Below eye level, Above eye level, near POS counters ) and its effect on the items purchased by a shopper during a visit.
Action trigger-4 : INTRODUCE LOYALTY CARD AND HAVE BEHAVIOR BASED RECOMMENDATION ENGINE
In order to track a customers specific behavior in terms of visit frequency, average spend, dispersion of spend across multiple product categories, recency of visit etc it is important that a loyalty card program be instituted. Once this is done we can use the torrent of purchase behavior transactions to create a behavior based recommendation engine. Techniques like collaborative filtering and market basket analysis can be used to derive cross sell recommendations. Once this engine gets trained on past purchase behavior it can be used to derive the top 5 recommendations for each of the loyalty card customer based on past behavior exhibited. This surgical approach has a greater chance of stimulating increase in basket count as opposed to 'carpet bombing' them with a generic recommendation on product
The competitive stores within the immediate 3-4 mile vicinity of our store are offering the same items at a higher cost. Shoppers find our store competitively priced for the items they shop .
Action trigger-5 : Incentivise staff based on customer recommendation and increasing store staff per 100 square feet of store space
There have been scenarios un earthed in stores where a customer comes to the storeand cannot find knowledgeable staff who can guide her on finding certain products . This problem can be alleviated by dynamically increasing the number of staff dynamically based on footfall observed during different shifts, ensuring store staff are sensitized to customers needs and incentivising store staff who have been recommended by shoppers who found the staff courteous and helpful

Action trigger-6 : NEGOTIATE VOLUME DISCOUNTS WITH VENDORS
If a competitor store in the vicinity offers SKU's at a price point below our store, then there is an opportunity to show past purchase and POS volume for the vendors SKU and negotiate a volume based discount which ensures that our store is competitive in terms of price for an item displayed resulting in the shopper purchasing the SKU and ensuring no dip to average basket count from our stores

Depending upon the dynamic nature of the market and competitive activities, one or more of the above levers can be used in conjunction to ensure that as a store manager one is able to manage a upward trend in the average no of items purchased per shopping visit

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

What are some of the right key performance indicators which can be used to track store performance ? 7 dimensions

In the retail sector, it is very important to optimize store on multiple dimensions .

What are the various dimensions on which a store needs to be optimized ?

There are 7 dimensions on which a store performance can be tracked. They are

1. Merchandising : Do I have the right mix of products/categories in my store ?)

2. Shelf space optimisation : Have I laid this mix out optimally across shelves - at eye level, above eye level, below eye level, near checkout counters for impulse buying etc ?)

3. Pricing : How is my store doing vis a vis alternative stores in 2-3 mile radius ?

4. Footfall : How many shoppers are visiting my store ?

5. Basket profile : What is the average basket size ? What do they spend on ? Are some products cannibalizing others ?

6. Promotions : Which are promotions are driving sales to my stores

7. Supply chain : Do I have frequent stockouts ? Are some items not moving past 90 days ?