Retail Analytics

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Retail Analytics: Case illustrations
Dec, 2013

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Traditional BI – Good enough?

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Definitely not! Advanced analytics is the need of the hour!

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Meet Ms. Jones. So much information but what are the actionable ?
She is tech savvy
She spends 45 minutes per
trip in the store on an
average
She works as a local nurse
in a Children's hospital
She lives in Canberra
Newspaper is her
primary media
influence
She loves experimenting
with new and local brands Her Average
basket size is $
50
She prefers Australian
made products
She lingers the longest in
sections which have
promotions/offers
She loves joggingLoves to entertain friends
at home
Demographic
attributes
Psychographic
attributes
Behavioural
attributes
In store attributes
Customer
profiles
Loyalty
analysis
Customer
segmentation
Customer lifestyle
and lifestage

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With right analytical frameworks, we build right solutions!
I just received coupons for
sportswear through my
newspaper! That’s just
awesome!
There is a promotional offer
on new local brands of diary
products! They just informed
me through SMS. That’s just
great.
Today is Friday. That’s means I
get 3% discount on all
Australian made products I
buy because of the loyalty
program I am in.
My reward points just
accumulated. I get a surprise
gift when I visit the store next
time. Hurray!
To the right customersThe right message Using the right channels
Campaign
management
Coupon
analysis
LTV modeling

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Customer Segmentation
Market Basket Analysis
Test and Learn
Forecasting
Product Recommendation
Supply Chain Analytics
Clustering
Fraud Detection and Prevention
Pricing Optimization
Shelf Space Optimization
Real Estate Optimization
A few successful case illustrations across the globe

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Right customer at the right place at the right time
Test and Learn
Forecasting
ProductRecommendation
Clustering
Techniques
k-Nearest Neighbors | Classification Trees | Cluster Analysis
Tesco
 80% of sales can be tracked through ClubCard.
 Provides rebates of 1% of customer purchase.
Historically by direct mail, but increasingly by
email.
 Customized coupons based on shopper behavior
are provided to customers
 Over 10 million variations in coupons for about
13 million customers.
Nieman Marcus
 Top 100,000customers in its complex (20
different levels) loyalty program,InCircle, account
for almost half of its revenues.
 Top customers can win free fur coats and even a
Lexus luxury car.
Do all the customerslook the same?
Which customer is likely to react to offers?
Are your campaigns reaching effectively?

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“Pen and Pencil” go-together better than “Pencil and Eraser”?
What items tends to be purchased together/purchased
sequentially?
How do you select your promotional offers?
What is your merchandising strategy?
Techniques
Association Rules
Limited Brands (Apparel and related retailer)
 “Buy two, get three” promotion campaigns are
successful, if market basket analyses are used in
order to determine the right products to be
promoted.
 “Buy a product, get a gift” sales promotion
campaignsare successful, if a basic product and a
gift are related and the basic product has high
marginrate.
Merkur (Trading company in Slovenia)
 Such related groups of goods are located side-by-
side in order to remind customers of related
items and to lead them through the centre in a
logicalmanner.
 Targeted marketing campaignto cross sell items
to those who have purchased certain product
groups.
Test and Learn
Forecasting
Clustering

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Where the rubber meets the road
What influences the purchasing decision of the
customer?
What are the factors that increases the sales?
Techniques
A/B testing | Multivariate Analysis
Food Lion (US food retailer)
 Testing to try out new retailing approaches and
uses of capital.
 New store formats, and at other times quite
tactical—suchas tests that determine whether
lobster tanks actually sell more lobsters, or
whether a fresh paint job in a store leads to
significantlyhigher sales.
eBay
 Conducts thousands of experiments with different
aspects of its website
 A/B experiments (comparing two versions of a
website) can be structured within a few days, and
they typically last at least a week
 Larger,multivariateexperiments may run for
morethan a month.
Test and Learn
Forecasting
Clustering

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Foretelling the future based on the past
Techniques
Time series forecasting (ARIMA, GARCH, etc)
Waitrose (UK based grocery retailer)
 Developed a new system for store-level sales
and demand forecasting.
 It takes into account holidays, promotions,
and seasonality for predicting demand and
feeding replenishment processes.
 40% reduction in order changes
J.C. Penney (US department store chain)
 Forecasts are also linked to assortments,
allocations, and pricing optimization systems.
 Five extra points of gross margin,
improvements in inventory turns of 10%, and
growth in top-line and comparable store sales
for four consecutive years-and double digit
increases in operating profit.
Are you efficientlyplanning ahead?
Are you able to differentiate slow movers vs fast
movers?
What is your planning horizon?
Test and Learn
Forecasting
Clustering

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Increasing product sales
Techniques
Association Rules | Sequential Patterns | Attribute based recommendation |
Demographic recommendation
Amazon.com
 Uses a pre-calculated item similarity matrix to
make real-time recommendations
Overstock.com (US based online retailer)
 Uses a Bayesian attribute-based technology
in its Gift Finder application.
 Asks consumers to specify the occasion, the
age of , the relationship to the recipient, and
the interests of the recipient, and then
recommends specific products.
 Gift Finder drives 2.5 times the average
purchase revenue for the site compared to
when customers don’t use it.
Are you selling the right product to the right customer?
Recognizing cross sell, up sell options.
Recommendingthe right complimentaryitems.
Test and Learn
Forecasting
Product Recommendation
Clustering

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Making supply chain efficient
Are you paying avoidable transportationcosts?
Are you prepared for unexpectedchanges?
Are you synchronizing your promotional activities with
supply chain?
Techniques
Integer Programming | Dynamic Programming | Non-linear Programming | Heuristic Algorithms
OfficeMax(US Office retailer)
 Attempts to achieve the highest availability (in-
stock by segmented SKU) at optimal inventory,
transportationcost, and warehouse investment.
 Analyses of store product movements drive both
differential assortments and restocking
frequencies.
Merkur(trading company in Slovenia)
 Such related groups of goods are located side-by-
side in order to remind customers of related
items and to lead them through the centre in a
logicalmanner.
 Targeted marketing campaignto cross sell items
to those who have purchased certain product
groups.
Test and Learn
Forecasting
Clustering

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Birds of a feather shop together
Are you able to effectively meet the customer
requirements?
Are you able to differentiatethe groups effectively?
Are all the groups same?
Techniques
Hierarchial Clustering | Principal Components | K-Nearest Neighbors |Naïve Bayes Classification |
Classification Trees | Artificial Neural Networks
Wal-Mart
 “store of the community” localization program
that tailors store formats, assortments, shelf
space allocations, and department layouts by
cluster.
 Stocking patterns are based on actual
consumer purchases, area demographics,
preferences from consumer surveys, and
inputs by local store managers.
American Eagle Outfitters
 Clustered its more than 750 stores based on
the types of assortments to which shoppers
were most responsive.
 The company found, for example, that
customers in Western Florida bought
merchandise similar to those in parts of Texas
and California.
Test and Learn
Forecasting
Clustering

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3% shrinkage due to fraudulent activities
How to minimize the fraudulent activities?
Help to come up with quick and corrective measures to
control fraudulent activities
What are the root causes of fraudulent activities?
Techniques
Benford’s Analysis | Data Mining Technique | Bayesian Learning | Neural Networks
CVS (US based pharmacy chain)
 Analyzingtrends in inventory movements at the
SKU level into, within, and out of the stores
 Nearly 1,600 key performanceindicators, including warehouse
invoices, transfers, returns, positive order adjustments and store
alarms.
 Analyzinglarge continuing discrepancies between
items sold and ordered.
 Prosecutingeight times as many suspected fraud
incidents as it did five years ago.
Jaeger (UK fashion retailer)
 Usingdata mining of point-of-sale data with
other, more complex data streams to identify
losses resulting from employee theft as well as
process-related errors.
 After only three months Jaeger determined that
its savings were significantly more than
predicted before implementation
Test and Learn
Forecasting
Clustering
Fraud Detection & Prevention

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Best bank for your buck
Are you gettingthe best pricing for your product?
Does higher mark-ups realizes higher gross margins?
Does pricing resultingin product cannibalization?
Techniques
Cost Profit Models | Elasticity Models | Efficient Frontier | Pricing optimization
D’Agostino Supermarkets (NY
grocery store chain)
 2002 trial of 10 stores and 13 categories,
found unit-volume gains in the categories
tested of over 6%, and sales increases of
9.7%. gross profit rose 16.1% and net profit
2%.
Northern Group Retail
(Canadian apparel retailer)
 Price optimization software has helped it
increase gross margins by 4.5% (a 2% gain
the first year and an additional 2.5% in the
second).
Test and Learn
Forecasting
Clustering
Fraud Detection & Prevention

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Eye level is the buy level
Are your items being placed at the right level?
Are you having the right planogram design?
Are you changing items during occasion / festive
season?
Techniques
Linear Programming | Non-linear Programming | Integer Programming | Dynamic Programming
Marks & Spencer
 Achieved $2.5 million in labor efficiencies and
$1.5million in operating improvements, through
planogramautomation and optimization
Lowes (US home improvement retailer)
 Managesover 800 planogramsper store in
collaborationwith over 150 key suppliers.
Test and Learn
Forecasting
Clustering

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Running efficient business in the neighbourhood
How do you identify your catchment area?
What is the best location for running your store?
Is your store designed to maximize the root faults?
Are you running the right format at the right
catchment?
Techniques
Demographic, psychographic and competitor analysis integrated with GIS data
OfficeMax (US Office retailer)
 New site optimization approach allowed it to
double its pace of new store openings.
 Takes current customer information into account
in terms of possible cannibalization
 Considers the proximity to existing distribution
networks
Jo-Ann Stores(Fabric and craft
retailer)
 Comparereturns and attributes of superstores
versus its traditional stores.
 Analyzed differences in customers between two
formats using their own customer database; the
customer base was similar, contrary to their
expectations. the superstore format tested very
positively, and the company used data
Test and Learn
Forecasting
Clustering
Pricing

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extensive global business experience working with Fortune 100
companies in North America and Asia Pacific. Established at IIT Madras
Research Park with a focus on researching and devising the
sophisticated analytical techniques to solve the pressing business
needs of corporations ranging from travel & logistics, finance,
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because they can- but because they should…”

Retail Analytics

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