Retail Analytics
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AAUM Confidential
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 jogging Loves to entertain friends
at home
Demographic attributes
Psychographic attributes
Behavioural attributes
In store attributes
Customer
profiles
Loyalty
analysis
Customer
segmentation
Customer lifestyle
and lifestage
- 5 -
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 customers The 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 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
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 100,000 customers 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 customers look 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 campaigns are successful, if a basic product and a gift are related and the basic product has high margin rate.
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 logical manner.
Targeted marketing campaign to cross sell items to those who have purchased certain product groups.
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
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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—such as tests that determine whether lobster tanks actually sell more lobsters, or whether a fresh paint job in a store leads to significantly higher 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, multivariate experiments may run for more than a month.
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
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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 efficiently planning ahead?
Are you able to differentiate slow movers vs fast movers?
What is your planning horizon?
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
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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.
Recommending the right complimentary items.
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
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Making supply chain efficient
Are you paying avoidable transportation costs? Are you prepared for unexpected changes? 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, transportation cost, 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 logical manner.
Targeted marketing campaign to cross sell items to those who have purchased certain product groups.
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
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Birds of a feather shop together
Are you able to effectively meet the customer requirements?
Are you able to differentiate the 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.
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
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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) Analyzing trends in inventory movements at the
SKU level into, within, and out of the stores Nearly 1,600 key performance indicators, including warehouse
invoices, transfers, returns, positive order adjustments and store alarms.
Analyzing large continuing discrepancies between items sold and ordered.
Prosecuting eight times as many suspected fraud incidents as it did five years ago.
Jaeger (UK fashion retailer) Using data 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
Customer Segmentation
Market Basket Analysis
Test and Learn
Forecasting
Product Recommendation
Supply Chain Analytics
Clustering
Fraud Detection & Prevention
Pricing Optimization
Shelf Space Optimization
Real Estate Optimization
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Best bank for your buck
Are you getting the best pricing for your product?
Does higher mark-ups realizes higher gross margins?
Does pricing resulting in 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).
Customer Segmentation
Market Basket Analysis
Test and Learn
Forecasting
Product Recommendation
Supply Chain Analytics
Clustering
Fraud Detection & Prevention
Pricing Optimization
Shelf Space Optimization
Real Estate Optimization
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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
$1.5 million in operating improvements, through planogram automation and optimization
Lowes (US home improvement retailer)
Manages over 800 planograms per store in collaboration with over 150 key suppliers.
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
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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)
Compare returns 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
Customer Segmentation
Market Basket Analysis
Test and Learn
Forecasting
Product Recommendation
Supply Chain Analytics
Clustering
Fraud Detection and Prevention
Pricing
Shelf Space Optimization
Real Estate Optimization
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