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 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

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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 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


Test and Learn

Forecasting



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 100,000 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.



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—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.



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 efficiently planning 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.

Recommending the right complimentary items.



Test and Learn

Forecasting



Clustering





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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.



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 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.



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) 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



Test and Learn

Forecasting



Clustering

Fraud Detection & Prevention




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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).



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

$1.5 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.



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)

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



Test and Learn

Forecasting



Clustering


Pricing



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sophisticated analytical techniques to solve the pressing business

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“Organizations are competing on analytics not just

because they can- but because they should…”

Retail Analytics

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Transcript of Retail Analytics