SAS Analytics2011 Retail final LI Post

Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
Emmett Cox
Retail Analytics:
Market Basket: Data Without Use Is
Overhead

The Journey Begins
Market Basket Data
Where does it come from?
What can you do with It?

4 /
Departments Shopped
Women’s Shoes
Women’s Dress Shoes
Grocery
Retailers Track The SKU Level Through POS
Luggage
Payment Type
Emmett’s
Everything Starts With The Receipt

Now The Magic Begins
Turning data into revenue
Most Companies use Market Basket
data for Replenishment
What Else is there ????????
Plenty

1,000,000 Total Transactions
200,000 Bottoms
50,000 Tops
20,000 Bottoms and Tops
Three evaluation criteria :
• Affinity Strength (a.k.a. confidence) is the percentage of cases in
which an affinity appears given that the primary has occurred.
• Penetration (a.k.a. support) is how frequently the primary and the
affinity occurs together in the market basket.
• Lift is a measure of the predictive power of the primary compared
with random case. Lift only makes sense when it is greater than 1,
otherwise the result of the rule is worse than random guessing.
TopsBottoms
RULE: If a customer purchases Bottoms, then
10% of the time he/she will purchase Tops.
Affinity Strength = 20,000/200,000 = 10%
Penetration = 20,000/1,000,000 = 2%
Lift = Affinity Strength / (50,000/1,000,000)
= 10% / 5%
= 2
Market Basket Terminology

What Sells With What: Affinity Analysis
One year of data was
provided.
There are 200,000,000
SKU level records and
71,000,000 transaction
level records (i.e.
baskets).
Observation: Insights build Margin Management
If a customer purchases BOTTOMS, 69% of the time
they will also purchase TOPS.
Take Away- Do not promote Bottoms with Tops, they
are purchased together anyway! This will eliminate
significant margin erosion.
Some particularly strong relationships (Affinity Strengths) are shown
# TRANS. Merchandise Relationships
# Trans.
Primary
# Trans.
Affinity
Affinity
Strength Penetration Lift
733,585 EVERYDAY WRAP ==> EVERYDAY CARDS 1,295,095 2,734,731 56.64% 1.29% 11.78
733,585 EVERYDAY CARDS ==> EVERYDAY WRAP 2,734,731 1,295,095 26.82% 1.29% 11.78
627,415 BOTTOMS -- OUTERWEAR ==> TOPS -- OUTERWEAR 901,041 1,293,799 69.63% 1.10% 30.62
627,415 TOPS -- OUTERWEAR ==> BOTTOMS -- OUTERWEAR 1,293,799 901,041 48.49% 1.10% 30.62
327,706 COMPACT DISC ==> VIDEO DVD 1,560,498 3,420,898 21.00% 0.58% 3.49
303,817 BRIEFS ==> SOCKS 924,992 1,633,417 32.85% 0.53% 11.44
151,302 PACKAGED ==> NOVELTY -- EASTER 397,979 451,275 38.02% 0.27% 47.93
151,302 NOVELTY -- EASTER ==> PACKAGED 451,275 397,979 33.53% 0.27% 47.93
148,566 GIRLS TOPS ==> GIRLS BOTTOMS 331,021 488,456 44.88% 0.26% 52.28
130,370 CAR CHEMICALS ==> CLEANING/AIR FRESH 407,278 460,320 32.01% 0.23% 39.57
125,863 SEEDLINGS ==> OUTDOOR PLANTS 398,139 673,835 31.61% 0.22% 26.69
124,833 MULTIEGG PACK ==> NOVELTY -- EASTER 292,119 451,275 42.73% 0.22% 53.88
116,656 MULTIEGG PACK ==> PACKAGED 292,119 397,979 39.93% 0.21% 57.09
94,052 COMBOS ==> TACKLE ACCESSORIES 169,248 729,682 55.57% 0.17% 43.33
Market Basket Analysis: Full Year Perspective

Affinity By Season
Observation:
Plant Categories (INDOOR PLANTS and OUTDOOR PLANTS) have a stronger
relationships in Spring.
A great opportunity to merchandise related products (Growth Sticks & Potting Soil)
with higher margins.
Some particularly strong relationships (Affinity Strengths) are shown
# TRANS. Merchandise Relationships
# Trans.
Primary
# Trans.
Affinity
Affinity
Strength Penetration Lift
184,427 BOTTOMS -- OUTERWEAR ==> TOPS -- OUTERWEAR 263,336 325,672 70.03% 1.26% 31.53
209,075 EVERYDAY WRAP ==> EVERYDAY CARDS 352,950 752,556 59.24% 1.43% 11.54
184,427 TOPS -- OUTERWEAR ==> BOTTOMS -- OUTERWEAR 325,672 263,336 56.63% 1.26% 31.53
35,271 SHORTS -- BOTTOMS ==> KNIT TOPS -- BOYS TOPS 75,123 105,876 46.95% 0.24% 65.02
38,555 GIRLS TOPS ==> GIRLS BOTTOMS 83,933 134,856 45.94% 0.26% 49.94
34,146 SHORTS -- BOTTOMS ==> KNIT TOPS -- TOPS 83,804 149,799 40.75% 0.23% 39.88
37,959 INDOOR PLANTS ==> OUTDOOR PLANTS 109,275 219,301 34.74% 0.26% 23.22
35,271 KNIT TOPS -- BOYS TOPS ==> SHORTS -- BOTTOMS 105,876 75,123 33.31% 0.24% 65.02
33,482 CAR CHEMICALS ==> CLEANING/AIR FRESH 103,105 120,513 32.47% 0.23% 39.51
72,383 BRIEFS ==> SOCKS 229,611 381,140 31.52% 0.49% 12.13
47,834 EVERYDAY SPECIALTY ==> EVERYDAY CARDS 154,685 752,556 30.92% 0.33% 6.02
35,807 KNIT TOPS -- TOPS ==> SHORTS -- BOTTOMS 119,724 93,799 29.91% 0.24% 46.75
46,519 SEEDLINGS ==> OUTDOOR PLANTS 160,178 219,301 29.04% 0.32% 19.42
53,186 LICENSED STATIONER ==> ART & GRAPHICS 185,607 490,620 28.66% 0.36% 8.56
38,555 GIRLS BOTTOMS ==> GIRLS TOPS 134,856 83,933 28.59% 0.26% 49.94
Seasonal Basket Analysis: Spring

Store Level Department Analysis

Manchester Entertainment
Children’s
Wear
Infant
Accessories
Women’s
Underwear
Cosmetics
Men’s
Underwear
Footwear
Menswear
Women’s
wear
Toys
Appliances Lighting Camping
& Fishing
Kitchen
Home
Org. &
Laundry
Sporting
Goods
Auto
Flexible Grocery
(Food)
Cards/
Party
Goods
Stationery Outdoor Living &
Indoor Furniture
Garden
Care
DIY
Books
> 30% 20%~30% 15%~20%Affinity Strength Level :
Store Layout (Affinity Strength)
In-Store Marketing

Index of Customer Segments by Category
92
105
107
100
90
92
94
96
98
100
102
104
106
108
110
Total
1. Premium
2. Valuable
3. Potential
4. Uncommitted
Category Reviews And Space Planning
Activate
Up-Sell
Gives Buying Team a view
of whether the Category &
Products appeal to
important customers
Low Core
Penetration
High
Value
Low Value
Bertolli
1ltr Extra
Virgin
Organic
Spray
Cooking
Oil
Blended 4lt
Economy Pack
High Core
Penetration
Colavita
Extra
Virgin
This shows which products are
swapped within Category and those
that are important to core customers.
This helps determine how much
space to give to specific products.
Item Space Based on Spend
Index Of Customer Segments By Category
Retain

SKU Rationalization:
How would you like to :
• Reduce SKU On Hands by 20%
• Increase Sales by 25%
• While Reducing Out Of Stock by 40%
Show Me The Money

SKU Rationalization: Intelligent Reduction
• In our “White Lake” test we were able to define , where
merchandise was placed based on their frequency of
purchase (Cross sell affinities).
• The basic idea: reduce the amount of time shoppers had to
hunt for their merchandise = more impulse items.
• The final design: 20% increase in sales, 22% reduction in
the number of items being carried.
White Lake Store Test

CROSS PURCHASE – Nappies and Beer
Basket Data can show many cross category purchase groups
Luvs and Life Savers
Diapers and Frozen
meals
Diapers and Advil
Diapers and Beer
Wal-Mart ran ads recently: Picture a register belt.
Crisco, Blue Bonnet Butter, Pam Non-Stick Spray and a
Stanley Saw….. Fade to Kid stuck in Bannister.
Market Basket Data

CROSS PURCHASE – ELECTRONICS
Television = Home Theater,
AV Accessory, DVD
Hardware, Cables & Cords
Desktop, Laptop, Monitor,
Accessory, Printer, Paper,
Ink, Computer Software
Telephone, Telephone
Accessory
Video game Software,
Hardware, Accessory
DVD Players = DVD Movies,
Batteries, Pop Corn
Portable DVD = AV Storage,
Batteries, Headphones,
Ear-Buds
Audio Hardware, Radio,
Digital Audio
Basket Data can show many cross category purchase groups
Telephone, Personalization
Accessories

Apparel Shoppers Buy More Jewelry
APPAREL OPPORTUNITY
Display Jewelry in close
proximity to Apparel.
$ Apparel Sales
+5%
$7.8
Million
$8.2
Million
Sales Potential
Sales
When a Basket Includes Apparel, Jewelry Merchandise,
Sales increase 5%

Store Segmentation / Clustering
Each store has its own characteristics, be it Population, Demographics,
Geographic Area or Square Footage.
Ranging by Demand Cluster is a process of systematically identifying the
similarities of the Stores and their Consumer Base, and then grouping
them into Clusters.
The next step is to identify the Merchandising (Ranging) Categories that
best suits the Cluster base. This knowledge is gained by evaluating all of
the Baskets and the Merchandise being purchased.
Finally you organize the Merchandise by Cluster, in-stead of individual
store.
Store Groups:
Ranging to Define Demand Clusters Through Market Basket

Develop Retail CRM Capabilities
Space Optimization
Cross Purchase
Merchandising
Collateral
Catalogs
Customer Database
Laura Spender
291 Main Street
Anytown, AL
Average Annual Spending $3500
Decile 1
Household Size 5
Age of Head Of Household 40-50
Number of Children 3
Homeowner Yes
Income $85,000
Credit Card Balance $2,000
Price Sensitivity Index 3
Out of Store Spend
Tesco – Grocery $200
Boots $32
Shell Royal Dutch $48
Receipt Store 4105
February 2, 2007
Jeans $12.99
Womens Belt $4.66
Pet Food $14.92
Lawn &
Garden Tools $34.66
2 ltr Shampoo $ 6.59
Tax $ 3.29
Total Due $77.00
Thank You For Shopping
Build Better Data Valuation by Partnering with your Card Provider
Customer Intimacy The New 1:1

Levels of Basket Analytics: The Journey Continues
Loyalty Marketing: With Market Basket
 Can reduce unnecessary promotions to a mass target
 Can help determine which Segments produce
the highest return
 Allows you to target geographies instead of
Blanket Marketing
 Can Identify the 30% of accounts that generate
70% of profit
In retailing, Market Basket Analysis can:
 Improve Margins by eliminating unnecessary Markdowns and Promotions
 Determine which items to place together (Build Impulse)
 Suggest new store layouts to optimize basket size
 Identify Brand Loyalties (Pepsi, Coke, Kraft, Evian, Perrier)

Understand the relationships between Customers and
Product sales
• Merchandise Cross Sell Opportunities
• SKU Rationalization
• Price sensitivity
Promotion Planning Opportunities
• What NOT to Advertise, cross sell characteristics
• Impulse Merchandise Purchases
Understand Your Customers
• Merchandise preferences (Brand Characteristics)
• Gender (Credit Card Geo Demographics)
Market Basket Analytics
Re-Group

Customer Basket Analytics
Combining Consumer data
with Purchase behaviour
Answers
the
Why Behind The Buy.

Thank You
Emmett Cox

SAS Analytics2011 Retail final LI Post

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