http://www.analytics-tools.com Here all you need to know about market basket analysis or Product Affinity

1. Cross Shop/Product Affinity/Market
Basket Analysis
http://www.Analytics-Tools.com
Analytics-Tools.com

2. Insights &
Introduction Metrics Involved Process
Application
What Are Co – Purchase Affinities ?
 A measure of how likely two products are to be purchased in same transaction/order or Basket. This is why
this analysis is also called Market Basket analysis
 Some products have natural affinities for example Bread & butter, Shoe &Polish, hamburgers & Fries etc.
 There are also some which are not the most obvious like Beer & Diapers, Tuna & Toothpaste, Barbie dolls &
some types of candy bars etc. These non-obvious affinities can only be discovered by analyzing the data
Obvious
Items #1 & #2
Affinity is seen in Green orders
Each Box is an Order
Non Obvious
Item #3

3. Insights &
Introduction Metrics Involved Process
Application
Why Assess Product Affinity ?
 Lot of interesting insights can be drawn out by analyzing consumer's purchase basket. These insights
can then be deployed across business units to improve overall profitability, productivity and consumer
experience
 An important thing to keep in mind while analyzing baskets is to remove the effect of impulse purchases
 Driver items (items which lead to purchase of other items) identification can also be done by
understanding co purchase behavior.
 Purchases of other products with drivers products is mostly done in the same order but for few products
(technology products) may be purchased after a certain period of time (defined by the business, more on
slide 10)
Insights at this 1
level help in Understand Purchase Behavior
Power Category
layout decisions
2
Stages of Application
Develop Cross Promotional Programs
Department
Applicability
3
Redesigning Store Layout / Design
Category
Insights at this 4
Discount Plans / Promotions
level help in
Sub Category
merchandise 5
decisions Plano gram Designing
6
Fine line / SKU Driver Items which lead to sales

4. Insights &
Introduction Metrics Involved Process
Application
What Metrics Are Involved?
 Support indicates prevalence of a product(s)
Out of all transactions N Bread
how often bread is
bought ? N Total
1
Support
Out of all transactions N Butter
how often butter is
bought ? N Total
Out of all
Indicator of transactions how N Bread  Butter
prevalence often both bread
of a
product(s) and butter were NTotal
bought ?

5. Insights &
Introduction Metrics Involved Process
Application
What Metrics Are Involved?
 Confidence Indicates predictability of one product given other product is already in the basket
How predictable is Bread given Butter in same basket ?
2 Support Bread
N Bread
Confidence
Support
BreadAndButter N Bread  Butter
How predictable is Butter given Bread in same basket ?
Indicator of
predictability of
one product given
Support Butter
N Butter
other product
Support
BreadAndButter N Bread  Butter

6. Insights &
Introduction Metrics Involved Process
Application
What Metrics Are Involved?
 Lift indicates the likelihood of two products going together
What is the likelihood of Bread and Butter in same basket ?
3
Lift
ConfidenceBread ConfidenceButter
SupportButter SupportBread
Indicator of
likelihood of two NTotal N Bread Butter
product going
together
N Bread N Buter

7. Insights &
Introduction Metrics Involved Process
Application
What Metrics Are Involved?
 Illustration of lift computation
Metric Description Number of Transactions with Bread 100
Proportion of transactions Number of Transactions with Butter 400
Support of Bread
with Bread
Number of Transactions with Bread and Butter 50
Proportion of transactions
Support of Butter
with Butter Total number of Transactions 1,000
Proportion of transactions
Support of Bread and Butter
with Bread and Butter
(Support of Bread & Butter) Measure Description
Confidence of Bread
/ (Support of Bread)
Support of Bread 100/1000 = 0.1
((Support of Bread & Butter)
Confidence of Butter Support of Butter 400/1000 = 0.4
/ (Support of Butter)
Support of Bread and Butter 50/1000 = 0.05
(Proportion of transactions
with Bread and Butter) / Confidence of Bread 0.05/0.1 = 0.5
(Expected Number of
Lift transactions with Bread and Confidence of Butter 0.05/0.4 = 0.125
Butter if not related)
0.05(0.1*0.4) = 1.25
(Confidence of Butter) / Lift
(Support of Bread only) 0.125/0.1 = 1.25

8. Insights &
Introduction Metrics Involved Process
Application
What is the process?
Theory 1 Driver Product
Product Purchased because
2 3 of Driver Product
30 Days Driver product drives
purchase for this period
Mar 12 Mar 13 Mar 14 Mar 18 May 01
1 March 12 Product and its driver product purchased in the same order
2 March 14 qualifies as driver product purchased was purchased March 13 th, within 30 days
3 May 01 does not qualify as the driver product was purchased more than 30 days back.
Following are the steps involved in the process
Transaction
1 2 3 4 5 6
Data
Transaction Data is If existence Stratified Final SAS SAS codes /
data is cleaned, of segments sampling is datasets are VBA macros
sourced sliced diced with unique done over created for are run to
to avoid purchase the dataset each generate
aberrant behavior is for analysis consumer product
seasonal seen, viability segment affinity maps
behavior datasets are for each
split for them segment

9. Insights &
Introduction Metrics Involved Process
Application
What is the process?
Digital Photography without Printers
Drives & Misc without HD and USB
Circuit Protection Cables Etc
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Multi-Function Devices
Mass Storage - Other
Avg Category Sales -
Portable Computers
Desktop Computers
Computer Monitors
Avg Basket Sales -
% Single Category
Product Services
(Cherry Picking)
Single Category
Multi Category
Flash Memory
Inkjet Printers
Transactions
Transactions
GPS Devices
PC Cameras
PC Memory
Networking
MP3
Row # Affinity Category
1 Portable Computers 25.6% $781 $970 H M H M L H L L H M L M H M L
2 Desktop Computers 17.5% $611 $819 M H H M H M L L H H M M H
3 Product Services 8.1% $31 $325 H H H L H M M H H M H H M M H H
4 PC Memory 52.7% $107 $160 M M L H L L M L M
5 Computer Monitors 43.4% $257 $398 L H H L H L L L M M L M M L
6 Networking 50.2% $69 $125 H M M L L H L I L H L M L I
7 Mass Storage - Other 49.4% $105 $152 L L M M L L H L L L M L
8 Digital Photography without Printers
16.9% $212 $295 L L H L I H M H L L L
9 Inkjet Printers 18.9% $109 $234 H H H M L M H L H L I
10 Flash Memory 43.0% $47 $111 M L H L H L L L
11 Circuit Protection Cables Etc
33.7% $31 $127 M H H M H L H H I L M H
12 GPS Devices 58.5% $262 $296 L H L I H I L
13 PC Cameras 46.9% $61 $122 M M M L L L L L L L H M
14 Drives & Misc without HD and USB
40.4% $44 $106 H M M M M M M L M M H L
15 Multi-Function Devices 19.0% $183 $285 M H H M L L H I L H
16 MP3 50.9% $101 $143 L H L I L L I L L H
Legend
H High likelihood of purchasing together  One easy way to visualize product affinity is to create
M Moderate likelihood of purchasing together Product Affinity Maps
L Low likelihood of purchasing together
May or may not be purchased together  Product affinity maps have categories as columns and
I Infrequently purchased togeher rows. The color of the cells tells how often the two
R Rarely purchased together
categories (row-column) have been purchased together

10. Insights &
Introduction Metrics Involved Process
Application
How to separate impulse behavior with externalities?
 Affinity grids are superimposed over grids with information on externalities
1 2
Men's Wear
Swim Wear
Boy's Wear
Men's Wear
Swim Wear
Sleepwear
Boy's Wear
Sleepwear
Intimates
Intimates
Hosiery
Infants
Hosiery
Ladies
Infants
Shoes
Socks
Wear
Ladies
Shoes
Socks
Wear
Men's Wear M M H H M M M I R Men's Wear P P P NP NP P NP P P
Boy's Wear M H I H H H R M M Boy's Wear P P NP NP P P P NP P
Shoes M H M M H M H H H Shoes P P P P P NP P NP NP
Infants H I M L I H H H M Infants P NP P P NP NP P P NP
Socks H H M L M M M H M Socks NP NP P P P P NP NP NP
Hosiery M H H I M H H I H Hosiery NP P P NP P P P NP NP
Sleepwear M H M H M H M M L Sleepwear P P NP NP P P P P P
Intimates M R H H M H M H L Intimates NP P P P NP P P P NP
Ladies Wear I M H H H I M H M Ladies Wear P NP NP P NP NP P P P
Swim Wear R M H M M H L L M Swim Wear P P NP NP NP NP P NP P
Promotion done for categories No Promotion done for categories
Hosiery
Intimat
Sleepw
Infants
Ladies
Men's
Shoes
Socks
Wear
Wear
Wear
Wear
Swim
Boy's
ear
es
1
Men's Wear 
Boy's Wear 
Shoes   Products that demonstrate impulse
Infants  based higher affinity
Socks   
Hosiery 
Sleepwear 
Intimates
2 Ladies Wear  
Swim Wear  

11. Insights &
Introduction Metrics Involved Process
Application
How to read insights ?
Organizational focus on Profitability
 Place high affinity products at an optimal
High distance so that Customer satisfaction is not
 Place High affinity products at a distance compromised
 Place high margin / impulsive and related
products in between
 Irrelevant case as organization has shed $ for
 Place all high affinity products together
analysis
Low
Organizational focus on Customer Satisfaction High
Lets take the example of Barbie and Candy bar.
 Placement- Highest margin candy can be placed near dolls or as Marketers know, that for every extra
minute of 'quality' time that one spends in a store, there is a high degree of likelihood that that person will
spend one extra dollar in that store (research states that this is true for large retail stores). With this in
mind, one can place Barbie dolls in one corner (Toys section) and Candy in a section that is further away
from the Toys section. And, in the path that lies between these two sections, place special 'Kid' items -
which may be either promotion items, high margin items, retailers own brand items etc.
 Promotions- By increasing the price of Barbie doll and giving the type of candy bar free, special
promotions for Barbie dolls with candy at a slightly higher margin, coupons for dolls and candy
 Discounts from Manufactures- Exploit discovered associations with the companies who manufacture the
products with tie-ins