Analytics can offer a key possibility identifying which products are sold together, because that information can be used to influence targeted communication efforts, store layouts, and in-store promotions.

1. Product Affinity
michel.bruley@teradata.com
Extract from various presentations: CRS, BUS 782, Aster Data …
January 2013
www.decideo.fr/bruley

2. Product affinity analysis is one of the
basket analysis techniques
Assortment analysis &
Management
Customer Analysis &
Marketing
Promotion evaluation &
management
•
•
Traffic Builder ID
Traffic analysis
•
•
Frequency of visits
Consumer Penetration
•
Av. Basket
Metrics
•
•
•
Item Contribution
Trx Builder ID
Price Point
Contribution
•
•
•
Value, av. Purchase
Discount behaviour
Customer Modelling
•
Promotional
Evaluation
Av. Dist. # of
Items &
Depts
•
•
•
Trx Builder ID
Item contribution
“Variety driver”
•
Purchase Variety
Behaviour
Customer Modelling
•
•
•
•
•
Item Deletion
Cross Sell
Lost Sales Prevention
Potential overstock
•
Co-marketing
opportunities by
customer
•
•
•
•
Cherry Picker
•
•
Item deletion
Item contribution
•
Cherry Picking
Behaviour
Consumer Profitability
•
Price Point
Identification
Price Elasticity
No. Of Baskets
Price Point
•
Transaction
analysis
•
Product quality
(returns)
•
•
In-Store activities
•
In-Store Activities
Payment Type
•
•
Local Store
Assortment
Pricing by segment
•
Store Performance
evaluation
Promotion Evaluation
Promo Item Selection
Event Strategy
Cross sell opportunity
•
Vendor Participation
•
Co Merchandising
Opportunities (visual
merchandising)
•
•
Promotion Evaluation
Promo item selection
•
•
Margin Protection
Vendor participation
Promotional Pricing
•
•
•
Price integrity
Fraud detection
Local pricing
•
•
Fraud Detection
Cashier productivity
•
•
Manpower planning
In-store activities
Payment type
relevance
•
Store layout & visual
merchandising
Payment influence
Trx. Profiling
Item Performance
Fraud detection
Time of Day
•
•
•
Traffic Analysis
(manpower planning)
•
•
•
•
Consumer Penetration
(with Cust.ID)
Traffic Builder ID
Traffic builder Opp.
•
Store Operations
Promotion Evaluation
(w/Cherry Picker)
Affinity
Analysis
Vendor & Supply Chain
Management
•
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•
•
•
Customer Profiling
Customer Modelling
Propensity to Buy
•
•
Event Strategy (“Early
Bird” opportunities)
Promotional
Evaluation (behaviour
change)
•
•
Item Performance
Consumer/retailer
relevance of item

3. Product Affinity Definition
Identify which products are sold together and use that
information to influence targeted marketing efforts, store
layouts, and in-store promotions
Product Affinity enables an organization to detect
product/service purchase patterns, linkages, and cross-sell
opportunities in order to increase revenues. Results from this
application will enable the organization to identify, with a
high degree of accuracy, those customers most interested in
specific products, services and product/service groupings
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4. Affinity Analysis
Affinity Analysis is a modeling technique based upon the
theory that if you buy a certain group of items, you are
more (or less) likely to buy another group of items.
The set of items a customer buys is referred to as an item
set, and market basket analysis seeks to find relationships
between purchases.
Typically the relationship will be in the form of a rule:
Example:
– IF {beer, no bar meal} THEN {chips}
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5. Product Affinity and Cross- Selling
For instance, customers are very likely to purchase
shampoo and conditioner together, so a retailer would not
put both items on promotion at the same time. The
promotion of one would likely drive sales of the other
A widely used example of cross selling on the internet with
market basket analysis is Amazon.com's use of suggestions
of the type:
– "Customers who bought book A also bought book B",
e.g.
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6. Product Affinity Analysis Process
Historic market basket data and analyzes are used to build more
effective marketing programs:
– Past customer purchase data is used to identify which products/services are acquired
by which customer groups
– Predictive analytics is applied to this data to discover profiles of customers most
likely to buy the products in each group
– These profiles are used to target those customers most likely to respond favorably to
specific cross-sell campaign
– Pair-wise product associations are also determined to enable the constructed of
offers featuring the purchase of these pair products
– Customer product dislikes are also identified so that company does not promote
unwanted products
Benefits that can be realized from utilizing this solution:
– Improve customer knowledge allowing company to better understand what their
customers are likely to buy and not buy.
– Increase revenue and decrease costs by identifying those customers most likely to
respond to cross-sell campaigns
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7. Behavior Prediction
This uses past consumer behavior to foresee the future
behavior of their customers.
This analysis includes several variations.
1.
Propensity-to-buy analysis- understanding what a
particular customer might buy.
2.
Next Sequential Purchase- predicting the customers next
buy.
3.
Product Affinity Analysis- Understanding which
products will be bought with others.
4.
Price elasticity modeling and dynamic pricing- determine
the best price for a given product.
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8. Product Affinity = Link Analysis
Aims to establish links (associations) between records,
or sets of records, in a database
There are three specializations
– Associations discovery
– Sequential pattern discovery
– Similar time sequence discovery
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9. Link Analysis: Associations
Discovery
Finds items that imply the presence of other items in the
same event
Affinities between items are represented by association
rules
– e.g. ‘When customer rents property for more than 2
years and is more than 25 years old, in 40% of cases,
customer will buy a property. Association happens in
35% of all customers who rent properties’.
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10. Link Analysis: Sequential Pattern Discovery
Finds patterns between events such that the presence of
one set of items is followed by another set of items in a
database of events over a period of time.
– e.g. Used to understand long-term customer buying
behaviour
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11. Link Analysis: Similar Time Sequence Discovery
Finds links between two sets of data that are timedependent, and is based on the degree of similarity
between the patterns that both time series demonstrate
– e.g. Within three months of buying property, new
home owners will purchase goods such as cookers,
freezers, and washing machines
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12. For Analytics: SQL or SQL-MapReduce
Teradata SQL
Aster SQL-MapReduce
SQL is better for:
SQL-MapReduce is better for:
•Standard transformations across
every element in a table
•Custom Transformations
•Standard aggregations using
GROUP BY on tables
• sum(), max(), stddev()
•Dimensional Joins
•Set Filtering
• Lookups, data pruning to limit a
table to a subset.
•Presentation formatting
• For example, “get me top K counts
only”
www.decideo.fr/bruley
• e.g. unstructured data, log extraction,
conditional manipulation
•Custom Aggregations
•Inter-row Analysis, like time-series
•Layered queries
• Nested queries, sub-queries, recursive
queries
•Analysis that requires reorganization
of data into new data structures
• Graph analysis, decision trees, etc.

13. Time Series Analysis
discover patterns in rows of sequential data
Sales
Transactions
{user, product, time}
Purchase 1
Purchase 2
Purchase 3
Purchase 4
Aster Data SQL/MR Approach
• Single-pass of data
• Linked list sequential analysis
• Gap recognition
Traditional SQL Approach
• Full Table Scans
• Self-Joins for sequencing
• Limited operators for ordered data
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14. Identify common product baskets of
interest
Cross-Channel Transactions
43M Customers Online Alone!
Teradata Aster solution
In-Store Transactions
Challenge
• Identify correlations between
purchases made over time
Impact
• Move beyond “people who
bought this also bought” to
time-based recommendations
EAN
Author
Store
time
15682817
823201
JK Rowling
100
12:00 PM
16816193
123101
Shakespeare
105
1:45 PM
19825996
182191
Rick Riordan
201
3:00 PM
15528047
With Aster Data
• SQL-MapReduce for market
basket analysis indicates
correlations between products
userID
823201
Walter Isaacson
100
4:20 PM
item_no
type
EAN
12334
book
823201
Product
13345
music
--
Catalog
21456
periodical
--
82673
toy
--
Online Transactions
EAN
Author
time
192.168.20.14
823201
John Grisham
12:00 PM
172.16.254.1
123101
Dostoevsky
1:45 PM
216.27.61.137
182191
Obama
3:00 PM
194.66.82.11
www.decideo.fr/bruley
IPAddress
823201
Stephen King
4:20 PM

15. Basket Affinity: Retail Business Need
Overview:
For most retailers, Market basket affinity is a well known tool for cross-promotions
and marketing.
However, there is very little affinity known “outside” the basket.
For example, there are many cases where the consumer will return to the store to get
the additional item(s) they did not purchase initially.
Examples:
Electronics retailer (Best Buy, Radio Shack, Fry’s):
– A Blue-Ray player is purchased online on a given date. The same customer
visits the store next week to buy HDMI cables and a B-R disc.
Fashion Retailer (Target, Macy’s, J Crew):
– A customer purchases a dress and hand bag one week. Returns within a
month to buy matching shoes.
With this sequential affinity analysis, the retailer can send very specific and timely
email marketing, to drive traffic and increase revenue.
www.decideo.fr/bruley

16. Overview of Cross-Basket Affinity
Challenge
•
Difficult to do in a relational DB due to
the sheer size of the combinatorial
permutations of the various purchasing
sequences.
Cross-Channel Transactions
X Customers X Marketing Campaigns
Transactional DB
Customer Loyalty
Requires good customer recognition via a
credit card database or a customer loyalty
card program.
With Teradata Aster
•
Use nPath/Sessionization to identify
“super” baskets within a time window.
Tighter time window implies higher
affinity.
TransID
UserId
Date/Time
Item
UPC
874143
10001
11/12/24
83321
543422
20001
11/12/28
73910
632735
•
30002
11/12/24
39503
452834
10001
11/12/30
Run Basket Generator to identify the
most frequent affinity items &
subcategories.
Impact
•
Enables more accurate targeting of
customer needs; reduce direct marketing
spend, increase revenue yield.
www.decideo.fr/bruley
Address
Phone
10001
10 Main St
555-3421
20001
24 Elm st
232-5451
30002
49019
534 Rich
232-5465
Retail EDW
Product/Item Hierachy
Item UPC
Category
Dept
83321
Heels
Shoes-Womens
73910
Handbags
Accessories
39503
•
UserId
Dresses
ApparelWomens
49019
Perfumes
Cosmetics
Marketing/Promotions
Date
CampaignID
UserId
11/12/24
3241
10001
11/12/28
2352
20001
11/12/24
3241
30002
11/12/30
2352
10001

17. Cross-Basket Affinity Example
UserId
Aster MapReduce
Platform
Address
Phone
10001
10 Main St
555-3421
TransID st UserId
Date/Time
24 Elm
232-5451
Item
UPC
30002
534
874143 Rich 10001 232-5465
11/12/24
83321
543422
20001
11/12/28
73910
632735
30002
11/12/24
39503
452834
Prepares multi-structured data
20001
10001
11/12/30
49019
•Stitches
rows together by customer in a timeordered view
Scans all records to produce complete set
of sequences
•No
need to define patterns in advance
•Fully parallelized for scalable performance
using MapReduce where not feasible with
SQL/SAS
Step 1: nPath/ Sessionization to
identify “super” baskets.
TransID
UserId
Date/Time
Item UPC
SuperSessi
on
SeqNum
874143
10001
11/12/24
83321
101
1
452834
10001
11/12/30
49019
101
2
Summarize sequential affinity output for
business exploration
•Rank
order the most popular sequential
purchase paths.
Step 2: run Basket Generator to
identify frequent affinity items.
ProductU
pcB
Support
Confiden
ce
Time
Window
Sequentia
lOrder
83321
49019
0.10
0.30
14 days
true
73910
www.decideo.fr/bruley
Product
UpcA
83321
0.11
0.25
7 days
false

18. Identifies the Cross-Basket Affinity
Products
The frequent sequence of purchased items identifies products B & C
which are likely to be sold when a customer buys a certain product A.
– Leverage this Cross-Affinity analysis to run more targeted marketing
campaigns; increase affinity purchases
– Personalized email offers yields higher customer retention and loyalty,
and reduces churn.
Aster SQL-MR functions nPath/Sessionization and Basket Generators are
key algorithmic differentiators; this process cannot be done in a scalable
manner in a relational DB and/or SAS
www.decideo.fr/bruley

19. Affinity Use Case 1/3
Analyzing item price movements and its impact on:
– Basket size over a long duration (6-10yrs) will
provide key insights into halo impact and
affinity contribution for items
– Basket composition over a long duration (610yrs) will provide key insights into price bands
for items
Analyzing Affinity of items over a long duration (6-10
yrs) will provide key insights into running better
promotions, planogram and price planning of around
affinity items
www.decideo.fr/bruley

20. Affinity Use Case
2/3
Affinity Analysis
•Analyzing Affinity of items over a long duration (6-10yrs) will provide key insights into
running better promotions, planogram and price planning using items affinity
•Time Frame: 8 Years, 1 Banner - Data Set: Transaction Data, Product hierarchy
Consumer Migration
•Analyzing declines in consumer segments over large timeframes.
•Time Frame: 3 Years - Data Set: Transaction Data, Segment Data, Competitor Data,
Pricing Data
Pricing Affinity
•Analyzing item price movement and its impact on basket size and affinity of items over a
long duration (6 years)
•Data Set: Transaction Data, Price data - Time Frame: 6 Years
Competitor Impact
•Data Set: Transaction/Consumer/Competitor/Pricing Data, Unit_Inf - Time Frame: 8
Years
Social Media
•Integrating consumer online data (Social Media - Facebook) with existing transaction
data and understand impact on consumer loyalty.
•Data Set: Should be collected by vendor
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21. Affinity Use Case 3/3
Data
– ~ 8 years of transaction data (2004 up to Sep-2011)
– 15 Billion baskets (or transactions)
– 225 Stores
– 367K Unique UPCs
– 12 Categories: Alcohol, Cereal, Frozen – Ice Cream, Laundry
Detergent, Cheese (Shredded/Sliced/Chunk/Other), Paper Towels,
Pizza & Shelf Stable Juice
Solution
– Aster SQL-MapReduce: Collaborative Filter
– Query Runtime: 48 minutes (4 Workers using Columnar)
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