The document discusses category management best practices supported by detailed data. It provides an overview of category management frameworks, emerging trends like obtaining a complete view of the customer using both online and offline data. It also presents several case studies on topics like customer segmentation, market basket analysis, SKU rationalization, promotional item selection, and localized assortments. The document concludes with a discussion on best practices implementation and price optimization testing along with supplier partnership cases.

1. CATEGORY MANAGEMENT
Frank Vullers
Lead Retail Practioner
Category Management supported by Detailed data
Moscow, June 5 , 2012 Teradata EMEA

2. AGENDA
Category Latest
Best Practices
management Technology
Category Management supported by Detailed data

3. Category Management Frameworks
Category Management supported by Detailed data 3

4. Category Management Frameworks
Develop
Retailer
Category
Strategy
Plans
Implemen-
Review
tation
Category Management supported by Detailed data 4

5. Emerging Trends
Category Management supported by Detailed data 5

6. Emerging Trends
Category Management supported by Detailed data 6

7. Emerging Trends
Category Management supported by Detailed data 7

8. The complete view of the customer
Traditional
Consumer/ Business
E-Pos
Shopper View
Extended
Contact History Models
Business
View Market
Web Data Research/
Text Data
Social
Media
Category Management supported by Detailed data 8

9. The complete view of the customer
Traditional
Consumer/ Business
E-Pos
Shopper View
Extended
Contact History Models
Business
View Market
Web Data Research/
Text Data
Social
Media
Category Management supported by Detailed data 9

10. The complete view of the customer
Traditional
Consumer/ Business
E-Pos
Shopper View
Extended
Contact History Models
Business
View Market
Web Data Research/
Text Data
Social
Media
Category Management supported by Detailed data 10

11. AGENDA
Category Latest
Best Practices
management Technology
11 CategoryCategory Management with Teradata
Management supported by Detailed data

12. Category Manager Pet Food
Should we Reduce the Assortment of Natural / Organic Pet
Food?
• What are the segment performance metrics?
• How does it vary by store?
• What are the item drivers?
• Which items can I remove from the assortment
with lowest impact / risk?
Category Management supported by Detailed data 12

13. Customer cases
Retailer Strategy
Case 1: Customer Segmentation
Case 2: Basket segmentation
Develop Category Plans
Case 3: SKU Rationalization
Case 4: Promotional Item Selection
Case 5: Assortments
Implementation
Case 6: (Promotional) Pricing optimization
Review
Case 7: Tesco Link
Case 8: Supplier cases
Category Management supported by Detailed data 13

14. Retailer
Case 1: Customer Segmentation Strategy
Objective Analysis & Actions Result
• Segmented 1.5 million customers
Distinguish • Identified “angels” and “devils”
between Sales gains
desirable and • Added merchandise and services double those of
undesirable targeted at high-spender angels traditional stores
customers • Cut back on promotions and loss
leader sales tactics to deter devils
Category Management supported by Detailed data 14

15. Retailer
Case 2: Market Basket Segmentation Strategy
Objective Analysis & Actions Result
• Identified several
• Build a market basket dozen distinct
Better
segmentation model shopping missions
understand
• For a unknown
customer • behaviors are common, you segment the basket
behavior in can gear your advertising size and frequency
absence of a and promotions to them even rose
loyalty without knowing each • A range of programs
program customer by name developed for other
segments
Category Management supported by Detailed data 15

16. Retailer
Case 2: Market Basket Segmentation Strategy
Objective Analysis & Actions Result
• Identified several
• Build a market basket dozen distinct
Better
segmentation model shopping missions
understand
• For a unknown
customer • behaviors are common, you segment the basket
behavior in can gear your advertising size and frequency
absence of a and promotions to them even rose
loyalty without knowing each • A range of programs
program customer by name developed for other
segments
Category Management supported by Detailed data 16

17. Develop
Category
Case 3: SKU Rationalization Plans
Objective Analysis & Actions Result
which items
should be
■ Score SKU’s sales value, volume and
remove from
profit contributions, Achieve product
their
range
assortment to
■ Vet SKUs based on customer, product, rationalization
make room for
and store dimensions,
new item
introductions
Category Management supported by Detailed data 17

18. Develop
Category
Case 3: SKU Rationalization Plans
Objective Analysis & Actions Result
which items
should be
■ Score SKU’s sales value, volume and
remove from
profit contributions, Achieve product
their
range
assortment to
■ Vet SKUs based on customer, product, rationalization
make room for
and store dimensions,
new item
introductions
Remove
Category Management supported by Detailed data 18

19. Develop
Category
Case 4: Promotional Item Selection Plans
Objective Analysis & Actions Result
This retailer ■ Which items drive the highest traffic
■ Insight in items that
desired a ■ Is item popular with preferred customers
drive store traffic and
solution to ■ What is sales history & promotional lift
increase basket size
avoid the (Pre, during & post) for past promotions?
■ More Revenue with
guesswork in ■ Determine promotional item placement.
increased store
selecting items ■ Merchandise promotional items to
traffic /basket sizes.
for Flyers maximize affinity sales
■ Reduced inventory
carrying costs.
Category Management supported by Detailed data 19

20. Develop
Category
Case 5: Localized Assortment Plans
Objective Analysis & Actions Result
■ Which product attributes perform well by
location? ■ Local/regional
Refine ■ Which locations sell small /large sizes? customer
assortments Small /Large Packaging? satisfaction
while better ■ Market / Customer/Suppliers assessment increases
managing ■ Adjust Assortment using preferences ■ Changes added 2.6-
in-store ■ Changed plan-o-grams and assortments 5.2% improvement
traffic flow ■ Recurrent Build and Analyze the to gross margin of
Assortment participating stores
Category Management supported by Detailed data 20

21. Imple-
Best Practices Implementation mentation
Some Remarks
■ Test fast, fail fast, adjust fast. Tom Peters
■ Test with real customers
■ Representative stores
■ One group of stores with new tactic versus Control group
■ 6-10 weeks Timeframe
■ Datalab in your datawarehouse
Category Management supported by Detailed data 21

22. Imple-
Case 6: Price Optimisation Test Catalog mentation
Objective Analysis & Actions Result
How to ensure ■ Calculated prices with Promotional Price
that products ■ Gross sales increase
Optimization solution & manually
of 15%
are priced for ■ 50% of the basic catalogues with
maximum ■ Total gross margin
traditional prices
increase of 11%
profitability ■ 50% of the basic catalogues with selected
products set at optimal prices
Category Management supported by Detailed data 22

23. Imple-
Case 6: Price Optimisation Test Catalog mentation
Objective Analysis & Actions Result
How to ensure ■ Calculated prices with Promotional Price
that products ■ Gross sales increase
Optimization solution & manually
of 15%
are priced for ■ 50% of the basic catalogues with
maximum ■ Total gross margin
traditional prices
increase of 11%
profitability ■ 50% of the basic catalogues with selected
products set at optimal prices
Category Management supported by Detailed data 23

24. Review
Case 7: Tesco Link
Objective Analysis & Actions Result
Leverage
■ Give Suppliers entrance to Tesco data
Suppliers ■ Lean backoffice
■ Sharing detailed information on sales data
knowledge ■ One consistent way
■ Not only viewing but also Downloading
on of working
data
categories
Category Management supported by Detailed data 24

25. Review
Case 7: Tesco Link
Objective Analysis & Actions Result
Leverage
■ Give Suppliers entrance to Tesco data
Suppliers ■ Lean backoffice
■ Sharing detailed information on sales data
knowledge ■ One consistent way
■ Not only viewing but also Downloading
on of working
data
categories
Category Management supported by Detailed data 25

26. Review
Case 8: Some Supplier Cases
Retail Execution & Monitoring
Anheuser Busch analyses store/SKU level data and push it out to field sales teams
to ensure availability, facings and stock levels are maintained
for the products.
attribute $12M benefit to this.
Trade PromotionManagement
Coca Cola Enterprises uses store level EPOS data, internal shipment plans and
profitability measures based on detailed invoice and off-invoice data to provide
real-time performance of promotions.
In 2 years ROI of promotions was doubled.
Category Management supported by Detailed data 26

27. Review
Case 8: Some Supplier Cases
Customer Relation Management
Pepsi and 3M have the ability to roll-up transaction level data by
customer to provide an overview of customer performance.
Sales, margin, customer service level data are recorded consistently
across geography to deliver a customer-level report by category or
geography.
Returns as high as 0.1% of net rev have been reported
Category Management supported by Detailed data 27

28. AGENDA
Category Latest
Best Practices
management Technology
28 CategoryCategory Management with Teradata
Management supported by Detailed data

29. Capturing browsing data on- & off line
Traditional
Consumer/ Business
E-Pos
Shopper View
Extended
Contact History Models
Business
View Market
Web Data Research/
Text Data
Social
Media
Browsing Purchase
Category Management supported by Detailed data 29

30. Big Data: From Transactions to Interactions
Supporting Technology
Detect &
Classical
Explore
Datawarehouse
platform
Category Management supported by Detailed data 30

31. AGENDA
Category Latest
Best Practices
management Technology
31 CategoryCategory Management with Teradata
Management supported by Detailed data

32. QUESTIONS ?
32 CategoryCategory Management with Teradata
Management supported by Detailed data

33. THANKS YOU FOR
ATTENTION
Frank Vullers
Lead Retail Practioner
Teradata EMEA
33 CategoryCategory Management with Teradata
Management supported by Detailed data
Frank.Vullers@Teradata.com