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Learn how Teradata customers use detailed data to support Category management
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Category Management supported by Detailed data
Frank VullersLead Retail PractionerTeradata EMEA
CATEGORY MANAGEMENT
Moscow, June 5 , 2012
Category Management supported by Detailed data
Category management
Best PracticesLatest
Technology
AGENDA
Category management
Best PracticesLatest
Technology
Category Management supported by Detailed data
Category Management Frameworks
3
Category Management supported by Detailed data
Category Management Frameworks
RetailerStrategy
Develop Category
Plans
Implemen-tation
Review
4
Category Management supported by Detailed data
Emerging Trends
5
Category Management supported by Detailed data
Emerging Trends
6
Category Management supported by Detailed data
Emerging Trends
7
Category Management supported by Detailed data
The complete view of the customer
Traditional Business
ViewConsumer/Shopper
ModelsContact History
E-Pos
Extended Business
View Market Research/ Text Data
Web Data
Social Media
8
Category Management supported by Detailed data
The complete view of the customer
Traditional Business
ViewConsumer/Shopper
ModelsContact History
E-Pos
Extended Business
View Market Research/ Text Data
Web Data
Social Media
9
Category Management supported by Detailed data
The complete view of the customer
Traditional Business
ViewConsumer/Shopper
ModelsContact History
E-Pos
Extended Business
View Market Research/ Text Data
Web Data
Social Media
10
Category Management supported by Detailed data11 Category Management with Teradata
AGENDA
Category management
Best PracticesLatest
Technology
Category Management supported by Detailed data
Category Manager Pet Food
12
• 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?
Should we Reduce the Assortment of Natural / Organic Pet Food?
Category Management supported by Detailed data
Customer cases
Case 1: Customer Segmentation
Case 2: Basket segmentation
Retailer Strategy
Case 3: SKU Rationalization
Case 4: Promotional Item Selection
Case 5: Assortments
Develop Category Plans
Case 6: (Promotional) Pricing optimization
Implementation
Case 7: Tesco Link
Case 8: Supplier cases
Review
13
Category Management supported by Detailed data
Distinguish between desirable and undesirable customers
Objective
• Segmented 1.5 million customers
• Identified “angels” and “devils”
• Added merchandise and services targeted at high-spender angels
• Cut back on promotions and loss leader sales tactics to deter devils
Analysis & Actions
Sales gains double those of traditional stores
Result
Case 1: Customer Segmentation
14
RetailerStrategy
Category Management supported by Detailed data
Better understand customer behavior in absence of a loyalty program
Objective
• Build a market basket segmentation model
• behaviors are common, you can gear your advertising and promotions to them even without knowing each customer by name
Analysis & Actions
• Identified several dozen distinct shopping missions
• For a unknown segment the basket size and frequency rose
• A range of programs developed for other segments
Result
Case 2: Market Basket Segmentation
15
RetailerStrategy
Category Management supported by Detailed data
Better understand customer behavior in absence of a loyalty program
Objective
• Build a market basket segmentation model
• behaviors are common, you can gear your advertising and promotions to them even without knowing each customer by name
Analysis & Actions
• Identified several dozen distinct shopping missions
• For a unknown segment the basket size and frequency rose
• A range of programs developed for other segments
Result
Case 2: Market Basket Segmentation
16
RetailerStrategy
Category Management supported by Detailed data
Case 3: SKU Rationalization
which items should be remove from their assortment to make room for new item introductions
Objective
Achieve product range rationalization
Result
■ Score SKU’s sales value, volume and profit contributions,
■ Vet SKUs based on customer, product, and store dimensions,
Analysis & Actions
17
Develop Category
Plans
Category Management supported by Detailed data
Case 3: SKU Rationalization
which items should be remove from their assortment to make room for new item introductions
Objective
Achieve product range rationalization
Result
■ Score SKU’s sales value, volume and profit contributions,
■ Vet SKUs based on customer, product, and store dimensions,
Analysis & Actions
18
Develop Category
Plans
Remove
Category Management supported by Detailed data
Case 4: Promotional Item Selection
This retailer desired a solution to avoid the guesswork in selecting items for Flyers
Objective
■ Insight in items that drive store traffic and increase basket size
■ More Revenue with increased store traffic /basket sizes.
■ Reduced inventory carrying costs.
Result
■ Which items drive the highest traffic■ Is item popular with preferred customers ■ What is sales history & promotional lift
(Pre, during & post) for past promotions?■ Determine promotional item placement.■ Merchandise promotional items to
maximize affinity sales
Analysis & Actions
19
Develop Category
Plans
Category Management supported by Detailed data
Case 5: Localized Assortment
Refine assortments while better managing in-store traffic flow
Objective
■ Local/regional customer satisfaction increases
■ Changes added 2.6-5.2% improvement to gross margin of participating stores
Result
■ Which product attributes perform well by location?
■ Which locations sell small /large sizes? Small /Large Packaging?
■ Market / Customer/Suppliers assessment■ Adjust Assortment using preferences■ Changed plan-o-grams and assortments■ Recurrent Build and Analyze the
Assortment
Analysis & Actions
20
Develop Category
Plans
Category Management supported by Detailed data
Best Practices Implementation
■ 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
Some Remarks
21
Imple-mentation
Category Management supported by Detailed data
Case 6: Price Optimisation Test Catalog
How to ensure that products are priced for maximum profitability
Objective
■ Gross sales increase of 15%
■ Total gross margin increase of 11%
Result
■ Calculated prices with Promotional Price Optimization solution & manually
■ 50% of the basic catalogues with traditional prices
■ 50% of the basic catalogues with selected products set at optimal prices
Analysis & Actions
22
Imple-mentation
Category Management supported by Detailed data
Case 6: Price Optimisation Test Catalog
How to ensure that products are priced for maximum profitability
Objective
■ Gross sales increase of 15%
■ Total gross margin increase of 11%
Result
■ Calculated prices with Promotional Price Optimization solution & manually
■ 50% of the basic catalogues with traditional prices
■ 50% of the basic catalogues with selected products set at optimal prices
Analysis & Actions
23
Imple-mentation
Category Management supported by Detailed data
Case 7: Tesco Link
Leverage Suppliers knowledge on categories
Objective
■ Lean backoffice■ One consistent way
of working
Result
■ Give Suppliers entrance to Tesco data■ Sharing detailed information on sales data■ Not only viewing but also Downloading
data
Analysis & Actions
24
Review
Category Management supported by Detailed data
Case 7: Tesco Link
Leverage Suppliers knowledge on categories
Objective
■ Lean backoffice■ One consistent way
of working
Result
■ Give Suppliers entrance to Tesco data■ Sharing detailed information on sales data■ Not only viewing but also Downloading
data
Analysis & Actions
25
Review
Category Management supported by Detailed data
Case 8: Some Supplier Cases
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.
Trade PromotionManagement
26
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.
Retail Execution & Monitoring
Review
Category Management supported by Detailed data
Case 8: Some Supplier Cases
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
Customer Relation Management
27
Review
Category Management supported by Detailed data28 Category Management with Teradata
AGENDA
Category management
Best PracticesLatest
Technology
Category Management supported by Detailed data 29
PurchaseBrowsing
Capturing browsing data on- & off line
Traditional Business
ViewConsumer/Shopper
ModelsContact History
E-Pos
Extended Business
View Market Research/ Text Data
Web Data
Social Media
Category Management supported by Detailed data
Big Data: From Transactions to Interactions
Supporting Technology
Classical
Datawarehouse
Detect &
Explore
platform
30
Category Management supported by Detailed data31 Category Management with Teradata
AGENDA
Category management
Best PracticesLatest
Technology
Category Management supported by Detailed data32 Category Management with Teradata
QUESTIONS ?
Category Management supported by Detailed data33 Category Management with Teradata
THANKS YOU FOR ATTENTION
Frank VullersLead Retail PractionerTeradata EMEAFrank.Vullers@Teradata.com
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