category management moscow
DESCRIPTION
Learn how Teradata customers use detailed data to support Category managementTRANSCRIPT
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Category Management supported by Detailed data
Frank VullersLead Retail PractionerTeradata EMEA
CATEGORY MANAGEMENT
Moscow, June 5 , 2012
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Category Management supported by Detailed data
Category management
Best PracticesLatest
Technology
AGENDA
Category management
Best PracticesLatest
Technology
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Category Management supported by Detailed data
Category Management Frameworks
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Category Management supported by Detailed data
Category Management Frameworks
RetailerStrategy
Develop Category
Plans
Implemen-tation
Review
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Category Management supported by Detailed data
Emerging Trends
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Category Management supported by Detailed data
Emerging Trends
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Category Management supported by Detailed data
Emerging Trends
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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
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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
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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
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Category Management supported by Detailed data11 Category Management with Teradata
AGENDA
Category management
Best PracticesLatest
Technology
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Category Management supported by Detailed data
Category Manager Pet Food
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• 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?
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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
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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
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RetailerStrategy
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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
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RetailerStrategy
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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
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RetailerStrategy
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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
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Develop Category
Plans
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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
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Develop Category
Plans
Remove
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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
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Develop Category
Plans
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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
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Develop Category
Plans
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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
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Imple-mentation
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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
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Imple-mentation
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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
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Imple-mentation
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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
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Review
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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
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Review
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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
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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
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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
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Review
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Category Management supported by Detailed data28 Category Management with Teradata
AGENDA
Category management
Best PracticesLatest
Technology
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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
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Category Management supported by Detailed data
Big Data: From Transactions to Interactions
Supporting Technology
Classical
Datawarehouse
Detect &
Explore
platform
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Category Management supported by Detailed data31 Category Management with Teradata
AGENDA
Category management
Best PracticesLatest
Technology
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Category Management supported by Detailed data32 Category Management with Teradata
QUESTIONS ?
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Category Management supported by Detailed data33 Category Management with Teradata
THANKS YOU FOR ATTENTION
Frank VullersLead Retail PractionerTeradata [email protected]