category management moscow

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

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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?

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

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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

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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

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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

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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

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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

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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

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

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