category management moscow

Category Management supported by Detailed data

Frank VullersLead Retail PractionerTeradata EMEA

CATEGORY MANAGEMENT

Moscow, June 5 , 2012


Category management

Best PracticesLatest

Technology

AGENDA

Category management


Technology


Category Management Frameworks

3


Category Management Frameworks

RetailerStrategy

Develop Category

Plans

Implemen-tation

Review

4


Emerging Trends

5


Emerging Trends

6


Emerging Trends

7


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






E-Pos

Extended Business


Web Data

Social Media

9






E-Pos

Extended Business


Web Data

Social Media

10

Category Management supported by Detailed data11 Category Management with Teradata

AGENDA

Category management


Technology


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?


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


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


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


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



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



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


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


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


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


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


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


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


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


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


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


AGENDA

Category management


Technology

Category Management supported by Detailed data 29

PurchaseBrowsing

Capturing browsing data on- & off line




E-Pos

Extended Business


Web Data

Social Media


Big Data: From Transactions to Interactions

Supporting Technology

Classical

Datawarehouse

Detect &

Explore

platform

30


AGENDA

Category management


Technology


QUESTIONS ?


THANKS YOU FOR ATTENTION

Frank VullersLead Retail PractionerTeradata [email protected]

category management moscow

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