Consulting

The Benefits of Store Clustering

Simon Smallwood Director Email – [email protected] Tel - +44 7786 387793

7 Garrick St Covent Garden London WC2E 9AR T - +44 (0)203 051 1375 www.riverheadconsulting.com

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Not so long ago.......

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Where everyone knew your name......

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But times they were a changing.....

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And the only constant is change.....

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Pick n Pay V & A Wharf Cape Town SA

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Mass Merchandise, Mass Market, Mass Range, Mass Inventory...

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So what’s in it for the.... Retailer: • Broadest possible range attracts broadest number of customers • Easy to manage – ‘One size fits all’

Customer:

• Buying & promotion efficiencies

• Vast range of choice

• Out range the competition

• All tastes catered for

• Logistics & Distribution efficiencies

• Secondary & Tertiary options

• Streamlined back office systems

• Competitive environment keeping prices down

Manufacturer:

• One stop shop

• Maximum distribution

• Bulk buying

• Optimum market penetration • Promotional Critical Mass • Minimum number of SKU’s

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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What is the real cost to retailers and do customers really benefit? 100

Sales Value

80

20

Inventory Value GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

100

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Studies have shown that the annual additional cost of holding excess inventory can be 25% to 32%.

The Diamond of Doom Excess Inventory Leads to

Poor Cash Flow: Pressure from suppliers

Leads to

Excessive Obsolescence Pilferage, maintenance, insurance etc

Leads to

Leads to

Excessive Debt servicing

Lower Gross Margin Leads to

Leads to

High Advertising & Selling Expenses (To eliminate the excess)

High Interest Expense Leads to

Leads to

Lower Operating Profits GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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Traditional Retail Models define both ends of the spectrum...

Range & Value

Sales Volumes

High

Low

Local Convenience Store: • Destination Store • 1:1 Service • Knowledgeable Staff • Awareness of Needs

Mass Market Grocers: • Destination Store • Low Cost Provider • Range Breadth & Depth • Broad Appeal Customer Engagement Operating Costs GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

High

New retail models combine service & value to achieve high loyalty & profits

Range & Value

High

SupaValu USA – La Jolla CA

Low

Customer Engagement

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

High

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Combining a strong commitment to service and value...

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Mission Statement To provide the finest assortment and highest quality fresh and specialty foods from around the world - in a warm, friendly, and uniquely designed atmosphere with service and value that exceeds the expectations of our customers. Service: Knowledgeable, Helpful Staff Each Bristol Farms store maintains a large staff who are always available to offer assistance to customers. Atmosphere: Bristol Farms' stores have been carefully designed and decorated to create a singular shopping experience that evokes the local area.

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Store Clustering - Why do it?

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• Introduce a ‘common language’ describing stores across the business • Improve store planning, assortment and merchandising • Tailor store space to match customer demand within each cluster • Provides the potential to offer differential cluster specific promotions

• At category and sub-category level determine optimum assortment • • • •

Enable informed predictions on demand levels for core range and new titles Optimise stockholding v demand Minimise overstocking Eliminate/reduce expensive returns of redundant stock

• Identify the external attributes that drive cluster performance to achieve a closer match to the needs of the customer profile store by store • Results in a higher rate of sale from a lower stock holding – improved ROCE

• Identify the internal factors driving optimum performance and enable the sharing of ‘best practice’ within the group

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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The Dynamics of Store Clustering

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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The dynamics of store clustering

Stores within a group do not perform in the same way despite how similar the product and price offers Both internal and external factors impact the performance of every store more or less In an ideal world we would treat every store as unique and range and merchandise to suit the customers who walk through each store In the real world we must seek to cluster stores by common attributes and performance patterns

Critical success factor – Simplicity. The entire company should be able to understand the clusters and describe the people and the stores that each cluster most strongly represents GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

The right store clustering programme results in increased customer satisfaction, compliance and improved supply chain efficiency

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External variables significantly determine store performance Percentage contribution to store performance variability.

6%

78%

7% 10% 25%

E D

C

30%

B

A

F

Examples of ‘External Variables’ are:

External Variables

A – Local population and Competition (Population, competition, grocery spend within 5,10,15 minutes) B – Store size variables (Revenue, payroll, sq m, opening hours, profit contribution etc) C – Wider demographics (10-15 minute drive time) D – Local demographics (5 minute drive time) E – Store productivity (Productivity index, wastage, shrinkage, FT/PT ratio etc) F – Variability explained (22% not measurable or identifiable i.e. internal variables such as how good store manager is) GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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There are several approaches to store clustering used by retailers...

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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Size, Format, Spend - Matrix Main+ Average Main Average

First for Food

Proposition

Q F

Range / Choice

P S+ S

E

P = Premium Brands S = Standard Brands S+ = Standard +Brands E = Economy Brands

Promotions Policy

Q v

Q

Q

v

v

Q = Quality

Service levels

J

Format

£ £ Basic Standard No Frills

Mixed Meals

Making Life Taste Better For Less

First for Foodies

First for Fresh

F

B

E

P S+ S

Q

Q

v

v

Q E

F

P S+ S

Q E

P S+ S

Mixed Grab & Go Fast, Fresh, New & Exciting

F

Q

F

E

P S+ S E

Q

Q

Q

v

v

v

V = Value

J

Extended

Business Benchmark

Environment

P S+ S

Main High Main Average

Q

Q F

B

Own Label Levels

Superior Food + GM For Family & Home First for Foodies & Typical Families

Main+ Low Main Low

£

J

J

J

J £

£

J £

J £

J

J £

Flagship

Average Size & Avg Spend

Avg Size & Low Spend

Avg Size & High Spend

Smaller Local Store; Mixed Shoppers

Smaller Local Store: Young Single Shoppers

Q = Quality (TTD, BGTY, Premium Brands, F = Families (Standard +, Standard, Some Economy), B = Budget (Extended Economy, Tertiary Brands) GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Asda Wal*Mart Spectra Advantage System

Asda WalMart describe all stores by one of four spending bands, Core, Core Plus, Core Plus Plus and Core Constrained, then refines at category level. Spectra system takes panel data (ACNielsen /TNS /GFK) and broadcasts national purchasing patterns through demographic profiles on to store trade areas to describe potential demand by each store

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Asda Wal*Mart Spectra Advantage System Store Clusters defined by opportunity – higher priced wines

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Strategic Customer Segmentation

Can’t stay away 3 monthly high spenders

Convenience

Shopping staff

Healthy Living GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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Strategic Customer Segmentation Tesco loyalty card analysis Lifestyles in Tesco

(8 Main Segments) Making Pennies Work

Staple Family Meals

Better off Families

Convenience Cooks

Quick Meals

Shoppers on a budget

Cheap and Easy Meals

High Spending Superstore Families

Cosmopolitan Cooks

Ready Meals Fans

Aspiring Foodies

Standard Superstore Families

Cooking from Jars

Calorie Counters

Stylish Foodies

Kids Choice

Eating for Health

Quiche Meals

16.4%

9.7%

13.0%

0.9%

Substantial Family Fodder

Basic Family Meals

Cost Conscious Cooks

Sausage and spuds families

3.4%

3.3%

4.8%

4.0%

11.8%

11.2%

4.2%

3.0%

4.6%

3.6%

5.2%

2.4%

Biscuits and quick meals

8.7%

3.4%

1.6%

2.0%

Well off Pizza Families

3.3%

1.7%

(Percentage of total number of Clubcard holders) GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Good Cuisine

9.4%

4.5%

2.6%

Good Taste is Green

2.3%

Conservative Quality

Traditional Living

Upmarket & Traditional

Traditional Elderly

First Rate Meals

Old Fashioned Brands

15.9%

4.0%

5.4%

Middle Market Conventionalists

3.8%

13.6%

7.4%

2.5%

Northern Band Loyalists

3.7%

Comfortable but Cautious

2.7%

(27 Sub-segments)

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Case Study

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Case Study

• Russian book retailer – Ranges include stationery, toys, music & video • Strong & sustained organic growth • 500 Stores throughout Russia and continuing to grow • Diverse locations • Large range of store sizes • Several ‘Banners’ • Introducing ‘Category Management’ • Implementing major new systems platform

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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Concept & benefits of ‘Clustering’ recognised... Different approaches had been tried, but without success

Store brand?

Store size? Store fascia?

Store geography? Store location?

Best practice is to develop a customer profile / shopping occasion based model

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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Diverse people, lifestyles & culture how do you profile & group them?

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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Shopper based clustering challenges... • Russian market evolving rapidly • Demographic data is difficult to obtain and not granular enough to be useful • Consumer data is patchy and non-existent in book retail channel • Customer profiles are too broad to be applied in this channel • Shopper behaviour understanding in this environment does not exist The only reliable data available was..... Store & Item Level POS Data:

Store Attributes:

Item type Item sales value, volume, history

Location, size, type of locality, adjacencies

Supplemented by observational data... Customer types: Age, single or family, children’s age, affluence

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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Analysis of similar stores indicated clear differences in sales profiles Media

Stationery Science & Technology Medicine, Economics, Law Culture & Society Languages & Dictionaries School, Education For child Fiction Home, Leisure, Life

-8

-6

-4

-2

0

Store 1

2

4

Store 2

Total sales values Store 1 = 6.5 million R, Store 2 = 5.8 million R GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

6

8

Analysis of similar stores indicate clear differences in sales profiles

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• Same size stores do not deliver the same mix of business • Clear evidence of a bias in store profiles.

Core Range Education bias store cluster

Family bias store cluster

Store 02 has 35% sales in education and sciences Store 01 has 77% sales in Home, fiction, children and stationery

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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A detailed analysis of the entire estate identified 6 ‘obvious’ clusters Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 6

Overall

Actual Sales index.

15

30

55

45

31

89

38

Projected Sales index using cluster 4 as a factor of 1

20

27

40

38

72

72

30

Actual

12

22

41

31

24

52

27

Projected

14

19

29

27

51

51

21

Business Economics Law

Culture And Society

Fiction Actual

39

69

132

98

86

124

82

Projected

43

59

87

82

156

155

65

Home Lifestyle, Leisure Actual

35

60

104

76

63

97

65

Projected

34

47

70

65

125

124

52

Actual

3

6

13

9

7

17

8

Projected

4

6

9

8

16

16

7

Actual

37

63

75

80

64

81

66

Projected

35

48

71

66

126

125

53

Actual

39

75

107

105

81

161

87

Projected

46

63

93

87

166

165

69

Actual

4

8

15

11

9

20

10

Projected

5

7

11

10

19

19

8

Actual

25

49

27

58

56

51

45

Projected

24

33

48

45

86

86

36

Linguistics

Literature for Children

Schools, education and Pedagogics

Science, Technology and Medicine

Toys

Significantly Low Sales

Reduce Space Allocation

Significantly High sales

Increase Space Allocation

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

A detailed analysis of the entire estate identified 6 customer-centric store clusters

1. “Counting the Roubles” Catering to less well off customers buying across all categories on a limited budget in smaller stores outside of major population centres

2. “Children First” Serving and middle income customers mainly buying children’s books and toys in mid-sized town centre and suburban stores

3. “Well Read” Attracting high traffic of high spending customers mainly buying books in larger town centre and suburban locations

6. “Young, better off & Well read”

4. “Middle of the Road” The average store attracting middle-income customers buying across all categories in all types of location

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5. “Stationery Stars” Providing an offer for a heavy flow of customers with a strong bias to buying a high number of low value stationery items in town centres and GS1 Baltics Retail Forum 5th November 2008 suburbs © Riverhead Consulting Ltd– 2008

Attracting the highest income, highest spending customers - mainly under 30 years of age, in large numbers, buying across all categories in town centre stores

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Cluster comparisons

Descriptor

Sales Profile

Customer Profile Store Profile

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 6

Counting the Roubles

Children First

Well Read

Middle of the Road

Stationery Stars

Young Better Off & Well Read

• Item sales value is rising • Toy sales lower v cluster 2 • Item sales value higher • Children’s books relatively • Lowest number of item • Value per item rising high sales • Book sales up on cluster 1, • High sales of business, • Value of each item is lowest toys, stationery & culture, fiction, linguistics, of all clusters children's books much science, home & life higher • Stationery sales flat v overall sales • Income profile is lowest of • all groups • Rising income profile • Age profile highest • • Age range & presence of • More households with children similar to cluster 1 children •

• • • •

• High performing cluster Average value of items sold • Highest total item sales of all stores • Stationery sales high but is reverse of cluster 3 lower than cluster 5 • Not the highest value Focus on lower value items • High book sales in every • Category sales of Sales of media, toys & category stationery & toys stationery high outperform all other • Overall value per item Book sales lower than clusters sold is higher than all cluster 3 other clusters • Books are in line with cluster 4

Income profile higher than • Income levels are higher • Income profile similar to • Highest income profile of cluster 1 & 2 than clusters 1 – 4 cluster 3 all categories • Age profile slightly Age range broadly same as • Age range & presence of • More shoppers under 30 younger 1&2 children similar to cluster 3 and fewer with children Less households with • More households with families older children

• Majority of stores are • Size slightly larger than • Sizes similar to cluster 1 smallest cluster 2 • Higher number of visitors • Traffic estimates are lowest • Traffic sharply higher than • Located in centres & of all stores cluster 1 & 2 suburbs, few in rural & • More stores in industrial & • No stores in rural or industrial rural areas industrial areas

• Sizes similar to cluster 3 • Traffic noticeably lower than cluster 3 • Located throughout most areas

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

• Store traffic is rising • Stores located mainly in centres & suburbs

• Highest traffic numbers of all clusters • All stores are in centres

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Cluster development... • Clusters were not developed... • ...based on store size • ...using only sales value or volume sales • Clusters were developed... • ...based on item sales mix of categories • ...using customer profile (customers who shopped in the store) • ...store attributes that determine the customer profile

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Customer centric Store Clustering drives benefits across the entire business.. Better understanding of the Value Chain Dynamics

Better understanding of the Market Dynamics

Better understanding of the Customer Dynamics

Factors influencing stores’ performance

Category Strategy

Inventory Management

Stock cover & replenishment planned and managed by cluster

Assortment

Core & discretionary category ranges planned and managed by cluster

Category Plans Space Allocation

Micro & macro category space allocation planned and managed by cluster GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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Revenue Management

Supplier Management

Promotional events tailored to cluster-specific requirements

Transparent communication of the implications of the store cluster model

Store assortment by category can be precisely targeted to customer profile For each cluster we can now define…..

Core Range • • • • •

Titles / SKUs Share of category space Position in store Stock levels / target availability Replenishment frequency

Discretionary Range • Based on cluster attributes – Store size – Category participation – Catchment preferences

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Promotions • Participation in promotion • Use of display materials • Position in store

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The results can be significant... • Sales uplift in underperforming test stores: +87% • Overall sales uplift: +22% • Availability: +18% • Overall reduction in inventory levels: -17% • Promotional response: +35% • Average spend per visit: + 12%

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Impact on Retailers business model... • Store Clustering enabled the retailer to improve efficiencies across a wide range of measures. • Retailer is now able to discuss ‘Ranging Solutions’ with suppliers on a ‘Cluster’ basis. • Macro & micro space allocation reflects customer demand – optimising stock holding and improving availability • The business has become more ‘Customer Centric’ in its approach and thinking. • Promotions are targeted to drive volume and profit in the stores where impact will be greatest. • Performance measures at store level are focused on ‘customer service’ • Stores are benchmarked ‘like for like’.

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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‘Store Cluster’ models should be developed using the best data available to a retailer... ... their own!

Effective ‘Store Cluster’ modelling should not be a ‘black box’ solution... ... it is a combination of high level analytics and retailing expertise.

‘Store Cluster’ modelling is a collaborative process within the retailer and with suppliers... ...the benefits can only be realised by working together .

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Effort, this is. But worth it, effort is. Interesting this may become.

GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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Consulting

The Benefits of Store Clustering

Simon Smallwood Director Email – [email protected] Tel - +44 7786 387793

7 Garrick St Covent Garden London WC2E 9AR T - +44 (0)203 051 1375 www.riverheadconsulting.com