14 the benefits_of_store_clustering

Consulting

The Benefits of Store Clustering

Simon Smallwood 7 Garrick St
Director Covent Garden
London
Email – simons@riverheadconsulting.com
WC2E 9AR
Tel - +44 7786 387793 T - +44 (0)203 051 1375
www.riverheadconsulting.com

Page: 2
Not so long ago.......

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

Page: 3
Where everyone knew your name......


Page: 4
But times they were a changing.....


Page: 5
And the only constant is change.....

Pick n Pay V & A Wharf Cape Town SA


Page: 6
Mass Merchandise, Mass Market, Mass Range, Mass Inventory...


Page: 7
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


Page: 8
What is the real cost to retailers and do customers really benefit?

100

80
Sales Value

20 100
Inventory Value

Page: 9

Studies have shown that the annual
additional cost of holding excess The Diamond of Doom
inventory can be 25% to 32%. Excess Inventory

Leads to Leads to

Poor Cash Flow: Excessive Obsolescence
Pressure from suppliers Pilferage, maintenance,
insurance etc

Leads to Leads to

Excessive Debt servicing
Lower Gross Margin

Leads to Leads to

High Advertising & Selling Expenses
High Interest Expense
(To eliminate the excess)

Leads to Leads to

Lower
Operating
Profits

Page: 10
Traditional Retail Models define both ends of the spectrum...

High
Local Convenience Store:
• Destination Store
• 1:1 Service
• Knowledgeable Staff
• Awareness of Needs
Range & Value
Sales Volumes

Mass Market Grocers:
• Destination Store
• Low Cost Provider
• Range Breadth & Depth
• Broad Appeal

Low High
Customer Engagement
Operating Costs


Page: 11
New retail models combine service & value to achieve high loyalty & profits

High
Range & Value

SupaValu USA – La Jolla CA

Low High
Customer Engagement


Page: 12
Combining a strong commitment to service and value...

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.


Store Clustering - Why do it?
Page: 13

• 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


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The Dynamics of Store Clustering


Page: 15
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

The right store clustering
programme results in
increased customer
Critical success factor – Simplicity. The entire company should be able to understand the satisfaction, compliance and
clusters and describe the people and the stores that each cluster most strongly represents improved supply chain
efficiency

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External variables significantly determine store performance

Percentage contribution
to store performance variability.

6% 78%
7%
10%
E
25% D
C
30%

B

External Variables
A F
Examples of ‘External Variables’ are:
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)


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


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Size, Format, Spend - Matrix
Main+ Average Main+ Low Main High Mixed Mixed
Main Average Main Low Main Average Meals Grab & Go

Superior Food + GM Making Life
For Family & Home Taste Better First for Fresh Fast, Fresh, New
First for Food First for Foodies
Proposition First for Foodies & For Less & Exciting
Typical Families

Range / Choice Q F Q F
Q
F Q
F
Q F Q
F
B B

Own Label Levels P S+ S E
P S+ S E P S+ S E P S+ S E P S+ S E P S+ S E
P = Premium Brands S = Standard Brands
S+ = Standard +Brands E = Economy Brands

Q Q Q Q
Q Q Q Q
Promotions Policy v v v
v v v v v
Q = Quality V = Value

Service levels
J
Business Benchmark
J
Extended
J J
J J J
J J J

£ £ £ £
£ £ £
Environment Basic
£
Standard Flagship
No Frills

Average Size Avg Size Avg Size Smaller Local Store; Smaller Local Store:
Format & Avg Spend & Low Spend & High Spend Mixed Shoppers Young Single
Shoppers
Q = Quality (TTD, BGTY, Premium Brands, F = Families (Standard +, Standard, Some Economy), B = Budget (Extended Economy, Tertiary Brands)


Page: 19
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


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Strategic Customer Segmentation
Tesco loyalty card analysis
Lifestyles
in Tesco
(8 Main Segments)

Making Pennies Staple Family Better off Convenience Conservative Traditional
Quick Meals Good Cuisine
Work Meals Families Cooks Quality Living
16.4% 13.0% 11.8% 11.2% 8.7% 9.4% 15.9% 13.6%

High Spending
Shoppers on Cheap and Cosmopolitan Ready Meals Upmarket & Traditional
Superstore Aspiring Foodies
a budget Easy Meals Cooks Fans Traditional Elderly
9.7% 0.9%
Families
4.2% 3.4% 4.5% 4.0% 7.4%
3.0%

Substantial Standard
Basic Family Cooking from Old Fashioned
Family Superstore Calorie Counters Stylish Foodies First Rate Meals
Meals Jars Brands
Fodder
4.8%
Families
4.6% 1.6% 2.6% 5.4% 2.5%
3.4% 3.6%

Cost Conscious Sausage and Good Taste is Middle Market Northern Band
Kids Choice Eating for Health Quiche Meals
Cooks spuds families Green Conventionalists Loyalists
3.3% 4.0% 5.2% 2.4% 2.0% 2.3% 3.8% 3.7%

Biscuits and Well off Pizza Comfortable but
quick meals Families Cautious
3.3% 1.7% 2.7%

(Percentage of total number of Clubcard holders) (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


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

Store size? Store brand?

Store fascia? Store geography?

Store location?

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


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


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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 Location, size, type of locality, adjacencies
Item sales value, volume, history

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


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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 2 4 6 8

Store 1 Store 2
Total sales values Store 1 = 6.5 million R, Store 2 = 5.8 million R

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Analysis of similar stores indicate clear differences in sales profiles
• Same size stores do not deliver the same mix of business
• Clear evidence of a bias in store profiles.

Core Range

Education bias
Family bias store cluster
store cluster

Store 02 has 35% sales in education and sciences

Store 01 has 77% sales in Home, fiction, children and stationery


Page: 30
A detailed analysis of the entire estate identified 6 ‘obvious’ clusters

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Overall
Business Economics Law
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
Culture And Society
Actual 12 22 41 31 24 52 27
Projected 14 19 29 27 51 51 21
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
Linguistics
Actual 3 6 13 9 7 17 8
Projected 4 6 9 8 16 16 7
Literature for Children
Actual 37 63 75 80 64 81 66
Projected 35 48 71 66 126 125 53
Schools, education and Pedagogics
Actual 39 75 107 105 81 161 87
Projected 46 63 93 87 166 165 69
Science, Technology and Medicine
Actual 4 8 15 11 9 20 10
Projected 5 7 11 10 19 19 8
Toys
Actual 25 49 27 58 56 51 45
Projected 24 33 48 45 86 86 36

Significantly Low Sales Reduce Space Allocation
Significantly High sales Increase Space Allocation


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

2. “Children First”
1. “Counting the Roubles” 3. “Well Read”
Serving and middle
Catering to less well off income customers mainly Attracting high traffic of
customers buying across buying children’s books high spending customers
all categories on a limited and toys in mid-sized town mainly buying books in
budget in smaller stores centre and suburban larger town centre and
outside of major stores suburban locations
population centres

6. “Young, better off &
4. “Middle of the Road”
Well read”
The average store Attracting the highest
attracting middle-income income, highest spending
customers buying across 5. “Stationery Stars” customers - mainly under
all categories in all types of 30 years of age, in large
location Providing an offer for a numbers, buying across all
heavy flow of customers categories in town centre
with a strong bias to stores
buying a high number of
low value stationery items
in town centres and
suburbs

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Cluster comparisons

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6
Stationery Young Better Off &
Descriptor Counting the Roubles Children First Well Read Middle of the Road
Stars Well Read
• High performing cluster
• Item sales value is rising
• Average value of items sold • Highest total item sales of
• Toy sales lower v cluster 2 is reverse of cluster 3 all stores • Stationery sales high but
• Item sales value higher
• Children’s books relatively • Not the highest value lower than cluster 5
• Lowest number of item • Value per item rising • Focus on lower value items
Sales sales • Book sales up on cluster 1,
high
• Sales of media, toys & • Category sales of • High book sales in every
• High sales of business, stationery & toys category
• Value of each item is lowest toys, stationery & stationery high
Profile of all clusters children's books much
culture, fiction, linguistics,
• Book sales lower than
outperform all other • Overall value per item
science, home & life clusters sold is higher than all
higher cluster 3
• Stationery sales flat v • Books are in line with other clusters
overall sales cluster 4

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

families older children
• Majority of stores are • Size slightly larger than
• Sizes similar to cluster 1 • Sizes similar to cluster 3
smallest cluster 2 • Store traffic is rising • Highest traffic numbers of
Store • Traffic estimates are lowest
• Higher number of visitors
• Traffic sharply higher than
• Traffic noticeably lower
• Stores located mainly in all clusters
• Located in centres & than cluster 3
of all stores cluster 1 & 2 centres & suburbs • All stores are in centres
Profile • More stores in industrial &
suburbs, few in rural &
• No stores in rural or
• Located throughout most
industrial areas
rural areas industrial areas


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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


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Customer centric Store Clustering drives benefits across the entire business..

Better understanding of Better understanding of Better understanding of the
the Value Chain Dynamics the Market Dynamics Customer Dynamics

Factors influencing stores’
performance

Category Strategy

Category Plans
Inventory Revenue Supplier
Assortment Space Allocation
Management Management Management

Stock cover & Core & Micro & macro Promotional Transparent
replenishment discretionary category space events tailored to communication of
planned and category ranges allocation planned cluster-specific the implications of
managed by planned and and managed by requirements the store cluster
cluster managed by cluster model
cluster

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Store assortment by category can be precisely targeted to customer profile

For each cluster we can now define…..

Core Range Discretionary Range Promotions
• Titles / SKUs • Based on cluster attributes • Participation in promotion
• Share of category space – Store size • Use of display materials
• Position in store – Category participation • Position in store
• Stock levels / target availability – Catchment preferences
• Replenishment frequency


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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%


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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’.


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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 .


Page: 39
Effort, this is. But worth it, effort is. Interesting this may become.


14 the benefits_of_store_clustering

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