14 the benefits_of_store_clustering

Consulting The Benefits of Store ClusteringSimon Smallwood 7 Garrick StDirector Covent Garden LondonEmail – simons@riverheadconsulting.com WC2E 9ARTel - +44 7786 387793 T - +44 (0)203 051 1375 www.riverheadconsulting.com

Page: 2Not so long ago....... GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 3Where everyone knew your name...... GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 4But times they were a changing..... GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 5And the only constant is change..... Pick n Pay V & A Wharf Cape Town SA GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 6Mass Merchandise, Mass Market, Mass Range, Mass Inventory... GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 7So 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

Page: 8What is the real cost to retailers and do customers really benefit? 100 80 Sales Value 20 100 Inventory Value GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 9Studies have shown that the annualadditional cost of holding excess The Diamond of Doominventory 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 10Traditional 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 11New retail models combine service & value to achieve high loyalty & profits High Range & Value SupaValu USA – La Jolla CA Low High Customer Engagement GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 12Combining 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. GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

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

Page: 14The Dynamics of Store Clustering GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 15The 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 customerCritical success factor – Simplicity. The entire company should be able to understand the satisfaction, compliance andclusters and describe the people and the stores that each cluster most strongly represents improved supply chain efficiency GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 16External 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) GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 17There are several approaches to store clustering used byretailers... GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 18Size, 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 EP = Premium Brands S = Standard BrandsS+ = 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) GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 19Asda 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 20Asda Wal*Mart Spectra Advantage SystemStore Clusters defined by opportunity – higher priced wines GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 21Strategic 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

Page: 22Strategic Customer SegmentationTesco 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) GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 23Case StudyGS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 24Case 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

Page: 25Concept & 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 26Diverse people, lifestyles & culture -how do you profile & group them? GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 27Shopper 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 28Analysis 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 29Analysis 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 30A 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 31A 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 32Cluster 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 childrens 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 – 4Customer • 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 33Cluster 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

Page: 34Customer 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 35Store assortment by category can be precisely targeted to customer profileFor 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 GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Page: 36The 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

Page: 37Impact 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

Page: 38‘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

Page: 39Effort, this is. But worth it, effort is. Interesting this may become. GS1 Baltics Retail Forum 5th November 2008 © Riverhead Consulting Ltd– 2008

Consulting The Benefits of Store ClusteringSimon Smallwood 7 Garrick StDirector Covent Garden LondonEmail – simons@riverheadconsulting.com WC2E 9ARTel - +44 7786 387793 T - +44 (0)203 051 1375 www.riverheadconsulting.com

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14 the benefits_of_store_clustering

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