Market Prioritisation & Distribution Footprint - Identifying Right Markets To Target At Actionable Levels Quintes Analytics

Copyright © 2010 Page 2 Business Questions Targeting Right and Planning Distribution Which geographic pockets in the state offer greater potential? How can we prioritise markets at actionable levels in a diverse and large country? Which are the key towns and villages to target to tap the potential of the region/state? How can we reach the potential markets efficiently? Can we identify ‘Anchor’ towns and villages for appointing/engaging with distributors and wholesalers?

Copyright © 2010 Page 3 Business Questions Improving Efficiency Of Current Distribution How can we optimise the current distribution? Can we identify areas for: Expansion Increasing focus Direct and/or wholesale activities in-market/store level initiatives Minimising wastage

Copyright © 2010 Page 4 Market Prioritisation – The Quintes Approach Our step-wise approach in aiding marketing and sales efforts Assessing Market Attractiveness Linking this with our Ability to Exploit Markets Identifying Markets for further Investment Targeting inhabited units (towns/villages) that give us the most optimum reach

Copyright © 2010 Page 5 Market Prioritisation – The Quintes Approach The strategic choice of markets and segments where the firm will compete – Marketing in the New Millennium (Doyle,1995) Marketers find it difficult to make segmentation operationally or strategically relevant (Brown et al, 1989) Chandler & Hanks’ (1994) survey - Connection between perceived market attractiveness, available resources & organisational success

Copyright © 2010 Page 6 Market Prioritisation Framework - Stage1 - For actionable segmentation and targeting of markets Economic Factors Population, Infrastructure Banking, Health, Education Macro-economic measures Category/ Consumer Factors Consumer Expenditure Product Penetration Asset Ownership Media penetration Weather Size Growth Power Company Ability Size of GDP Category Size Number of Retail Stores GDP Growth (Long & Short-term) GDP Growth Consistency Category Growth (Short-term) Per Capita Income Per Capita Consumer Expenditure Asset Ownership Per Capita Category Value Company Distribution Cost of Reach (Stores/ sq km) Company Performance Data Sources Market Attractiveness Key Variables

Copyright © 2010 Page 7 Ability Target districts based on Stage1 results Market Attractiveness Identification of Geographical clusters Sub-district or tehsil (administrative) Anchor-towns (target agglomerations that cater to both urban and rural areas. Can be used to set-up distributors/ hubs and to plan localised marketing initiatives) Identification of Final Units List of towns/ villages that should be targeted as last-mile. Can be used for developing spokes /micro-distributors. Linked directly to anchor-towns Market Prioritisation Framework - Stage2 - Planning distribution footprint to tap the identified potential Level 1 Level 2 Level 3 Prioritisation done at all three levels With a measure of GDP, Cat Size and Stores*

Copyright © 2010 Page 8 Market Prioritisation – Benefits of this approach Considers both Size and Growth for first stage of market prioritisation Customised to categories of interest Leverages data from multiple sources Results can be customised to client’s sales area territories Actionability for Sales/ Activation teams Clear identification of towns/ villages for action in selected markets/ areas Factors contiguity through the concept of anchor towns Can be directly fed into planning of coverage, distribution points and routes

Copyright © 2010 Page 9 Market Prioritisation – Benefits of this approach Focus on Potential and Efficiency Identifies optimal number of areas/ towns or villages to reach, so as to achieve a pre-specified target potential (E.g. 80% GDP, 80% Category etc) Flexibility of integrating additional internal data Distributor locations, Hubs & Spokes, Routes etc Consumer Preference results from other studies

Copyright © 2010 Page 10 Market Prioritization Output

Copyright © 2010 Page 11 Linking Market Attractiveness to Firm’s Ability to Exploit Markets Firm’s Ability Market Attractiveness A B C Excellent Average Low Invest Invest Invest Maintain Maintain Maintain Evaluate Evaluate Evaluate Illustration Only

Copyright © 2010 Page 12 Size Dimension - Methodology Details – Scoring districts on size Factors considered GDP Size Category Size Category Stores Each factor estimated using a group of variables * Group of Variables Overall Score on Size Estimated factors weighted to arrive at an overall score on size Size Segment (A, B &C) Ranked on size and grouped into Top 20%, Next 30% & Bottom 50%

Copyright © 2010 Page 13 Size Dimension - Methodology Details - An example of GDP estimation (breaking down GDP to lower levels) Scheduled commercial banks Agricultural Banks Bank deposits Credit Primary schools Middle/ secondary schools Senior Secondary schools Colleges, Adult literacy centers Health Centers Dispensary Nursing Homes, Hospitals Family welfare, TB Clinics Bicycle Telephone, Television Two and Four wheelers Banking Services Shops & Establishments Hotels & Lodges Factories Consumption Expenditure Banking (G1) Education (G2) Health (G3) Amenities & Assets (G4) Business Activity (G5) Consumer Expenditure (G6) GDP = fn (Banking, Education, Health, HH Assets, Business Activity, Consumer Expenditure) ∑ Gi Wi I =1 I = 6 The multiple models are then weighted based on accuracy/ degree of determination Models, Variables Coefficients Illustration

Copyright © 2010 Page 14 Growth Dimension - Methodology Details – Scoring districts on growth Factors considered GDP Growth - Short-term (3 years) GDP Growth - Long-term (8 years) GDP Growth Consistency Category Growth Each factor estimated using a group of variables * Group of Variables Overall Score on Growth Growth Segment (A, B &C) Ranked on growth and grouped into 3 (Top20%, Next 30%, Bottom 50%)

Copyright © 2010 Page 15 Power Dimension - Methodology Details – Scoring districts on power Variables considered Per Capita Income Per Capita Consumer Expenditure Per Capita Category Consumption Asset Ownership Other Category specific variables like per capita segment (premium etc) would be considered later if available Finally districts are ranked and grouped into three A, B & C

Validation with Planning Commission Reports

Copyright © 2010 Page 17 Validation with Planning Commission Reports In order to improve the robustness of the GDP estimates (size and growth), various checks and comparisons are being done The Planning Commission of India compiles Gross District Domestic Product (GDDP) information from several states This information is prepared by the Directorate of Economics and Statistics/ other statistical bodies within the respective states using a standard method Figures not available for the recent periods, but are available for multiple years Data are not available for all states Some of the factors used for computation are derived by breaking down the state level figures using surrogates/other variables Comparison of GDP estimates with GDDP figures available with the Planning Commission of India is one of the checks carried out

Copyright © 2010 Page 18 Across years, a high degree of correlation has been observed – indicating representativeness and usability Across periods, the correlation is high Around 0.9 at district level (All India; For available districts) Between .80 and 1.0 at a state*district level for most states for which figures are available Consistency in the correlation across years and at lower geographic levels GDP estimates are fairly robust and provide a good base for Classification/Grouping of Markets by Size and Growth Correlation reflective of range observed for multiple years Based on available data. (recent figures not available yet)

Market Size Assessing Market Attractiveness

Copyright © 2010 Page 20 Market Size – Districts of India The Top 20% of the districts in the country account for 56% of the GDP and almost 3 times the average district GDP in the country *A: Top 20%, B: Next 30%; C: Bottom 50%

Copyright © 2010 Page 21 Market Size – A Class Districts of India

Copyright © 2010 Page 22 Market Size - Top 10 districts of India

Market Growth Assessing Market Attractiveness

Copyright © 2010 Page 24 Market Growth - Districts of India The Top 20% districts are growing at double the rate of the Bottom 50% of the districts in the country – high variation in growth rate and growth consistency *A: Top 20%, B: Next 30%; C: Bottom 50%

Copyright © 2010 Page 25 Market Growth – A Class Districts of India Gujarat registers a high and consistent growth. Pockets of TN, Maharashtra & even slightly less developed states like Bihar & Orissa are growing at a good pace

Market Power (Premiumness) Assessing Market Attractiveness

Copyright © 2010 Page 27 Premium Markets - Districts of India *A: Top 20%, B: Next 30%; C: Bottom 50%

Copyright © 2010 Page 28 Premium Markets – Top 10 Premium A districts

Market Size, Growth & Premiumness Assessing Market Attractiveness

Copyright © 2010 Page 30 Before we begin All districts have been classified on Size, Growth and Power A district will be either A, B or C based on Size Similarly it will be A, B or C based on Growth and A, B or C based on Power In total there are 27 cells Each district will fall in one of these cells

Copyright © 2010 Page 31 Market Attractiveness Grid GDP of ‘A++’ group districts is almost 10 times the ‘C++’ group GDP. Groups like the AAA and BAA are growing at a significantly higher pace and need to be focused on. High variation in size, growth & power – indicates the need for prioritisation.

Copyright © 2010 Page 32 Market Attractiveness Grid – AAA districts in India

Copyright © 2010 Page 33 Market Attractiveness Grid – AAA districts in India

Copyright © 2010 Page 34 Planning Distribution to Tap The Identified Potential - Approach allows prioritisation at multiple levels Sub-district level Pin-code groups Town/Village Clusters (around anchor towns) Market Prioritisation & Distribution Footprint Results Individual Towns/Villages (prioritised towns/villages)

Market Prioritization Stage2 Tapping The Potential Through Effective Distribution

Copyright © 2010 Page 36 Ability Target districts based on Stage1 results Market Attractiveness Identification of Geographical clusters Sub-district or tehsil (administrative) Anchor-towns (target agglomerations that cater to both urban and rural. Can be used to set-up distributors/ hubs) Identification of Final Units List of towns/ villages for developing spokes /micro-distributors. Linked directly to agglomerations Market Prioritisation Framework - Stage2 - Planning distribution footprint to tap the identified potential Level 1 Level 2 Level 3 Prioritisation done at all three levels With a measure of market size and retail stores

Copyright © 2010 Page 37 What is an Anchor Town/ Agglomeration? It is a geographical cluster that includes the core-town as well as the neighboring towns/ villages. These are typically areas with adequate infrastructure and facility support (e.g. banks, schools, health centre, market etc) People from neighbouring areas visit frequently to use services May not necessarily have a high resident population Have a higher retail development (within/around) Can be used as a geographical cluster Through which nearby towns and villages can be serviced’ E.g. Satara can be an anchor for 102 villages Tarale (3 km) Parali (15 km) Nagthane (18 km) Kinhai (27 km) Pusegaon (36 km) Nimsod (55 km) SATARA example

Copyright © 2010 Page 38 Maharashtra – one of the top states as an example Some basic facts 35 districts, 353 tehsils/subdistricts, 378 towns, 41095 villages; 307000+ sq.km. 5 SCR’s – Konkan, Vidharba, Marathwada, Khandesh, Desh (Western Ghat) 8200+ banks, 83000+ schools, 2200+ health centres HH Penetration: TV 45%, Four wheeler 5% Nagpur Amravati Nashik Konkan Pune Aurangabad Illustration Only

Copyright © 2010 Page 39 First Stage Prioritisation Segments - Key Districts - Nashik, Kolhapur, Ahmadnagar and Aurangabad good candidates for second stage action These 4 districts contribute to 12% of total and 21.5% of Rural Maharashtra. Improvement of distribution efforts here will benefit overall state Illustration

Copyright © 2010 Page 40 Stage2 Example - Ahmadnagar district - A total of 18 towns and 1578 villages. It is critical to select the right towns and villages for planning distributors, coverage and routes Number of towns/ villages (1596) FMCG Market (%) Illustration

Copyright © 2010 Page 41 Stage2 - Summary of Distribution Anchors Stage2 Example - Ahmadnagar district - 89% of FMCG market can be served through distribution points in 12 agglomerations (out of which 3 are shared with bordering districts) Direct Distribution Primarily Wholesale Anchor towns ** Based on a national classification of agglomerations (into A, B, C&D) This needs to be kept in mind while prioritizing resources at regional/ national levels In Ahmadnagar, there is no agglomeration which is A class. Three are B class and the rest are C

Copyright © 2010 Page 42 ** Based on a national classification of units (towns/ villages) into A, B, C&D This needs to be kept in mind while prioritizing resources at regional/ national levels Stage2 Example - Ahmadnagar district - All villages under each ‘anchor town’ are also prioritized. A list of villages is generated along with the distance from the agglomeration. This can be used for coverage and route planning Illustration Stage2 – List of individual units (towns/ villages)

Copyright © 2010 Page 43 Stage2 Example - Sangli district - Anchor and Focus points for Direct Distribution in Sangli District Takari Kasegaon Burli Kurlap Shirala Kokarud Kavathe Ek Bhilwadi Waifale Palus Kharsundi Atpadi Lengre Khanapur Kadegaon Kavathe Mahakal` Malgaon Miraj* Dafalapur Arag Jat Sankh Nevri Shalgaon Arala Umadi Mangale Bagani Gothkindi Bavchi

Copyright © 2010 Page 44 Pin-Code Grouping (State Level) - An approach to move from a vast number of low yield points to manageable presence Large number of small units Fewer units with higher avg. popn. Prioritising pincode regions to service and selecting key villages as nodal points/for presence would help improve efficiency *:Per pincode for pincodes and per villages for ‘all villages’ Illustration Only

Copyright © 2010 Page 45 Pin-Code Grouping High potential pin-code regions could be targeted and serviced through anchor towns and select key villages in the pincode areas Illustration Only

Copyright © 2010 Page 46 Integrating with internal distribution Step 1 Integrate internal distribution data (direct coverage, wholesale) with above results Arrive at distribution footprint (ideal vs. current) Step 2 Carve and quantify expansion/ wastage minimisation opportunities

Thank You

Copyright © 2010 Page 48 Topics Current Presentation Market Prioritisation and Distribution Footprint Some of our other services Internal Sales Data Analytics Market share drivers - Integrated learning Identifying future category/segment opportunities Short-term forecasting for budget planning MMM and other analytics solutions