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Research in the age of Pragmatism

Research in the age of Pragmatism. XXII Annual . Seminar. Reinterpret Redesign Reinvent. Open New Doors… . XXII Annual . Seminar. Making Consumer Segmentation a Real Time Process. Anjan Kumar Ghosh , Senior Vice President Bhavesh Mansinghani , Research Director Hansa Research.

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Research in the age of Pragmatism

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  1. Research in the age of Pragmatism XXIIAnnual Seminar Reinterpret Redesign Reinvent

  2. Open New Doors… XXIIAnnual Seminar Making Consumer Segmentation a Real Time Process Anjan Kumar Ghosh, Senior Vice President BhaveshMansinghani, Research Director Hansa Research

  3. Background • Our client is a leading player in the financial services space offering consumer finance and financing capital requirements of SMEs • Client product portfolio: • Consumer durable loan • Personal Loan • Small Business Loan • Loan against property • Current business environment suggests the following facts and hypotheses: • Higher the affluence of the customers, higher is the ticket size for their loan • Servicing cost as a proportion of ticket size is lower for the high ticket loans • Although CIBIL and internal audits ensure that creditworthiness of customers is evaluated properly before loan disbursal, the system currently does not ensure appropriate targeting of the loan to the consumer segment • So the need was to set up a system that facilitated effective targeting of offerings in the market

  4. Key client questions and thought for research ‘Key questions • Are we reaching the affluent audience? • At the time of loan application, is it possible to assess the affluence level of an individual? Thought for Research • Identify indicators to measure the affluence level and create affluence segments of current customers • Basis the affluence segment of the current customer base, identify cross-selling and up-selling opportunities • Identify variables that can be included in the loan application form, to assess the affluence segment for new acquisitions; thus aid in better targeting

  5. Are we reaching the affluent audience? Decoding Affluence Applying the HPI Construct on the existing customer base Affluence profile across business verticals Implications for the marketer

  6. Decoding affluence • MHI – Patently unstable on account of hesitance to disclose the correct MHI leading to over claims or under claims • Problem maybe accentuated amongst SMEs MHI • Handicapped in situations where the target group is intrinsically homogenous • Conventional SEC not strongly correlated with affluence • Possible number of segments limited to currently available options SEC HPI • Scientific method to segregate and target the consumers’ basis a more direct measure of affluence

  7. Household Premiumness Index (HPI) • An established system of classifying consumers, based on indicators from the Indian Readership Survey (IRS) • It covers ownership of durables, consumption of FMCG categories, and demographic measures. • The HPI philosophy : If a home is premium, it necessarily gets reflected in product consumption or ownership. • Defining premiumness : Something that is desired by most but affordable for few. Hence, we define premiumness as inverse of penetration. • Non-judgmental approach • Consistent across all types of products/services • Scores obtained for all indicators are aggregated for a household and indexed to a maximum of 1000. • Provides the opportunity to define as many segments as desired by the marketer

  8. Applying the HPI Construct on the existing customer base • Task involves profiling client customers through variables that determine affluence and go into computing HPI • Census being unrealistic, estimation needs to be done through a sample survey of client’s customers • Sample selection needs to ensure representation of customers for its various product verticals • Within a vertical, the sample of current customers ought to match the universe profile on key variables, in order to have an accurate measure of affluence

  9. Ensuring representation of the sample • The primary research to ascertain HPI was conducted amongst a representative sample that was drawn randomly from the current customer database, after stratifying them on basis of relevant parameters for each business vertical. • The sampling variables used are listed below… • Weighting at the branch level was done in accordance with the universe proportions of client’s customers.

  10. Research Design • Method : Quantitative, face to face interviews using a structured questionnaire. • Target Group : Client customers from the following business segments • Consumer Durables | Personal Loans | Small Business Loans | Loan against Property • Markets : Chosen basis size of existing customer base and share of revenue contribution • Ahmedabad, Bangalore, Chandigarh, Chennai, Cochin, Delhi, Jaipur, Kolkata, Hyderabad, Ludhiana, Lucknow, Mumbai, Nagpur, Pune, Vellore • Sample Size – Total sample of 1352 customers • CD – 593 • PL – 397 • LAP – 129 • SBL - 233

  11. Affluence profile across business verticals • The LAP customer is the most affluent amongst the customer base followed by the SBL, CD and PL All India Median HPI - 212 190 430 80 595

  12. HPI based targeting: identifying markets that need action • Current set of consumers in general are significantly more affluent as compared to average city profile • However, there are certain verticals across markets where Targeting can be made sharper

  13. Opportunity analysisIRS (SEC A,B1) vis-à-vis Client (All Business Segments) • Differential reach of the client’s business in the high HPI segment highlights markets which are untapped and can be explored UNTAPPED MARKETS UNTAPPED MARKET POTENTIAL No. of HHs above the client customers median HPI CHANDIGARH(5%) POTENTIAL MARKET HH – 669000 (100%) DELHI (81%) LUDHIANA (77%) Untapped potential HH – 417000 (62%) Current reach HH – 252000 (38%) JAIPUR (64%) LUCKNOW (42%) AHMEDABAD (57%) NAGPUR (78%) KOLKATA (53%) MUMBAI (49%) HYDERABAD (15%) PUNE (17%) BANGALORE (53%) COCHIN (61%) CHENNAI (85%) No. of HHs above the median HPI where Client’s product has been availed No. of HHs above the median HPI where Client’s product is yet to reach Figures in ( ) indicate mean untapped potential in %

  14. Implications for the marketer • Benchmarking the HPI of client’s customers with market average indicates that client has reached the affluent set • HPI clearly emerges a prospective variable to create an affluence hierarchy • Therefore, a suitable segmentation variable • Re-think required on business strategy for individual verticals to reach out to more affluent customers • Assessing market size (above median HPI) helps to identify markets with huge potential • Indexing city HPI vs. the average and comparing across business verticals provides pointers on markets that need to work on their current acquisition strategy

  15. Classifying customers into manageable segments Need for customer segmentation Use of Latent Class Modeling (LCM) Validating the segment profiles generated through LCM Implications for the marketer

  16. Need for segmentation of the customer base • The first stage provided us HPIs for each customer • Customers could hence be classified on the affluence hierarchy on the basis of the range in which their HPI fell • However, there were some limitations with this classification / segmentation, to meet the business objective : • Segmentation of customers into affluence tiers ought to be completely objective – Judgmentally classification on the basis of HPI range may be biased • Segmentation should be replicable on new set of customers – Here, the computation of HPI was done using a set of 50 variables; this was too large a set of information to capture for assigning a segment to new acquisitions • Hence the need to run a segmentation algorithm and not rely solely on HPI

  17. Using LCM for customer segmentation • We chose Latent Class Modeling (LCM) as a segmentation tool for the following reasons: • LCM is flexible enough to accommodate a variety of variable types, from categorical to continuous • LCM allows statistical testing and retesting of the model fit to assess the quality of a segmentation scheme. This is not possible with factor/cluster approaches. • The algorithm to classify customers is a direct output from the segmentation function in LCM. In cluster/factor approaches it is developed separately using a discriminant solution. The direct approach produces a more accurate categorization scheme because it does not lose any information. • Basis the LCM, a shorter list of 14 variables was identified through an iterative process to ensure adequate discrimination between the segments • These variables can be used in the loan application form, going forward – so that customers can be classified on the affluence hierarchy at the time of acquisition

  18. Cluster size and Median HPI • As we move from C1 to C4, the affluence profile of the customers improve • C4, which is the most affluent group accounts for only 9% of the current customer base

  19. How would the SEC segments be like • HPI segments brings out the hierarchy in a more effective way SEC Segments HPI Segments

  20. Incidence of household durables across segments Household Durables

  21. Incidence of PC/Laptop and printer across segments PC / Laptop and Printer

  22. Incidence of Automobiles across segments Transportation

  23. Exploring variation in ticket size by segments • Ticket size for loan increases as we move from less affluent to more affluent segment, validating our hypothesis • However, consumer durable financing has been affluence independent to some extent Figures above indicate Avg loan amount

  24. Profiling business verticals on the basis of clusters • Although majority of the PL customers belong to C1, there are some customers in C3 and C4 as well – points to cross-selling opportunity amongst C3/C4 for PL • Product verticals like SBL and LAP which deal with higher ticket size show a sizeable chunk in C1/C2 - a need for an effective targeting mechanism

  25. Thus, LCM helps to : Target customers with the correct affluence profile Identify cross-selling / up-selling opportunities on the basis of the cluster to which the customer belongs Also points out business verticals where affluence profile may not be relevant even though the vertical may be a big contributor to overall asset portfolio

  26. Identifying variables for customer targeting Making the segmentation process actionable and real-time The new loan application form

  27. Identifying variables for customer targeting • While including all variables covered under LCM segmentation were desirable, it may not have been feasible for our client • Keeping in mind the above, we re-worked the classification algorithm with lesser number of variables through an iterative process • The final list of variables was chosen to ensure that prediction can be done at 95% accuracy • Solution of 9 key variables was suggested to be included in the loan application form

  28. Application form

  29. What did we achieve? • We could evolve a set of classification variables which are good enough to identify heterogeneity in a largely homogenous audience • Apply an algorithm to categorize customers into a particular segment on a real time basis, at the time of loan application HPI coupled with LCM opens an opportunity for a REAL TIME SEGMENTATION PROCESS

  30. Thank You

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