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Retail Banking Analytics_Marketelligent


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Retail Banking Analytics_Marketelligent

Retail Banking Analytics_Marketelligent

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  • 1. Application of Decision Sciences to Solve Business Problems Retail Banking Industry
  • 2. Analytics for Retail Banks
  • 3. A Strong P&L Discipline to all Analytics New Accounts Acquired Accounts Closed Account Activation rate Payment Rate Total Ending Receivables Interest Cost of Funds Net Interest Margin Risk-based Fees Interchange Affinity Rebates Cross-Sell Annual Fees Net Credit Losses Net Credit Margin Operating Expenses Loan Loss reserve Net Income REVENUESEXPENSES Bank P&L Acquire New Customers - Segments X Products X Channel - Mailbase Expansion - Pricing Reduce Customer Attrition - Voluntary / Involuntary - Retention Strategies - Winback Increasing Activation Rates - Deepening Engagement - Inactive Customer Treatment Improve Profitability of Assets - Balance Transfer - Credit Line Strategies - Pricing Maximizing Interest Revenue - Product Pricing - Customer Behavior – Revolvers, transactors, etc Maximizing Fee Revenue - Over Credit limit - Delinquency - Bad Check Reduce Net Credit Losses - Credit Line strategies - Pricing strategies - Collections Increasing Cross-sell Revenues - Revenue Enhancing Products - Breadth of relationships Marketelligent brings a top-down approach to all Analytics. Our analytics expertise impacts all line items of a Business P&L
  • 4. Strategic Reporting Strategic MIS & Reporting Having a multi-dimensional view of critical business metrics available in real-time is key to effective management of a Business. Marketelligent helps banks in putting together Strategic MIS across Product and Functions without having to invest heavily in bespoke IT systems and BI packages. Our capabilities include ETL, development of analytical databases, identification of key metrics and views, SAS-based data manipulation and report delivery on visually-rich platforms. Sample Reports include:  Acquisitions and profile of New Customers  Portfolio & Vintage performance  Credit lines and Utilization  Delinquency, Risk, Loss and Collections  Product P&L’s and trends across time
  • 5. Marketing Analytics Marketing Analytics Marketing Analytics covers all functions of a Bank that help the business get a better understanding of its Customers thereby leading to a deeper Customer Engagement and enhanced revenues. Marketelligent brings unique expertise in key functions across the Customer Lifecycle:  Customer Segmentation  Customer Lifetime Value (CLV)  Increasing Usage and building profitable balances  Campaign design, management and tracking  Measurement of marketing effectiveness  Cross-sell and up-sell  Customer Retention and win-back  Marketing Response Scorecard development, validation and maintenance Acquisition Usage & Loyalty Value Time Retention Activation
  • 6. Marketing Analytics Customer Segmentation In today’s competitive business scenario with customers having a multitude of options, their preferences and buying patterns have been constantly evolving. For retaining the profitable and loyal customers, it is therefore necessary to keep track of changing customer trends and accordingly tailor the offerings. Segmentation is the practice of identifying homogenous groups of customers based on their needs, attitudes, interests and purchase behavior. It enables identifying profitable customer segments and customizing product and service offerings and marketing campaigns to target them effectively. It is typically done using a combination of transaction data, demographic data and psychographic information. It aids in answering critical business questions like:  Which are the most profitable and loyal customer segments and how do we have tailored offerings for these segments?  How do we have special promotion campaigns, specifically to reach the high value customer segments?  What are the revenue and profit contributions by different customer segments? Platinum: Current investment > 50K Gold: Current investment > 5K; < 50K Silver: Current investment < 5K Tenure<12mo All Customers 1,889 1,637 MM EAD 87k AED/Customer New Customers 4,568 (24%) 433 MM EAD (27%) 95k AED/Customer Existing Customers 11,573 (76%) 1,203 MM EAD (73%) 84k AED/Customer Savers 2,944 (16%) 39 MM EAD (2%) 13k AED/Customer Investors 7,316 (38%) 812 MM EAD (50%) 111k AED/Customer Redeemers 871 (5%) 60 MM EAD (4%) 69k AED/Customer Revolvers 3,190 (17%) 292 MM EAD (18%) 92k AED/Customer Platinum Gold Silver Platinum Gold Silver Platinum Gold Silver Platinum Gold Silver Platinum Gold Silver
  • 7. Marketing Analytics Customer Lifetime Value Customer Lifetime Value (CLV) represents how much a customer is worth in monetary terms and is based on customer’s expected retention and spending rate. It can be defined as the present value of the total profit expected from the customers during the entire period they do business with the company. CLV analysis uses customers’ past transaction data and employs predictive modelling techniques to forecast how much each customer would contribute to the company’s revenues and profits till they remain with the company and do not attrite. The analysis can also be extended to estimate the lifetime values of new customers. CLV analysis takes into account estimated annual profits from customers, duration of business relation of the customer, and the discount rate to assess the net present value of the customers. CLV analysis is used for:  Forecasting the expected revenue from new customers and weighing it against the acquisition and retention cost for them  Deciding how much to spend on marketing programs for different customers  Identifying the high value customer segments that can contribute the maximum to company’s revenue and have special offers for them  Identify the prospects who can become profitable for the company Monthly Expenses Monthly Net Revenues Customer Tenure Net Margin Accumulated Margin Acquisition Costs Customer Lifetime Value Predict monthly Revenues Predict Customer Attrition Predict Response RatesFrom existing P&L’s
  • 8. Marketing Analytics Profitability & Loyalty analysis For the sustainable growth of any enterprise, it is very important to identify the most profitable and loyal customers. Having special schemes for these customers in form of offers and discounts, can help in realizing the long term goals of increasing profits and expanding customer base. Organizations use customer profitability and loyalty analysis to identify the most valuable customer segments to prioritize marketing, sales and service investments. Transactional behaviour is analysed for creating a Customer Value Score (C-score) for each customer, which explains their engagement levels. The C-Score can be leveraged for proactive action to defend, retain and grow the customer base. This can help answer key business questions like:  Which are the most profitable and loyal customer segments and how much they contribute to the firm’s profit?  Which are the customer segments to be targeted for marketing programs and special offers?  Which are the customer segments that can have a negative impact on the company’s profitability? 0% 10% 20% 30% 50% ProfitContribution 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Watch Out: Customers accounting for losses. Focus: Customers who have growth potential Sustain: Highly profitable customers Invest & Sustain: Profitable customers %Customers Invest : Highly profitable customers 40%
  • 9. Cross Selling & Upselling Strategies It is not just enough to retain the profitable and loyal customers, but it has become a necessity to increase the revenue contribution from the existing customer base. Cross Sell involves the sale of additional items in order to increase the wallet share from the customers. Market basket analysis is the technique used to evaluate customers’ purchasing behaviour and to identify the different items bought together in the same shopping session. It uses transactional data and employs predictive modelling techniques to identify customers’ preferences based on the associations between the products recently purchased. It helps to determine which products are to be offered and which are the customer segments most likely to be receptive to these cross selling propositions. It aids in strengthening relations with customers by:  Customizing layouts, product assortments and pricing so that it appeals to the customers  Designing effective affinity promotions • Stimulating trials and increase customer awareness during launch of new products and variants • Handling excess stock by designing offers among associated products Marketing Analytics CONFIDENCE Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product8 Product 1 100% 25% 9% 6% 18% 2% 28% 31% Product 2 42% 100% 7% 8% 22% 6% 29% 22% Product 3 31% 16% 100% 5% 10% 4% 18% 17% Product 4 35% 29% 8% 100% 28% 7% 26% 12% Product 5 47% 35% 8% 12% 100% 3% 37% 24% Product 6 37% 66% 18% 19% 21% 100% 25% 21% Product 7 45% 28% 8% 7% 23% 2% 100% 25% Product 8 57% 24% 9% 3% 17% 2% 29% 100% Probability that Product 8 is purchased given that Product 1 is bought is 31% Probability that Product 1 is purchased given that Product 8 is bought is 57% Increasing sales by creating cross-selling opportunities using MBA
  • 10. Marketing Analytics Customer Retention To retain customers, it is very essential to keep tracking customers’ activity regularly — their frequency of shopping, evolution of their shopping patterns, how often do they shop and so on. Customers attrite on a definite path to inactivity which can be identified and therefore managed. Also, acquiring new customers has become far more expensive than retaining existing ones and hence customer retention has become a major corporate priority. By employing attrition analysis, customers whose engagement levels have lowered and who are likely to attrite can be identified and appropriate retention strategies can be formulated. Churn analysis helps answer key business questions like:  Which are the customer segments, to be targeted for retention programs?  How do we identify the factors which are most likely to drive customers to remain: • Creating segments based on preferences and buying patterns so that right offers can be made to the right people • Understand the variables that make high-value customers most likely to purchase and offer incentives and personalized service AttritionRate% 0% 10% 20% 30% 40% 50% 60% 70% - 1 2 3 4 5 0 - 0.5 year 0.5 - 1 year 1 - 3 years 3 - 5 years > 5 years #Customers(MM)
  • 11. Risk & Fraud Analytics Risk & Fraud Analytics Risk Analytics covers all areas of the business that directly impact loan losses and charge-off’s. Marketelligent brings deep expertise in designing and implementing profit-based strategies that enable a business to limit its losses; at the same time not compromising on revenue opportunities.  Acquisition Credit Policy  Pricing and Credit Line Management  Collections and Recoveries  Fraud detection and management  Scorecard development, validation and maintenance across functions : approval, delinquency, collections, etc Behavioral Models Revenue and Cost Drivers Optimal Line Determination Optimal Line Drivers Balance Model Revolve Model Risk Model CMV LOCOptimalLOC Revenue LOC Cost LOC Predicted V/s Actual Inactivity 0.00 50.00 100.00 150.00 200.00 250.00 300.00 0 50 100 150 200 250 300 350 400 Predicted Inactivity Ideal Actual Predicted Illustrative process for assigning Optimal Line of Credit (LOC) Other Models
  • 12. Business Situation : The insurance provider generated leads through cross-selling. Potential customers were targeted in a 4-stage process, and they generally displayed 4 possible outcomes: Resources were being wasted on pursuing unlikely Prospects classified in red boxes above. The insurance provider wanted to determine which members were more likely to complete at each stage , and then fast track the application through the approval process. By reducing the proportion of declined and incomplete applications, operating costs could be optimized. The Task : - Develop a framework of predictive models to calculate the probability of a prospect purchasing the insurance product - Get a more targeted base of Prospects, and hence reduce costs by removing prospects with least probability of buying the Product Analytical Framework : - Historical data , which contained information from both, internal and external sources, was analyzed - Logistic models were built for identifying separate probabilities for each stage of the approval process - Testing was done if Oversampling or Undersampling would improve the performance of the predictive models - All the models were then combined to identify the ‘best’ leads - The models were validated and implemented as SAS Macros to enable real-time scoring The Result : • The framework of 3 models provided useful insights on probabilities associated with approvals and important factors affecting it • Up to 25% of total applications were removed with a loss of just 5% of Paid customers • With costs going down by 25%, we were able to achieve an increment 14% net profits. Client : An Insurance Provider in the US Targeted Prospects Approved Completed Forms Paid Premium Didn’t PayDid notDeclined Target : 100 Approved : 85 Completed: 68 Paid : 58 Predictive Models Target : 75 Approved : 70 Completed: 62 Paid : 55 Analytics in Action Targeted Prospecting. Increasing Profits by 14%.
  • 13. Business Situation: The client, a South-east Asia based Retail Bank encountered a significant increase in customer churn on their Card Portfolio despite having a tried and tested loyalty program in place. This resulted in a 4.6 % drop in Balances during Q2 2012. The business wanted to monitor and control Customer churn at regular intervals. The Task: To develop and implement a program that monitors Customer engagement levels and attrition risk, measure business impact from Customer churn, and develop actionable strategies to manage Customer Attrition. Analytical Framework: High value customers that left the business impacted Balances significantly. Segments were developed to slot each high-value Customer on the basis of recent purchase patterns. Movement of Customers across segments and over time was used to identify the level of ‘engagement’ the Customer had with the Business. Segment-specific offers and campaigns were implemented to manage customer attrition. Results from the campaigns were used to continuously refine targeting and messaging. The Result: • Based on the analysis, the Business was able to identify high value Customers at risk of attrition. Suitable Retention programs were designed and implemented for these Customers. • Business was able to more efficiently utilize its Retention budget as targeted customers consisted of only 15% of the overall customer base • Balances in Q4 2012 were up by an average of 2.1 % as compared to the previous two quarters. Analytics in Action Proactively Retaining your most Valuable Customers Client: A Leading South-east Asian Bank 10% 39%21% 32% 39% 24%30% 6% # of customers Revenue contribution 0% 25% 50% 75% 100% High Medium-High Medium-Low Low
  • 14. Business Situation : The Client - a provider of short-term payday loans - was experiencing high default rates in its loan portfolio. Payday loans are instant small value, short term loans. These loans are received by the borrower and are to be returned back on their next payday along with requisite fees to the lender. Default rates were highly seasonal and geography specific. The Task : - Develop and implement a ‘First Payday Default (FPD)’ Risk Model. The model will be used at the point of Acquisition to screen out Prospects with a high probability of first payment default. - Refine Acquisition Business strategy across key functions – pricing, loan amount and loan term. Analytical Framework : A 3-step analytical process was used: 1. Segmentation of Customer Base: Segmented Customers based on loan performance, demographics and credit bureau profile. Identified and tagged High/Medium/Low FPD Customer Segments 2. Building a Default Scorecard: Identified Customers that have defaulted by Segment. Built predictive models for each segment. Model identified Customers most at risk of defaulting in the first payment cycle 3. Building a holistic Strategy for execution: Created a strategy grid for executing business strategies. Recommend appropriate treatments by Strategy grid Segmentation Predictive Modeling Business Strategy The Result : The New FPD model was implemented in a robust test-control mode and results tracked: • Geographic location of the Prospect was identified as a significant segmentation variable. 6 geographic segments were created and a FPD model built for each segment. • Seven credit bureau variables were identified as significant in identifying potential high-risk Prospects. Of these, ‘# inquiries in past 30 days’ and “# loans given in past 12 months’ were identified as most significant. • Once implemented, the new predictive FPD models helped lower portfolio loan losses by 19%. Analytics in Action Lowering Portfolio Defaults by 19% Client : A Consumer Finance Company making short-term Unsecured Loans 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Random New Model %DefaultersCaptured % Customers Approval Model Conversion Model FPD Model 5+ cycle Risk Model 180 day Revenues Reactivation Model Acquisition Risk Revenue • Individual Scores • Strategy Matrix • Joint Scores Old Model 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
  • 15. Business Situation: Being a service provider of online remittances, the client faced significant problems with fraud and money laundering. A lot of time was being invested in manual review of all transactions, which in turn delayed the overall transaction processing time, and also impacted customer satisfaction. The Task: To develop a process where only high risk transactions are sent for manual review, and rest are approved automatically. The Analysis: • Transaction history, compliance, service data along with external data sources like SSN responses and AMLOCK (Anti- Money Laundering Database) data for high risk Customers was leveraged • Critical variables defined that would serve as inputs to the Risk Score were divided into 5 broad categories - Geographic, Historic, Identity, Transaction and Demographic - to make it easier to understand the reason for high risk during manual review process • Along with these variables some rules were also created based on compliance policy and Government regulations, which when triggered automatically moved transaction to manual review irrespective of the risk score • Multiple linear regression was performed to arrive at a transaction-level Risk score, and 4 Risk classes were correspondingly defined (<25: Low, 25-50: Moderate, 50-75: High, 75-100: Extremely High) The Implementation: • A schedule of data extraction was setup to generate the Risk score before the KYC forms were prepared. • The process was streamlined to ensure that all KYC forms carried a Risk score • The Risk score and Risk class were populated on the KYC sheets • Compliance committee continues to give it’s opinion on each KYC form (Low Risk to Extremely High Risk) and the predictive model continues to be bootstrapped The Result: • The new Process reduced number of transactions reviewed manually from 700-800/day to 200/day, and also reduced the average turnaround time for transactions from from 3.0 to 1.5 days. • The process also helped the compliance team to identify which documents/clarifications to get from customer to process the transaction. Analytics in Action Transaction-level Risk Assessment Client : A Leading Service Provider of Online Remittances
  • 16. Business Situation : The Credit Card issuer was facing significant losses due to fraud in spite of having a real-time transaction scoring application. It flagged suspicious transactions and declined them with a large false positive ratio, leading to bad customer experience and attrition. There were also gaps in the process of identifying fraud with good accuracy due to constraints within the Fraud Operations group. The Task : - Develop optimized authorization rules that efficiently capture fraud with minimum impact on genuine transactions. - Revisit the transaction scoring mechanism and suggest a methodology that is more customized for certain type of transactions. Retire non- performing authorization rules. - Develop new strategies/rules to better identify fraudulent transactions. - Measure ‘agent performance’ in Fraud Operations Queues and suggest areas of improvement to Fraud Operations. Analytical Framework : A 5-step analytical process was used: 1. Historical card transaction logs, data on confirmed fraud cases for the past two years, Credit Bureau data and Card Association notifications were leveraged for the analysis. 2. For Customer attrition analysis, accounts with a minimum 12 months-on-book at the time they were wrongly queued/declined were used, their attrition rates, were then measured using a test-control approach. 3. Apart from an existing real-time fraud scoring engine, further analysis was conducted to identify domestic and international fraud hotspots. 4. Detailed segmentation was carried out on recent transactions to segregate fraud population with least impact on non-fraud population – this led to new authorization rules. 5. Developed automated MIS reporting measuring existing rule performance, agent performance in Operation queues and a framework to retire non-performing rules. The Result : • Fraud Detection rates improves by 70 basis points Year-on-year after implementing the new scoring layer incorporating localised scoring. • Fraud Operations agents false positives and false negative ratios (identifying true fraud and non-fraud respectively) improved significantly within 2 months of implementing Decision Quality framework. Missed dollar opportunity due to not identifying true fraud reduced by 15% Year-on-year. Analytics in Action Combating Credit Card Transaction Fraud Client : A Credit Card Issuer facing high Transaction Fraud
  • 17. MANAGEMENT TEAM GLOBAL EXPERIENCE. PROVEN RESULTS. Roy K. Cherian CEO Roy has over 20 years of rich experience in marketing, advertising and media in organizations like Nestle India, United Breweries, FCB and Feedback Ventures. He holds an MBA from IIM Ahmedabad. Anunay Gupta, PhD COO & Head of Analytics Anunay has over 15 years of experience, with a significant portion focused on Analytics in Consumer Finance. In his last assignment at Citigroup, he was responsible for all Decision Management functions for the US Cards portfolio of Citigroup, covering approx $150B in assets. Anunay holds an MBA in Finance from NYU Stern School of Business. Greg Ferdinand EVP, Business Development Greg has over 20 years of experience in global marketing, strategic planning, business development and analytics at Dell, Capital One and AT&T. He has successfully developed and embedded analytic-driven programs into a variety of go-to-market, customer and operational functions. Greg holds an MBA from NYU Stern School of Business Kakul Paul Business Head, CPG & Retail Kakul has over 8 years of experience within the CPG industry. She was previously part of the Analytics practice as WNS, leading analytic initiatives for top Fortune 50 clients globally. She has extensive experience in what drives Consumer purchase behavior, market mix modeling, pricing & promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad. ADVANCED ANALYTICAL SOLUTIONS MARKETELLIGENT, INC. 80 Broad Street, 5th Floor, New York, NY 10004 1.212.837.7827 (o) 1.208.439.5551 (fax) CONTACT Industry Business Focus Tools and Techniques Consumer Finance Investment Optimization SAS, SPSS, R, VBA Credit Cards Revenue Maximization Cluster analysis Loans and Mortgages Cost and Process Efficiencies Factor analysis Retail Banking & Insurance Forecasting Structural Equation Modeling Wealth Management Predictive Modeling Conjoint analysis Consumer Goods and Retail Risk Management Perceptual maps CPG & Retail Pricing Optimization Neural Networks Consumer Durables Customer Segmentation Chaid / CART Manufacturing and Supply Chain Drivers Analysis Genetic Algorithms High Tech OEM’s Supply Chain Management Support Vector Machines Automotive Sentiment Analysis Logistics & Distribution YOUR PARTNER FOR DATA ANALYTICS SERVICES