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Tibil Capabilities

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    Tibil Capabilities Tibil Capabilities Presentation Transcript

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    •                4
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    • Our People: Empirically Driven Consultants Our Portfolio : Full BI & Analytics-chain  Led by experienced technocrats  Extract – Data capture forms, Data marts & Data  Team with deep business experience reconciliation  Sourcing is based on stringent 7i filters  Monitor – Performance monitoring tools,  Continuous learning Dashboards, Data cubes, Basic analytics  Development centers in Bangalore and Hyderabad  Predict – predictive and forecasting models for strategic planning Our Processes : Flexible & Efficient Our Platforms : Technology agnostic  Ability to balance analytical prowess with pragmatic  Simple, Scalable and Robust business application  Capable of handling offline Excel files to complex  Driven by customers’ business objectives databases  Proven record of delivering step-up change in business  Experience of working on varied source systems KPIs and P&L lines  Our platforms integrate seamlessly with existing  Demonstrated ability of high-octane delivery infrastructure6
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    • Your Data feeds Our Engagement Single version of Truth Your P&L impact Consulting Auto Revenue Decision Concurrent BI and Analytics chain Maximization Sciences Auto Analytics Cost BI/Reporting Layered Optimization Data Auto Management Products & Solutions Services Impairment Personalized Manual Control Strategic Outsourcing Advanced Analytics &… with constant focus on delivering a positive P&L impact Strategic Consulting Key tools : SAS, SQL, Knowledge seeker, COGNOS, Informatica • Product/platform Key techniques : Regression. CHAID, Clustering, Neural networks Decision Support selection • Product-market strategy • Customer segmentation Business Analytics • Campaign & loyalty • Portfolio management Data Value management frameworks Partial List • Cross-sell models Monitoring & Reporting • Fraud mangement • Retention strategy and • Revenue & profit models • Collection & recoveries frameworks • BusinessEye decision • Credit risk models incl. optimization • Customer life-time value Data Management support portal Basel II • Growth models with • Financial forecasting • Value at risk (VaR) ROEC / NPV triggers frameworks • Data modeling • CustomerEye analytical • Product-channel mix • Create data stores CRM • Loss forecasting models optimization • Data repair & ETL • Report automation Strategic Impact8
    • Analytical Data Marts Business Forecasting Finance • Portfolio and P&L Business Intelligence Collections Engine forecasting Credit & analytics engine Collections • Prescriptive Dashboard with a 12-16 Decisioning tool with configurable rules week implementation Customer • Pre-packaged metrics & proprietary Customer data models for retail banking Contact • X-sell platform productsManagement • Rule-based • Performance management analytics Campaign Portfolio • Sales management , incentive ProductManagement calculation • Campaign mgmt tool, with Product /Campaign • Basic Segmentation configurable rule profitability tool based Sales & engine on vintage engine Marketing • Light software Data Monitoring & Basic Analytics Decision Advanced Analytics & Management Reporting Support Strategic consulting …. in addition to a host of customized services across the spectrum9
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    • Our Delivery Model allows Clients to Flex both Quality and Cost Levers, while sourcing globally Quality Lever (On-site/In-house)  Requires senior analytics staff/domain experts Quality Lever 6 1  Advanced education required Deploy Define  Ability to interact with stakeholders Results Business Objectives 5 ONSITE Evaluate & Iterate Results OFFSHORE CLIENT 2 Analytical Design & Data Selection 4 Modeling & 3 Analysis Data Preparation Cost Lever Cost Lever (Offshore)  Requires large volume of data work  Repetitive tasks, easily productionalised  Rules based  Time Consuming (60-75% of total time spent)11
    • Our analytical process is driven by customers’ business objectives Service Delivery Model ‘Key Steps at Each Stage 1 Define • Develop clear problem statements and Business performance metrics Quality Lever Objectives • Understand larger business and market context 6 1 Deploy Define 2 • Develop analytical solution and establish key Results Business Analytical hypothesis Objectives Design & Data • Identify data sources and validation sources Selection • Establish availability of key data 5 3 • Clean and merge data using visual and statistical Data methods Evaluate & • Create Meta data ,construct new variables Iterate Results Preparation CLIENT • Perform high level reconciliation of the data 2 Analytical Design & Data 4 • Iterate different modelling alternatives and Selection Modeling & evaluate fir vs. objectives Analysis • Recommend one model with key assumptions 4 Modeling & 3 5 • Pressure test and validate the model Analysis Data Evaluate & • Iterate results to improve accuracy Preparation Iterate Results • Agree new analysis requirements / priorities • Translate model results into tangible business Cost Lever impact 6 • Identify process changes required to implement Deploy • Implement the model/solution ONSITE Results • Monitor for accuracy and performance OFFSHORE12
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    • Analytical Data Marting Business Intelligence Analytics & Predictive Modelling  ✔ ✔  ✔ ✔  ✔ ✔  ✔ ✔  ✔ ✔  ✔ ✔  ✔ ✔  ✔ ✔             14
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    • Presentation Layer Customizable dashboards 1. High degree of user customization Privilege on the Presentation Layer Manager permissible Report Automation Engine Administrator 2. Information privilege mirrors Generates reports basis rules set Control organization structure 3. High level of administrator rights – Analytical Engine on rules, formats and access Define business rules and triggers for monitoring and reporting 4. Post implementation, our involvement needed only if data Benefits sets or rule dimensions need to be Analytical Data Mart altered Consolidating & reconciling data from disparate sources and mapping onto Logical Data Model 5. Cost of scaling up for more data Data Source sources is marginal & proportional Auto Auto Auto Manual16
    • Onsite Offshore Define Business Analytical design & Development & Data preparation Evaluate & Deploy Objectives Data selection Coding …. and governed by 3 pillars of strength Prescriptive requirements Pre-built modules Embed thru Technology • Ability to understand business • Fundamental Sciences - • Tools & Applications to hardwire requirement and context, quickly Statistics, Econometrics, Ops analytics in day-to-day ops • Proactively think through cascading Research • Tools : SAS, SQL, VB, .NET, C++ and x-functional impact • Techniques - Predictive • Dbases: Oracle, MS SQL, MS • Quick solutions to issues Modeling, Forecasting / Access, DB2 Simulation, Optimization • Min time from client on briefing • ETL Tools : SQL server, SAS • Pre built data adapters ETL, Informatica • Pre-configured KPIs , dashboards & reports - rapidly customizable17
    • Business  Solutions developed with business needs as focus Solutions  Addresses functional issues and operational challenges  Pre built data adapters to crunch time and cost Quick Delivery  Pre-packaged metrics & dashboard templates  Well defined Requirements documents  Data format agnostic – works with data dumps from core systems & Simple and other offline sources Scalable  Light and low-cost IT infrastructure  Fully customizable  Senior management has a “dashboard” view Cuts across org.  Functional executives have “drill-down” view structure  Business analysts have a “scratch-pad” view  Automated generation, transmission and distribution One version of  Detailed reconciliation across GL, Risk and business the TRUTH  Well defined sign off processes18
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    • Mortgage Commercial Insurance Credit Cards Retail Banking Banking Banking  Propensity Modeling  Risk scorecards  Risk scorecards  Risk scorecards  Campaign management  Response scorecards  Response scorecards  Response scorecards Acquisition Customer  Marketing campaign  Campaign management  Campaign management  Smart leads to offer new analytics  Cross Sell/ Up Sell  Lifecycle profiling lines of credit  Acquisition Analysis Analytics  Loyalty / customer  Acquisition Analysis lifetime value (CLTV) modeling  Churn prediction  Churn prediction  Customer profitability  Product alignment /  Renewal strategy Customer Retention  Renewal analytics  Credit line management  Loyalty programs design  Retention & Elasticity  delinquency forecasting  Surveys modeling Loss Mitigation  Forecasting claims  Loss Forecasting  Collection analytics  Collection strategy  Payment risk scorecard severity / frequency  Collections analytics  Fraud prediction  Foreclosure prediction  Loss Ratio Analysis  Fraud prediction  Fraud prediction “Speed” underwriting Optimization  Automated underwriting  Authorization analytics  ATM optimization   A/P analytics Process  Sales force analysis  Campaign management  Branch optimization  Sales force analytics  A/R analytics  Approval optimization  Optimizing end customer versus intermediary interest20
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    • http://www.fewgoodpeople.com/demos/tibil_telecom Username: demo Password: demo24
    • 1. Wealth Modeling 2. Customer Lifetime Value Customer Lifetime Value (CLV) is long term and dynamic value that •Identify the customers with the potential to be can help you optimize your decisions for long term profitability upwardly mobile (to migrate) through this segment scheme to help drive product development and Average Profit portfolio actions Optimum Short Term Strategy • The Near Term Wealth Score will assess how close a customer is to the target wealth profile Long Term Strategy within their given life stage. The higher the Customer B score, the more closely they resemble the target Over a longer period customer B wealth profile. is more profitable than customer A • The Lifetime Wealth Score will assess how close Customer A a customer is to the ideal target wealth profile across all life stages. The higher the score, the more closely they resemble the target wealth profile. Today Time 120% 100% 80% 3. Retention Modelling 60% The models capture 40% Models that identify those customers most likely to close their 40% of the potential attriters in accounts and Triggered Based Retention strategies 20% the first two deciles. 0% 0 1 2 3 4 5 6 7 8 9 Random% Closed %25
    • Objectives Results • Identify Upwardly Mobile Customers: Identify the customers • Developed five Near Term Wealth Score models, one for each of the life with the potential to be upwardly mobile (to migrate) through this stages segment scheme to help drive product development and portfolio • Model for Life stage 1 has a maximum KS of 80% actions • Life stage 4 is chosen as the ideal “wealthy” profile for the entire • Improve Value Understanding: To develop a value profile for portfolio the different customers and segments. • Model for Life stage 4 has a maximum KS of 78% • Develop a Reusable Segmentation: Develop a segmentation • The top two deciles in Value model capture 87% of total value which that can be reused globally gives good separation • There are sixteen actionable segments based on Wealth score and Value score Approach Business Impact • We developed two different scores: 1. A Near Term Wealth Score and 2. A Lifetime Wealth Score • Targeted marketing based on Wealth profile and Value profile • The Near Term Wealth Score will access how close a customer is to the target wealth profile within their given life stage. The higher • Clear strategies can be drawn to move customers from “Mass” to the score, the more closely they resemble the target wealth “Advanced” and “Premier” segments based on scores profile. • The Lifetime Wealth Score will access how close a customer is to • Plug and Play SAS codes the ideal target wealth profile across all life stages. The higher the score, the more closely they resemble the target wealth profile. • Developed historical data model that provides monthly account level profit estimates. These estimates are then converted into Value Score • Developed a two dimensional segmentation based on Wealth Score and Value Score26
    • Designed an approach that will measure the wealth potential of a customer both within the lifestage that the customer is in and across all lifestages We broke down the 1 portfolio into 5 different lifestages Lifestage based on age L1 L2 L3 L4 L5 High (Premier)2For the Near Term Wealthmodels, we defined targetwealth customers for each of Wealththe different lifestages 4 For the Lifetime Wealth Scores, we established the idealThe Near Term Wealth Scores target wealth profileprovide a measure of howclose a customer resemblesthe target wealth profile in their The Lifetime Wealth Scoreslife stage provide a measure of how close a customer resembles the ideal target wealth profile across all 3 lifestages Low (Mass) 527
    • Lifestages Model Variables Below 25 25-35 35-45 45-60 Above 60 Average over 6 months - ATM Transactions × × × Average over last 6 months - Number of TD Transactions × × × × × Average over last 6 months - Outgoing EFT Trans Amt × × × × × Average over last 6 months - Total CA Balance × × Education × × Professional Group × × × × Residential Status × × × × Revolver Segment × × × × × Total # of products × × × Transaction Band × • TD Transactions, EFT Transactions, and Revolver Segment are the three variables that are significant in all the Lifestage segments • Education and Transaction Band are the least significant across all Lifestage segments28
    • The scorecard was used along with other criteria in creating multi dimensional segmentation. Usage and activation strategies were based on this segmentation. Life stage Probability band 0-0.4 0.4-0.7 0.7+ Missing Overall % of Customers 7.30% 0.56% 0.24% 0.01% 8.11% % of profit -4.09% -0.38% -2.06% -0.03% -6.57% <=0 Avg. Balance A1 2,032.2 A215,035.3 A3 45,737.3 A4 35,271.4 21,564.7 Strategies Avg. spend 2,611.3 16,158.0 49,492.7 35,513.1 15,066.6 developed to % of Customers 16.63% 01.65% 0.43% 0.05% 18.77% move % of profit 1.14% 0.10% 0.03% 0.00% 1.26% customers to 0-40 B1 1,068.1 B2 2,648.2 B3 B4 Avg. Balance 12,162.9 1,997.9 3,307.0 higher value Avg. spend 2,264.2 4,171.8 14,375.1 1,997.9 4,257.6 bands Value band % of Customers 53.01% 2.92% 1.96% 0.04% 57.93% 40-500 % of profit C1 33.52% C2 1.57% C3 1.33% 0.01% C4 11,105.8 36.43% Avg. Balance 2,186.4 5,516.1 19,850.9 6,045.6 Avg. spend 4,801.6 10,890.0 25,464.4 11,105.8 8,707.7 % of Customers 11.51% 0.85% 1.79% 0.00% 14.15% % of profit D1 47.48% D2 4.60% 16.76% 0.03% 68.87% 500+ D3 D4 Avg. Balance 9,314.5 23,797.9 106,071.5 163,365.1 55,780.2 Avg. spend 13,337.3 33,074.8 118,127.3 163,365.1 58,874.2 % of Customers 0.79% 0.16% 0.07% 0.00% 1.03% Missing % of profit 0.00% 0.00% 0.00% 0.00% 0.00% Avg. Balance 18,932.5 19,108.2 52,394.9 2,143.5 36,503.8 Avg. spend 18,603.6 20,792.6 53,633.4 2,143.5 37,380.0 % of Customers 6.16% 4.49% 0.11% 100.00% Overall 89.24% % of profit 5.89% 16.05% 0.01% 100.00% 78.05% Avg. Balance 12,068.9 69,323.5 36,385.8 24,744.9 3,909.6 Avg. spend 17,959.4 77,900.2 36,440.9 25,125.2 6,291.929
    • Objectives Results • The Bank was using Behavior Segments/Scores to drive • 95% of the value comes from two segments that have the portfolio management strategy. This strategy focused 24% of the accounts on incremental lifts in response rates, with no insight/control • 9% of the accounts destroy 27.5% of the value on the profitability of the customers • Two thirds of the accounts are neutral in value • Develop algorithms & easy-to-use interfaces that calculate account level profitability based on forecasted revenue/cost • The Least Value Contributor is Transactor and not drivers and use these outputs in conjunction with behavior High Loss group segments/scores to drive profitable portfolio growth Approach Business Impact • Forecasting assumptions used on pre-defined segments • Detailed analyses of CLV drivers can help in designing • Forecast revenue drivers instead of actual P&L line of campaigns to maximize value items • Leverage historical value data and CLV index for • Vintage based forecasting approach balance building activities • Seasonality of revenue drivers built into the forecasting • Leverage historical value data and CLV index for the methodology evaluation of credit line strategies and for determining • Event based cost allocation methodology new opportunities • Attrition and delinquency handled using probabilistic rates30
    • Vintage based forecasting engine is the cornerstone of this architecture. This methodology provides the granularity that is required to achieve accuracy and consistency. Functional Forms from Historical account Normalized level data – Portfolio Vintage Curves KPIs and detailed & Forecasting revenue lines assumptions for pre-defined Segments Forecasting Engine CLV Segmentation Framework Forecast Output for Revenue Drivers Revenue Drivers – Key Portfolio parameters which Calculation drive portfolio P & L Algorithm for creation of account level P&L (SAS code)31
    • Average Spends Average Balance Average Revenue Limit Camp Accounts Average CLV CLV DRIVERS 7955 15732 509 27% 489 YTL High Value CLV RESISTORS Average Cost of Funds Average loss amount Average cost 244 0.02 -20 In depth understanding of what is driving different levels of CLV on two products Average Spends Average Balance Average Revenue Limit Camp Accounts CLV DRIVERS 10,409 13323 12 10% Loss Makers Average cost of Funds Average loss amount Average cost Average CLV CLV RESISTORS -235 YTL 209 228 -1732
    • Less than 20% of Accounts contribute more than 90% of total profitability Profit Contribution by Decile (%) Top 20% contributes 100% 97% of total profit 80% Bottom 40% destroys 25% of 60% total profit 40% 20% 0% 1 2 3 4 5 6 7 8 9 10 -20% -40%33
    • Segmentation is built based on value and spend groups Value SPEND + VALUE Segmentation GROUPS Low Spend(0 High to <=3000) Spend(>3000) Loss Makers Loss Makers Marginal Marginal Low Low Medium Medium High High34
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    • Objectives Results • To identify those customers that are likely to close their • For each behavior segment, we have identified possible high level cards. marketing strategies that address the key customer opportunities • Perform behaviour segmentation based on their likeliness to attrite. • For each behavior segment, Identify high level marketing strategies that address the key customer Summary Gains Table concerns and issues. Number of Cumulative Marginal Non KS Prob Deciles Non Non Closed Closed Closed Closed Closed Non Closed Rate (Closed) Closed Closed % Rate % 0.0% 0.0% 0.0% Approach 0 575 4444 575 4444 20.7% 9.4% 11.3 % 11.5% 88.5% 11.5% 16.6 1 416 4603 991 9047 35.7% 19.1% 8.3% 91.7% 8.3% % All 19.6 2 357 4662 1348 13709 48.5% 28.9% 7.1% 92.9% 7.1% Cards % 20.2 3 293 4726 1641 18435 59.1% 38.9% 5.8% 94.2% 5.8% % 20.3 All Active All Inactive 4 280 4739 1921 23174 69.2% 48.9% 5.6% 94.4% 5.6% Activity Breakout % Cards Cards 17.8 5 212 4807 2133 27981 76.8% 59.0% 4.2% 95.8% 4.2% % 15.2 6 210 4809 2343 32790 84.4% 69.2% 4.2% 95.8% 4.2% % Shop & All other 11.0 Product Breakout Bonus 7 168 4851 2511 37641 90.4% 79.4% 3.3% 96.7% 3.3% Miles products % 8 144 4875 2655 42516 95.6% 89.7% 5.9% 2.9% 97.1% 2.9% 9 122 4897 2777 47413 100.0% 100.0% 0.0% 2.4% 97.6% 2.4% 1 2 3 4 Models These figures show the cumulative percentage of cards. Here 36% of the attrition has been captured within the first two deciles.36
    • The process we follow considers an exhaustive list of independent variables to make sure that predictive power of the model is maximized. 1 2 3 4 5 Data Variable Model Scorecard Validation Validation Selection Building Development We started with over We reduced the We then ran stepwise We then validated the We then developed 300 variables in the variable set down to regression to models based on the an appropriate modelling universe. about 60 based on determine the final statistical results. scorecard. the bivariate analysis variables in the We conducted and the overall model. bivariate and information value. univariate analysis for the categorical and Then we conducted continuous variables correlation analysis to make sure the and eliminated any trends were correct. variables that were highly correlated.37
    • • Modelling has been done Open Cards Closed Cards at a card level 55% sample 85% sample Data Used Customer Credit Burearu Card Usage EFT Data Revenue Data Product Holdings Data Current Month 11 Months 11 Months 11 Months When Pulled Customer Transactional Authorisation Call Center Data Demographics Data Data Current Month 11 Months 11 Months 11 Months Customer Complaint Data Current Month38
    • Parameter Description of variables Bonus Shop & Miles Other Total volume of non instalment purchases in last 3 AMT_SPEND_3M_6mnths statement periods x x woe_age_band The age of the card x x x The value of transactions done in home improvement woe_AMT_HOUSEWARE_3_6mnths category lst 3 months x woe_AMT_SPEND_3M_Ratio Change in spend over the past six months. x Ratio of spend in Bonus network / total spend (as volume of woe_AMT_WEB_RATIO_6_3mnths transactions) x x woe_ASSETS_TOTAL_6mnths Average YTL value of all assets in bank last calendar month x woe_ASSETS_TOTAL_CURR_3mnths Total current YTL value of all assets in bank x woe_ASSETS_TOTAL_CURR_ratio Total assets change in past six months. x woe_BHVR_SCORE_CURR_6mnths Last calculated behaviour score (scores calculated monthly) x Whether the customer has been or is enrolled in a spending woe_BNS_PROM_FLAG commitment for Bonus card x woe_CURR_CUST_LIMIT_A_ratio Current available customer limit x woe_CURR_DEBT_6mnths Customers Current outstanding balance total (of all cards) x woe_CURR_DEBT_ratio Current debt change in past six months. x Total new transactions in last statement / Maximum total of woe_LAST_PUR2MAX_PUR new transactions in last 6 statements x x woe_limit_band Limit of product x x woe_mob_band The month on book group that the card is in x x A flag that indicates if the owner has multiple cards with woe_multi_card_flg Garanti x Whether customer is payroll customer and receives salaries woe_PAYROLL_FLAG in current account x woe_segmentation The business segment that the card is in. x x39
    • A heat map was created based on the scorecard. Clear actionable groups were identified and appropriate strategies were designed. • % of Closed Cards Number of Cards VIP VG1 VG2 HS Others TOTAL VIP VG1 VG2 HS Others TOTAL High High 38 4,931 3,976 73 11,058 20,076 Attrition 3% 8% 8% 14% 8% 8% Attrition 0% 10% 8% 0% 22% 40% Risk Risk Med Med 163 5,413 3,844 423 5,215 15,058 Attrition 8% 4% 4% 6% 5% 5% Attrition 0% 11% 8% 1% 10% 30% Risk Risk Low Low 339 5,887 4,406 2,104 2,321 15,057 Attrition 3% 3% 3% 3% 3% 3% Attrition 1% 12% 9% 4% 5% 30% Risk Risk 540 16,231 12,226 2,600 18,594 50,191 TOTAL 4% 5% 5% 4% 7% 6% TOTAL 1% 32% 24% 5% 37% 100% • 10% of the cards • 11% of the cards • Take more urgent proactive measures to • Take moderate proactive measures to ensure that these customers are happy strengthen the relationship • 26% of the cards • 17% of the cards • Stay focused on BAU activities and • Take moderate proactive measures to promoting the benefits of the product reinforce use of the product40
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