Analytics Case Studies

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Analytics Case Studies

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Analytics Case Studies

  1. 1. Building Intelligent Enterprises Case Studies Top 10 Emerging Analytics startups in India for 2015 by Analytics India Magazine
  2. 2. Work Abstract www.valiancesolutions.com Product recommendation model for prominent Life Insurer identifying top 2 products existing customers are likely to purchase. Analysis was used in email and direct marketing campaigns. Prediction Model for identifying customers who are unlikely to pay insurance premium within 30 days grace period. Results were used to formulate pro-active customer retention strategies. Monthly sales forecasting model for prominent direct sales retailer in US using Neural Networks. Achieved average forecasting accuracy of 7 percent with 5 to 10 percent error range. Cross Sell Customer Churn Sales ForecastFraud Prevention Real time Fraud Detection algorithm for unsecured consumer lending. Substantial decrease in loan disbursement to fraudulent cases at Point of Sale
  3. 3. Case Study: Product Recommendation www.valiancesolutions.com Company Profile Project Profile Technology Used Business Need Solution Benefits to Customer Location India Industry Insurance Project type Propensity Modeling R SQL Excel To identify the propensity to cross- sell a policy • To proactively identify the policy holders who have high likelihood to purchase more than one policy • Use agent characteristics as main lever to predict cross-sell propensity Propensity Algorithm to score customers using Logistic Regression • Cross Sell propensity scores at product category level for each customer. • Scores normalized to recommend top 2 products customer is likely to purhacase. • Recommendations used to power email and call center campaigns. Tailored marketing campaigns across modes of marketing • Efficient Marketing Campaigns • Incremental Revenue of USD 100,000 in 3 months • Lower cost of Marketing Campaigns
  4. 4. Case Study Details www.valiancesolutions.com Input Data Data Cleaning Exploratory Data Analysis Data Enrichment Propensity Modeling Algorithm Implementation Customer Attributes Product Attributes Transactional Behavior Interaction behavior Missing value Treatment Correcting incorrect values Removal of duplicate records Uni-Variate Analysis Bi-Variate Analysis Creation of new variables Variable transformations Multiple versions of Models basis different variable selection Model Comparison Choice of best model Modify marketing campaigns. Feedback monitoring Algorithm tweaking (if needed) Solution: Propensity Algorithm to score customers using Logistic Regression Objective: To identify the propensity to cross-sell a policy To proactively identify the policy holders who have high likelihood to purchase more than one policy Use agent characteristics as main lever to predict cross-sell propensity
  5. 5. Cross Sell Model www.valiancesolutions.com Illustrative All the customers acquired in Analysis Window Characteristics Characteristics Scoring model Likelihood to Cross-sell Scoring Algorithm for Calculation Propensity to Cross-sell Identify the Last Agent of a particular customer for Agencies- which maximize propensity to cross-sell Customers holding multiple policies in Analysis window Customers holding single policy in Analysis window If a customer cross-sold more than one policies during analysis window, then each cross-sell instance will be considered as cross-sell opportunity (one customer might appear more than once in modeling window) Identify Best Agent for ARD - which maximize propensity to cross-sell Orphan Customers of Agencies* Cross-sell Campaigning
  6. 6. Performance Results www.valiancesolutions.com Illustrative 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 %ofCrossSellPopulation % of Population Random Validation Model Cross-sell Deciles Model Lift Validation Lift 10 27% 23% 20 44% 38% 30 57% 52% 40 67% 63% 50 75% 71% 60 82% 79% 70 88% 87% 80 93% 92% 90 97% 97% 100 100% 100% Model captured nearly 70% cross sell customers within 40% of population.
  7. 7. Cross Sell Solution High Purchase Propensity Medium Purchase Propensity Low Purchase Propensity Tailored marketing campaigns across modes of marketing Efficient Marketing Campaigns Incremental Revenue of USD 100,000 in 3 months Lower cost of Marketing Campaigns Cross Sell Algorithm www.valiancesolutions.com
  8. 8. Case Study : Customer Retention www.valiancesolutions.com Company Profile Project Profile Technology Used Business Need Solution Benefits to Customer Location India Industry Insurance Project type Lapse Modeling R SQL Excel Logistic Regression Random Forest Improving Customer Retention • To identify policy holders who are likely to lapse and move out of the program • Take proactive measures to keep them in the program Quantitative Analysis of Lapsation • What are the reasons for attrition? • What are patterns in customer attrition across different tenure of policy? • How does the attrition rates change by changing factors? • What is the probability of a customer to attrite? • What channel or combination of channels which will deliver the most conversion? Churn Scoring algorithm based on machine learning. • Upcoming renewals scored on monthly basis in a batch mode. • Customer Segments created on basis of churn score and Annual Premium. • Contact Strategy finalized on basis on churn score and premium at stake. • Customers with higher churn score and premium >25k pursued through calls and visits if needed. • Customers with lower churn score and lower premium contacted via sms and emails. • Frequency of emails, call s to be adjusted as per segment. Customer Churn • Policy Persistency increased by 20% over 1 year • Incremental Revenue of 3M USD in 1 year • Lower cost of retention Campaigns
  9. 9. Case Study Details www.valiancesolutions.com What are the reasons for attrition? What are patterns in customer attrition across different tenure of policy? How does the attrition rates change by changing factors? What is the probability of a customer to attrite? What channel or combination of channels which will deliver the most conversion? Quantitative Analysis of Lapsation Objective : Improving Customer Retention To identify policy holders who are likely to lapse and move out of the program Take proactive measures to keep them in the program
  10. 10. Solution: Lapse Model www.valiancesolutions.com Illustrative Policies for renewal between Analysis Window Characteristics Characteristics Scoring model Likelihood to lapse Policies lapsed between Analysis window are bad Policies lapsed between Analysis window are good Retention Campaigning Application on policies coming for renewals in following month Scoring Algorithm for Calculation Propensity to lapse Lapsed and Reinstated Lapsed Non Lapsed
  11. 11. Sample Deliverable: Customer Risk Profiling www.valiancesolutions.com Illustrative Customers were segmented on basis the probability to lapse and APE band APE BAND Risk Group <18K Between 18K and 25K >25K Total High 18% 8% 14% 40% Medium 15% 8% 7% 30% Low 10% 7% 13% 30% Total 43% 23% 34% 100% Customers were segmented in High, Medium and Low risk profiles on basis of Annual Premium and their probability to lapse. Cut off probability band for High, Medium & Low group was identified from customer deciles. i.e. For High band probability cut off was based on top 30 percent of lapsers. Proactive campaigning to customers with higher likelihood to lapse Risk_Group Probability of Lapsation H >0.18 M 0.03-0.18 L <=0.03 High Risk Priority 1 Medium Risk Priority 2 Low Risk Priority 3 Legend
  12. 12. Customer Churn Solution High Churn Propensity Medium Churn Propensity Low Churn Propensity High risk customers to be reached pro-actively through calls and visits if needed. Medium risk customers to be reached through calls, emails and sms’s Low risk customers to be reached through sms’s and emails. Policy Persistency increased by 20% over 1 year Incremental Revenue of 3M USD in 1 year Lower cost of retention Campaigns Churn Propensity Algorithm www.valiancesolutions.com
  13. 13. Case Study : Credit Default Modeling for Unsecured Loans www.valiancesolutions.com Company Profile Project Profile Technology Used Business Need Solution Benefits to Customer Develop Credit Risk framework for POS loan approvals • To identify customers who are more likely to commit fraud/default on consumer durable loans. • To streamline loan approval process according to customer risk profiles. Real time Fraud Propensity score at Point of Sale • Machine Learning based fraud engine integrated with CRM • Assigns fraud score for applicant at point of lending. • Higher fraud score applications routed through stringent verification process. Substantial decrease in fraud thus improving the Bottom Line • Substantial decrease in loan disbursement to fraudulent cases at Point of Sale • Almost 10% of the originations are referred to ‘Normal process’ in which the fraud incidence is as high as 5% which translates into a gross saving of almost 1.5 million USD i.e. 50% of the VaR • Substantial decrease in the third party cost of loan amount recovery from the fraudulent cases. Location India Industry Banking Project type Fraud Likelihood Model SAS SQL Java Excel
  14. 14. Case Study Details www.valiancesolutions.com Identify attributes of customers who are most likely to commit fraud? What are patterns in customer default across cities/income/ profession segments? What is the probability of a customer to default? Quantitative Analysis of Credit Risk Objective: Develop Credit Risk framework for POS loan approvals To identify customers who are more likely to commit fraud/default on consumer durable loans. To streamline loan approval process according to customer risk profiles.
  15. 15. Solution www.valiancesolutions.com Text Mining Fraud Likelihood Model Development of technology solution Implementation framework Strategy roll-out and testing • Hypothesis building • Data cleansing • Conducting field visits to understand typical trends in fraud patterns • Profiling patterns • Algorithm for fraud prediction • Build a Java based algorithm • Ensure compatibility with client’s Sales CRM system • Host the algorithm on the client’s system • Cross-validate the scores generated by the system • Roll-out the algorithm on the live system • Continuous monitoring of through the door population for any changes in patterns
  16. 16. Fraud Likelihood Model www.valiancesolutions.com Illustrative All account sourced Characteristics Characteristics Scoring model Likelihood to Default Customers identified as not fraud Customer s identified as Fraud Loan application coming for renewal at POS Scoring Algorithm for Calculation Propensity to default Medium Risk High Risk Low Risk
  17. 17. Implementation Framework www.valiancesolutions.com Customer walks-in to outlet for purchasing products Proposal to convert invoice amount to EMI’s Customer Details fed into the system The algorithm developed will return fraud score based on inputs The algorithm developed will return fraud score based on inputs Instant mode Approvals are made instantly within 30 min Normal Mode Approvals are after rigorous verification Medium Risk Feedback Process FeedbackLoop
  18. 18. ROI of Modeling Exercise www.valiancesolutions.com Substantial decrease in loan disbursement to fraudulent cases at Point of Sale Almost 10% of the originations are referred to ‘Normal process’ in which the fraud incidence is as high as 5% which translates into a gross saving of almost 1.5 million USD i.e. 50% of the VaR Substantial decrease in the third party cost of loan amount recovery from the fraudulent cases. Default Model led to substantial decrease in fraud thus improving the Bottom Line
  19. 19. Case Study : Monthly Sales Forecasting for Direct Seller www.valiancesolutions.com Company Profile Project Profile Technology Used Business Need Solution Benefits to Customer Location United States Industry Retail Project type Sales Forecasting R ARIMA Linear/Non-Linear Regression Neural Networks Develop Sales Forecasting Model for Monthly Sales • To build Monthly forecasting Model with high degree of accuracy. • Forecast Monthly sales for next 9-12 Months Neural Network based monthly sales forecasting algorithm. • Sales in last 1 year plus external factors as inputs. Model Techniques Error Moving Average And Exp Smoothening 47% ARIMA 32% Linear Regression 20% **Neural Networks 6%
  20. 20. Case Study Details www.valiancesolutions.com To identify Seasonal patterns and factor affecting monthly sales. Segment Agent workforce, to improve forecasting Accuracy. Forecast Monthly Sales for next 9- 12 months. Quantitative Analysis of Monthly Sales Trend Objective: Develop Sales Forecasting Model for Monthly Sales To build Monthly forecasting Model with high degree of accuracy. Forecast Monthly sales for next 9-12 Months
  21. 21. Forecasting Solution www.valiancesolutions.com Monthly Sales Raw Data Sales Lag Creation for last 12 Months Train Neural Network Forecast for next 6 Months & Calculate Error Optimize Network Weights Forecast Sales for Next 12 Months • Various Forecasting Techniques are used and best results are selected. • Neural Network use Single Hidden Layer Network with 24 Neurons. Feedback Process
  22. 22. Forecasting Solution www.valiancesolutions.com 0 20 40 60 80 100 120 140 160 180 200 Jan' 2013 Feb' 2013 Mar' 2013 Apr' 2013 May' 2013 June' 2013 July' 2013 Aug' 2013 Sept' 2013 Oct' 2013 Nov' 2013 Dec' 2013 Actual Sales Forecasted Sales Actual Sales Vs Forecasted Sales Model Techniques Error Moving Average And Exp Smoothening 47% ARIMA 32% Linear Regression 20% **Neural Networks 6%
  23. 23. Implementation Framework www.valiancesolutions.com R Stat is used for Model Implementation Past Monthly Sales Forecasting Model Sales Forecast for Next 12 Months
  24. 24. Let’s Engage Vikas Kamra Co-founder & CEO E: vikas.kamra@valiancesolutions.com T: +91 8750068961 Shailendra Kathait Co-founder & Head of Analytics E: shailendra.kathait@valiancesolutions.com T: +91 9873343019 Ankit Goel Co-founder & Head of Technology E: ankit.goel@valiancesolutions.com T: +91 8750919666 www.valiancesolutions.com Valiance Solutions Private Limited | A-146, Opposite TCS building | Sector 63, Noida, U.P – 201306 | India +1 347 708 0143 | +91 120 411 9409

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