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Application of predictive analytics

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How Analytics is being used in Different Fields

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Application of predictive analytics

  1. 1. PRASAD NARASIMHAN – Technical Architect APPLICATIONS OF PREDICTIVE ANALYTICS
  2. 2.  Encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events. PREDICTIVE ANALYTICS
  3. 3.  POS data  Social media  External feeds  Payments  Log data  Telephone conversations  RFID Scans  Events  Emails  Sensors  Free-form text  Geospatial  Audio  Still images/videos  Transactions  Call center notes USES OF ANALYTICS IN VARIOUS FIELDS
  4. 4.  Is powered by Synapse algorithm.  It learns about its users ( as Amazon, Neflix, and Pandora ) to recommend new products, movies, or songs based on a user’s preferences ,  Excludes certain variables or take a multi-dimensional scoring approach with different weights . EG1: Match.com
  5. 5. EG 2 : WEBSITES
  6. 6.  Customer needs and wants : Techniques  analytical quality control,  reliability modeling,  streamlined services and  expedited application processing For example, predictive modeling assist in :  Moving from mass marketing to more personalized,  Targeted campaigns and offers.  Provide insights into where airlines are or are not meeting traveler expectations.  Pricing products,  Managing inventory or staffing,  Customer and operational data to improve efficiency,  Reduce risk and  Increase profits. EG 3 : AIRLINES
  7. 7. Things driving the use of predictive analytics in HR :  Getting better at using operational processes and technology by collecting good-quality data to make better decision-making.  These rich data sources referring to the inclusion of both external and internal data.  Vendors of HR solutions are increasingly building analytics into their core platforms. EG 4 : HR
  8. 8. HRIS vs PREDICTIVE HR ANALYTICS HRIS Predictive HR analytics Looks for trends at the macro or aggregated levels of the business, and then drills up, down, or across the data to identify areas of under- and over- performance. Builds analytic models at the lowest levels of the business—at the individual employee level—and looks for predictable behaviours, propensities, and business rules (as can be expressed by an analytic or mathematical formula) that can be used to predict the future likelihood of certain behaviours and actions. Is about descriptive analytics (or looking at what happened), slicing-and- dicing across dimensional models with massive dissemination to all business users. Is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes.
  9. 9.  Retailers accumulate huge amounts of data on a day-to-day basis.  Using predictive analytics and data from loyalty cards, computers in real-time crunch terabytes and terabytes of historical purchases to figure out that favorite ice-cream was the one item missing from shopping basket that week.  With bill, a coupon is received for the item that are most likely to buy next time.  The shift toward contextual marketing and retailing is driven by data — big data EG 6 : COUPONS IN GROCERY STORES
  10. 10. Objectives are:  Data to enable cross-channel and multi-stage marketing.  Dynamic, personalized content across touch points  Social marketing as buying cycles being online and consumers make decisions before engaging with the company (people buying from influencer’s comments and feedback)
  11. 11. EG 7 : BUSINESS Business Application: What Is Predicted : Customer retention customer defection/churn/attrition Direct marketing customer response Product recommendations what each customer wants/likes Behavior-based advertising which ad customer will click on Email targeting which message customer will respond to Credit scoring debtor risk Fundraising for nonprofits donation amount Insurance pricing and selection applicant response, insured risk
  12. 12. BENEFITS IN BUSINESS Understand your customer • A substantial analytic infrastructure is a necessary basis for new approach. Technology developed in the past decade more efficiently synthesizes information that customers have already willingly shared with a company. • Recordings from call centers, emails, letters, and social media make up the raw material, commonly referred to as big data. • Analytic programs distill this information nearly instantly to give FIs a clear portrait of individual customer attributes:  demographics,  sentiment,  transaction history,  life cycle needs,  risk tolerance,  cloud score,  channel preference,  utilization, and more.
  13. 13. 2. Improve the customer experience  Improving the quality of each customer interaction and  The efficiency of each sales effort is the major goal of analytics. Predictive analytics can  Minimize intrusions upon the customer and  Reduce hand-offs and hold times. • With these insights available at the moment of contact, an FI can now create an interaction that more directly supports each customer's needs. Decision engines can suggest specific responses, questions, and offers during a personalized service interaction. • Applied to the entire customer database, upfront analytics support new and more insightful customer segmentations. • These more refined segments are available to contact centers for immediate use; marketing forces rely on them to create highly targeted campaigns and more relevant products and offers.
  14. 14. 3. Prevent attrition • Predictive analytics can also identify dissatisfied customers and the nature of their complaints. • When these customers call again, service agents have a conversation path prepared to address and resolve their issues. • FIs can now identify particular areas of concern early on, and take proactive steps to mitigate customer dissatisfaction. These steps range from simply acknowledging a problem to extending special offers to instill trust and loyalty. 4. Improve relevant offers • For targeted cross-sell efforts, predictive analytics illuminate what your customers need and when they are willing to buy. • Higher product close rates deliver long-term sustainability. As noted, analytics helps an FI determine the nature and timing of offers. Transaction histories suggest the "next best product" for a cross-sell; predictive modeling may also recommend the "next best action" if customer sentiment does not warrant a cross-sell at that moment. • A customer's life events—retirement, divorce, moving—continually affect the nature and timing of offers.
  15. 15.  The objective of customer segmentation is :  to enable the companies to identify the most profitable customers and  to target them with focused marketing efforts to maximize the ROI of different campaigns.  Advanced analytical techniques used :  cluster analysis,  decision tress,  Random Forest,  Regression models etc. ( to create meaningful clusters from the client’s customer base.) o Advanced statistical modeling conducted on :  customer’s transactional ,  profile and  appended data ( to divide customers into groups ) o Groups based on shared characteristics like :  profitability,  customer life time value,  loyalty index,  customer requirement and  transaction history. EG 8 : CUSTOMER SEGMENTATION
  16. 16. BENEFITS IN MARKETING & OPERATIONS
  17. 17.  It is directly related to the profit and loss of the organization  Job is to recover loan from the “happy” customers without offending or hurting his feeling.  Requires the perfect mix of :  efficient operations ,  man management skills (imagine angry and yelling customers on the other side of phone) and  intelligent collection strategies (think how discreet you have to be with your friends and relatives to ask back the money you lent them at the time of their most “urgent” need).  Collection teams have to pay for the misdeeds of aggressive acquisition policies of sales and marketing team which results into accumulation of bad portfolio . EG 9 : COLLECTION
  18. 18.  Collections and recovery predictive models helps :  To calculate the accurate estimates of a customer’s propensity to repay.  To distinguish between self-cures and potential long term delinquent accounts only to maximize the collection from the delinquent accounts while preserving valuable customer relationship.  The self cure are those account which need minimum follow up where as potential long term are those account which are usually difficult to crack.  Differential treatments are done to different segment to maximize revenue in limited budget.  Some of the predictive model that are used in collection processes are as follows :  Early warning Delinquency Scorecard :The objective of this model will be to raise early alerts about the customers who are most likely to default or most likely to miss the payment in the next collection cycle  Normalization Scorecard : Normalization model helps to identify the customers having greater propensity to clear their entire due amount and return to bucket zero or regular payment cycle.  Rollback Scorecard: Rollback predictive model helps to segment the customers in collection who are more likely to make some payment and come back to lower collection levels.  Recovery Scorecard: Once the account is written off /charge off the account moves to recovery. Recovery model predicts the propensity of some settlement or recovery from the customers. Deep Vintage and early vintage recovery customers are very different in behavior. They should be treated differently.
  19. 19.  PDL is a small and short term unsecured loan where an individual borrows a small amount at very high rate of interest till the next payday.  These loans are also known as cash advance or check advance loan as lenders give loan against cheques, debit cards etc.  The loan amount is usually very small with very high rate of interest.  Since this is Payday Lending, the repayment cycle is also very small (averaging about 15 days).  Most of its applicants are employed as the loan is linked with the employment status of the customers. The payment is usually done through cheques or debit cards.  The advent of open sources like R, MySql etc.; has removed the cost factor - only deterrent in the use of predictive modeling in payday lending. EG 10 : PAYDAY LENDING
  20. 20. Different applications of analytics for payday lending can be summarized as follows: •Application Approval Scorecard: To develop a model that helps in application approvals using customer’s profile data. •Conversion Scorecard: To develop a model to identify leads that will convert to loan. •First Pay Default: To develop a model to identify customers who are more likely to default. •Credit Risk Scorecard: To develop a model to identify customers who can be given long term loans. •Customer Retention Scorecard based on profitability: To develop a model that identifies high value customers who will come back to avail the services again.
  21. 21.  The predictive model is used :  to identify the customers who are more likely to not clear their dues and  eventually get terminated in next six month.  They used the scores to design their customer reach program like sending emails to self cure customers.  Data analytics helps:  In enabling the intelligent and smart reporting  To automate smart decision process based on scientific insights backed by historic data.  The business managers to understand the complex relationship of  different customer behaviors,  micro/macro-economic variables with the sales etc. and  To use this knowledge effectively to promote sales,  Build brands and  Increase profit for their companies. EG 11 : TELECOM
  22. 22.  Designing Involves :  Customer segmentation,  Churn scores,  Usage pattern,  Recharge history  Campaigns are designed for :  retention,  revenue enhancement (increasing customer wallet share) and  cross-sell /up sell etc.  The techniques like :  regression models ,  Clustering,  Decision Tree etc. are widely used to do customer segmentation to design the campaign and target the customer more effectively. a) CAMPAIGN MANAGEMENT
  23. 23.  The challenge : Is to identify different level of profitability differentiated target strategies could be adopted for customers at different points of the profitability matrix.  Different statistical techniques like  GLM( Genaralized Linear Model),  Survival Analysis etc. is used to determine the life time value of a customer.  The insight provided by the model is used by business manager  In developing the strategies for customer services  Retention and  Churn prevention b) LIFE TIME VALUE OF CUSTOMER
  24. 24.  In the Telecom industry, where churn rates are very high, it affects profitability of the company if a customer churns before the company can even earn back the expenses it incurred in acquiring the customer.  Predictive analytics model can be used in early identification of the customers which are more likely to Churn. c) Churn and Retention Predictive Models
  25. 25.  Cross-Sell analytics helps :  To increase the value of Customer Relationships,  Enhance product penetration and  Increase revenue per customer and profitability.  Data analytics method like :  Market basket analysis,  Regression model etc. could be used in identification of customers who are more likely to buy a particular product.  Analytics can be used :  To identify the important factors that affects the cross sell and  Also helps in designing customized product bundling offerings based on customer profile. d) CROSS-SELL/UP-SELL MODELS
  26. 26.  For any effective campaign, proper customer segmentation is a must.  For a service provider, it is a major challenge  to recognize the preferences of its customers and then  to effectively offer products and services that enhance customer loyalty.  Based on the analysis of various parameters like  incoming/outgoing voice usage,  recharge,  VAS etc. customer base can be segmented in groups whose behavior and needs are very different from each other. e) CUSTOMER SEGMENTATION
  27. 27.  One of the major credit risk mitigation challenges is  to identify potential fraud and  bad debt at application level itself.  Early Identification of subscribers who are more likely to turn fraud or bad-debt within first three or six months of coming on board help  in avoiding future credit loss and  improves the quality of the portfolio. f) APPLICATION FRAUD/BAD-DEBT MODEL
  28. 28.  Because of the huge portfolio size, credit exposure of telecom service providers is very high.  Because of the limitation of collection resources it is imperative to have smart credit risk management systems to optimize the collection revenues and related costs.  Delinquency predictor scorecards rate the customer based on  his profile,  credit dues and  historical behaviors.  These scorecards can be used  to design effective treatment programs.  to decide what multimedia campaign will be run to what customer segments.  For example a customer with low credit score will be dealt seriously as compared to customer with high credit score g) CREDIT RISK MANAGEMENT
  29. 29.  When signing players, they didn’t just look at basic productivity values such as  RBIs,  home runs, and  earned-run averages.  Instead, they analyzed hundreds of variables from every player and every game, attempting to predict future performance and production.  Past performance as a predictor of the future.  Some statistics were even obtained from game footage by using video recognition techniques for  equally productive on the field  Fantasy football,  sports betting, and  point spreads. EG 12 : SPORTS: “MONEYBALL” WITH OAKLAND A’S
  30. 30.  The first phase was around new and innovative collaboration capabilities such as Facebook, Twitter, Digg, Yammer or LinkedIn. In this phase, the focus was better customer engagement through Twitter or Facebook.  The second phase is enterprise social — social embedded in apps such as CRM, Sales force management, marketing Intelligence or Data Management tools to embrace a more real-time streaming, “crowdsouring” architecture.  In the third phase we are seeing the trend of business applications taking on attributes of these consumer-facing sites to develop better predictive insight. For example, better data management (structured + unstructured; inside the four walls + outside data) within a CRM system could allow operations staff to give greater context to sales forecasts that show steep drops in certain product category sales.  Social data leverage brings in new capabilities so problems are identified more quickly and the resulting relevant insights can be explored. B2C techniques are coming to B2B and B2E interactions EG 13 : SOCIAL ENTERPRISE - CONNECT DATA, INSIGHTS, AND PEOPLE IN THE ORGANIZATION
  31. 31. EG 14 : INSURANCE To identify the customers who are more likely to churn or not likely to pay premium after minimum lock in period. Regression analysis (Logistic Model) to solve this problem.
  32. 32. BENEFITS IN INSURANCE •Pricing advantages for better risks: When policyholders with favorable claims outcomes and risk profiles are more easily and reliably identified, they will receive better pricing. •More relevant, individualized policy reviews: Instead of making wholesale judgments about certain types of businesses or homes, underwriters using more relevant data make better-informed decisions on individual policies. For instance, an underwriter can use predictive analytics to discern that Roofing Company A is a better risk than Roofing Company B. •Greater efficiency: A big part of providing good customer service today depends on the speed of your response. Customers expect information to be instantly available and insurance carriers incorporating predictive analytics are able to quote business faster and more accurately. •Maintain choice and market stability: Carriers suffering from poor systemic performance negatively impact their ability to pay claims. You want to choose the best carrier for your customer and have confidence that the carrier will be around for the long term.
  33. 33. EG 15 : OIL & GAS
  34. 34. EG 16 : BANKING Leading Indian bank used predictive analytics to cross sell their LAP(Loan against property) to their Current account and saving account portfolio.
  35. 35. EG 17 : RETAIL , DEFENCE & CASINO • A London based retailer used predictive analytics to forecast their weekly sales product wise and used that information for inventory management. • The Defense Department has long employed predictive analytics to model nuclear war scenarios or optimize the order of battle. • Casino gaming industries have also invested heavily in programs that help them calculate their odds of success.
  36. 36. EG 18 : HEALTH CARE  Clinical observations can also improve the accuracy of predictors.  To illustrate, a patient wellness metric known as the Rothman index requires users to input not only structured data such as lab values and blood pressure readings but also the nursing assessment of the patient.  The predictor would be a failure without the nursing notes, because it would be an incomplete snapshot of the patient.  But the combination of the nursing assessment with the lab values and the vitals makes the Rothman index fairly accurate.
  37. 37. Predictive modeling in healthcare is at the forefront of  Improving quality of care,  Reducing costs, and  Improving population health (triple aim). BENEFITS IN HEALTH CARE PREDICTIVE MODELING

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