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Analytics cross-selling-retail-banking

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Analytics cross-selling-retail-banking

  1. 1. Analytics in Financial Services Business Analytics Tap into the true value of analytics Organize, analyze, and apply data to compete decisively
  2. 2. ContentPrefaceFrom the Editors’ DeskAnalytics for a New Decade01. Post-Crisis Analytics: Six Imperatives 0502. Structuring the Unstructured Data: The Convergence of 13 Structured and Unstructured AnalyticsRevitalize Risk Management03. Fusing Economic Forecasts with Credit Risk Analysis 2104. Unstructured Data Analytics for Enterprise Resilience 2905. Why Real-Time Risk Decisions Require Transaction Analytics 37Optimize to Drive Profits06. Ten Questions to Ask of Your Optimization Solution 4707. Practical Challenges of Portfolio Optimization 55Understand Your Customer08. Analytics in Cross Selling – A Retail Banking Perspective 6109. Analytics as a Solution for Attrition 6910. Customer Spend Analysis: Unlocking the True Value of a Transaction 77 011. A Dynamic 360 Dashboard: A Solution for Comprehensive 85 Customer UnderstandingFight Fraud More Effectively12. Developing a Smarter Solution for Card Fraud Protection 9313. Using Adaptive Analytics to Combat New Fraud Schemes 10314. To Fight Fraud, Connecting Decisions is a Must 109Improve Model Performance15. Productizing Analytic Innovation: The Quest for Quality, 117 Standardization and Technology GovernanceLeverage Analytics Across Lines of Business16. Analytics in Retail Banking: Why and How? 12517. Business Analytics in the Wealth Management Space 135
  3. 3. Analytics in Financial Services08 Yamini Aparna Kona Balwant C. SurtiAnalytics in Cross Selling – Senior Consultant, Industry Principal and Infosys Technologies Head-Solutions ArchitectureA Retail Banking Perspective Limited and Design Group, Finacle Solutions Consulting Practice, Infosys Technologies LimitedThe case for cross-selling to the existing customers of a bank is an easy one—the difficultpart is executing it. Today, there are several different techniques for cross-selling effectively.The common thread that runs across them is data and analytics. Predictive analytics basedon various models have created offers that are just right, just in time. Data mining andanalytics have helped in discovering trends and populating models that are the backboneof predictive analytics. Value analytics is another approach to cross-selling that is available.The call center, the branch, the web—every distribution/ service channel—all leverageanalytics in some way to cater to the entire gamut of customer needs—not just what thecustomer seeks. This article analyzes the different ways in which cross-selling workswith analytics, its intrinsic challenges, and the emerging trends in the analytics field. clients becomes increasingly difficult and Why Cross-Selling is Imperative expensive in a highly commoditized industry, selling more products to existing customersThe experience of many financial institutions makes great business sense for a bank. It is anshows that the cost of selling an additional excellent way to increase revenues and indirectlyproduct to a current customer is one-fifth improve customer retention, because customersthe cost of selling the same product to a with more products tend to be more loyal.new customer. This explains why cross- Customer attrition rates are inversely proportionalselling, i.e., selling a bundle of products and to the number of products held—the more productsservices to the client (usually an existing one), you sell to the customer, the lesser is the chance ofis being increasingly considered the cornerstone the customer leaving you. As a result, movingof the retail financial industry. from a silo-product mentality to a consultativeAs other sources of organic growth (for example, selling approach has resulted in a proliferation ofloan demand) have slowed, and adding new cross-sell initiatives in the banking segment.
  4. 4. effective in the hands of a skilled advisor Approaches to Cross-Selling who can extract portfolio-related information from a client. This approach Cross-selling is selling additional products also has the advantage of revaluing the to existing customers or prospects. It may portfolio at periodic intervals and happen along with the initial sale or after coming up with other opportunities for the initial sale is made. Often, the customer cross-selling. may not explicitly mention specific needs 4. Predictive Analytics-based Approach: or be aware that the bank offers products This refers to a set of approaches where a that meet their needs—cross-selling taps into model (or a set of models) characterizes this unmet potential using a variety of customer buying behavior for financial techniques: products. Past customer data is used to 1. Person-based Approach: This is based build, refine and modify predictive on either the skill of the Customer models. These models are used to predict Service Representative (CSR) or through future customer buying—information a structured question-based approach. In used to generate customer offers. either case, the emphasis here is to elicit In many circumstances, current or recent the need through customer interaction. transactions are used as trigger points in Often, the skill of the CSR is the deciding the system, and very often, the current factor of success, and little or no use of customer interaction is used as the means analytics is made. to deliver the offer. Trigger-based models 2. Rules-based Approach: The system can range from simple to sophisticated. defines a set of rules and uses the Advanced versions can analyze a current information collected from the customer online transaction and couple it with past to arrive at a cross-selling offer. Some data to present relevant offers. Offline analysis of the customer data is made. For offers are also often analyzed to come up example, while processing a loan with the best channel for delivery of the application, enough information is offer (for example, by mail, through a available to decide whether the prospect call, etc.) and some offers may be made qualifies for a credit card as well. using a combination of channels used in an orchestrated manner to get the 3. Value-based Approach: This follows a customer hooked (for example, a teaser portfolio approach to the customers mail, with a click to a website or a phone assets and liabilities with the bank. Here, number to call or meet a particular a customer is given a scenario with one branch officer). The success or failure of product that he or she has asked for. an offer is also an input to the model to Then, based on other information improve future success rate. obtained from the customer, alternate scenarios are offered. Certain value 5. Social Networking-based Approaches: metrics (for example, net assets, These are not yet prevalent in retail installments per month, average rate of banking, but here again, a persons social interest paid, etc.) under multiple networks, likes, dislikes, preferences, scenarios with additional products are recommendations from network friends, presented to the customer— highlighting and products used by others in the benefits and opportunities for growth. network, can be analyzed using Value-based approaches are often more sophisticated models to arrive at probable cross-selling opportunities. One relevant62
  5. 5. Increased role of data and analytics in cross-selling Figure 1 Predictive Value Social networks Rules Person non-financial example is Amazons 1. Data Mining can uncover potential product recommendation engine that is customers who can be targets for cross-selling, based on users who make similar and lead to generation of off-line offers. purchases. (Refer Figure – 1 for “Increased 2. CRM Systems for sales, marketing and Role of Analytics in Cross-Selling”.) servicing, can use online analytics toBarring the first approach, where the number make cross-selling offers.crunching is done mostly in a persons brain,every other approach calls for heavy use of 3. Predictive Analytics can be used toanalytics—the analysis of data, as well as the make both online and offline offers bycreation of models, rules engines, and offer predicting most likely choices of thedatabases. customer based on past data. Analytics in cross-selling Figure 2 Other technology used in cross-selling includes event Reporting processing, rules engines and more. Text Business Analytics Intelligence Cross- selling Predictive Data Analytics Mining 63
  6. 6. Role of Analytics in purpose of cross-selling. Though they Cross-selling may not be part of a suite of products, point solutions are easy to integrate with existing point-of-sale/ service solutions. The role of analytics in cross-selling is Often, these solutions are an easy way of described in Figure 3. bringing cross-selling to an existing environment with minimal changes to Cross-Selling Solutions existing systems. Most of them rely on specific technologies and some rely on a combination of technologies. Examples 1. Home-grown or Assembled Solutions: include Finacle Customer Analytics, Amongst internal initiatives to use Customer XPs, and TIBCOs Cross- predictive analytics, the most common Selling Solutions. application is often cross-selling. In- 4. Channel-specific Solutions: Some house data warehouses provide the data, solutions are designed around specific and business intelligence tools, predictive channels—a call center, for example. These analytics tools, rules engines and coding solutions can monitor call center volumes, provide cross-selling solutions. and trigger extensive cross-selling with 2. CRM Solutions: CRM solutions from incoming calls if the call volume is low. leading vendors—such as SAP, Oracle, When call volumes are high, opportunities etc.—come with cross-selling modules, for follow-up are generated. Similarly, which can be configured and used along outbound call prioritization can be done, with the sales and marketing modules of based not only on probable success rates, the solution. CRM analytics are used to but also based on higher probability of provide the data and power the cross- cross-selling. selling engine, with the operational CRM providing the delivery. Some core Challenges in Leveraging banking solution suites that offer a CRM Analytics solution also offer cross-selling solutions Analytics certainly present a summative view through their customer analytics module of customer transactional and behavioral (for example, Finacle Analyz). patterns. However, the following challenges 3. Point Solutions: These are specific are slowing down the adoption of analytics by solutions that are made for the primary financial institutions: Role of analytics in cross-selling Figure 3 Role Illustrative Examples of Analytics Used 1. Actual process of cross-selling Predictive Analytics, Portfolio Analysis 2. Analyzing past data to uncover trends Data Mining, Reporting, Business and changes in customer preferences Intelligence 3. Measuring effectiveness of cross-selling Reporting, Web-analytics, Channel Analytics64
  7. 7. n Expertise: A combination ofLack of and software. This adds to the cost ofdomain knowledge and data analysis implementing analytics models, whichability, a pre-requisite for effective are already considered on the priceyimplementation of analytics, continues side—especially by small and mediumto be elusive. A banking end-user, banking enterprises. In addition, lengthy,though an expert in his domain, interactive database queries and complexoften faces a challenge to interpret analytics scoring processes can congestand analyze the myriad statistics networks and adversely affect databasethrown up by the analytics platform. performance.A data analyst can compile the statistics · Need for Real-time and Advancedquickly, but is dependent on the business Analytics: End users are no longerusers domain expertise to organize content with analyzing historical dataand analyze the data and communicate and understanding past sales patterns.it in the form the end-user needs it, to Financial organizations now want real-facilitate an actionable decision. time data streaming and analysis thatThe whole process may involve several facilitates on-the-spot business decisions.iterations, resulting in a significant User demands are fast moving fromlag time between data collection and “what happened” scenarios to “whataction and frustration on both sides. may/ will happen” to be prepared with aPredictive analytics, especially, are ready action plan. Analytics models areconsidered a niche realm, requiring expected to answer what will be theextensive training for effective possible outcomes out of action A vs.implementation. action B. This requires high performancen for Clean Data: Statistical· Need analytics models that are capable of real- models are only as good as the data time data analysis. There is growing fed into them. The majority of statistical interest among banks in advanced models not only demand accurate data analytics—though implementation has with the least possible approximations, yet to pick up. (Refer Figure - 4 for but also require that data be scrubbed “Industry Level Advanced Analytics and neatly formatted in a particular Adoption Trends”.) way to ensure quick and meaningful/ actionable recommendations. However, Emerging Trends in the a significant portion of the customer Analytics Field data, maintained by banks happens to be inconsistent and siloed, making it Over the past couple of years, business difficult to meet the formatting standards intelligence—of which analytics are a of analytics models. part—has been catching the attention of financial services industry decision-n· Operational Difficulties: The process makers, who are realizing the need to of deploying sophisticated analytics transform the increased amount of models usually involves accessing available disparate customer transaction data from and/ or transferring data pattern data into actionable information. among numerous machines and Keeping with the growing interest, the operating platforms—requiring seamless following important trends are observed in interoperability of various applications the analytics field: 65
  8. 8. Industry-level advanced analytics adoption trends Figure 4 “What are your firm’s plans to adopt the following business intelligence technologies?” Expanding/ Implementing/ Planning to Planning to Interested Not Don’t upgrading implemented implement in implement in but no interested know implementation the next 12 a year or more plans months Reporting tools 31% 31% 12% 9% 10% 5% 2% Data visualization, dashboards 17% 22% 18% 13% 19% 9% 3% Specialized database engines 18% 15% 9% 8% 21% 22% 7% Business performance solutions 16% 11% 10% 11% 27% 16% 8% Decision support solutions 15% 11% 10% 10% 28% 20% 7% Data quality Management 15% 10% 11% 10% 28% 18% 8% Advanced analytics 9% 11% 10% 10% 29% 22% 9% Complex event processing 8% 5% 6% 6% 28% 34% 13% Text analytics 9% 3% 7% 6% 28% 33% 13% 1% In-process analytics 3% 29% 41% 19% 2% 4% Base: 853 North American and European software decision-makers responsible for packaged applications (percentages may not total 100 because of rounding) Source: "The State Of Business Intelligence Software And Emerging Trends: 2010." Forrester Research. May 10, 2010 n Analytics Applications are Packaged business intelligence vendors are in Demand – Business users, especially expected to find great traction. Many financial institutions, are increasingly small to medium-sized banks are leaning demanding packaged analytic towards SaaS models that allow the user applications that are specifically to use the application through designed for online marketing/ cross- affordable monthly subscriptions selling, fraud detection, online credit without heavy IT or manpower analysis, online trading/ investment investments. Small and medium-sized advisory, and others. To date, many banks will leverage SaaS to architect organizations have attempted in-house analytics applications that meet with customization of analytics applications their specific requirements. to meet such specific ends. Such n Open Source Solutions Gain Traction re-architecture may no longer be – Open source analytics solutions are fast necessary with the emergence of eating into the market share of on- sophisticated event-driven/ complex premise solution providers. Apart event-processing products and predictive from low cost, convenience is also a analytics platforms that can support contributing factor—open source these capabilities. solutions can be deployed alongside on- n as a Service (SaaS) Finds Software premise solutions. Open source is Demand with Smaller Banks – SaaS providing an opportunity for recession-66
  9. 9. hit organizations to experiment with a features that will support simulation mix-and-match model and acquire using historical data, which helps components of analytics solutions from experimentation before starting the various providers at a fraction of the actual analysis. price. Just as one might assemble spare nInitiatives will Catch Up with Green parts in the backyard, businesses are Analytics Vendors –Initial green efforts toying with the concept of reaching out in the analytics/ business intelligence to best-of-breed open source vendors for field have come from hardware vendors, various phases of the analytics resulting in reduced energy consumption. process—from charting to data Software vendors are expected to enter the crunching, statistic modeling, predicting, market with offerings that will enable and reporting. The soaring sales of companies to monitor their emissions vendors—such as Pentaho and and sustainability exercises. JasperSoft—bear testimony to the growing popularity of open source in the Conclusion analytics field.nMash-ups Make an Entry – Over the Analytics have a key role to play innext couple of years, many analytics helping the banks to increase revenueapplications are expected to be deployed by discovering and fulfilling genuinethrough coarse-grained application customer needs. The pressure to increasemash-ups, which provide a cost-effective sales is even more urgent now than evermeans to embed analytics into business before and the use of online analytics andprocess—without involving major predictive analytics can make the job ofre-architecture work. cross-selling a non-invasive, seamless partnImproving Analytics Literacy – of every customer interaction. PredictiveVendors are realizing that providing analytics provide the much-needed,applications with rich graphical data-based support to cross-selling, whichrepresentations and complex will convert the task of “selling more” intodashboards is not enough to satisfy an act of “fulfilling a customer need” bybusiness users, unless the users have preemption. By ensuring that the cross-sella means of deciphering the output. That is aimed at optimizing value to theis why we will begin to see vendors customer, banks can gain additionalchurning out flexible and user- business as well as customer loyalty andfriendly models with built-in training stickiness. 67

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