Customer analytics in the banking industry

Data Analytics is helping banks in reducing their risk exposure, cutting down on customer acquisition costs and extracting better profitability from the existing customers. One sure sign of the fact that analytics is here to stay is the fact that many banks are now setting integral customer analytics cells. And it is not just restricted to MNC banks like Citibank and Standard Chartered who are emulating the best practices of their parents abroad but also homegrown banks like ICICI Bank. Analytics service providers such as Fractal Analytics has developed several predictive analytics based models for credit risk management, cross-sell, customer retention, customer segmentation etc.One of the oldest area in which banks have been using analytics to great results is credit scoring.

Statistical credit score-cards serve up as a better alternative to the traditional judgmental methods of risk appraisal when a bank is making a decision whether to lend to a customer or give him a credit card or not. Risk Scorecards combine historical loan default data with the demographic and transaction details to arrive at a risk score for an applicant. Statistical techniques are applied to data on existing customers to generate equations that can accurately distinguish good customers (customers who repay on time) from bad customers (customers who don’t repay on time or don’t repay at all). This equation or scorecard is used to score new applicants. Statistical scorecards lend themselves to automation. From the consumer’s point of view this ensures quick turnaround time in the evaluation process as well as total consistency, eliminating any bias, which may be present in a human analyst.

Another instance of analytics in banking and where results are apparent almost instantaneously is cross-selling. Banks are leveraging their existing databases of customers more judiciously to rope in customers for lending products like credit cards and loans. Since banks are sitting on wealth of information like liability transaction which sets the base for response models predicting their response to another marketing offer. The cross sell models throw up interesting triggers about the customer setting the stage for life-cycle based marketing or event based marketing. Already, close to 70% of credit cards portfolios of most banks are sourced through cross-sell from their own bank account customers.

One crucial fallout of analytics based marketing campaigns is the tremendous cost savings accomplished by the bank by restricting its soliciting efforts to the customers who are predicted to be active rather than widening its efforts onto the entire customer base and incurring huge costs there. Using customer segmentation solutions, a bank can get 1.5 times more eventual customers to a particular offer while actually contacting a much narrower customer base.

Nowhere else the effect of analytics based marketing is more apparent than in the credit cards companies where analytics have become a way of life. In a fiercely competed battle for wallet share where an average credit card holder holds 3 to 4 credit cards and free credit cards have become the norm, getting a credit card customer to spend on your credit card and ensuring that he sticks to your credit card. Analytics based customer marketing and value management solutions will help you to design optimal customer development strategies, maximize your customer’s profitability by widening the relationship across different banking products and optimize existing customer relationships.

Customer Segmentation strategies which help a portfolio manager to know smaller cohesive groups sitting within his larger customer base, understanding their transaction patterns and hence preempting his requirements goes a long way towards customizing campaigns, offers linked to campaigns and even the tone of the communication directed towards the customers.

Credit Scoring – Analytics approach for credit risk management

Credit scoring is a set of statistical tools to objectively assess credit risk of consumers. Application credit scoring can help determine the probability of default of a consumer based on the application form parameters when he/she applies for credit. A credit scoring model is optimized to maximize profits from lending operations. Credit scoring helps a bank (or any credit granting firm) reduce its defaults, improve process efficiencies & approval times and move to an objective and consistent method of making credit decisions.

A sample size of about 3000 customers (1500 good customers and 1500 bad customers) can give a very robust credit scoring model. Data on rejected applicants is also vital for building the scoring model. Usually, a base of 1500 bad customers requires a large base of overall credit population. Good scoring models (with restricted scope) can be built even with as many as 250 bad customers.

If an organization doesn’t have sufficient past data on credit performance and no credit bureau information is available, scoring models of different kind can be built. These are expert judgment models which extract the business rules and the intelligence implicit the lending practices of the bank’s analysts. These models are less accurate but can still attain objectivity and consistency in lending practices.

Scoring models need to be optimized for the business at hand incorporating the opportunity loss on a rejected consumer who might have been good and the credit loss on accepting a consumer who may be bad. Once a scoring model is implemented, approval/acceptance rates of consumer applications can be predicted with fair degree of confidence. A scoring model can be optimized to maximize profitability of the business or meet a given acceptance rate requirement.

Few niche analytics service providers such as Fractal Analytics provide modeling solutions that enable businesses to develop credit scoring models. They are known to use cutting edge mathematics and technology, and back it with exhaustive data models built off bureau and transaction data.

A credit scorecard should be tracked closely for its stability and its ability to distinguish between the less risky and more risky consumers. A risk analysis and reporting system can perform model tracking and give warning signals when the underlying population or business practices are undergoing a change. A model should be recalibrated at this stage. Analysis and reporting system can also help to assess the portfolio concentration and performance and estimate portfolio profitability.

A credit scoring solution is built on a modeling population. This model when ‘tested’ with external data (“out of sample”) and simulate the savings that can accrue to the firm on using the scoring model instead of judgment based credit decision. This is the acid test for model performance. After the model implementation (either as a “challenger” or a “champion”) – actual benefits can start to accrue to the firm over a period of time.

Marketing Mix Modeling

Marketing is a broad topic that covers a range of aspects, including advertising, public relations, sales and promotions. Strategies in marketing have changed enormously. Tactics that were considered radical earlier are almost main stream now. With so many messages bombarding the consumer in the marketplace today, it is now more difficult than ever to get your product noticed, so marketers have learned to be creative. Companies without a marketing mindset are at a disadvantage in today’s business world.

Marketing can be done in a number of ways via various marketing channels but choosing an optimum marketing channel is not an easy task. Data Analytics has proven to be a very useful tool for the companies to identify the right marketing channels and optimize their marketing spends with increase in ROI. Companies are spending up to 40 per cent of the top-line sales on such marketing activities. Mathematical data modeling can help increase efficiency of this spend by up to 30 per cent. With the increasing number of channels to advertise their brands, marketing managers are facing huge challenges identifying the right mix of channels for advertisement.

Marketing Mix Modeling (MMM) is an analytical approach that uses past data to quantify the sales impact of various marketing activities. Mathematically, this is done by establishing a simultaneous relation of various marketing activities with sales in the form of a linear or a non-linear equation, through the statistical technique of regression.

Marketing Mix Modeling defines the effectiveness of each of the marketing elements in terms of its contribution to sales-volume, effectiveness, efficiency (volume generated by each rupee spend) and ROI (return of investment). This learning is then adopted to adjust marketing tactics and strategies, optimize the marketing plan and also to forecast sales while simulating various scenarios.

Few niche analytics service providers such as Fractal Analytics have helped their clients increase their marketing ROI with the help of their Marketing Mix Modeling solutions. Marketing Mix Modeling solutions helped them identify the right mix of channels for advertising and hence enable them to make better business decisions in terms of allocating marketing spend and media planning not just allocation to various channels of marketing but also the allocation to each channel across time periods. The impact of pricing and distribution can also be gauged through Marketing Mix Modeling, which helps managers identify the optimal price against competition and the right markets to be present in with optimal spread and the optimal number of items.

Please refer below for case studies on Marketing Mix Modeling:

Get the mix right

Promotion analysis enables CPG company to increase promotions effectiveness by 25%

CPG brands uses marketing mix modeling to increase revenues by 5% while staying spend netural

Marketing Mix Modeling – Optimize Spend and Increase Marketing ROI

With the emergence of new media i.e. Internet, viral marketing, event marketing, sports marketing, product placement, cell phones etc, there has been a substantial effect of traditional media i.e. the television. With upcoming technologies, one can even skip commercials while watching television. Thus, a significant amount of marketing spend has shifted to the new media. But with so many marketing channels to advertise, managers need to optimize the combination of marketing and advertising investments in order to increase sales and overall improve marketing ROI. With the help of data analytics, one can measure potential value of all these factors and hence identify the right marketing investment channel. Analysts call it Marketing Mix Modeling.

Marketing Mix Modeling is a statistical analysis technique used by marketers to understand the individual and combined contributions that multi-media marketing investments have on business results. Thus, marketing mix models help estimate the ROI associated with historical marketing spending, as well as forecast the prospective business results that future spending will generate.

Marketing Mix Modeling involves breaking up of sales volume into various components, and analyzing spend on each of them to calculate ROI from each of these components. After knowing ROI at different levels of marketing activity, threshold and saturation levels, one can forecast sales through each of these activities and hence optimize the marketing spends to gain maximum value.

In the last 10 years many CPG companies have adopted MMM. Many Fortune 500 companies such as P&G, Kraft, Coca-Cola and Pepsi have made MMM an integral part of their marketing planning. This has also been made possible due to the availability of specialist firms such as Fractal Analytics, who have developed Marketing Mix Modeling solutions to help their clients optimize their marketing spends and hence improve their brands across markets. Statistics show that with same budget, clients have improved their brand sales by up to 35%.

To know more about Marketing Mix Modeling and its success stories, please refer to the links below:

Marketing Mix Modeling

Marketing Mix Modeling–Analytics Solution to increase marketing ROI

Marketing Mix Model: A Genetic Approach

Analytics in Retail Industry

Retail analytics is an emerging area which aims at analyzing every aspect of Retail activity right from sales performance, to marketing effectiveness to customer preferences, to loyalty and shift. Retailers need to identify their profitable customers, understand their behavior and utilize optimized customer retention strategy to retain them. Apart from customers, they need to optimize their pricing and sales strategy to manage their business more effectively.

Data Analytics have proven to be a very useful tool for the retailers because of enormous amount of data available for analysis. Customer Analytics has emerged as a new field helping firms in customer acquisition, customer retention, customer segmentation and customer value management. With the help of predictive analytics, analytics service providers have been able to predict customer behavior and thus help retailers to understand their customers.

Analytics service providers such as Fractal Analytics have specifically designed a suit of solutions for Retail industry through its Retail Analytics Toolkit which help them in various aspects such as customer analysis, sales strategy, pricing strategy and brand promotion. They provide retailers customer loyalty analytics (to help retailers improve customer acquisition and retain right customers), Merchandise Analytics (to manage efficiently inventory and stock), Performance Analysis (to analyze sales performance and devise optimized sales strategy), and Marketing ROI Analytics (to optimize marketing spend and pricing strategy).

Retail Analytics in Grocery Industry

In the early 1990’s, the retail grocery industry began leaving the growth stage and entered the maturity stage in the industry life cycle. The vast number of grocery stores that had been built in growth stage and the emergence of new grocery retail formats such as warehouse clubs and dollar stores led to increase in competition forcing firms to compete with each other for the same customers by lowering prices

Consumer reactions to prices are driven by a wide range of interdependent variables, including price elasticity, regional differences, seasonality, trade promotions, shelf placement and more. With so many variables to consider retailers must adopt robust, science-based decision processes and analysis tools to enable retail price optimization.

Instead of simply basing pricing on past methods such as cost plus strategies or reacting to competitors pricing, today’s need is to optimize retail pricing for every item in the store in order to achieve volume, sales, profitability and price image goals. Retail Analytics have proven to be a major tool for decision makers to optimize their pricing strategy.

Several niche analytics service providers such as Fractal Analytics provide pricing elasticity solutions enabling grocers define and optimize consumer-centric pricing strategies based on consumer, demand, and market insights. Known Value Items are often used to compare prices across stores and hence it is very important to price them appropriately and merchandise & communicate effectively about them. Identifying these KVI Items requires multi-stage data mining and analytics.

For more information on pricing analytics for grocery industry, attend the webinar on Grocery Price Optimization to be held on June 12.

Data Analytics – Customer Analytics

For most businesses, the primary means of growth involves the acquisition of new customers which involves finding customers who previously were not aware of your product, were not candidates for purchasing your product, or customers who in the past have bought from your competitors. Some of these customers might have been your customers previously, which could be an advantage (more data might be available about them) or a disadvantage (they might have switched as a result of poor service). In any case, data mining can often help segment these prospective customers and increase the response rates that an acquisition marketing campaign can achieve.

Customer acquisition solutions like response modeling, campaign analytics and segmentation rank order prospects and identify population clusters that are most likely to respond product campaigns. Through the use of predictive models, test and learn frameworks, and tracking mechanisms businesses can support a multitude of tailored offers that not only enhance market share but also successfully reduce acquisition costs.

Research shows that acquiring a new customer can be 7 to 8 times more expensive that retaining an existing customer. Also since customer acquisition costs are very high, customer retention and customer value management are very important in domains like credit cards industry, insurance industry and retail.

Predictive analytics provides the power to assign the likelihood of attrition to existing customers. Industries can then identify the profitable customers amongst those that have a high likelihood of attrition and implement programs to proactively retain these valuable customers. Proactive identification of likely attriters combined with proactive retention programs can add millions by way of incremental customer revenue.

Fractal Analytics, an analytics outsourcing firm based in India has deployed several CRM analytics solutions particularly for customer acquisition, attrition management, customer retention, customer value management and customer segmentation for clients in verticals like banks, insurance firms, retail and CPG.

Related Links:

• Customer Analytics – CRM Analytics
• Use of data analytics in consumer finance companies
• Consumer Finance: Predictive Analytics for competitive advantage
• CRM Analytics in Retail
• Customer Retention Strategies in Insurance Industry

CRM Analytics – Customer Analytics

Executives in the areas of consumer marketing, customer relationship management, customer retention, revenue management have to take care of issues such as customer attrition, response rate, marketing ROI as these factors have a direct impact on business revenues and profits. Data Analytics have proven to be a good tool for them to take good decisions upon such matters. Customer analytics have enabled them to manage better customer lifecycle, know their customers and predict their behavior.

CRM analytics comprises all programming that analyzes data about an enterprise’s customers and presents it so that better and quicker business decisions can be made. CRM analytics can be considered a form of online analytical processing and may employ data mining. As Web sites have added a new and often faster way to interact with customers, the opportunity and the need to turn data collected about customers into useful information has become generally apparent. As a result, a number of software companies have developed products that do customer data analysis.

CRM analytics can provide customer segmentation groupings, response modeling, attrition modeling, loyalty analytics, cross-sell modeling and RFM analysis. Data collection and analysis are viewed as a continuing and iterative process and ideally over time business decisions are refined based on feedback from earlier analysis and consequent decisions.

Companies like Fractal Analytics have lead their clients to have a better productive customer relations in terms of sales and service and improve in supply chain management (lower inventory and speedier delivery) and thus lower costs and more competitive pricing.

For more information:

• CRM Analytics
• Customer Acquisition Analytics for Insurance Vertical
• Customer Analytics in Retail
• Retailing- The age of customer intelligence
• Customer Value Management with Cross-Sell Models

Analytics in India

Analytics is basically about digging deep into data to discover relationships, find causation, and describe phenomena. It is an attempt to understand historical patterns to predict future performance or, in the a few cases, provide information to support decisions in real-time. Today, the latest trend in outsourcing has been the outsourcing of high-end knowledge work to India. Indian analytics service providers have been providing global organizations with technology-driven analytics services. Analytics outsourcing decisions are driven by factors like Focus on Core business, specialists taking care of high-end analytics work and Leverage Cost arbitrage advantage.

From finance sector to retail and telecom, analytical solutions have proven to be highly beneficial for business performance. Where you have a data, you can have a solution to analyze that data and provide you information which can be used to optimize your business processes. For example: In retail, by knowing your customer, analyzing their behavior you can optimize your marketing expenses and spend per customer. Similarly, in CPG sector companies use analytics to optimize their pricing and manage their brand and portfolio and hence improve profitability and increase their market share and containing costs.

As rises to become a major hub for analytic services, MNC’s such as HSBC and American Express have already set up their captive centers here. Few global analytic firms such as Fair Isaac have started their Indian operations to take advantage of raising trend of analytics outsourcing. There are few niche analytics providers such as Fractal Analytics who have established a benchmark by providing analytic solutions to various sectors ranging from financial services, retail to telecom and Insurance. Solutions like customer acquisition, marketing mix modeling, consumer insights and risk analytics have proven to be very beneficial for these sectors to enhance their sales and overall business performance. To sum it up, analytics market has grown up in a very significant manner
and soon India is going to become a primary hub for the analytic services. Estimates suggest that India accounts for one third of the total $17 billion global market. Analysts believe that the true potential of

analytics hasn’t been realized yet and this is just the beginning. For latest information on analytics, please refer to following links:

• Analytics Outsourcing – Why Fractal?
• Data Analytics: A Huge Opportunity
• The Rise of Analytic Service Providers
• Interview With Srikanth Velamakanni: CEO Fractal Analytics
• Business Analytics in India
• Leveraging analytic solutions to improve insight, performance and customer experiences
• Analytics outsourcing – emerging models in India
• Insurers Seek Competitive Edge Through Underwriting Analytics
• Financial Analytics: Delivering metrics that matter
• Retail risk management software launched
• What senior executives think about analytic priorities
• Blog Analytics Outsourcing – the new buzzword

Analytics Outsourcing

Data analytics helps solve complex business problems by analyzing data and generating useful business insights. Analytics outsourcing has helped several global firms leverage the power of data to generate useful insights and make strategic business decisions. Cost savings, operational efficiencies, access to a highly talented workforce and improved quality are all underlying expectations in outsourcing analytics processes to India.

Analytics outsourcing, like other forms of knowledge process outsourcing helps companies get their analytics needs met without having to take on the responsibility of another new process. The success of knowledge process outsourcing in India was the main driver of outsourcing analytics processes as well. Analytics outsourcing to India can help a company generate savings of at least 30% and get high quality results as well. Further, analytics outsourcing eliminates the need for companies to invest in data analytics technologies and infrastructure. Knowledge process outsourcing to India has helped several companies cut costs and improve bottom lines. Analytics outsourcing in India is seeing a similar trend and is bound to grow at a rapid pace in near future.

Fractal Analytics has helped several large Fortune 500 clients meet their data analytics needs in areas of Marketing, Risk and CRM analytics using sophisticated tools and technologies. Fractal is a pioneer in analytics outsourcing and has been engaged by clients across industries financial services, insurance, telecom, retail, and CPG. The company is one of the leading analytics outsourcing providers in India and more than 70% of its business is repeat business that speaks of the clients’ trust in the company’s abilities.