The Hindu Business Line : Analysing consumer behaviour

To reap the maximum benefits from data analytics, firms have to invest in the right technology, hire the right people and develop standardised and robust processes of data collection, data retrieval, data analysis and strategy implementation.

UNDERSTANDING consumer behaviour is the key to success in the marketplace. Companies are constantly looking at customer behavioural patterns to predict future trends. Among the many tools is data analytics. Broadly speaking, data analytics can be described as the process of collecting, analysing and using data (related to demographic information, past behaviour trends, etc) to better understand and predict the behaviour of existing and prospective customers for business decision-making.

The common tools used to conduct data analytics range from simple cross tabulations and segmentation analysis to more sophisticated statistical methods such as multivariate and logistic regression, discriminant analysis and cluster analysis. In the last few years, optimisation tools and machine learning algorithms such as neural networks and genetic algorithms have also been used to perform advanced data analysis.

The recent years have seen increased use of data analytics in driving business strategies across various industries. While the data analytics methods have been extensively used in FMCG, pharma and telecom companies, their mainstay has been the consumer finance industry.

The wide scale applications of predictive data analytics started almost four decades ago in the form of credit scoring models pioneered by Fair, Isaac & Company (FICO) in the United States. These credit scoring models or scorecards were used to predict customer default. Today, the FICO Risk score is the benchmark for credit decision process in the US, so much so that the `Prime' and `Sub Prime' markets are defined on the basis of this score.

With the exponential increase in computing power and application of information technology in business processes, more and more data analytics techniques and statistical tools are now being applied for Marketing, Risk Management, Pricing and NPI functions in the consumer finance industry.

In India, it is common for major banks and financial services companies to use data analytics to better manage their credit card, housing, personal and auto loan and insurance portfolios.

But why are businesses increasingly adopting the use of data analytics in their day-to-day working? Clearly because it allows these firms to predict the behaviour of existing and potential customers. Empowered with this information, firms are able to devise suitable strategies to better manage their respective businesses.

On the risk management front, data analytics techniques can help a bank develop an approval strategy for its mortgage and auto loan applications and also help to determine the optimal lending rate.

The same techniques can help an insurance firm decide the premium for its policyholders. The data analytics techniques have been extensively used in the credit card businesses to decide on credit and cash line assignments and dynamic authorisation and fraud detection activities.

Data analytics is also effectively used in managing the collections functions of the consumer finance companies. Using statistical modelling, the companies are able to predict the likelihood of contacting a customer and chances of receiving a payment from him. This information is helpful in choosing the right collections strategies that optimise collection efficiency and effectiveness.

On the marketing side, the use of data analytics in the form of response models helps companies design and execute cross sell, up sell, deep sell and retention strategies. In the long run, creative use of past customer data through predictive modelling helps companies in building powerful and effective analytical CRM (customer relationship management) platforms.

These analytical CRM platforms allow firms to make suitable offers to its customers and optimise campaigns through e-mail, direct mail, telemarketing and inbound call channels. Consumer finance companies in the US, where the credit bureaus are fairly developed, use data analytics to evaluate the quality of consumer loan and insurance portfolios during mergers, acquisitions and securitisation deals. What do companies need to do to use data analytics effectively? Experts believe that to reap the maximum benefits from data analytics, firms have to invest in the right technology, hire the right people and last but not the least develop standardised and robust processes of data collection, data retrieval, data analysis and strategy implementation.

For example, a company may invest in a separate analytics data mart to capture the relevant customer data. This data are mainly of three types: demographic, behavioural and contact information. While demographic data refers to information about customer characteristics like age, income, etc., behavioural data includes information of customer's prior performance like transaction history and delinquency behaviour. Contact information includes history of prior offers and contacts made to the customer.

Once the data mart is ready, the company needs to build efficient and robust systems for extracting and analyzing data from the data mart. After the required data analysis is completed and a suitable strategy using data analytics has been devised, it is important to ensure that strategies are implemented efficiently and accurately.

The implementation of analytically driven strategies has been rather `painful' process for most companies. However if the right IT infrastructure exists and process planning is rigorous then implementation can be accomplished with minimal disruption of business processes and limited impact on the company's resources.

To facilitate easier and faster implementation, software that integrate with a company's work flow and account receivable systems to implement the risk and marketing strategies are now available. Campaign management packages, systems that enable easy execution and tracking of analytically driven targeted marketing campaigns are also being increasingly used by consumer finance companies.

After a particular business strategy (a new risk policy or marketing campaign) has been implemented, the companies need to measure the performance of the business strategy and make sure that the results can be tracked effectively for future use. The process of continuous designing, executing, and tracking and allows companies to `test and learn' and thereby helps them gain a competitive edge.

The above process requires firms to make investments in technology — database packages, statistical software, implementation platforms, and reporting and analysis tools. Most major software companies have developed data mining and analytics software, however the use of specialised statistical software such as SAS, SPSS for predictive modelling and of reporting and analysis tools such as Business Objects and COGNOS is common.

A team of systems specialists and data analysts is required to develop and maintain efficient data marts and robust implementation and analysis systems. To conduct data analytics, teams of econometric and statistical modellers and business analysts that can effectively perform strategic analysis and build predictive models need to be developed.

Major financial services firms in India have built internal data analytics and business intelligence teams of data analysts and statistical modellers that support marketing and risk management activities. A significant number of independent third party data analytics companies that provide end-to-end data analytics solutions have also mushroomed in the last couple of years.

The market for consumer finance products is growing at a rapid rate in India. To seize this opportunity, new financial services firms are entering the industry and the existing banks are increasingly focussing on retail portfolios. The pressures to make high profits remain high in the face of increasing competition. For consumer finance companies, use of data analytics is no more a luxury but a necessity. Firms that invest in data analytics now will reap in the benefits for a long time to come.

(The author is Business Analytics Manager, GE-SBI Credit Cards.)