With banking products becoming increasingly commoditized, Analytics can help banks differentiate themselves and gain a competitive edge. This paper delineates the various ways that banks can use Analytics at every stage of the customer lifecycle. In addition, it talks about how banks can prepare themselves to embark on this journey.
A typical day at a bank
Picture this: It is a typical start of the day at a bank that has a daily heavy foot fall. Shutters open to let in the stream of customers waiting outside the branch. Branch officers are neatly seated at their respective counters such as the ‘Demand Draft,’ or the ‘May I Help You’ desk. A peek inside the back office shows the branch manager conducting his daily sales huddle with his relationship managers, sales officers and field staff. The mood is highly charged, almost aggressive and abrasive. They are being asked about the numbers planned for the day, and those not keeping up with the daily run rate are being reprimanded.
The mood is somber and tense; the meeting ends with a strict warning to the sales team and a reminder to meet the numbers, else face severe consequences. Does this sound familiar to bankers or sales personnel?
If the answer is yes, then it is time to take stock of the traditional way of doing business. Banking products are getting commoditized, and the features of all banks’ products — be they current account, savings account, fixed deposits, personal loans, or credit cards — are very similar. How, then, can banks differentiate and grow their business? No matter how much strategizing happens at the top level, it is these sales officers at the bottom of the pyramid who are going to bring in the business. If they are not given direction, and not shown the way, the business model will not be sustainable. As an old proverb says, “If you don’t know where you are, you won’t know where to go.” Brute force and threats to bring in business may bear results in the short run, but not in the long run. It could also lead to malpractices by the sales officers due to the immense pressure being put on them by their managers, and can also lead to talent attrition.
Analytics is the answer
So how do we provide the sales force with enough ammunition that they can go to the market with confidence and conviction rather than fear and despair? How do we ensure that the leadership and senior management guide their teams rather than whip them endlessly?
The answer to this is: Analytics
The sales process cycle in a bank comprises the following steps:
- Knowing your target audience
- Ascertaining whether they fit into your criteria(eligibility, profiling)
- Conducting a sales call
- Following up
- Ensuring closure
And from the perspective of senior management, another final step — monitoring and tracking numbers.
Sales personnel know that the following information can greatly help them at various stages in the sales process:
- A list of probable customers obtained by doing a basic check of products purchased and the ones not yet purchased by them\ (from existing data)
- A list of products most likely to be purchased by them, based on his/her behavioral trends and buying habits
- The likes and dislikes of the customers and their preferences
However, solutions like Next Best Offer (the use of predictive analytics to identify the products or services that customers are most likely to be interested in for their next purchase) and Locational Intelligence already provide the above data. The question then arises — are there any other, smarter ways to provide this information?
Credit risk and Collection
- Lend to right type of customers.
- Monitor collections.
- Predict and reduce delinquencies.
- Reduce NPA and increase profitability.
HR and performance management.
- Track performance V/s business objectives.
- Get the best out of people.
- Reduce attrition.
Finance and treasury
- Determine interest rates and forecast NII .
- Monitor and control interest rate risk.
- Establish risk tolerance levels and submit Intelligence to ALCO.
- Manage overall funds situation and FTP.
Marketing and sales
- Design products and make customers aware through various marketing channels.
- Maximize sales at minimum cost through optimizing revenue.
- Increase customer loyalty and reduce attrition.
The officer who monitors credit risk will need to understand the types of customers, monitor collections, predict and reduce delinquencies and reduce non-performing assets. Analytics is essential if a high level of KPIs is to be achieved. Similarly, departmental heads managing functions like marketing and sales, HR, finance and treasury must rely on Analytics to enhance their performance and work.
While the Management Information System (MIS) can provide numbers to the functional head, it cannot bestow decisionmaking powers; the latter requires that some more processes/ algorithms are run on the MIS to provide deeper insights. For example, MIS can provide the Asset Liability Management (ALM) function with information on the various types of deposits and the amount held in each, or loans coupled with repayment cash flows etc. However, to conduct a ‘What If’ analysis and play around with the numbers to develop various scenarios, Analytics is required.
Equipping a bank for the analytics jump/Building analytics capabilities
The first step towards building capabilities is recognizing the fact that it is imperative to business and growth. As simple as it may sound, many banks are yet to recognize Analytics as an important and strategic pillar. Having once recognized the same, the bank needs to have buy-in from senior management.
We believe that any medium or large sized bank should necessarily have an Analytics department. This department would cut across different verticals or Line of businesses like Retail Banking, Corporate Banking, Investment Banking, subgroups like trade finance, Home Loans, Personal Loans and so on.
The analytics team should be a healthy mix of statisticians, resources who have experience on analytical tools and software, thought leaders, and leaders from line of businesses. The next step is to arrive at a high level problem statement and a probable solution around it. For example, reducing NPA on retail loans or increasing customer stickiness is a high level problem statement. The next level would be on arriving at a solution and how one can use analytics to overcome the problem.
This would involve finding the right set of tools, finding a right partner OEM and/or SI, articulating the business problems which the solution partner would translate into processes and the end state architecture. It may involve building propensity models and statistical models. This would need active participation from the core team as established by the bank.
There would be challenges at each step, right from identifying the problem statement to articulating business imperatives, identifying right set of tools and solutions etc. But once all of it falls in place the results that it would yield would be worth every effort put in.
Benefits of Analytics in the selling process
Analytics can provide the senior management with valuable inputs at each stage in the customer lifecycle. Figure 2 below gives a detailed perspective on the typical life cycle of the banking customer and its various stages from onboarding onwards. It also provides the type of information and insights that analytics can provide at each stage.