How Banks Meet Customer Expectations

The relationship between company and customer is based on trust, particularly in the case of financial organizations. Customers seek financial products to save, invest or take risk. This relationship works in the backdrop of macro and micro conditions. Financial organizations like banks or investment houses can try and control micro conditions with meaningful customer oriented policies. Macro conditions such as the financial crisis of 2000 or the instability of the European markets, thanks to Brexit or non-compliance to austerity measures by the Greeks lie outside the purview of organizations. Unfortunately, organizations have to bear the brunt of the customer fears, aspirations and concerns.

E&Y reports that it costs banks six times more to acquire new customers as compared to keeping them.  This is a clear case for implementing measures to retain existing customers. How does a bank understand the underlying concerns of customers, what they value currently and what are their future expectations?

Earlier customers banked with one bank through generations. The volatile economic environment has changed all that, with customers holding multiple accounts and quick to migrate if they find their objectives not being met,

In this situation, banks (or other financial organizations) enlist the service of a market researcher, who conducts a survey to capture consumer insights and therefore help formulate strategies for retention and growth,

The researcher will spend time with the financial organization to understand the problem before formulating the survey. The survey questions will go through a few rounds of discussion and revision before a finalized list is arrived at. Traditionally surveyors were sent out to seek respondents. However this did not always result in a targeted audience. Though this method is still in use, paid surveys are increasingly gaining traction.

Respondents get paid a small fee for answering a survey and works very well when a large number of respondents are required. Furthermore, respondents can take the survey online, anytime which helps deepen the outcomes of the survey.

Bank customers could for instance rate trust as the first criteria for staying on with a bank. The survey could throw up reasons for having a second account with another bank – better interest rate, specific financial product – which help the bank fine-tune its marketing strategy; feedback can also be relayed to the product development team. The product team can use this information to design better products such as loans, credit cards, saving accounts and even offer premium features that could increase their ROI. The survey can also capture what the bank is doing right that customers are choosing to be loyal. In this the bank can arrive at what to change and what to keep retaining its customers.

Market research have become an integral part of customer understanding and strategy.  If it is observed that trust has diminished, whereas there are no complaints with customer attention, the bank can unroll communication that addresses this concern. Communicating that the bank is Basel compliant and takes strong objection to any deviance from ethical practices is one way of improving trust. Greater customer interactions via workshops or face to face meetings to list how the bank  intends to protect itself from market risk or any other sudden downturns instills customer confidence.

The advent of technology has made banking and financial products fairly commoditized.  The survey can help ask questions that will help the bank differentiate it from others. For instance on a scale of one to ten where do customers rate the banking experience? A closer look at customers with low satisfaction scores will reveal the cause for dissatisfaction and therefore redressal.

Capturing customer perception through surveys can help organizations do a course correction, retain existing customers and acquire new ones.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.