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How big data & business analytics are used by banks to detect & avoid customer attrition.

Commercial banks have seen a shift in customers’ number. Currently, most banks are facing problems of Acquiring, growing and retaining customers.

Many customers are changing banks, and that urgent action is needed to detect and prevent customer churn.

But what is the cause of this trend? And how much are the activities of the banks to blame?

 Doing more marketing and promotions are neither valid nor helpful.

The best solution, I believe, is to use big data and business analysis in three key areas related to customers:


  1. Risk analytics, 
  2. Customer experience,
  3. Operations optimisation.


The power of all this data can enable better product creation,  accurate business forecasting,  mitigation of business risks, and more efficient and streamlined marketing and business systems.

Banks have been losing customers to their competitors.

According to the study, Consumer Banking Survey done by Ernst & Young in 2012, over 50% of customers either changed their bank or were planning to change.

This churning of customers has the potential to create serious problems.

Fewer customers being retained and acquired means that in the long term.

A smaller proportion of the customers will be actively transacting with the banks, whilst the banks will be struggling to make money from the business to finance its operations.

Most experts agree that, without customers, organisations would not exist let alone survive in this competitive global environment.

So what are the cause of this trend and what can be done to stop it?

One common approach has been to use big data and business analytics.

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A data mining technique, which is an automated process of analysing, organisation or grouping a large set of data from different perspectives and summarising it into useful information using special algorithms.

Financial institutions hold volumes of unstructured data.

Big Data - Envestreet Financial

Historically, this data has been largely underutilised but with advances in technology

They are now able to tap into different types of unstructured data like:

Machine data from ATMs & servers, 
Social data from Facebook & Twitter, 
Click-stream data from websites, 
Voice logs from call centers, 
Communication data from e-mails etc.

It is argued that, banks possess far more data about customers than any other industry,

But in spite of all the data, customer-centric companies like banks are often unable to deliver effective personalised service.

Banks are turning data and analytics into competitive advantage.

Most of commercial banks are investing in platforms that combine structured and unstructured data.

They leverage data science and analytics to obtain powerful insights.

These helps them to understand market traction and opportunities to drive better and seamless customer experience across channels and reduce operating expenses.

Banks are now incorporating new technology and techniques like Neural Network.

This technique has a valuable forecast tool in financial economics.

This is because of its abilities of learning, generalisation and nonlinear behaviour properties.

It’s a powerful general-purpose software tool used for a number of data analysis tasks such as prediction, classification and clustering.

Neural networks are used by financial institutions in areas such as credit rating and predicting bankruptcy, forecasting exchange rates and cash forecasting in order to achieve a reliable decision-making process through scientific approaches.

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In order to be competitive in this market, banks are trying to predict possible churners and take proactive actions to retain valuable loyal customers.

Cross-selling is one of the most important ways to increase the profitability of existing customers.

Banks are using big data to analyse consumer behaviours to identify customer buying trends and needs for new product recommendation.

women working in a data center

Using these trends and behaviours, banks are now Selling additional products to customers they associate with thus increasing their loyalty.

Analysing the data available, banks can determine the next best offer for a particular client. For example, bank could offer car insurance together with the car loan.

By doing these, Consumers on their part will enjoy high-quality products and services that are specifically targeted to their needs and behaviour, with enhanced availability and convenience.

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