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How banks can use data mining for direct, personalized offers

Customer Experience Management has become a key topic in modern retail banking industry in recent years because banks are trying to improve upon their traditional product and transaction-focused business model. They are now trying to transition to a customer centric approach and to improve cross-selling, up-selling and enhance share of wallet.

Customer Experience Management (CEM) is the practice of designing and reacting to all customers interactions in a way that meets or exceeds customer expectations. The main aim is to increase customer satisfaction and loyalty. Of course this process consists of many stages and depends on particular banking goals and activities, for example, creating a good product and preparing marketing materials. One specifically valuable task is training personnel to use Data Mining models to predict customer responses and create personalized offers.

We’re focusing on CEM in this post, because we found that it’s a top priority for bankers and can be enriched with data mining. Here we will try to describe in more detail how data mining helps banks, specifically how data mining analytical models of customer data can improve CEM performance and help banks differentiate by offering products that address customer needs.

The challenge: Customer centricity

It’s very convenient for banks to imagine that they can “put the customer first”, while also have customers neatly ask (and use) the products that are essential to the bank’s sales plan. But the reality is not so neat. Unfortunately, there are many competitors, customers have become both more discerning and more demanding, and expensive advertising “for all” doesn’t work so well. So a “customer first” strategy must be transformed to a “bank first” strategy: when should the bank initiate the conversation with customers and offer some products. Is that the way to successful sales? Yes, of course… but only if the bank offers something interesting for customers, in the most convenient way.

This “return to basics” trend – how to switch the business model from a product-centric to a customer-centric approach – is the most significant challenge for many retail banks because it demands new technologies in extracting knowledge about customers’ needs from available data.

How Big Data analytics improves CEM performance

Banks already have a variety of customer data. In addition to personal information and data about accounts and transactions, banks can collect data such as purchase histories, channel usage, and geo-locational preferences to create a 360-degree view of the customer. After collecting and “mining” new knowledge about customer needs this analytics could be the key of successful CEM practice. The picture below shows the channels from where customer data is acquired, and what can be done with it for CEM after analysis. It emphasizes two levels of using customer data in the CEM practice.

Customer experience management for banks with big data analytics and data mining

Level 1: Personalization

To realize the Level 1 tasks, banks can use a combination of Big Data management, Predictive Data Mining models such as Regression, Neuron Networks, Machine Learning and Clustering and Segmentation algorithms with the following aims:

  • Predict customer responses to product offers and evaluate their needs;
  • Predict customer reactions to some changes in product offers and create personalized offers.

After realizing these tasks, banks can work with predicted estimation of customer responses to offer fitting products and to create personal offers that could raise probability of positive responses.

Level 2: Flexibility

To achieve the tasks in Level 2, banks can implement the practice of real-time offers in current business models. It means that each customer can get the offer about the most interesting product (or product bundles) in the most convenient way at the proper time.

Dynamic personalized pricing is also the way of increasing sales. A range of products a bank offers is relatively limited, so innovation in product bundling and personalized pricing is essential for a customer-centric approach to sales.

Offers designed around customer needs

In a nutshell, switching to a customer-centric approach is a challenge for bankers that can be tackled with data mining tools. And the payoffs should be worth the investment.

The challenge:

The task of switching the business model from a product-centric to a customer-centric approach is the most significant challenge for many retail banks. It demands new technologies of knowledge extraction about customer needs from available data.

The solution:

Using a combination of Big Data Management and Predictive Data Mining models can help banks predict customer responses to product offers and create personal offers that raise probability of positive responses.

Business value:

Evaluating customer needs, predicting their responses and practicing real-time offers based on predicted needs could be the key to increasing business efficiency, employee productivity and reducing costs.

By applying the methods of big data management and predictive data mining to their already existing customer data, banks gain a much better insight into the products their customers actually want. They can create offers which are more likely to be successful, which lead to increasing sales and business efficiency, rising employee productivity and reducing costs for unsuccessful contacts with customers.

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