25 May 2017

Using Natural Language Processing (NLP) to Improve Your Bank’s Customer Engagement and Retention Processes

While banks have been comparatively conservative business entities, in recent years they have found themselves in a business space that is usually more competitive than other verticals. Mobile and digital technology, the fading of customer loyalty, and the dramatically reduced consumer attention span have all spurred many financial institutions into giving up their traditional business approaches.

Customer churn being a greater a problem than ever, banks have become a lot more proactive in their interactions with clients. However, the very nature of a bank’s customer relationships hampers the marketing efforts of many institutions.

In corporate banking, decision makers are often occupied or out of reach; while in retail banking, customers are always in too much of a hurry to take part in marketing surveys. Moreover, they are easily lured into opting for your competitors' product or service when those offerings are instantly available, and they often maintain accounts with several entities.

To make matters worse, the more traditional marketing techniques tend to show you only what a client or group of clients have done. They leave you guessing about the reasons for this kind of consumer behavior. Besides, they are not “fast” enough to allow you to reach out to your public with the right offer at the right time.

Luckily, there are two intertwined NLP techniques that can both help you identify your problem areas and make your customer relationships more profitable. They are Sentiment Analysis and Text Mining also referred to as Text Analytics.

Sentiment Analysis and Text Mining in Banking

Sentiment Analysis allows you to tap into the Internet grape vine and other communication channels (e.g., email, phone) in order to learn your customers’ opinions about your institution’s service or product. You can identify your clientele’s sentiment toward some phenomenon associated with your bank as positive, negative or neutral, as well as gauge the degree of polarity that exists in this sentiment.

Although the above can be very useful in examining and improving a wide range of areas and activities (i.e., branch banking, teller service, the user experience provided by your website, – you name it), your organization’s true treasure lode is its raw textual data. This data includes names, figures, feedback, SMS messages, transactional data, financial reports, u representatives’ notes in your CRM, survey data, and more. Valuable raw data can only be rendered usable and meaningful with the help of Text Mining.

Thus, Text Mining can take your data analysis to a whole new level. It can reveal many viable opportunities for your bank.   What exactly are those opportunities?

Identifying At-Risk Customers

With Text Mining, you can identify those customers who are at risk of withdrawing. Identification can be done based on these users’ interactions with your customer service (via email, messenger conversations, or call transcripts), forum posts, posts on social media, among other routes. You can then reach out to these patrons in order to retain them with your exclusive offers.

Preempting Customer Loss through Complaint Analysis

Text Mining makes it possible to identify customer complaints in your incoming mail and prioritizes them so that accounts in jeopardy can be addressed by your employees without delay.

Enhancing Your Credit Scoring Models

Text Mining can be incorporated into your credit scoring models as a set of processing steps. These steps will detect words or phrases that you designate meaningful in your decision-making process.

Refining Your Lending Process and Making It More Reliable

If your credit risk officers are precluded from making a lending decision by a lack of information on a customer, with Text Mining they will be able to probe the Web for any particulars that they deem serviceable. Results include financial reports, press releases, customer reviews, and information on the customer's Executive Board with related changes.

Enhancing Your Bank’s Marketing Scoring Models

Most banks use scoring models to target their marketing campaigns. Depending on consumer type, these models can take into account such factors as job title, company size, industry segment, demographic data, as well as credit and transaction history. With Text Mining, your existing scoring models can be further enhanced to include customer-specific insights derived from your unstructured data. You might also use unstructured data from your customer’s posts on social networks. In a financial market with increasing competition, your institution can manage customer churn with NLP.  Text mining will enrich your organization with the resources to maintain and enhance your market share.  


Beyond Big Data: Big Text Analytics

Is “Big Text” next big thing?

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