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Reduce Customer Churn With Artificial Intelligence As A Service For Financial Services

Reduce Customer Churn With Artificial Intelligence As A Service For Financial Services

Time has changed for banking and finance sectors, from exhibiting low customer churn rates to customers having a bundle of options to choose to manage their finances. The financial services industry is flooded with competition from both existing players and new entrants. With so many options, customer churn is an increasingly important battleground for financial institutions.

According to research conducted by Bain & Company, “The cost of acquiring a new customer can be higher than that of retaining a customer by as much as 700%. And, increasing customer retention rates by a mere 5% could increase profits by 25% to 95%.”

Now that being said, let’s jump straight to address the elephant in the room.

Elephant in the room

What is churn & how to manage it?

Churn or customer attrition is the net percentage of customers who discontinued the services of a business within a specified time-period, netting off the new customer acquisitions. Customer churn for financial services can take many forms, like transfer of loans to other lenders, silent attrition in the way of a slight decrease in customer card spend, insurance policy closures, and many more.

In order to reduce customer churn, it is necessary to understand customer’s switching behavior and to identify high-risk clusters to predict churn. It could be either internal or external stimuli that trigger a churn. For example, a product/service performance, agent relationship with customers, market dynamics, technology advancements, and other similar touchpoints. Each churn has different contextual reasoning that makes each attrition unique. The figure below provides various customer touchpoints to better predict fading customer behaviors based on past customer interaction data.

Customer interaction data

However, this requires careful consideration of two critical steps to locate and understand high-risk clusters.

Two critical steps to locate and understand high-risk clusters

1. Recognize and define churn or customer attrition

Financial institutions should have a well thought out definition for customer attrition. The description should consider and capture the tiny details that trigger the churn. A few factors to consider before defining a churn are as follows.

‘Absolute’ vs. ‘Presumed’ churn

When a customer entirely halts a relationship with a bank or credit union, then it’s an absolute churn. For example, a bank account holder closing his account and all other services. On the flip side, if a customer who may stop engaging with the services is presumed churn. For example, a slight decrease in customer card spend. Identify which of the above two categories, your high-risk customers will fall into.

Time-period for churn

In the financial services sector, banks occupy the major pie. But financial services cover much broader sub-categories like lending, leasing, factoring, advisory, wealth management, mutual funds houses, insurance, and brokerage firms, to name a few. While it’s difficult to define the churn period for each of the sub-categories, it’s recommended to categorize based on the business nature and the customer life cycle.

‘Reactive’ vs. ‘Prospective’ churn

Churn due to specific adverse events or experiences is coined as ‘Reactive’ churn. For example, when a customer experience events like unexpected charges, unsatisfactory customer service experience, a tedious dispute resolution process, and other similar instances. On the contrary, a gradual disengagement without any external trigger is a ‘Silent’ or ‘Prospective’ churn. Financial service providers should be proactive to identify customer behavior and bucket them.

Each financial institution might be in a different state of their digital offerings, and it is crucial to recognize and agree to the definition of the potential attritors as described above to predict the early signals.

2. Develop a data bank that enables churn prediction and triggers

Companies today have abundant data sitting somewhere, which is not given the attention it should get. Data on your organizational functions will provide you with the actual functioning of where you stand and how you could progress ahead. Data analysis is essential in business to understand the fading customer behaviors using past customer interaction data. Building a Data Bank should be the ideal step to run any further machine learning and AI-based technologies to generate near-real-time signals for managers to take action on the high-risk clusters.

Overcome churn with AIaaS

For companies that can’t or are unwilling to build, test, and run their artificial intelligence-based churn model, AI-as-a-service [AIaaS] is the solution. Like other “as a service” options, AIaaS allows the company to focus on its core business operations, significantly lowering the risk of investment and increasing the profits by reducing the churn percentage.

To summarize: Start with a sound definition for your customer churn, leverage the available customer data to build a data warehouse, and then use machine learning to develop a churn prediction model.

By following the above steps, financial institutions can identify and predict potential customer attrition and take proactive measures to impact profitability positively.

Click here to download our brochure and know more about Zuci’s AIaaS solution.


Janaha Vivek

I write about fintech, data, and everything around it | Assistant Marketing Manager @ Zuci Systems.