Blog article
See all stories »

The Subtle Art of Segmenting your Customer Base at a Bank with AI

Introduction

With widespread population growth across various geographies, and with each customer’s expectations skyrocketing, it is imperative to segment customers based on their preferences and uniqueness. However, there exists a major bottleneck in the whole process- i.e. the stage of collecting data across several customer touchpoints to gain some sort of insights from them. Hence, it is important for banks to learn the art of segmenting customers efficiently and effectively, to gain insights on how customers would want to be treated.

For example, let’s take the case of Netflix. Netflix, using the approach of collaborative filtering, created their customer base out of several thousand taste groups based on their customer’s behavior. Netflix collected data based on the customer usage on the platform, such as fast-forwarding and rewinding behavior on select movies and TV shows. This enabled them to segregate customers effectively, which also enabled them to have a self-learning, machine learning algorithm which provided top quality recommendation engines for customers on the platform.

Building customer experiences and enriching them, is similar to building a bridge – a bridge which is a two-way street, where the customer as well as the bank can share challenges and talk on the latest trends that one needs to pick up or discover for better quality services.

At present, with the banking and financial services industry booming, there is a vacuum where, the opportunity is present to enhance customer experience. With immense terabytes of data that is available in a company or banks’ storage, it is important to extract insights to obtain quality customer experience. Data ingestion does take place at banks. However, banks at times, fail to extract data across all customer touchpoints. At the same time, it is important to use the data available to segment customers and treat each taste group uniquely.

Customer Segmentation – Why is it Important and what does it Lead to?

Let us take a scenario where, the bank completes efficient customer segmentation through a complete and thorough data ingestion process where the database sucks in quality data from different customer touch points and pain points. The bank now has quality data and has effectively placed it across various taste groups based on previous customer behaviour, transactions, payments, social media behaviour and so on.

So where does Customer Segmentation lead to? Once there is effective segmentation of your data, the creation of taste groups takes place with different customers in each bucket, the machine-learning algorithm sucks the cleansed data, through which predictive insights and future prognosis take place.

The functionalities around customer segmentation:  At the same time, it is vital to note, that there should be cleansed data before sending it for obtaining quality customer intelligence. Often, data tends to be present as cluttered and messy without any real structure. Often data appears as outliers or incorrect values, which change the direction in which banks should predict and provide insightful information. Hence, it is vital to obtain a one-true version of your customer data before having a self-learnt algorithm to study the data. This happens through a clear master data management process, which would govern the flow of data within the bank.

What is In It for Me? – Basing Your Strategy from a Customer’s Perspective

Security and privacy of data is a major concern among all customers. However, there are several footprints of customer data that are present across different platforms and these data points – both quantitative and qualitative data – influence customer satisfaction and enrich the experience of the customer greatly.

“Customers are looking for results, not features. Benefits overpower the value of multiple features.”

Why Act Now?

Only 26% of customers prefer to stay based on brand while 74% of customers prefer to stay with the brand based on the product.

Customer Loyalty is plummeting and so is patience. Customers are not willing to wait long enough with a bank unless they get quality profits in return. The retail industry reached the point a couple of years back and now banks have reached it too. With the millennial crowd coming, and online, mobile banking and new payment options coming up, it is vital for banks to act now to engage instead of disengaging.

Classification of Your Customer – What’s the Right Approach?

Classification of customers at banks has already been in place for quite some time. This type of segregation bases itself on monetary value that the customer brings to the bank. However this results in a huge split in customer satisfaction and engagement. High Monetary valued (HMV) customers feel enhanced customer satisfaction while the experience is missing for the rest of the customers.

So how do you segregate your customers and make each one unique? Well, quality customer segregation depends on quality data ingestion and extraction. Each bank should know where to look, to obtain relevant data across platforms. Some of the platforms from which banks can collect data are transaction history details such as payments, bills, invoices and statements. Data could also be in the form of feedbacks, suggestions and complaints as well as social media behaviors of the customer. Analysis to responses from SMS, calls and other engagement metrics could also take place in order to find deeper customer behavior. The frequency of usage of banking services and products is also another vital metric which helps banks to move forward to obtain quality customer intelligence.

Recommendation Engines: The Result of Quality Customer Segmentation

Quality Customer Segmentation leads to quality recommendation engines which could provide a probability of how much the customer would be able to adapt to the product or service. It could provide higher accuracy for the customer in what they are actually looking for and customer loyalty would start to go on a linear curve again. More importantly, little or minimal risk would be present for the customers while they choose the product of their choice.

Conclusion

With data collection reaching new highs and inadequate use of technology to make use of the available data, it is extremely important that banks act now and have a customer segmentation strategy, so that there is effective use of data which leads to ROI generation. Banks and other financial institutions have prioritized customer experience and satisfaction for quite a while. With several outsider threats from retail giants who are moving into financial payments and online wallets, it is extremely vital for banks to pick up this threat with quality enhancement of their current technology usage. Besides with several challenges and issues blocking the ultimate customer satisfaction, segmenting your customer and using data to draw insights could be the key to enhanced experiences at banks.

  

 

157
External | what does this mean?
This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.

Comments: (0)

Ashish Cherian

Ashish Cherian

Banking Research Analyst

Aspire Systems

Member since

26 Mar

Location

India

Blog posts

2

Comments

0

This post is from a series of posts in the group:

Fintech

Fintech discussions and conversations around the development of fintech.


See all