The rise and prevalence of an approach of personalization have created a lot of scope for healthy competition among businesses to get creative about ways to acquire and retain customers. Customer segmentation is the first step toward effective personalization and can guide businesses in making decisions about marketing and pricing strategies, new features, and new products, and even get effective at delivering the right in-app recommendations which can nudge the customer towards making a purchase. Customer segmentation was done manually previously, but this approach used to be time-consuming and ineffective to get faster insights. So, why not use machine learning to do this process? This blog explores the domain of customer segmentation, the reasons why it should be done with machine learning, and some of the unique benefits it bring to businesses in the BFSI and retail industries.

What is Customer Segmentation?

Customer Segmentation is segmenting customers into groups according to certain parameters such as age, location, gender, etc. It is a very useful way for businesses to know their customers well to make strategic decisions in marketing and product growth. The characteristics by which an organization can group customers can start from basic ones such as age, gender, and location, and up to ones such as ‘time spent on the app’, ‘time spent on the website’ etc. The potential to split and segment is endless and mainly depends on the amount of customer data available.
There are four types of parameters for customer segmentation:
⦁ Geographic – country, city, zip code, etc.
⦁ Demographic – age, gender, income, occupation, etc.
⦁ Behavioral – past observed behaviors of customers such as products purchased, peak spending and purchase times, etc.
⦁ Psychological – personality traits, attitudes, beliefs, etc.

Customer Segmentation using AI
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Why use Machine Learning for Customer Segmentation?

Machine Learning is an excellent tool for customer segmentation as it can analyze huge volumes of customer data at a pace far exceeding manual methods of segmentation. Machine learning can efficiently and precisely identify customer segments that are harder to do manually or with other conventional methods such as rule-based programming.

Less time

Customer segmentation was previously done manually which took months or years. An analyst would acquire reams of data and populate different tables and then apply statistics to find patterns like a detective with a magnifying glass. The insights obtained with this approach, would have become obsolete as new data reflecting changes in customer behavior and business conditions pours in. It’s now a lot easier to apply machine learning with the support of the tools and software packages are available to use and get insights that can be used to make decisions.

Easy to retrain

The customer segmentation use case is not a ‘develop once and use forever project. After developing the first model, data keeps pouring into the databases, business environments fluctuate, and customer preferences keep changing in line with the trends prevailing in the market. A solution to this problem would be to keep the first model as a starting point and keep retraining it with new data as it keeps pouring in.

Scalability

Scalability refers to the ability to scale up or down with demand. Employing a cloud infrastructure increases your ability to scale up your resources as you acquire and retain customers. Let’s say an e-commerce platform is starting up and presently has ten thousand customers. After two years from now, it will have two million customers. Given this increase in customers, a team of data scientists without cloud infrastructure at their disposal would have to ask for additional expensive infrastructure to handle their work. But with cloud computing, this would not happen as they can just keep on working as usual and all the additional needs for infrastructure like servers, databases, and containers would scale up with demand.

ML Algorithms

In general, there are numerous machine learning algorithms each of which is appropriate for specific class of problems. For customer segmentation, clustering algorithms are the most appropriate as the number of segments are not known beforehand. Let’s take a look at a few ML algorithms used in customer segmentation.

⦁ Collaborative Filtering

Collaborative filtering is used to segment customers with similar purchasing habits or buying behaviors. It is one of the techniques used in the more recent wave of customer segmentation.

⦁ Clustering algorithms

Clustering algorithms divide the points in the dataset into groups of points whose members share similar characteristics. There are several clustering algorithms each of which differs in its understanding of what constitutes a cluster and the method by which they achieve clustering:
⦁ Hierarchical clustering (also referred to as connectivity-based clustering) algorithms
⦁ Partitioning clustering (also referred to as Centroid-based clustering) algorithms
⦁ Density-based clustering algorithms
⦁ Distribution-based clustering algorithms

Benefits of Customer Segmentation in BFSI

Banks and insurance companies operate in a highly saturated and competitive market. There is a need for small and medium-sized players to deeply understand their customers better to compete and thrive against the big incumbents. To do this, BFSI companies need to segment their customers by more than just demographic parameters such as age, gender, location, occupation, etc. Fortunately, companies in this sector have massively large volumes of detailed data about their customers which puts them at an early advantage. There are several advantages of employing customer segmentation using Machine Learning and AI.

Better Insights on Customers for Acquisition and Retention

Applying and optimizing the right machine learning clustering algorithms on your customer data could yield insights into which customer segments are underserved and could indicate potential strategies and actions to serve them better. Customer segmentation can also be used on CRM data to acquire new customers. For example, customer segmentation could indicate that men living in urban areas in their 20s tend to avail of a loan to buy their first or second vehicle. Sending the right marketing messages targeting the right customers of your competitors who fit in this segment with lesser interest rates can make a sizable percentage of the shift to your company’s loan product.

Better Customer Experience and Loyalty

The intelligence derived from customer segmentation can be used to target customers with appropriate content and also offer them customized products and services specific to their needs and financial status to retain and grow your organization’s relationship with them.

Benefits of Customer Segmentation in Retail

The landscape of the retail industry and the customer expectations associated with it has undergone a dramatic change and upheaval in the last few recent years. Instead, customers look and prioritize shopping with retailers who cater to their needs and ever-changing expectations in line with the changing trends in popular culture. In this new environment, customer segmentation has become the need of the hour for retailers to identify what their customers need and serve them effectively. The benefits for retailers including customer segmentation as a tool in the arsenal of their AI tech stack are listed here.

Better serve the customers

Customer segmentation is all about segmenting your customers into groups with similar needs. Defining your segments better will allow you to employ specialized marketing strategies and target those segments better by creating personalized incentives to shop for products of different ranges. For example, suppose that one of the segments is women under the age of thirty-five shopping for clothes at the lower end of the spectrum. This will allow you to create personalized marketing messages with discounts for clothes that are fashionable and are also priced appropriately for their respective budgets. This will incentivize the customers of that segment to shop with your brand as you create a personal connection by addressing their necessities.

Greater focus for your Brand

It is necessary for any brand that its marketing budget is utilized efficiently. Instead, the marketing resources of many brands are wasted on either targeting the wrong segment or delivering general messages to all customers irrespective of their needs and wants. Customer segmentation with machine learning ensures that the right segments of customers are targeted with personalized marketing communication that resonates with their needs and aspirations, bringing more visibility and focus to your brand.

Market expansion

Demographic customer segmentation will identify groups of customers who will purchase and do business with the brand but are presently not served. This will allow you to invest capital and resources to expand your market while potentially resulting in greater ROI on your investments if done appropriately.

Conclusion

Customer segmentation, if done appropriately can ensure an unbeatable edge for your organization in the market, if it is also utilized to the hilt. It is unwise to target your customer base with a one-size-fits-all approach, especially with current generation where customers differ greatly in terms of their needs and aspirations. Customer segmentation performed with ML and AI results in greater customer experience and brings more revenue and profits for any business that utilizes it properly.

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