By focusing on specific subsets of customers, businesses can better reach and appeal to their core demographic. Using customer segmentation analysis, companies can better target the most profitable segments of their markets with the most suitable offerings at optimal times. To boost revenue and reach a wider demographic, businesses would do well to familiarize themselves with this method.
In this article, we will cover the basics of customer segmentation analysis, including its definition, benefits, methods, implementation in Python via RFM analysis, and a few helpful pointers.
To begin, let us define “customer segmentation.”
Customer segmentation is the practice of dividing a company’s clientele into subsets with similar needs and wants. The purpose of customer segmentation is to determine how to interact with different types of customers so that the lifetime value of each customer is maximized.
What is a customer segmentation analysis?
Customer segmentation analysis is the method used to find out what makes certain groups of customers what they are. Marketers and brands use this process to decide which campaigns, offers, or products to use when speaking to various segments.
For example, a retail brand that wants to figure out how to bring back old customers might create a group of customers who have bought from the eCommerce store in the past but have not bought anything or looked around in the last 30 days. After identifying a specific demographic, the company could study its customers’ purchasing habits, reactions to price cuts, and other factors. With this knowledge, the marketing department can devise a strategy for reengaging these inactive users.
Similarly, a company can use customer segmentation analysis to estimate the worth of different customer subsets by looking at metrics like their predicted Future Value, average order value, loyalty tier distribution, and more.
What are the benefits of customer segmentation?
Customer segmentation has the potential to allow marketers to reach out to each customer in the most effective way possible. Marketers can use the wealth of information about their customers (and potential customers) to conduct a customer segmentation analysis and pinpoint specific groups of customers on the basis of demographic, behavioral, and other indicators.
In order to maximize value (revenue and/or profit) from each customer, it is essential for marketers to anticipate the effects of their actions. Such “action-centric” customer segmentation, ideally, will not prioritize the short-term value of a marketing action over the long-term impact that action will have on customer lifetime value (CLV). Therefore, customers must be categorized (or “segmented”) based on their CLV.
Methods of Customer Segmentation
Decide on a strategy for creating customer segments after you have settled on the categories and attributes you will use as your foundation. Customers are typically divided using either a rule-based or cluster-based method.
A method of customer segmentation known as rule-based segmentation involves manually dividing up groups of customers in accordance with predetermined criteria. It is a simple and easy-to-understand method of segmenting your clientele, but it might not pick up on subtle relationships.
Cluster-based segmentation, on the other hand, uses algorithms to automatically group customers into subgroups based on similarities in multiple attributes, thus elucidating underlying patterns in the data. Although this method is more adaptable and can handle complex datasets, it may be harder to interpret.
The decision between these strategies should be based on the complexity of the data, the need for human intervention, and the organization’s unique customer segmentation objectives.
Define RFM Analysis.
The RFM model classifies customers based on their recency of purchase, frequency of purchase, and monetary value. All three of these RFM metrics—recency, frequency, and monetary value—have significant effects on retention, a proxy for customer engagement.
The Role of RFM Analysis in Customer Segmentation and How to Apply It
Using RFM, marketers can make strategic decisions based on data from customer segments. Marketers can now easily categorize users into like-minded groups, allowing for more targeted and individualized campaigns. As a result, user satisfaction and loyalty are boosted.
Marketers can benefit from RFM analysis by answering the following questions:
- Which clients have proven to be the most profitable for you?
- Who among your clients is at risk of increasing your churn rate?
- Who are the people most likely to become loyal clients?
- Which of your clients can be kept on as regulars?
- Which of your customers is most likely to respond to engagement campaigns?
Customer Segmentation Using RFM Analysis In Python
Recency, frequency, and monetary (RFM) analysis in Python is a customer segmentation method. Businesses can better target specific demographics of consumers by analyzing their purchasing histories in the form of customer segments.
The process includes preparing the data, calculating RFM metrics, possibly changing the data, and standardizing it. Then, based on their RFM scores, K-Means clustering is used to divide customers into groups. The resulting clusters make it easier to find different groups of customers for targeted marketing.
Customer segmentation analysis: Step-by-step guide
When conducting an analysis of a customer segment, it is important to take the following steps:
1. Identify customers The first step in conducting a customer analysis is to determine who the company’s customers are. Additional study is needed. Doing so will aid in the development of subgroup profiles down the road.
2. Organizing clients into groups
- Audiences are divided after thorough research has been conducted.
- Audience segment sizes should be estimated.
- Make segments stand out.
- Eliminate or reorganize small groups.
3. Make customer personas: Use customer data, criteria, and market knowledge to create customer personas. This can help you make marketing content for real people instead of making assumptions about who they are. Think about how you will handle any complaints about your product or service. The brand’s voice should also match how a group communicates.
4. Tell customers what they want: A brand’s products and services will do well in the market if they meet the needs of the people who buy them.
5. Focus on how specific brand features aid customers and users in resolving issues, and adjust your products and services accordingly. Connecting features to what customers want can improve the customer experience, customer insights, sales, product development, and product focus.
6. Put key segments in order of importance. Use your data and initial customer analysis to rank your brand’s most important customers, the ones who respond well to advertising and bring in the most money.
7. Create marketing plans. To reach more customers, use the group’s interests, platforms, and search keywords. You can boost the brand’s ROI by targeting its highest-value segments with marketing funds.
8. Assess the strategy’s effectiveness. Effective analysis requires continuous evaluation strategies.
In order to better understand their clientele, businesses can benefit from segmenting their customers. An organization can gain an edge in the market and boost customer satisfaction, loyalty, and resource efficiency by targeting specific demographic groups with their marketing efforts.
After reading this, I hope you have a better grasp of what customer segmentation is and how it operates.