RFM ANALYSIS

Introduction:

Before getting into RFM analysis, we must first understand marketing analysis. Marketers gather information and conduct proper research to determine whether the product they intend to promote will address customers’ demands and pain concerns. A company needs marketing analysis to strengthen its position in a particular market, stay competitive, and provide value to customers. After a thorough investigation, entrepreneurs gain useful knowledge about market trends, rivals, demography, economic movements, and customer purchasing patterns.

Now that we understood a little about marketing strategies every company tries to segment its customers based on various parameters like demographics, purchase patterns etc., the most effective way to communicate with your target customers is by making them part of a group.

RFM analysis is one kind of marketing analysis to segment a firm’s customers and identify its best customers based on their spending habits.

What is RFM Analysis:

RFM (Recency, Frequency, and Monetary Value) refer to three important client characteristics.

It specifically assesses:

• Recency: how recently a purchase was made. Businesses typically measure recency in days, but depending on the product, they may measure it in years, weeks, or even hours. Customers who have just made a purchase are more likely to make another one in the future compared to those who haven’t done so in months or even longer time frames. Such data can be utilized to entice previous consumers to return and spend more money.

• Frequency: how often a customer makes purchases. Marketing initiatives may be targeted at encouraging customers to visit the firm when essential things run out if the purchase cycle can be forecast, such as when a consumer wants to buy additional food. In order to turn first-time clients into loyal customers, follow-up advertising may make them a viable target.

• Monetary: how much money one spends on purchases. Spending customers are valuable to a company since they are more likely to make future purchases. While this may result in a higher return on investment (ROI) for marketing and customer service, it also runs the risk of losing loyal consumers who might not be spending as much per transaction but have remained loyal nonetheless.

How RFM analysis works:

Each of the three key variables receives a score in RFM analysis for customers. Typically, a score between 1 and 5, with 5 being the highest, is assigned. Finally, by adding up these customers’ individual R, F, and M scores, we may rate them using an overall RFM score. To determine the final score, you may increase or reduce the relative value of each RFM variable depending on the nature of your businesses.

Significance of RFM Score:

Comparisons between potential contributors and clients are possible thanks to RFM analysis. It helps businesses understand how much of their revenue comes from returning customers (as opposed to new customers) and what levers they can use to try to make customers happier so they will buy from them again

Customer Segmentation using RFM Score:

Best Clients: These are the clients who receive the highest ratings across the board. They are dependable, generous with their money, and likely to make further purchases shortly. You can pitch them for higher ticket size products and recommending products based on past purchases.

Devoted Clients: These customers are really good at frequency score. Even if they buy frequently, they might not be your largest spenders, so think about rewarding them with free delivery or other deals.

Top Spenders: These customers are scored highest in terms of monetary score. Usually, luxury deals, higher subscription levels, and value-added cross- and upsells that boost average order value are used by marketers to target this group. Once more, it makes logical not to reduce margins by giving discounts.

Risky Customers: These customers are least scored in terms of all three factors. Targeting consumers with messages focused at retention, such discounted prices, limited-time deals, and new product releases, is something marketers should think about doing.

RFM Analysis in Health Insurance:

Though we can’t expect customers to buy multiple health insurance as compared to usual retail industry since the scope is bit limited. So RFM analysis at customer level is bit insignificant in health insurance space. However, we can replicate the same method to our sales personnel.

We can build an RFM model to segment our Agents and take necessary actions. We can identify the group of agents who are really great in terms of doing business and can offer customized incentive plans for them. And we can launch few interesting schemes to motivate the group which are not so active.

Conclusion:

RFM is a data-driven approach to consumer segmentation that enables marketers to make strategic choices. It enables marketers to swiftly classify people into uniform categories, segment them, and then target them with specialised and individualised marketing campaigns. This enhances user retention and engagement.