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How the RFM Analysis Can Help You Segment Your Customers
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How the RFM Analysis Can Help You Segment Your Customers

One of the most popular techniques for customer base segmentation is the RFM Analysis. RFM segmentation helps you identify groups of customers for personalized, targeted marketing messaging. By grouping your customers, you can target them with personalized communications that are relevant for them, thus generating a higher response rate and an improved overall customer experience.

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How Customer Behavior Helps You Define The RFM Segments
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How Customer Behavior Helps You Define The RFM Segments

The key idea is that not all customers are created equal so it’s vital to know where to draw the lines and who to target in your marketing strategies and campaigns.

Let’s continue the “Know your customer” part of this series by understanding when a customer is no longer a new customer, how many times they should buy before we should propose a subscription model to them and what is the threshold beyond which we should consider promoting them to VIP status?

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How Data Driven Segmentation Supports Personalized Marketing
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How Data Driven Segmentation Supports Personalized Marketing

In the previous article we saw how combining the 3 dimensions of the RFM analysis will result in clearly defined customer segments. Now that we have a full picture of what our customer base is made of and we understand that each segment has different needs, it’s time to move forward and put in place a marketing strategy that would maximize the engagement and customer experience of each segment.

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Doctor in a modern office reviewing patient health data analytics on a large TV screen.

The Power of Data Analytics in Healthcare

Data analytics in healthcare is driving improved patient outcomes and operational efficiency. Dive into this comprehensive exploration of its key uses, successful case studies, and future potential, and understand how we can overcome challenges to

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Diverse group of business professionals discussing digital and printed charts at a table.

The Challenge of Premature ML and AI Deployment

Many organizations rush into advanced analytics like machine learning and artificial intelligence, aiming for a competitive edge. However, without a foundational strategy in data collection, storage, and reporting, this approach risks inefficiency and unexpected costs.

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