The path through market segmentation models was full of challenges. Still, combining expert knowledge with fresh ideas led to a new framework. The RFM-based Customer Segmentation Model turned out to be a useful tool. It helped the client find valuable customer groups for more focused marketing.
About Client
A company was having trouble with market segmentation. They wanted to improve their large customer database to better target their marketing.
Challenge
They needed a solid system to group customers by different demographic and economic details, so they could target their marketing better and improve customer relationships.
Our Solution to Get their Desired Results
The RFM (Recency, Frequency, Monetary value) Segmentation Model is a tool used to analyse customer buying habits and group them into clear categories. Our team used SQL to pull data and mixed clustering methods (DB scan, Agglomerative clustering, and K-means) with Ensemble learning. This helped us build an algorithm that groups customers based on their financial and demographic details.
Recency
They looked at how recent customer interactions were to measure engagement.
Frequency
Measured the frequency of purchases to gauge loyalty.
Monetary
Evaluated spending patterns to determine customer value.
What it can do
Diverse Data Integration
Merging a broad spectrum of data to paint a detailed picture of customer profiles.
Instant Lending Decisions
We set up APIs that give fast lending decisions, resulting in happier customers.
Behavioural Analysis
Tools that dive into purchasing habits, illuminating spending behaviours and preferences.
Tailored Insights
We derived clear and practical insights from customer data to guide tailored marketing plans.
Got an idea for your product?
We will build it for you from scratch and share the structured documentation to help you keep your system in top shape.