Data-Driven Solutions to Help You Elevate Your Marketing Efforts Today
Elevating marketing with data science: segmentation, recommenders, RFM analysis.
2 mins reading time
Over the last few years, I’ve had the privilege of assisting leaders and marketing teams in implementing several marketing and CX analytics solutions.
Ranging from descriptive (reporting, dashboards, KPIs) to predictive (Churn, Segmentation, Recommenders, etc.). And throughout most of the engagements I’ve noticed a few ideas that can have a real and immediate impact.
In this article I wanted to share three pivotal projects that can quickly augment any business and marketing approach using data science and analytics: customer segmentation models, product recommender systems, and the good old RFM model.
These transformative solutions hold the potential to revamp businesses’ go-to-market efforts and drive success further.
Segmentation Models
Segmentation models allow businesses to group their customers into distinct categories based on shared characteristics. A retailer, for instance, could use these models to identify high-value customers who frequently make large purchases. The benefits are substantial: increased customer engagement, improved conversion rates, and enhanced customer satisfaction. Without segmentation models, businesses are essentially in the dark, employing a one-size-fits-all approach that might miss the mark for different customer groups.
Recommender Systems
Think of the movie recommendations on Netflix or product suggestions on Amazon. These systems analyse user behaviour and preferences to suggest products or content that the user is likely to enjoy. This translates into higher sales and user engagement. Without recommendation engines, businesses may struggle to cross-sell or up-sell effectively, potentially leaving substantial commercial opportunities on the table.
RFM Analysis
Recency, Frequency, Monetary (RFM) analysis is such an old approach to classifying customers. But it is also a vital tool for understanding customer behavior and segmenting them accordingly. An online business, for example, might use RFM analysis to identify customers who frequently make purchases (high frequency), recently made a purchase (high recency), and spend generously (high monetary value). Without an RFM analysis, businesses risk losing valuable insights into customer preferences, waste valuable marketing dollars, have a suboptimal customer retention, and consequently, lower customer lifetime value.
Now we all agree that these strategies are not groundbreaking or novel; in fact, they have been tried and tested with great success in many industries. However, there are still a bunch of organisations that haven’t done it at all or haven’t done it well and consistently enough. What makes them worth mentioning though is how effective and the swift they are to implement.
So, for all business leaders out there, consider adopting these tried-and-true solutions to unlock the full potential of your hard earn budgets today.