Perfecting Segmentation: Bolstering your ESP with AI

Email Service Providers (ESPs) are a great starting point when trying to segment your customers, but nowadays, you're going to need more than just an ESP to make your campaigns the best of the best. We can't live without ESPs — I mean, they’re absolutely critical for delivering messages to consumers across various platforms.

And while ESP’s are great at delivery and compliance, we’ve found the key to amplifying their existing impact even further: a robust RFM (or AI) model that enables brands to have more granular and predictive user segmentation.

Deeper Segmentation

Because ESPs are so focused on the delivery of the message, they often lack more complex segmentation tools and models. This makes it harder for brands to group customers into more meaningful segments without the help of a separate tool.

Personalization is key when it comes to making an impression on customers, which is why integrating an external RFM model could help enhance ESP’s existing segmentation capabilities.

Expanding on Retention Insights

Understanding retention is becoming more and more important for retail and ecommerce companies. The way you appeal to new customers, loyal customers, and at-risk customers is going to be very different.

If you have a new customer who just made their first purchase, your goal is to get this person to stick around. In an early email campaign, you would probably want to make a few personalized product recommendations based on their purchase and browsing history to make a good impression. On the other hand, if you have a customer who is more at-risk of churning, you may want to throw in a personalized promotion alongside strong product recommendations.

ESPs already provide foundational insights into customer retention levels, but simply incorporating an extra AI model or experimenting with external RFM would ensure precision and accuracy are optimized.

Predicting Complex Patterns

Finally, ESPs prioritize reliable distribution over complex prediction. Any predictive capabilities are more focused on delivery. However, being able to predict more nuanced patterns gives you time to adjust for these changes before it’s too late and show a deeper understanding of your customers.

For example, if you can easily tell that a group of your customers is making fewer and fewer orders and has a high chance of churning in the future, you know to start taking action as soon as possible to prevent this from happening. Luckily RFM, along with a multitude of other tools, can help you dive even deeper into potential future risks and changes earlier on.

Summary

The point is, it goes both ways. An RFM model wouldn't do good on its own without an ESP, and an ESP wouldn't do good on its own without RFM.

• RFM adds depth to your ESP. If you want to appeal to your customers, you need to understand their specific needs and behaviors to personalize your campaigns.
• RFM further explores the nuances of retention. If you can’t tell whether or not a customer is new, loyal, or at-risk, your retention efforts will be unsuccessful.
• RFM makes predictions to help you optimize your ESP. You need to be able to predict future behaviors and patterns if you want to prevent slip-ups and always stay one step ahead of your customers.

We depend on ESPs to deliver the message, and we rely on tools like RFM to deliver the right one.