If you have a substantial customer base, including repeat customers, here’s a valuable tip to enhance the Return on Ad Spend (ROAS) from your dynamic remarketing efforts.
Segmentation plays a pivotal role in digital marketing, primarily serving two purposes: delivering the right message to the user at the right time.
Dynamic remarketing efficiently addresses the timing aspect (post-website visit within a specified timeframe), and it effectively communicates the message (showcasing viewed items, related category items, and products from the same brand).
However, there’s an additional dimension that can further elevate performance—using segmentation to optimize budget allocation and media exposure.
Introducing the RFM segmentation model.
This model not only improves budget allocation but also introduces clear distinctions in communication. It provides fascinating insights into your customers and customer base, contributing to a more nuanced and effective marketing strategy.
The RFM segmentation model is a method used to categorize customers based on three key factors: Recency, Frequency, and Monetary value.
Recency:
This factor considers how recently a customer made a purchase. For instance:
Frequency:
This factor looks at how often a customer makes purchases. It can be divided, for example, into four groups.
Monetary:
This factor considers the amount of money a customer has spent. Like Frequency, it can also be divided into four groups.
Each customer is assigned an RFM score, with 111 being the best (indicating a recent purchase, frequent buying, and high spending).
Conversely, a 444 profile represents a customer who made a purchase a long time ago, bought only once, and spent a small amount.
The segmentation provides a total of 64 segments, allowing for insightful and nuanced analysis.
Grouping customers based on RFM scores provides a simple yet powerful way to understand and engage with different customer profiles.
The RFM segmentation model provides valuable insights into customer behavior, allowing you to categorize customers based on recency, frequency, and monetary factors.
Once you’ve segmented your customers, you can tailor your advertising strategies to different groups.
Here are three examples:
Best Customers Segment:
First-Time Buyers but Lost/In Hibernation Segment:
Do Not Lose Segment:
Adding the monetary dimension provides even more insights, allowing you to understand customer responses to discounts, basket size preferences, and other factors.
Implementing segmentation doesn’t have to be complex initially.
Start with a manual approach, creating customer extracts enriched with relevant information.
Assess the impact of segmented campaigns and, over time, plan to automate the process.
Automation can involve tools like Google Cloud and BigQuery for daily updates and audience uploads to advertising platforms.
However, even a manual process, updated weekly, can provide valuable insights and targeted advertising strategies for existing customers.
Remember to prioritize customers with permission and update mailing lists accordingly for effective segmentation in various campaigns, including DPAs.
#1 Tip
In addition to examining the overall distribution of customers, it’s also valuable to analyze the distribution in relation to permission.
This provides insights into the share within each segment with permission.
By doing so, you may identify significant variations in leave rates across different groups.
Understanding these nuances can be instrumental in refining your strategies for each segment.
#2 Tip
Geting permission is paramount for your success in the aforementioned strategy.
To facilitate this, promptly launch a lead ad specifically targeting users who have landed on your receipt page (i.e., buyers).
Use your existing permission list as a negative segment to ensure that you are precisely targeting those who have not yet granted permission.
This targeted approach aims to boost the permission rate among customers who have already made a purchase, enhancing your overall customer engagement strategy.
This blog discusses leveraging customer segmentation, particularly using the RFM (Recency, Frequency, Monetary value) segmentation model, to enhance the Return on Ad Spend (ROAS) in dynamic remarketing efforts.
The RFM model categorizes customers based on their recency of purchase, frequency of purchases, and monetary value spent.
Each customer is assigned an RFM score, providing a comprehensive view of their behavior.
The key segments highlighted in the content include:
Best Customers Segment:
First-Time Buyers but Lost/In Hibernation Segment:
Do Not Lose Segment:
The content emphasizes that adding the monetary dimension provides additional insights, such as understanding customer responses to discounts and basket size preferences.
It suggests starting with a manual approach for segmentation, assessing the impact of segmented campaigns, and gradually moving towards automation.
Two additional tips provided are:
Examining Distribution in Relation to Permission:
Getting Permission:
