Effective Email Segmentation: How to Use RFM Strategy

RFM stands for Recency, Frequency, and Monetary. It is the easiest form of customer database segmentation and is based on user activity data. RFM segmentation can be applied to activity-related data that has measurable value and repeatable. Therefore, it is a perfect strategy for eCommerce, because purchase history and website visits can be measured and tracked. Let's look at each of the RFM components in more detail:

• Recency: Recency is the most important predictor of who is more likely to respond to an offer. Customers who have purchased recently from you are more likely to purchase again from you compared to those who did not purchase recently.
• Frequency: The second most important factor is how frequently these customers purchase from you. The higher the frequency, the higher is the chances of them responding to your offers.
• Monetary: The third factor is the amount of money that customers have spent on purchases. Customers, who have spent higher amounts, are more likely to purchase compared to those who have spent less.

To perform an RFM analysis, each customer is assigned a score for recency, frequency, and monetary value, and then a final RFM score is calculated. Recency score is calculated based on the date of their most recent purchase. The scores are generally categorized based on the values. Similarly, the frequency score is calculated based on the number of times the customers purchased. Customers with higher frequency receive a higher score.

Finally, customers are assigned a score based on the amount they spent on their purchases. For calculating this score, you may consider the actual amount spent or the average spent per visit. By combining these three scores, a final RFM score is calculated. The customers with the highest RFM score are considered to be the ones that are most likely to respond to their offers.

Good news is that RFM is based on past customer results and does not require heavy data analysis.

Here is a step by step guide on how to perform RFM segmentation:

1. Sort your list for recency, in order of highest to lowest.
2. Divide the list into five equal segments.
3. Give the top 20 percent a recency score of 5, the next 20 percent a score of 4 and so on.
4. Sort each recency segment by frequency and divide into five equal segments, resulting in 25 recency plus frequency segments.
5. Each of these segments is then sorted for monetary and divided into five equal segments, leaving you with 125 segments that have RFM scores ranging from 555 to 111. This is your RFM index. Customers with a score of 555 are the best customers.

If you are very familiar with your database, you could also simply use intuitive groupings, such as "purchased in last month, last three months, last six months or greater than six months," as the basis for your RFM classifications.

RFM analysis can help retailers segment the customers and design offers and promotions based on their profile. Below are some ideas for the types of campaigns that may work best with different RFM segments:

• Customers with an overall high RFM score represent the best customers.
• High recency, high frequency and high monetary: Reward your most loyal customers and prospects with exclusive email privileges that make them feel special. For example, some retailers automatically offer free shipping and other perks to their best online customers.
• High recency, low frequency and low monetary: This segment includes your newest customers or subscribers. Give them a good first impression of your company with welcome offers, product usage tips or other information that newbies would find helpful.
• Low recency, low frequency and low monetary: As in direct marketing, your least-engaged recipients simply may not be worth mailing to. But in email marketing, they may be great candidates for a re-opt-in campaign. Double-check whether they still want to hear from you, and remove them from your list if they don't.
• High frequency, low recency: Customers that used to visit your store quite often but have not been visiting recently. For these customers, the company needs to offer promotions to bring them back to the store, or run surveys to find out why they abandoned the store.

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