Tour operator – personalized marketing through RFM customer segmentation

RFM is a technique used to create segment of customers based on their past purchasing behavior with the hopes that history will repeat itself.  RFM is analyzed by using the last purchase date (recency), purchase frequency (frequency) and amount spent (monetary value) (Peelen & Beltman, 2013). RFM is a crucial data analysis method in CRM.

In order to perform RFM analysis, each customer is given a score for recency, frequency, and monetary value, and then a final RFM score is calculated. It starts by creating a recency list from highest to lowest into five segments. Each recency segment is then arranged for frequency and separated into five equal segments. Finally, the same is done for monetary value and it results to 125 segments that have RFM scores ranging from 555 to 111. The customers with the highest RFM score are considered to be the ones that are most likely to respond to the tour operator’s offers.

Even though RFM analysis is a useful data analysis method, it does have its restrictions. Tour operators must be careful that customers with the highest RFM score are not overwhelmed with for example excessive emails/offers. Also customers with low RFM scores should not be neglected, but instead should be cultivated to become better customers. (Techtargetcom, 2016) Tour operators can use RFM to deliver better targeted unique messaging and personalized offers. An example is Thomson Holidays which is a UK-based travel operator and subsidiary of TUI Group. Thompson uses RFM customer segmentation to targeting purposes. RFM helps Thomson to focus its marketing effort on customers who are more likely to give a return on marketing investment. (Bournemouthacuk, 2016)

There are three types of customers that would be interesting for the tour operating industry to provide more personalized marketing as explained in Peelen and Beltman (2013).

  • The first type of customers is high value customers with high recency, high frequency and high monetary scores. Tour operators should reward the customers with exclusive offers of package tours and special privileges which would make the customers happy and appreciated in order to keep making bookings.
  • The second type of customers is newest customers with high recency, low frequency and low monetary scores. Tour operators should make sure they put their best foot forward, by sending them welcome offers and relevant information to get them accustomed to your tour operator. Since this type of customer have low frequency scores, it is better to send more promotions to them.
  • The third type of customers is inactive or least engaged customers with low recency, low frequency and low monetary scores. Tour operators need to decide whether to attempt to re-activate them or let them go. They should send these customers promotions since they are low frequency customers and also have an option to unsubscribe. When unsubscribe is clicked, following will be a question of what they want to do. The options are to unsubscribe completely, receive promotions once a month or once a week. Therefore the customers will feel that they are heard and actually start booking with the tour operator. Moreover, the three types of customers that came from the RFM customer segmentation could help tour operators with targeting customers and personalized offers.

In conclusion, RFM customer segmentation will be very useful for tour operators to identify their customers and provide more personalized marketing. RFM makes it easier because it groups people with similar scores which are ideal for tour operators. The three types of customers should be the core focus of the tour operators because they are going to be the main one that book with them and they need to find ways in order to personalized marketing efforts to therefore create customer engagement and make more profit.


Bournemouthacuk. (2016). Bournemouthacuk. Retrieved 27 September, 2016, from

Peelen, E., & Beltman, R. (2013). Customer Relationship Management.Pearson.

Sailthrucom. (2016). Sailthrucom. Retrieved 27 September, 2016, from

Techtargetcom. (2016). SearchDataManagement. Retrieved 27 September, 2016, from

Leave a reaction

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.