What if you knew the exact number of customers who have purchased with you recently? What if you also knew how often they have purchased and how much they have spent? You may be wondering how all of this information would help your company in the long term. By segmenting your customer database, you would be able to focus directly on customers who most likely would find your offers suitable to their needs and desires.
As the airline industry grows, segmenting your customers can significantly enhance your marketing performances, making products and services more relevant to more of your customers. The goal of segmentation is to approach customer groups in a differentiated manner so that they become more satisfied, loyal and spend more with the supplier. But, before you get to know who your best customers are you need to identify them by understanding their historical actions.
According to (Peelen & Beltman, 2013), direct marketers find that behavioral segmentation produces the most useful results. Behavior observed in the past appears to be a better predictor of future behavior than pronouncements made by customers regarding their purchasing intentions and attitudes. And that is where the customer segmentation technique called RFM analysis can help airlines maximize the return on their marketing investments.
The RFM analysis suggests that each customer is scored based on three attributes, specifically Recency, Frequency, and Monetary value.
•Recency: The most important factor of customers who have purchased recently from you and are more likely to purchase again.
•Frequency: The second factor is how frequently these customers have purchased from you because the higher the frequency, the greater the chances are of them responding to your offers.
•Monetary: The third factor is the amount of money these customers have spent on purchases, this is a great way to distinguish heavy and light spenders.
To calculate RFM scores, you will need the values of the three factors such as most recent purchase date, amount of transactions followed by the total number of sales for each customer. For example an airline may follow a system from 1 to 5, with 5 being the highest score. By choosing this method of scoring, customers who purchased a ticket within the last month will have a score of 5, customers who purchased a ticket within the last 1-3 months will have a score of 4 and therefore you can continue scoring. Likewise, the frequency is calculated based on the amount of times a customer has purchased so the higher the frequency the greater the score. Lastly, the last scoring is based on the amount that a customer has spent which is calculated by the actual amount spent per visit. To receive a final score, you must then combine all three scores and the customers with the highest scores are considered to be the ones most likely to respond to your offers.
After this, airlines can take the results and form RFM segments for example frequent travelers, business travelers or even budget travelers. In this case airlines can focus on frequent travelers as they are a more lucrative market who tend to fly more often throughout the year.
Customers who have a high overall RFM score but a frequency score of 1 are new customers. Airlines should then provide special offers for these customers in order to increase their booking behavior. Customers who have a high frequency score but a low recency score are those customers that used to book quite often but have not been travelling recently. For these customers, airlines should then offer promotions to bring them back to frequent travelers, or can also run surveys to find out why they have not been travelling recently.
Ultimately, it is apparent that the RFM analysis is a powerful segmentation technique that allows you to identify your best customers to create better marketing performances. It will increase customer loyalty while increasing response rates, customer retention and sales revenue.
Customer Segmentation Using RFM Analysis. (2014, 11 26). Retrieved from Gain Insights, Better Decisions, Better Solutions: http://gain-insights.com/solutions/retail-analytics/customer-segmentation-using-rfm-analysis/
Peelen, E., & Beltman, R. (2013). Customer Relationship Management (Second Edition). United Kingdom: Pearson Education Benelux BV.