LRFM Analysis as a Customer Segmentation Tool in the Tourism Sector

LRFM Analysis as a Customer Segmentation Tool in the Tourism Sector

Pınar Özkan
Copyright: © 2020 |Pages: 27
DOI: 10.4018/978-1-7998-3030-6.ch012
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Increasing competition in the tourism industry forces industry professionals to try increasingly harder to develop stronger ties with their customers to increase customer loyalty. Many firms invest heavily in customer relations management (CRM) practices to monitor changes in customer needs so as to be able to offer personalized services, differentiate themselves from competitors, and create competitive advantage. RFM is a popular CRM technique used to effectively classify large amounts of customer data according to how recently, how frequently, and how much a customer spends. RFM has been extended in different directions over time, for the purpose of coming up with more detailed segmentations. One such extension adds “length” of the relationship with the customer to the criteria, making LRFM a more effective segmentation method than RFM, especially for the service sector. This chapter describes the use and benefits of using LRFM as a CRM tool in the tourism sector.
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Rapid growth of the global tourism industry and the marked increase in the variety of services and destinations offered over the past two decades have led to a significant increase in the number of people engaging in touristic activities. Various developments such as advances in communication and transportation technologies, changes in living conditions, increasing variety of vacation alternatives, the impact of social media on people’s expectations, increasing interest of people (particularly of Generation-Y) in discovering the world have all contributed to the rising popularity of tourism. Recent emergence of independent review sites like TripAdvisor and online booking companies such as, and, along with share economy-compatible business models in the provision of accommodations (e.g., AirBNB) and transportation services (e.g., Uber) have also changed the structure and the modus operandi of the sector. These services have become indispensable for today's tourists by enabling them to access to huge amounts information about destinations and facilities, and to get seats and rooms reserved quickly and in a cost efficient way. They also have boosted cooperation among firms offering complementary services and created synergies to let suppliers of tourism-related services reach larger markets at reduced costs. As a matter of fact, continuously finding solutions to increase customer satisfaction and customer loyalty has become an essential practice for firms to survive and grow in an increasingly competitive environment.

This rings especially true for smaller businesses operating in tourism industry. Since these firms and businesses are typically required to offer a wide variety of (complementary or integrated) services to people from different walks of life with all sorts of different expectations, one of the biggest challenges they face is to categorize customers based on their characteristics shaping up the nature of their demand. The most effective tool for the firms and businesses to meet this challenge is processed data on characteristics of (existing and potential) customers. Yet, since customers of tourism industry often seek new experiences in new locations, different settings etc., limited repetitiveness of customer behavior may make CRM (customer relationship management) investments too costly especially for small and boutique businesses, often forcing them to operate without using detailed customer databases. Even when they have some database, these smaller enterprises typically lack the capacity to employ the right data mining techniques. . Furthermore, given the complementary nature of services offered by firms and businesses working in the tourism industry, an integrated CRM approach could be desirable.

Increasing popularity of CRM practices over the last 20 years is due to the common need of firms and businesses everywhere to classify existing and potential customers into different categories based on their characteristics. Among the various data mining tools and techniques employed for this purpose, RFM analysis stands out as one of the most popular. The technique is meant to classify customers into categories based on an analysis of data concerning the (R)ecency and (F)requency of their contact with the firm in question, and the (M)onetary amount involved in the business they create for the firm. RFM analysis allows customers to be placed into categories like the most valuable or the most loyal customers, irregulars, those with potential etc. Such a categorization enables the firms to create group-specific product or service packages.

There have been proposals in the literature to extend the RFM analysis to cover additional dimensions or characteristics of the firm’s relations with its customers so as to come with a more effective segmentation of the firm’s customer base. One such extension has been proposed by Chang and Tsay (2004) who recommend a switch from the RFM analysis to the LRFM analysis by including the (L)ength of the customers’ relationship with the firm. The added L(ength) dimension here measures the duration between the first and last contacts (transactions) that the customer had with the firm and it is often argued to improve the modeling and analysis of the customer loyalty behavior.

Key Terms in this Chapter

LRFM (Length, Recency, Frequency, Monetary) Analysis: An analysis that also considers the relationship length between the organization and customer additional to recency, frequency, and monetary values.

Customer Value Matrix: A matrix that divides customers into four groups (uncertain, spender, frequent, best) based on their frequency and monetary values in RFM analysis.

Data Mining: The task of accessing and using meaningful data from databases where a lot of information is stored for purposes of making predictions.

CRM (Customer Relationship Management): A comprehensive strategy and process of acquiring, retaining and partnering with selective customers to create superior value for the company and the customer.

Customer Relationship Matrix: A matrix that divides customers into four groups (lost, potential, establishing, close) based on their recency and length values in LRFM analysis.

Customer Segmentation: The practice of dividing a customer base into groups of individuals that is similar in specific ways relevant to marketing, such as age, gender, interests, and spending habits.

RFM (Recency, Frequency, Monetary) Analysis: A marketing technique used to determine quantitatively which customers are the best ones by examining how recently a customer has purchased (recency), how often they purchase (frequency), and how much the customer spends (monetary).

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