In travel retailing, some customers are more equal than others (part 1)

In this article, we share how we predict Customer Lifetime Value (CLV) at OpenJaw and explain why this metric is so important for travel retailing. This is the first part of a three part article on this topic.

By John Carney, Yuxiao Wang, Auren Ferguson, Beibei Flynn and Gavin Kiernan.

Introduction

In the travel industry, some travellers are more equal than others. This is evident every time you step on to an aircraft — some passengers sit comfortably sipping champagne in business class awaiting departure, whilst others struggle to cram their carry-on baggage into the overhead bins in economy. But of course, this added luxury comes at a cost; on average, a business class ticket costs four times as much as an economy ticket and sometimes this difference can be much larger, even on the same flight.

These differences in ticket price translate to large differences in customer value for a travel retailer over time. This is particularly important when profit margin is considered e.g. a very frequent traveller who only purchases the lowest cost economy seats may have negligible value to an airline in profit margin terms, compared to an infrequent traveller who only purchases business or first class tickets.

In this respect, it is critical that any travel retailer engages with customers in a differentiated, personalised fashion given the dramatic differences that customers have in terms of margin contribution to the business. Despite this reality, travel retailers struggle today to even measure the relative value of their customers.

There are two primary mechanisms travel retailers use to measure the relative value of their customers in travel today. The first is through points accumulated in a loyalty program and the resultant ‘loyalty tier’ that a customer qualifies for. The second is through the class of flight or accommodation they have booked for a specific trip.

However, in both cases, these measures are incomplete and in some scenarios completely inaccurate. And it is obvious why — although a loyalty tier does capture a measure of customer value over time, it is a coarse measure that doesn’t differentiate the value of customers inside a tier, or account for the fact that some ‘loyal’ customers don’t participate in the loyalty program at all.

The fare class for a specific trip is also incomplete, but in a different way — a customer may be flying economy today because she is travelling for leisure, but this customer could in fact be a very high-value customer for the travel retailer overall, as she travels exclusively in business class for work purposes.

A better way to measure customer value in travel is to quantitatively measure and predict Customer Lifetime Value (CLV). This approach is well established in other areas of consumer retail, especially financial services, telecommunications and online retail. In this article we describe the CLV methodology we have developed at OpenJaw to measure and predict customer value in travel.

Background to CLV and its variants

Fundamentally, the purpose of CLV is to assess the financial value of each customer. It is different to other customer metrics such as customer profitability, in that it is predictive. A popular, formal definition goes as follows:

CLV is the present value of the future cashflows attributed to the customer during his / her entire relationship with the company. (1)

The predictive model used to estimate CLV can have varying levels of sophistication, ranging from complex machine learning methods to simple heuristics. CLV follows the concept of applying present value to cash flows attributed to the customer relationship. In this respect, it is a measure of the financial value of a customer to the firm, or a theoretical upper bound on the amount a firm should invest to acquire the customer, or to retain the customer.

CLV methods have traditionally been used in relationship focused businesses such as financial services and telecommunications, where there is also contract with the customer. However, recently, CLV has been applied to other areas of consumer retail that are more transactional in nature e.g. travel and e-commerce. This is mainly because it is very useful in practice. For example, if you want to offer a financial incentive to a customer (e.g. a discount) to increase the probability of conversion, then CLV can be used to determine at what level this discount should be set at, for the discount to make economic sense to the retailer.

One of the confusing things about CLV is that there are many variants, especially when you read articles that fall outside the rigour of peer-reviewed scholarly publications. This is generally understood amongst experienced data scientists in the field — in fact, there are a few papers that attempt to provide some clarity by identifying all of the variants of CLV and highlighting their differences. A good example of this can be found in (2). In this paper true CLV is compared with:

• Post Acquisition Value (PAV);
• Residual Lifetime Value (RLV);
• Historical Value (HV);
• Sales CLV (S-CLV);
• Finite horizon CLV;
• Un-discounted CLV;
• Cash-flow CLV and
• Accrual-based CLV.

As you can see, the field of CLV is a minefield, with many variants and a myriad of vague publications online in web forums that confuse one approach over another. In this article we attempt to provide some clarity and describe the methodology we have developed at OpenJaw for estimating CLV in travel, mindful of some of the practical hurdles in this setting.

Applying the principles of CLV to travel retailing

There are several challenges in estimating CLV in travel retailing. Many of these relate to the fact that there is significant variability in pricing and profitability for the same or similar products across multiple dimensions e.g. there are multiple fare buckets for seats on the same airplane and route, plus these can vary at different times of the day, year and sales channel.

Similarly, for accommodation, price for the same hotel room can vary by sales channel and time of the year or promotion. And it gets even more complex when products are bundled into packages as they are by tour operators that sell holidays. Dynamic bundles are created with discounts relative to purchasing products individually e.g. flight and hotel, but the bundle is priced opaquely with pricing driven by contracts negotiated with individual suppliers such as hotel chains.

Therefore, the profit earned on each customer transaction can be highly variable in travel retailing.

Another important difference in travel retailing, which largely stems from the fact that travel retailing is transactional in nature (versus contractual), is that customer acquisition costs are rarely tracked, especially at the customer level.

In part 2 of this article…

As you can see, it is not easy to model and predict CLV in the travel domain. In part 2 of this article we will describe how we navigate this minefield of complexity at OpenJaw to generate accurate estimates of CLV.

References

(1) Farris, Paul W.; Neil T. Bendle; Phillip E. Pfeifer; David J. Reibstein (2010). Marketing Metrics: The Definitive Guide to Measuring Marketing Performance.

(2) McCarthy, Daniel. A general framework for customer lifetime value. https://www.dropbox.com/s/xjak7pezn6i9m06/CLV%20framework.pptx?dl=0