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Airline loyalty programs, often operating as separate, highly profitable entities, can be likened to a cunning piggy bank within the corporate structure. Lufthansa’s Miles & More (MaM), for instance, made an earnings contribution of €137 million for 2023, and €185 million for 2024. Driven by the inherent profitability of unredeemed miles (breakage) and immensely lucrative partnerships, particularly with credit card issuers, loyalty programs are almost certainly designed to be profitable. But even within this success, lies a fascinating strategic question that many airlines may not have properly examined: should they actively pursue casual flyers?

The Casual Flyer Segment

Consider the traveler who flies once or twice yearly. Perhaps a vacation to Spain, a business trip to Berlin. They are unlikely to apply for a co-branded Miles & More credit card, viewing annual fees as unjustified for their limited travel. They rarely accumulate enough miles for meaningful redemptions before the 36-month expiration hits. Yet airlines spend marketing dollars acquiring these customers and enrolling them in loyalty programs.

The question is deceptively simple: Is it worth it?

Let’s assume a ticket is sold for €1000, and on this, €100 could potentially go into the free cash flow to equity (FCFE). To convey the general idea, this example, although oversimplified, highlights the gist of the core tension between immediate cash returns for shareholders and the deferred value locked within the loyalty program.

(A technical note for clarity: Assuming a portion of a ticket’s price is direct FCFE is a simplification to illustrate the marginal contribution of the sale. Furthermore, while the future €10 gain from breakage is a non-cash accounting profit, our net present value—NPV—analysis remains the valid approach as it correctly compares the present economic value of these two different outcomes: immediate cash versus a future increase in shareholder equity.)

  • Scenario 1: No Loyalty Program Enrollment

In this scenario, it is straightforward. The entire €100 cash is available at the time of sale.

Net Present Value: €100.

  • Scenario 2: Incentivized Loyalty Program Enrollment

In this scenario, we invest to incentivize the passenger to enroll into the MaM program. Let’s assume we spend €10 on marketing or equivalent. Additionally, Lufthansa internally transfers €10 to Miles & More to cover the earned miles from the flight. This creates a deferred revenue liability, i.e., cash received today by MaM, but an obligation to deliver future value. That €10 sits locked up for potentially three years until the miles expire.

So, from the initial €100 FCFE from the ticket, shareholders effectively receive €80 in cash at the time of sale (€100 minus the €10 marketing cost and the €10 transferred to MaM). They also gain an almost guaranteed €10 in three years due to breakage.

The Time Value of Money Problem

The opportunity cost is real. Let’s quantify the drag on shareholder returns using two financial perspectives.

A Conservative View: The Risk-Free Rate

First, let’s look at the risk of the future cash flow itself. The €10 payment from breakage in three years is almost guaranteed. Because this cash flow is so low-risk, we can discount it using a conservative risk-free rate, which we will set at 4%.

  • Net Present Value (Scenario 2):

    \[€80 + \frac{€10}{(1.04)^3} \approx €88.89\]

This is already more than 11% lower than the €100 NPV of receiving the full amount upfront.

A More Accurate View: The Shareholder’s Opportunity Cost

However, that calculation understates the true cost to shareholders. That €10 is not just delayed; it is capital that the Lufthansa Group could have otherwise used for strategic development. (While decisions to invest in new planes or pay down debt reduce FCFE in the short term, they are made with the clear expectation of increasing long-term shareholder value.)

Essentially, the choice for that capital is between two productive paths:

  1. Reinvesting it in strategic, value-accretive projects like fleet modernization or deleveraging the balance sheet.
  2. Returning it to shareholders via dividends or buybacks, who can then reinvest the proceeds as they see fit.

For either path, the benchmark for an acceptable return is the company’s average risk profile, which is inexorably higher than the risk-free rate. This benchmark is best represented by the Weighted Average Cost of Capital (WACC), which is the blended return a company must generate on average to satisfy all its capital providers. (Strictly speaking, since WACC is used, the cash flow being evaluated is the Free Cash Flow to the Firm, or FCFF, which differs from FCFE by also including the cash flows available to debt holders—which means in reality, equity holders expect an even higher rate.) According to Lufthansa’s 2024 Annual Report, the WACC for 2024 was 8.5%. Using this as the opportunity cost of the tied-up capital gives a more accurate picture:

  • Revised Net Present Value (Scenario 2):

    \[€80 + \frac{€10}{(1.085)^3} \approx €87.83\]

From this more appropriate perspective, the drag on shareholder value increases to over 12%.

While the financial drag on a single ticket may seem small, the impact is magnified. Scale this across millions of casual flyers, and the capital tied up becomes substantial.

A potential counterargument is that this financial drag is driven by the one-time marketing cost, and that the difference between the two scenarios will shrink and become negligible with repeat business. Let’s look at this.

Assuming two flights per year at six-month intervals, we can calculate the net present values over an additional three-year period (i.e., 6 more flights) for both scenarios, using the WACC to discount future cash flows.

In Scenario 1, the NPV of the initial flight plus six subsequent flights is:

\[€100 + \sum_{k=1}^{6}\frac{€100}{(1.085)^{k/2}} \approx €621.44.\]

In Scenario 2, the NPV for the same period is:

\[\left(€80 + \frac{€10}{(1.085)^{3}}\right) + \sum_{k=1}^{6}\left(\frac{€90 + \frac{€10}{(1.085)^{3}}}{(1.085)^{k/2}}\right) \approx €597.95.\]

The conclusion, therefore, remains unchanged. While the percentage drag narrows over time (from 12% to 3.8% in our simplified example), it never disappears, representing a permanent loss of value. When scaled across millions of casual flyers—the very bulk of a major airline’s customer base—this drag amounts to a substantial and perpetual drain on shareholder returns.

Furthermore, this analysis only considers the initial cost. It does not account for the continuous marketing dollars spent trying to leverage the sparse data from these casual flyers, efforts that, as we will argue in the following section, are often futile. This turns the speculative promise of data into a tangible, ongoing cost.

This leaves airlines with a fundamental question: Is the speculative value of this data worth the certain, and continually growing, opportunity cost of capital?

The Data Value Promise

The counterargument is seductive: customer data. Every casual flyer enrolled provides behavioral insights, purchase patterns, and demographic information. Marketing teams present compelling business cases about personalized campaigns, targeted offers, and optimized customer lifetime value. The theoretical potential seems obvious: better data should drive better decisions and higher profits.

But here is where theory meets reality.

The Execution Gap

First, the obvious: casual travelers may not generate enough data to reliably extract a purchase pattern due to their infrequent interactions. Many advanced data leveraging techniques, e.g., CLV modeling, collaborative filtering, variational autoencoders, etc., simply do not apply to low-frequency flyers. These models require behavioral density. But casual travelers generate sparse, noisy signals that defy most known recommender logic. Airlines do not have the equivalent of “watched movies” or “finished books.” They have one trip to Mallorca and a sandwich.

Second, I have witnessed firsthand how organizations may sometimes struggle to monetize customer data effectively. A recent example from the industry: dashboards showing that customers with loyalty accounts spend more money led to a discussion about investing in incentivized account creation for all customers. The logic seemed sound: if members spend more, create more members.

This represents a fundamental misunderstanding of causation versus correlation. The data indeed shows an association, but we are most likely dealing with reverse causation. People who fly more frequently are naturally more likely to create accounts, not the other way around. Heavy travelers join loyalty programs; loyalty programs do not create heavy travelers.

This is not an isolated incident. McKinsey and BCG regularly report that digital transformation initiatives at traditional firms often fail spectacularly. The gap between claimed data analytics capabilities and actual execution is enormous. Organizations build sophisticated data infrastructure but may struggle with basic statistical reasoning.

More problematic, the likely lack of an experimentation framework, the backbone of data-drivenness. This issue may be deeply rooted in traditional corporate culture at many established firms. And without a true experimentation framework, even the best models risk reinforcing biases rather than uncovering insight. “Data-driven” decisions require more than dashboards, they demand causal evidence. Google runs over 100,000 experiments annually; LinkedIn more than 50,000 (Iansiti & Lakhani, 2020). Amazon likely runs even more.

Third, leveraging data incurs additional costs, including infrastructure for storage and computing, analysis and model development, testing, experimentation, monitoring, corrections/improvement, etc. This is a loop that never ends when done properly, and its costs cannot possibly be negligible, potentially further depleting the FCFE. One could make the argument that most tools and resources are already in place (except for proper experimentation frameworks) for dealing with higher-frequency customer segments, and thus the marginal cost for dealing with the infrequent flyer may not be that consequential.

What About High-Frequency Flyers?

This model can be parameterized to show that even if a customer were to fly forever—and even at higher frequency—the loyalty program strategy never closes the value gap. The total difference in net present value converges to a strictly positive value representing a permanent deficit for the shareholder.

Crucially, the business model for this segment is entirely different. High-frequency flyers redeem their miles, so the airline cannot rely on breakage for its profit. Instead, profitability comes from the spread between what partners (like credit card companies) pay for the miles and the lower, internal cost of the redeemed flight. The financial drag, in this case, is the permanent discount offered on these redemptions.

The key distinction, therefore, lies on the other side of the equation: the data. For a high-frequency flyer, the airline has a more realistic chance of leveraging their dense behavioral data to generate incremental revenue that might one day offset this permanent financial drag.

This brings us back to the central problem with targeting casual flyers, where the futility of the data-leveraging attempts—as we have just discussed—potentially makes the financial drag a stark and uncompensated cost.

The Unclear Trade-off

The airline industry is often a classic example of the Pareto principle, where a small minority of frequent travelers drives the vast majority of activity. Research on the U.S. market, for instance, found that just 12% of the population takes two-thirds of all flights (Gössling, 2020), leaving the vast majority as infrequent, casual flyers. The business case for enrolling this latter group—the bulk of an airline’s customer volume—therefore remains genuinely unclear, depending entirely on an organization’s actual (not claimed) data analytics capabilities. Given typical execution gaps, the time value of money cost may outweigh practical, not theoretical, data benefits.

For casual flyers who will not get co-branded credit cards, airlines face a fundamental question: Is the speculative value of customer data worth the certain opportunity cost of capital? The answer depends on honest self-assessment of analytical capabilities, not wishful thinking about data monetization.

I would not be surprised if most airlines are making this bet without properly quantifying either side of the equation. They are tying up capital based on theoretical data benefits while sometimes struggling to execute basic customer analytics. Until organizations can demonstrate practical value extraction from customer data—not just dashboard correlations—the time value of money argument deserves serious consideration.

The casual flyer dilemma reveals a broader truth about modern business strategy: the gap between data promise and data reality could be wider than we admit.