Rethinking the “Lowest Logical Fare” Obsession in Corporate Travel
Introduction: Questioning Our Data Habits
Every corporate travel manager knows the drill: we dutifully capture the “Lowest Logical Fare” (LLF) for every flight booking and log the so-called savings or exception codes. It’s been standard practice for decades, intended to show how much we would have spent versus what we actually spent. But take a step back – why are we collecting this data in the first place? Is it even reliable or useful? This thought-provoking discussion challenges the status quo of LLF tracking in corporate travel management. We’ll explore whether this data is relevant, what we actually do with it, and imagine smarter metrics that could truly benefit travel managers and corporate buyers.
The Tradition of Tracking “Lowest Logical Fare”
Lowest Logical Fare (LLF) entered the corporate travel lexicon in the late 1990s with the rise of online booking tools. The concept was simple: identify the cheapest available airfare that meets the company’s travel policy (the “logical” lowest fare), and then compare it to what the traveler actually booked. The difference between the two was recorded as “savings.”
Over time, LLF became a fixture in travel policies. Many programs explicitly require employees to book the “lowest logical fare” that fits their needs. On paper, it’s a sensible goal to minimize costs. In fact, LLF compliance rate – the percentage of trips where the traveler chose the lowest logical option – is a common KPI for travel programs. Travel management companies (TMCs) routinely report on how often travelers accept the lowest fare offered, and use exception codes when they don’t. The underlying idea is that by tracking missed opportunities for cheaper fares, corporate buyers can identify overspending and enforce policies to reduce it.
But here’s the catch: while the intention behind LLF tracking is sound, the execution and data quality are often questionable. The result is a lot of numbers on “savings” that might be more fiction than fact, leading us to ask if we are measuring something meaningful or just feeding a ritual.
When “Lowest” Becomes Garbage Data
Travel managers often whisper a dirty little secret: much of the captured LLF and savings data is garbage. Why? Because how we capture the lowest fare data is fundamentally flawed. In many cases, the “lowest logical fare” in a booking record isn’t coming from a robust data source – it might be generated by a back-office script or even entered manually based on an agent’s best guess. Traditional TMCs have relied on methods like PNR remarks and manual scripts to insert the LLF into each PNR which eventually makes its way to the back office. This means the “baseline” fare you see in reports could be the product of an automated script (which might not account for every nuance) or an individual agent’s interpretation of what the cheapest logical fare was at that moment. In other words, we’re often dealing with subjective or incomplete data masquerading as a precise metric.
Consistency is another issue. “Lowest logical” can vary wildly depending on interpretation. One company might define it as “lowest fare with no more than one stop,” another might allow a wider time window or exclude certain budget airlines. Even within a single organization, definitions may include squishy terms like “reasonable connection” or “as much as possible,” leaving room for personal judgment. For example, an agent may consider a 14-hour layover unacceptable and thus not truly “logical,” while another agent (or an algorithm) might count that ultra-cheap flight with the long layover as the LLF because it technically meets policy. The result? The data captured as the “lowest logical fare” can differ case by case, making it an unreliable yardstick.
Furthermore, consider what baseline fare we’re comparing against. In many reports, the benchmark for savings is the fully flexible “Y-class” fare – essentially the full-price economy ticket. That’s the highest fare in economy, which could be 5-10 times more expensive than the cheapest ticket in the market. Using full Y as a baseline makes any discounted fare look like a huge win, but let’s be honest: not many people buy full Y fares. Airlines themselves acknowledge that only a handful of last-minute or flexibility-needing travelers (mostly corporate) pay those top fares.. For the vast majority of trips, travelers would have bought some discounted fare anyway. So measuring “savings” as the difference between a full Y fare and what was booked is often a phantom victory – it overstates the benefit, since no one seriously considered buying the Y fare in the first place.
Why Do We Keep Collecting This Information?
If the lowest fare and savings data is so often inaccurate or misleading, why do we keep obsessively collecting it? Several reasons explain the persistence of this habit:
Legacy of Travel Management: We’ve always done it. LLF compliance and savings reports became standard in an era when data was scarce. Travel managers needed some way to demonstrate their value – “We saved the company $X by steering travelers to cheaper flights.” It became ingrained as a measure of program success, even if its relevance may have eroded.
Expectation from Buyers: Corporate buyers (like procurement and finance teams) expect to see something quantifying savings. It’s often baked into quarterly business reviews with TMCs or in annual travel program reports. Without an easy metric, travel managers might struggle to show ROI. So, we keep using LLF data because it’s there and provides a talking point.
Policy Compliance Monitoring: On a practical level, recording exceptions to the lowest fare helps identify when travelers aren’t following policy. For example, if an employee books a pricier flight, the system or agent prompts for an exception code (like “personal preference,” “schedule constraint,” or “preferred airline”). Capturing these reasons was supposed to help managers address non-compliance issues (e.g., coaching the traveler or adjusting policy if many cite the same legitimate reason).
Is the Data Even Relevant?
Let’s play devil’s advocate: suppose the “lowest logical fare” capture was 100% accurate – perfectly calculated for each booking. Even then, we must ask how relevant is this information, and what do we actually do with it?
Often, the answer is “not much.” Consider a scenario: your report shows an average saving of $50 per ticket because travelers booked the lowest logical option most of the time. Great – but that’s expected if your policy forces it. Conversely, say you see that on certain trips travelers spent $300 more than the lowest logical fare. Now what? Here are some ways one could use that data, theoretically:
Travel Policy Enforcement: If certain employees consistently bypass cheaper options, you might educate them or mandate stricter compliance. But chances are, they had a rationale (maybe the cheaper flight had two layovers or a 6 AM departure). If their reasons fall under allowed exception codes (e.g., “schedule convenience” or “trip length”), then their choice was arguably policy compliant under the circumstances. You already collected an exception code to justify it – so the data point simply gets filed away.
Policy Adjustment: If you notice a pattern – for example, no one is taking the 10% cheaper flight with a connection on the New York to Los Angeles route – you might conclude that your policy definition of “lowest logical” should exclude connecting flights for transcontinental trips. That could be a useful tweak. But did we need a precise dollar figure of savings missed, or just the insight that travelers overwhelmingly prefer nonstop and will override policy to do it? The latter could be gleaned qualitatively too.
Supplier Conversations: If a certain airline’s flights are never the lowest logical but employees pick them due to preference or loyalty, that’s useful to know. It might inform negotiations (“Our travelers really prefer your airline, even when you’re not cheapest; give us a better deal and we’ll both win”). Again, that insight is more about market pricing and traveler preference than about absolute savings dollars.
The uncomfortable truth is many organizations gather LLF data “because it’s what we’ve always done” but don’t derive actionable tasks from it. It often ends up as a line in a PowerPoint: “Policy compliance 95%, missed savings $XYZ.” If missed savings are huge, does anyone truly recalibrate the program because of it? Or do we shrug and say “well, those were mostly unavoidable due to business reasons.”
Moreover, the relevancy is declining in today’s travel market complexity. With airlines introducing new types of fares and add-ons (like Basic Economy vs. regular Economy, paid seat selections, bundled fares, etc.), what constitutes the “lowest” total cost isn’t straightforward. Does your LLF include the baggage fee that your traveler would have to pay on that ultra-low fare? Did it account for the fact that the “cheapest” ticket was non-refundable and your traveler might need flexibility? These nuances matter. An overly simplistic lowest-fare metric could encourage choices that save pennies but cost dollars in the long run (like change fees or lost productivity). Some forward-thinking programs are already asking: Should we count a fare with no checked bag and no flexibility as “logical” for a week-long business trip? Probably not, but if your scripts aren’t that smart, they might still flag it as the lowest. All this begs the question: Are we measuring all the right things?
What If We Captured Better Data?
It’s time to get imaginative. What if, instead of fixating on one “lowest” fare, we captured a richer picture of the market fares available at the time of booking? Would that be more helpful? We believe yes – and here’s what that could look like and how it could add value:
Fare Spectrum Snapshot: Imagine every time a traveler books a flight, the system records not just the chosen fare, but the range of other options in the market at that moment. For example, Fare options on NYC–LAX (booking date X): Nonstop options ranged from $300 (basic economy) to $500 (flexible economy); one-stop options ranged from $250 to $400. The traveler chose a $480 nonstop economy ticket with standard restrictions. This contextual data tells a story: the traveler passed up a $300 fare (likely basic economy or with a bad schedule) and a $400 connecting flight, opting to pay a bit more for convenience or policy reasons.
Contextualizing the Decision: With a snapshot like that, travel managers can analyze why the cheaper options weren’t taken. Was the $300 fare basic economy with heavy restrictions? Was the $400 fare on a carrier outside policy or with a 3-hour layover? Knowing this, we can validate that the $480 choice was sensible. Or, if there was a $250 one-stop on a preferred carrier that the traveler ignored, that’s a flag to investigate (did they even see it, was the OBT display biasing results, or did they choose comfort over cost knowingly?).
Market Benchmarking: Over time, collecting fare ranges can help build a market benchmark for your routes. Instead of comparing to a perhaps irrelevant “full Y” fare, you could compare your average ticket price on a route to the average lowest logical in the market. For instance, if on 100 bookings the average cheapest logical fare was $400 and your travelers’ average purchase was $420, you know the real gap is about $20 (5%) overspend due to various reasons. That could be a more realistic measure of potential savings than saying “we saved 50% off full Y fares” when no one would pay full Y anyway.
Identifying Policy Gaps: Richer data can reveal if your travel policy is aligned with reality. If the data consistently shows that travelers are not choosing fares under a certain price point because of layovers or basic economy restrictions, it might be time to refine the policy definition of “logical fare” (e.g., exclude basic economy for flights over 3 hours, or allow nonstops within X% of the cheapest fare). It moves the conversation from punitive (“you missed savings”) to constructive (“our policy might need to adapt to market conditions or traveler well-being”).
Negotiation Leverage Based on Real Choices: Knowing the fare landscape also aids negotiations with airlines. If Airline A is often $50 more expensive than Airline B on a key route but employees still choose A (maybe due to loyalty programs or better schedules), you have a case to bring to Airline A: “We could shift share to you even more if you narrow that $50 gap – our folks like you, but budget pushes them to B sometimes.” Conversely, if a preferred airline is consistently the cheapest but travelers aren’t choosing it, perhaps service issues or preferences are at play – something to discuss with that carrier or address with traveler education.
Conclusion: Evolving Beyond a Checkbox Metric
In a world where data-driven decisions are the gold standard, clinging to outdated or unreliable metrics is a risk we shouldn’t take. The practice of capturing lowest logical fares and savings had its moment, but it’s time to evolve. We need to shift from collecting data for tradition’s sake to collecting data that informs strategy.
Let’s remember why corporate travel programs exist: to enable business growth and productivity, while controlling costs and ensuring traveler well-being. Any metric we track should serve those ends. If lowest logical fare tracking isn’t doing that, we should either fix it (make it dynamic, accurate, contextual) or replace it with metrics that do.
So, why do we collect this data? Perhaps there are 2 possible use cases from the corporate point of view:
# 1: Is the traveler or travel arranger making the optimal decision
# 2: Is the booking channel providing all the available fares
For the TMC, because it is of value to the corporate, maybe an automated, reliable way of capturing this market data would be helpful for internal processes. And having accurate data can remove any anomalies you could be currently receiving as well as highlighting the value of a TMC partner.
To all the TMC operations and corporate buyers reading this: Reach out if you have interest in improving the data you are comparing your booked data against. We are evolving this process and appreciate your inputs and any other suggestions regarding this topic.
