Timing and Features Valuation for Reservation Systems Selection

February 21, 2010
Principal Author: Furio Ciacci
Contributors: Curt LaMontagne, Brian Cimaglia
Reservation Systems: Empty Airport Terminal


Reservation system selection is strategically critical, and inevitable, for any passenger transportation provider.

In addition to a range of capabilities inherent in the options considered, the question of timing inevitably arises because of the interaction of the systems options considered with the operator’s environment, the condition of the incumbent solutions, financial and contractual constraints, and the evolution of system’s capabilities.

By applying the Investment Value and Timing module of fareENOUGH’s proprietary quantitative methodology fQOM (fareENOUGH Quantitative Operational Methodology ) to the specifics of the operator’s situation, we can effectively compare the importance of the decision timing to the capabilities of the selection made, making it possible to mutually optimize timing and scope, or harmonize them with other requirements, for best returns to the operator.


The typical time between major changes in reservations system for the contemporary passenger operator is three and a half years. Even more frequent is the significant overhaul and updating of the capabilities of a system already installed. These decisions are made taking into account many factors that relate to timing. However, the decision of what to adopt is often made independently of the timing, and in many cases before, and in such a way that the implication of timing on the system selection that precedes it are understood only in a very superficial and static way.

Compounding this, the uncertainty about timing and typically short timeframes reduces the confidence in the relative benefits estimated for the systems options considered, leaving room for bias, forced linearity, and other cognitive problems latent in any decision process.

As in other cases, in addition to a suboptimal decision in the first place, a solution adopted under faulty or unclear premises will also risk inadequate internal deployment and marketing, and eventually reduced actual profitability relative to its potential. This is especially important for a critical system connected to customers, such as reservations.

With its proprietary quantitative fQOM methodology, fareENOUGH is able to optimize concurrently and dynamically both decisions of scope and timing, providing the opportunity for the adopter to schedule the acquisition of new capabilities in ways that maximize the benefits. fQOM does that by identifying the relationship between the relevant elements of value and solution performance indicators on one hand, and other time-related results drivers on the other. By focusing directly on already-understood, relevant drivers for a given set of functional capabilities, fQOM delivers timely answers when timing is critical.

In general, In general, fQOM contemplates three cases::

  • Adoption of a new system, for example by a startup operator;
  • Replacement of an installed and operating system with another, with different functionalities and performance measures;
  • Combination of two or more different systems into one, for example as a result of a merger or acquisition;

The methodology is applicable to any system with potential implications on the economics of its operation or business model. For the purpose of classification, we identify a system with the collection of functionalities it embodies, so that, depending on the degree of interaction with the original functions, a significant increase in capabilities for an extant system can be considered either as a sub-case of adoption, or of a replacement.

For the purpose of discussion in this paper, we will stay for the moment with Case No.2, as it offers a relatively complete case of broad relevance, without complications that are not strictly pertinent to the topic or to a majority of operators.

In the case of replacement of an existing reservation system with another, fQOM distinguishes many Contributors to operator value:

  • Ongoing, post-conversion savings from new system cost differential;
  • Ongoing old system maintenance pre-conversion savings;
  • Ongoing savings from old system user familiarization;
  • Ongoing new system maintenance post-conversion savings;
  • Ongoing savings pre-conversion from old system perfecting;
  • Ongoing savings post-conversion from new system tailoring and perfectioning.

Additionally, value is impacted by revenue opportunities:

  • Net revenue enabled by old system and forgone post-conversion;
  • Net revenue permitted by new system and enabled post-conversion;
  • Option value, enabled or forgone, from the potential extendability of the capabilities of any of the systems considered.

Finally, some of the savings and additional revenue are offset by these costs:

  • Event-specific conversion costs;
  • Ongoing old system pre-conversion deterioration;
  • Costs relating to the acquisition of new system capabilities (bought or made)

The majority of these Contributors, including revenues, go directly to the bottom line of the operator. But it is not out of the question that some user savings may be obtainable and recovered via appropriate pricing. For an example of that, see the fareENOUGH work on In-Flight Passenger Connectivity.

The contributors to value change over time under the effect of changing underlying parameters, and the interaction with the solution contract length, and other events. They are represented as the economic benefits delivered over the relevant period of time, within a given relevant decision horizon for the operator, during which the implementation generally takes place. It follows that both decision and implementation timing have a specific and non-trivial effect on the benefits to the operator.

In addition to timing, the relative importance of each Contributor depends to a large extent on the performance indicators for the solutions considered and on specific indicators for the operator of the system. These, in particular the latter, are dealt in greater detail in other fareENOUGH literature. Although solution performance indicators, such as percentage of successful transactions, are nominally distinct from time, in many cases they are themselves constrained if not determined in relation to time, which magnifies the importance of timing.

The economic relevance of events that take place much later is modeled differently. For reasons of clarity, in this analysis, we are considering a decision horizon between 3 and 4 years, at the limit of the short-term range, which is typical for evaluating a newly adopted reservation system. For more information, see the fareENOUGH work and upcoming paper on turbulence and the economic modeling of significant and distant events.

Benefit Quantification

Figure 1 - Net Ancillary Sales Value per Reservation
vs. Timing and Relevant Performance

Figure 1 - Net Ancillary Sales Value per Reservation vs. Timing and Relevant Performance

It can be seen in Figure 1 how the net new sales revenue enabled by a new reservation system (Contributor #8) changes with implementation time, and one of a limited number of key measures of solution performance, namely transaction time saved ds.

Figure 1 shows that in some cases, the effect of solution performance and solution timing are comparable, and to the extent that solution performance depends on its timing, tradable as well. In a short while, we will see that depending on other relevant conditions of the technology, adopters, and users, one of those two elements may dominate over the other.

In Figure 2, the relative sensitivity of net new revenue enabled value to transaction time saved ds and timing is compared for 3 different Operator-Type Scenarios. Scenario sS1 applies to a predominantly domestic airline that is relatively homogeneous in terms of markets served. S2 applies to a heavily international airline, with complex and heterogeneous market requirements. S3 applies to a regional, no-frills airline that can take advantage of a simpler, easier to use system. To facilitate comparisons, the airlines in these 3 scenarios have the same market share of reservations.

Figure 2: Performance and Timing Sensitivity
by Operator-Type Scenario

Figure 2: Performance and Timing Sensitivity by Operator-Type Scenario

The implication of Figure 2 is that operator attributes that are at once specific, variable, and measurable, significantly influence the extent that reservation system features and timing result in benefits for the operator itself.

Figure 3 shows the increments in expected adopter value per reservation for the 3 Operator-Type Scenarios, as ds and timing are first adjusted individually (dark and light bottom 2 column segments, respectively) and finally together (lightest, top column segment).

Figure 3: Value-per-Transaction Increments
with Performance-Timing Combination
by Operator-Type Scenario

Figure 3: Value-per-Transaction Increments with Performance-Timing Combination, by Operator-Type Scenario

The implication of Figure 3 is that operator structures respond to optimization of performance and timing in ways that are qualitatively, as well as quantitatively, different. For example, the effect of intermediate performance adjustments is comparable (slightly smaller or greater, respectively) to that of timing for S1 and S2 operators that are structurally more complex, while timing is a comparatively much bigger lever for S3 type operators that offer a more rapid establishment curve to the adopter and its customers. The outcome could change if other performance measures than ds are altered, or other value dimensions are considered. However, in general, different operator types tend to respond differently to the mix of value levers available.

Adoption Implications

The above findings are a specific illustration of the more general concept that the combined interaction of timing and performance on the benefits of a reservations system conversion cannot be reliably assumed a priori, while at the same time they can be understood via the fQOM methodology, to an extent sufficient to guide decisions of timing, scope, capability, and cost.

Figure 4: Projected and Breakeven Value
per Reservation and Value Reference Ratio
vs. Conversion Timing

Figure 4: Projected and Breakeven Value per Reservation, and Value Reference Ratio, vs. Conversion Timing

Although net new sales revenue enabled is specifically concerned with ancillary, non-fare sales transactions, its behavior as modeled is representative of that of many other components of operator value. In particular, its dependency on the structural attributes of the operator is the norm, rather than the exception, among the possible components of value. As the Operator-Type Scenario differences in Figure 3 show, in the understanding of that dependency lies one of the most significant sources of value of the fQOM approach discussed in this paper, easily covering the distance that separates project failure and success.

To better illustrate that, Figure 4 compares the projected to the breakeven benefits per reservation, showing that for the case discussed in this paper, the breakeven value is exceeded only for conversion in 9 months time or later, because of the expected improvement in system performance with time. At the same time, Figure 4 shows also that the overall benefits relative to an immediate conversion decline progressively, as the conversion date is moved further into the future. This illustrates the existence, in the case of the S1 operator, of a preferential window around the 9-to-15-month interval.

As the executive team in charge of the conversion approaches a decision, it is useful to know that fareENOUGH’s fQOM methodology is able to pinpoint the best timing and performance tradeoff for each individual reservation system operator.