This paper provides a comprehensive review of the lifecycle of promotional campaigns within online travel platforms (OTPs) based on real world experience from Booking.com. It emphasizes the critical aspects that must be addressed to define, optimize and monitor successfully these promotions. Initially, we present an overview of promotions in OTPs, highlighting their unique characteristics in comparison to other industries. We continue by identifying the diferent aspects of a promotional campaign with focus on those that can be tuned for campaign optimization. Following this, we propose best practices for experimentation, evaluation and steering to make sure that running promotional campaigns stay compliant with the business requirements.
to a complex ecosystem in which an intervention in any of them—such as a promotion—
can afect the rest. These efects can be either positive synergies but also unwanted
negative results such as cannibalisation. Proper promotion management should be aware
of these efects and take actions to maximize positive outcomes and mitigate the negative
ones.
• Shared supply: Travel products are not only limited but also often listed on several
competing OTPs and on the business itself. Promotions can play a role in diferentiating
the value provided to the customer by ofering perks like Free Cancellation, Free Breakfast
or Room Upgrade.
• Promotional costs: In funnel OTP’s promotional campaigns only incur costs when the
customer materializes the given promotion by making a purchase. For example a 10 €
discount for a hotel room will only incur in a cost if the customer books that room. Unlike
other promotional channels such as Pay Per Click or marketing.
• Low interaction frequency: as a reflection of the tourism seasonality, customers don’t
engage with OTPs on a frequent basis [
Turning an hypothesis into a successful promotional campaign is not an easy task and typically takes multiple iterations to optimize. There are multiple aspects which define a promotional campaign, but in general, those can be divided between settings or constraints and levers. The settings define the scope of the campaign, these are properties that cannot typically be modified and any campaign must comply with them, these include: • Goal: The business objective of the campaign. it can be a certain amount of profit, increase in sales or user engagement, among others. • Budget: the finite resource that the company allows itself to invest in the campaign. • Eligibility: The subset of OTP’s trafic susceptible to the promotion. i.e in the case of cross-sell campaigns, only customers that already purchased a product can be targeted. The levers are those aspects that can be tuned to optimize the promotional campaign, inside the framework defined by the constrains or settings. These include aspects of the campaign setup, and audience targeting. The former drives coarse changes in volumes and economics whereas the latter represents finer trade-ofs between the two
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The main levers of a promotional campaign set up are the following:
• Placement: Promotions can be ofered at various stages, such as the upper funnel of
the webpage, the lower funnel (post-purchase), or even through diferent contexts like
marketing channels. The placement of the campaign predominantly afects campaign
audiences and the quantity and quality of the customer information. Generally speaking
the audience is reduced along the funnel and the amount of the customer information
is increased. Therefore the campaign placement will determine target audience and the
quantity and quality of the customer data available
• Benefit type: A fixed discount is easier for the user to perceive and for predicting costs
due its linear nature. However, in the travel industry, transaction value may vary
significantly between diferent countries and destinations, thus making a fixed discount
very appealing for cheaper destinations, where the expected revenue is small, making
it economically ineficient. On the other hand, a percentage discount, or a benefit with
monetary equivalent which is proportional to the transaction value, e.g., free breakfast,
is more eficient across destinations but actual costs can be harder to predict. A fixed
discount is therefore more suitable where the expected transactions value and/or expected
revenue are relatively similar across items.
• Benefit size: This is the actual amount or percentage of discount. The law of demand
suggests that for most goods, there’s an inverse relationship between the price and
demanded quantity, or, in the context of promotions and benefits, the higher the benefit
size, the higher the demand. However, increasing benefit size is very costly, and is also
sufering from diminishing returns. Once the target audience and discount type are
set, setting the sweet spot for discount value or benefit size would be such that the
incrementality is maximized with economics (iROI, cost of acquisition, etc.) slightly
under-performing, leaving room for further optimization [
In the context of promotional campaigns, the customer audience can be divided in four segments
according to their response to a given discount [
• Complier: customers who respond positively only when targeted • Always-taker: Customers who respond positively, regardless of targeting • Never-taker: customers who won’t respond regardless of being targeted.
• Defier: customers who are less likely to respond positively if they are targeted
The ideal targeting, in the context of promotion and benefits, is giving the discount or benefit
only to the complier group. This way the promotion budget would be allocated in the most
efective way. To that end, Uplift models are commonly employed to predict the causal efect of
a treatment at the individual level based on data collected from a Randomized Control Trial
(RCT) [
A/B testing is the standard methodology to evaluate any new promotion campaign [
Continuous experimentation can be defined as a diferent paradigm in which both experimental features and consolidated policies are continuously running. All the running policies conform a portfolio that is continuously monitored and modified if needed. This method ofers several key advantages: • Flexibility and Adaptability: Continuous experimentation allows real-time adjustments to test parameters and variations. Leveraging adaptive algorithms and real-time data analysis facilitates dynamic refinement based on user interactions, ensuring the testing process evolves in response to emerging insights. • Continuous baseline. By continuously allocating trafic to a baseline, campaign managers can monitor and detect seasonality efects or any disruption in the market. • Scalability and Comprehensive Insight: The methodology supports concurrent testing of multiple variations, providing a holistic view of user preferences and interactions. This capability enhances the depth of analysis and facilitates informed decision-making regarding feature optimization and deployment strategies.
Typically, in a continuous experimentation set up, we are working with three distinct sets of policies: Baseline, Benchmark and Experimental Policies. Each one is designed to test the efectiveness of discount strategies in a controlled and systematic manner. Below is a detailed explanation of each approach: 1. Baseline — a control group of trafic that does not receive any discounts or treatments.
This group remain untreated to provide a baseline for comparison. 2. Benchmark — Well established promotion campaigns. 3. Experimental Policy— Any experimental policy to be tested.
Portfolio Average (Arbitrary metric)
Steer
Steer
Time
While each of these policies is important on its own, one of the significant advantages of using this framework is the ability to manage all of them together as a portfolio of discounts strategies. Similar to a stock portfolio, this framework enables us to create a mixture of policies with diferent characteristics. Continuous monitoring of the portfolio allows promotional campaign managers to steer the portfolio. Steering refers to the change of the amount of trafic allocated to each of the policies of the portfolio in order to bring the portfolio average performance to the desired value. Figure 3 illustrates a steering scenario in which trafic is reallocated from one policy to another. This figure highlights two pivotal steering points where the portfolio performance metric is adjusted in response to shifts in economic or business priorities.
This work highlights the critical aspects of promotional campaigns in the online travel industry. We describe the unique characteristics of this domain, such as seasonality and interconnected verticals. We elaborate on the diferent features of a promotional campaign, emphasizing the distinction between constraints and levers, and list several options for leveraging the levers, such as targeting compliers. Additionally, we discuss the need for continuous experimentation and metric monitoring in contrast to traditional A/B testing. Finally, we emphasize the benefits of managing all promotions together as a single portfolio, which allows for balanced experimentation and alignment with business goals through strategic steering.