Virgin Holidays:
Reaching New Heights with “Look to Book”

A bespoke platform that predicts the likelihood of travel bookings post media interactions.

Case Study

Package holidays are not typically impulsive purchases, and as such, it can take up to three months for a media interaction to result in a travel booking. This presents an enormous challenge for performance marketing channels that rely on timely and current data to make real-time decisions, and where most technology is built to serve simple 30-day cookie windows.

To help Virgin Holidays solve this problem, ForwardPMX built a bespoke forecasting tool dubbed “Look to Book”, which can, with precision, predict the likelihood of any of our media interactions leading to a booking.

This allowed our teams to make more informed optimization decisions, leading to increased investment in non-brand paid search by 40% year-on-year, ultimately delivering a 65% increase in bookings year on year.

Goals & Objectives

Analyze and visualize historic booking data, broken down by booking type, destination, campaign type, audience profile (demographic, destination and stay length) etc.

Use this data to build a forecasting model that could predict the outcome of current media interactions.

Test the impact of optimizing to the forecasted return rather than the existing methods (a small number of confirmed bookings).

Program Execution

We identified that the lack of timely data to make optimization decisions was due to Virgin Holiday’s use of a 90-day attributed purchase window, with corresponding cookie lags. This was a major obstacle to being able provide timely, accurate optimization and drive significant performance increases for the account.

With no solution in the market available for us to use, ForwardPMX built a bespoke forecasting and attribution model to overcome the challenges of the purchase window. This tool enabled Virgin Holidays to predict the likelihood of any of our media interactions leading to a booking, with new precision – this, in turn, would lead to a clear view of the current actual return, inform decisions made for forecasted returns and targets, and further enable Virgin Holidays to make longer term decisions about strategy and budget phasing. 

We split the project into three stages:

Stage 1

Using a combination of our own data from the advertising engines, raw data pulled from Virgin Holidays’ attribution partner and their own first party booking and customer data, we first built a dashboard dubbed “Look to Book”. This visualizes all bookings, broken down to a highly granular level such as demographic, destination and stay length. All this data is tied back to the media interactions which played a part in that booking. Importantly, this allowed us to understand changes due to seasonal trends and peaks, such as sale periods, and ensure our forecasting was accurate.

Stage 2

Our data science team worked with the paid search team to produce and refine forecasts predicting the return of campaigns. Over time, the forecasts were refined to a point where we were confident they were highly accurate. Using machine learning technology ensures this refinement continues as long as the tool is running, without the need for human intervention. When we were comfortable that our forecast was accurate, we could begin to test optimizing to these new numbers.

Stage 3

Starting out with a small number of campaigns, we A/B tested optimizing to the forecasted numbers and reviewed performance. As the actual results came in, we grew in confidence about the effectiveness of the new approach, and scaled the test across more campaigns. For this process to be as efficient as possible for the team, a new version of “Look to Book” was built for purely optimization purposes. This enhanced version could visualize the forecasted return, the current actual return and our targets, allowing us to make longer term decisions about strategy and budget phasing, rather than being restricted to just bid and keyword changes. Finally, our teams used our own technology platform, Stage to build automated processes that allow the optimization steps to be as streamlined as possible, meaning that they could move on to focusing on the next test.

Results

65%

Increase
in Bookings

40%

Increase in Spend Across Competitive Non-brand Campaigns

The increase in spend is a measure of the confidence Virgin Holidays has placed in us, and the tools we have built. The return on investment reinforces the accuracy of our approach and the benefits it brings.

This optimization approach is now being rolled out across all performance channels we manage, and is being considered for implementation for Virgin Holidays sister business, Virgin Atlantic. “Look to Book” has now become a widely used tool across Virgin Holidays for budget and campaign planning as well as real-time analysis of performance.

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