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Intelligent Price Testing

Test #6: “PRICE INCREASE TEST”

Goal:

Maximize revenue per passenger within VivaAerobus’ ancillary booking path by increasing the price of specific ancillary products.

Key Performance Indicator (KPI):

Revenue per Passenger

Traffic Source / Segment:

Desktop and Mobile Web traffic

Summary of difference(s) between variations:

Fusion performed a deep exploration of data into the strong predictors of price for Seats and Baggage.  Booking Window was identified as a predictor for Seats and Route (Distance)/Season for Baggage. A new pricing framework for each product was created for their respective offers.Sample Size & Test length:

Approx. 40,000 visitors were randomly shown either variation over the same 16-day period in 2015.

Hypothesis:

By focusing on Seats and Baggage products, specific increases in price based on flying distance and booking window will yield a higher revenue per passenger.

Results:

Seats Pricing Test – Initial Analysis:

  • ANCILLARY REVENUE CHANGE: +1.7%
  • POPULATION GIVEN VARIABLE: 100%

 

Baggage Pricing Test – Initial Analysis

  • ANCILLARY REVENUE CHANGE: -1.4%
  • POPULATION GIVEN VARIABLE: 100%

 

After all the data had been collected, a second, comprehensive post-test analysis was completed within 48 hours. During this process, Fusion measured the overall performance of each experiment at a high level.

While the initial results of the experiment were determined to be fairly neutral, Fusion was then able to dive further into the data and identify variables of success to be isolated at a granular level. The targeted populations were then grouped within the test and control populations, to be analyzed as if the multivariate tests had only been executed within those groups.

The results were then measured against the established KPI’s and the potential incremental revenue impact for VivaAerobus was calculated.

 

Seats Pricing Test – Refined Analysis

  • ANCILLARY REVENUE CHANGE: +80%
  • POPULATION GIVEN VARIABLE: 16.3%
  • OVERALL ANCILLARY REVENUE CHANGE +13%

 

Baggage Pricing Test – Refined Analysis

  • ANCILLARY REVENUE CHANGE: +71%
  • POPULATION GIVEN VARIABLE: 11.3%
  • OVERALL ANCILLARY REVENUE CHANGE +8%
TOTAL OVERALL ANCILLARY REVENUE CHANGE +21%

Actionable Takeaways:

The pricing eligibility logic was then implemented to ensure that only the population of VivaAerobus’ customers that exhibited positive responses would receive the pricing from the test. This process continued to repeat indefinitely as Fusion’s pricing models gain intelligence.

By re-analyzing the test data and having the capability to understand where in the data the test worked with significance, you can implement complex pricing models targeted to specific customers and therefore increase revenue.

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Jason Keough

Director, Consulting Services at Fusion

Jason is a responsible for leading and managing the consulting engagements for the Fusion Agency. He is also a CUA (Certified Usability Analyst) and has over 10 years’ experience leading teams that deliver results for Fusion partners. He is passionate about optimization, testing, and making data-driven decisions.

Jason holds a Bachelor’s degree in Business from the Belmont University and has over 20 years’ experience in e-commerce strategy and web development and design.

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