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An AI powered flight and hotel recommendations engine

About Rocketrip

Rocketrip is an enterprise traveler rewards program. Rocketrip offers travelers 50% of the total savings on a trip by booking under the Price to Beat.


For example, Jane is a corporate traveler. She books a flight for $500 while the Price to Beat on that flight is $800, so Jane saved $300 by booking below the $800 Price to Beat, thereby earning $150.


In an effort to prove value of its product, Rocketrip wanted to drive adoption and increase captured spend from 65% to 75% and push for higher gross savings for the top clients thereby increasing their ROIs.

Role & responsiblities
  • Led the discovery & design process- user research, customer interviews, ideation, information architecture, wireframing, creating mockups and prototyping.

  • Launched the 'User feedback panel' - a traveler outreach program to facilitate generative user research. For this initiative, I teamed up with the customer success and support teams for recruiting travelers into this program.

  • Collaborated with different stakeholders by sharing learnings & insights from discovery, brainstorming different strategies and solutions.

Setting up user feedback panel

I launched a traveler outreach program, 'User feedback panel', to interact with travelers. During this project, I teamed up with the customer success and technical support teams for recruiting travelers into this panel. I used this panel to interview travelers to understand their booking behaviors, motivations, booking process and pain-points.

User research & traveler interviews
  • Interviewed corporate travelers (end user persona) to understand their booking behaviors, motivations, factors affecting their booking decisions, workflows and pain points.

  • Studied some of the best practices for effect of social norms on user behavior.

  • Reviewed the in-page analytics and mapped user flow patterns.

  • Reviewed the in-page analytics and mapped user flow patterns.

Target areas identified from interviews

  • Safety: All female participants in the study mentioned safety as priority while almost none of their male counterparts mentioned it. Women used information about hotel neighbourhoods, hotel reviews and colleague feedback.

  • Loyalty programs: Travelers use their own loyalty programs for business travel to get upgrades and earn points.

  • External reviews: Travelers searched outside of Concur for checking the ratings and reviews of hotels. Some travelers also checked pictures of hotel interiors, rooms and amenities on other travel sites.

  • Social norms: Travelers showed higher probability of selecting hotels recommended by their colleagues.

  • Unknown earning potential: Travelers don’t know how much potential they have to earn $ on a trip.

Most women travelers described safety as their highest priority whereas none of their male counterparts considered it as a concern. 

Booking experience on Concur​

  • Concur's booking flow is very complex. It requires the traveler to input at least 6 fields upfront to start searching flights. Each search results into 10-15 pages of options.
    For example, a flight search between New York and San Francisco will show an average 200 flight results and an almost the same no. of hotels with each hotel offering an average of 5 different room options.

  • Therefore, travelers on Concur undergo,

    • Cognitive overload

      Travelers need to know and enter a lot of information upfront before searching.

    • Decision fatigue

      Endless options and combinations leaving traveler confused.


Based on the learnings of user interviews & research, we created a recommendation engine. This would create hotel recommendations that travelers are more likely to select. Thus earning more points for the travelers and save cost for the company. A win-win for both!


Recommendations MVP released showing recommendations for 'High Rewards' and 'Convenient Locations'.​

~8.2% CTR

Click through rates (CTR) for the MVP

~80% conversion rate

Confirmations by travelers who clicked on a Recommendation


of total spend captured. Team OKR was to move Captured spend  metric from current 65 to 75%.

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