Caviair is a tool/platform that helps air-travelers make the best decision around using their accumulated loyalty points.


Product Experience

Problem Space 

Problem Statement  

How might we optimize the redemption process for credit card points and airline miles, so that travelers can maximize their value and travel in the most comfortable way?

Problem Background  

The airline industry is just coming out of the biggest shakeup in its history. The Covid-19 pandemic shut down almost all operations for over 12 months in most countries. Something that would be considered unthinkable in the recent past. As we come back from a travel hiatus, there are two key phenomena I noticed. 

1. The people are really eager to travel again after being forbidden during the pandemic.

2. Users have racked up a large number of credit card points and previously accrued miles that are ready to be spent, and are losing value over time.

The other major element in this problem space is the ambiguity and complexity around the estimation of the value of a point or mile when redeeming them for travel experiences. Every single one of the people I talked to had issues pin-pointing the value of points they have collected and even the process of redeeming them was deemed complex and daunting.

On top of this the airlines are trying to recoup their losses and the recent increase in inflation by devaluing the points in several ways. Most people are completely oblivious to the changes made by the companies and the fact that their points, just like their cash, are losing value everyday.

The loyalty points business is also one of the fastest growing industries within Travel and Hospitality. Just the North American market was valued at $1.7 Billion and global markets are valued at around $5.29 billion in 2022 & is projected to grow from $6.47 billion in 2023 to $28.65 billion by 2030. Growing at almost 23% y.o.y. 

I believe the market is ripe for a product that can facilitate transactions that offer better value to the users and guide them on how to best make use of their points in an economy that's increasingly tightening its belt and trying to make every penny go that extra mile.

Research Insights

User Pain Points 

During our research I spoke to 8 individuals between the ages of 35 and 52. 7 of them live in Canada and 1 of them lives in the United States. I asked them about their past behavior around airline miles and credit card points and their preferences around air travel.

From these interviews I was able to uncover the following key pain points:

  1. People don't have an accurate idea of their total points across all of the loyalty programs they are participating in. 

None of the people I talked to had a complete picture of what their total points were across all the CCs or loyalty points they participated in. Or whether the points across different programs were open to conversions between each other, potential loss in value when doing it. If they had a better idea of the totals they would be more likely to check/redeem higher value options.

  1. It is really difficult to find and compare the cost of a flight across economy and business in both cash and points cost.

To check the value in dollars and points for each category/class of flights available the users have to search 4 times and there is absolutely no way to compare them side by side except opening multiple windows.

  1. There is no easy way to find cheaper routes across different airlines or reward systems when trying to redeem points.

There are several search engines like Kayak for example that provide low cost itineraries between different airlines and with different numbers of stops to the destination. However there is no simple tool to do this when trying to book using points. Since there are several ‘groups' that don't play well with each other.

Supporting Data

  1. People want to use their points to “elevate their experiences”

7 out of 8 people I talked to want to use points to enjoy a higher class of travel (Business or First class upgrade from economy or premium economy) and all the comforts and perks that come with it. They are also more likely to spend to upgrade other parts of the trip, for example hotel rooms.

  1. Most people will trade more time in higher class for less time in economy even if they have to spend more points (not same with cash)

At least 50% of the people I talked to would happily spend more time in the air if they are in a higher class that they can upgrade to using points than take a shorter flight in a lower class of flight e.g. economy. They are also willing to use way more points for the longer flights.

  1. Money saved now trumps value per point.

Most people viewed a flight booked via points as an immediate saving of the equivalent ticket price when purchasing in dollars/cash. E.g. Lets say Jane Doe booked a flight for 50k points when it would have cost them $500. They see it as an immediate saving to the tune of 500$


Based on our findings I gathered that the points ecosystem at least in North America is ripe for disruption and most users are fumbling around without much help when they are trying to make the best use of the accumulated points. Caviair can become a very powerful tool for people to save money in the current economy (Winter 2023) and help them with their aspirations for travel and other leisure activities.


Based on the perceived effort vs perceived value for each job story we can group them into buckets and start working towards an MVP. It's important to start providing value as soon as a user comes to our platform or installs a plugin or we will not see them again.

The travel booking arena is very competitive and given our off-kilter value prop of using points which are not always front-of-mind we need to deliver a great experience where the user is rather than try to lure them off top another platform. 

Based on this I broke the product roadmap into 4 stages, depending on increasing levels of complexity and integrations needed to provide value to the users.

Landing on the Solution 

First Stage (MVP)

First stage has the minimum effort (on users behalf) and immediate reduction in cognitive load in their decision making process. The user visits their own airline/ travel website and we show additional data like cost per point or comparison of points vs cash on all fare classes etc.

We launch as a browser extention that uses user login to parse on screen data and add more information like other fare classes and points vs cash figures as well as per point cost etc. This doesn't make the user leave their usual flow but manages to alleviate some of the key pain points in the user's journey.

We target Chrome and Firefox first and start collecting data to populate the timeline trends and average points cost to evaluate good deals vs bad deals.

Second Stage

Second stage is onboarding the user onto caviair platform so that they provide us information like their loyalty programs and points etc so that we can give them better contextual offers and alternatives. For e.g. if the user only has 120k points we dont show alternate flights routes that need 150K points.

We build our own search engine that keeps all of our users' info in context when presenting them with the best options. We can start to abstract the minute details like cents per point and start selling on value they desire most like higher class travel, best value for points, alternate itineraries between non code-sharing airlines etc. 
The users however still have to be redirected (with autofill) onto their airlines to complete transactions.

Third Stage

Third stage is capitalizing on users' transactions and searches to build a predictive engine to analyze the best time/ routes to use. This also involves community building and sharing rewards for identifying great deals. Think secretflying.com meets hopper but for points based travel.

Fourth Stage

The fourth and last stage is to get deep integrations with all loyalty programs and get capabilities to buy and sell and *loan* loyalty points to help users capture the maximum value even if they don't have the necessary points immediately. For e.g. you spend 3000$ a month, but only have 60K points at the moment, we know you will earn enough points to book 160k in 10 months so Caviair loans you the 100k points and you can “Buy Now, Earn Later” or purchasing the gap in points from our platform to bridge the delta, eventually. (we charge a fee for the loaned points and interoperate between multiple systems to transfer earned points back to us)

Explanation of Solution

The MVP of the Caviair product is a browser extention that helps users that are trying to book air travel with contextual information that can help them make better decisions around choosing the best way to pay for the airfare when there are multiple options available. The key features in the browser plugin are:
1. The users will be show their current maximum redeemable points combination between compatible (for exchange) programs 

2. The extension also shows all the possible combinations of fare-types and the available points and cash prices for each.

3. The extension also shows the user the cents-per-point redemption value and a signal informing them if this is considered a good deal or not based on a pre-determined rubrik. (For e.g. 1 cent > Bad , 2 cents > average, 2cents to 3 cents > Good, 3.1 cents and above > amazing)

Future Steps

The main focus is on the MVP to start collecting anonymized data about the points redemption values offered at different times and routes so that we can start training our models to predict the trends in the future. 

Stage 2 through 4 will be built incrementally and take in learnings from the previous stage’s user behavior and redemption data trends. I hope to explore this idea from a technical perspective and perhaps build a working prototype with some mentorship soon. 


Product Manager Learnings:

Apurv Ray

Co.Lab was a tremendous learning experience for me as someone trying to change their career from UX Design to Product Manager roles. The biggest learning was the importance on prioritization of the possible solutions and incrementally provide more value by getting to the market quickly and learning from their data.

Another major part was to move away from a usability lens when conducting research and focus more on the business angle and how to provide the most value to the users while balancing the resources and time taken to deliver the solutions.

Designer Learnings:

Developer Learnings:

Developers Learnings:


Full Team Learning