PRD - Solve

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PRD - Improving Debt optimizer engagement

PRD for Debt optimizer’s referral feature


Target Audience

People with debt
Users who are looking to improve their financial situation by reducing their debt. These users have a goal to either reduce their total debt by either paying off quickly OR reducing their monthly borrowing costs.

Problem overview

Solve recently launched a debt optimizer. Users who are registered with solve are using the newly launched debt optimizer to find their personalized recommendations but not coming back to the platform causing a decline in overall user engagement.

Goals & Success

User goals are either “lower my monthly borrowing costs by as much as possible” or “pay off debts as quickly as possible”. The goal of this feature is to improve the overall user engagement. To that end, the metric we want to improve are
New User Signups
Monthly Active Users

Success of this feature would be an uptick in both of these metric month over month post launch of the referral feature system.


Technical risk - Breaking the debt optimizer functionality and introducing a critical bug.
Financial risk -Lack of funding to support development and maintenance


Key features

Below are the 3 main feature recommendations to improve the user engagement issue
For guests or non-registered users - For users that are guests and have not registered yet, the debt optimizer should be structured to allow users to reach the end of the survey and then be provided an opportunity to share their email address and subscribe to a personalized content on the basis of their respective goal. The content that is available on the website under blog can be utilized to send these users drip campaign emails with CTAs that invite them to come back to the website to take one or the other action
For guests or non-registered users - Another recommendation for non-registered users is to not display the recommendation on the website at the end of the survey and have it sent to them in an email. The users have already come this far to find the financial recommendations and would very likely be happy to have the recommendations sent to their email. This will allow us to segment these users on the basis of their goals and run email campaigns to reach out to them and drive them back to the website just like recommendation 1
Registered users - Lastly, below is what I recommend for registered users, a referral system. Using this referral system we will be able to A, acquire new users and B, notify our registered users via emails whenever their referrals have signed up with us, providing them with some incentive and thus increasing the probability of them to visit the site again thus increasing user engagement on both new and returning user front.

Referral feature mockup
Final Rec.JPG

Key flows

Happy user flow path
User will click on the Debt Optimizer CTA on the website
User will land on the first modal where, they will select 1 of the 3 options whether they want to shop, refinance or make loan payments as the options
Once a user selects one of these options, they will asked for some information such as their credit score range, zip code, address etc. that will help us run data in the backend to find the best recommendations for them
After providing their information, user will be asked to provide their goal i.e. lower their monthly payment or pay off quickly as possible
Final screen will be of the recommendations that the user can take to achieve their goal. As this is the point where the user satisfaction will be the most, it makes sense to ask users to refer to their family and friends. Below the recommendations, we can give users two options to refer: either send emails to friends or copy their personalized referral link.The incentive to refer will need to be discussed.

💡 The user flow is below

Launch Plan


Operational Checklist


Reviewer Signoffs

Pulse Check - How do you feel about this feature?

Check to show everyone's sentiment (
submitted with average sentiment of
Submitted by
Love the new debt optimizer flow!
Ankit Verma
Potential improvement on UX
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