Empowering everyone to make better pay decisions. Salarybands is a web app that crowdsource compensation data for marginalized and underrepresented groups in tech.
Wage gap is one of the important measures for evaluating equity in the workplaces. A widespread demographic descrimination continues to be present in society irrespective of companies’ publicly acclaimed Diversity, Equity and Inclusion (DEI) commitments.
Research shows that certain minority groups experience persistent economic inequality due to large gaps in wages when compared with their counterparts. Thus, people from certain marginalized backgrounds based on gender, race, disability, and neurodevelopment backgrounds are more likely to be paid below the job market value.
There is an opportunity to democratize pay information by making salary information readily available to professionals to enable them to initiate fair and informed salary negotiations.
Using our network from forums and social media, we surveyed and interviewed different categories of prospective users from diverse backgrounds. From the survey received from over 262 survey respondents, the common themes were that:
Our preliminary survey and interviews with job seekers and professionals revealed the gaps in salary transparency. The main issue was first-time job seekers found existing salary databases to be too generic and lacked detailed salary information that was focused on relevant demographics and data they cared about. Another problem was that some professionals found it daunting and unnerving to discuss topics about salary with their colleagues or company to assess pay equity because of their minority profiles. Other participants expressed that they lack confidence and an action plan on how best to negotiate their salaries.
Based on our target users’ pain points, we knew we wanted to work on a solution that provided the following benefits::
Using the insights gained from our user research, we leveraged the RICE framework to help us prioritize features that highlight the core values and goals of the solution.
Our learnings: The RICE scoring model helped the team to quickly evaluate and prioritize the value of the features that were planned for the sprints.
In our MVP, we initially decided to prioritize both the database view and the salary upload as the main features due to implementation complexity and time constraints.
Even though features such as salary advice and demographic filters were going to provide high value for our users, the PM made a decision to descope and deprioritize those features because of the high technical risk so that we can focus on the main features that will enhance the intuitiveness and user experience of the product.
After the main features were deployed into production, we revisited and reprioritized the two features that later shipped successfully.
After we showcased our prototype to the users again, we learned that the design resulted in 95% average task completion rate of submitting data. However, a couple users demonstrated challenges understanding what input fields were optional versus required. Therefore, we identified two opportunities to improve the design.
Overall, users expressed that they would use this tool in the future to determine the market salary of their desired role.
We used Rest Api for the backend and fetched the data in the frontend. The backend is deployed on Render.com and the Frontend on Netlify.com.
About the stack, we used:
After we received the low fidelity design handoff, the backend developer started working in the backend to implement an API to store the required fields for submitting salary information. In parallel, frontend developer started working in the frontend to implement web components following the hi-fi mockup design.
For the authentication process to verify contributors, the team made a decision to implement a one-time-authentication using an automatically generated magic link instead of a two-factor authentication. Our backend developer had not had experience implementing this type of authentication, he stepped out to the challenges and explored ways to to implement it.
The dev team had recurring meetings to integrate the backend with the frontend. Finally, we achieved the integration without including the authentication in the first version.
On the backend side, the most challenging part was to implement the one-time-authentication from scratch. It took time to figure out how to implement it. The process to achieve that was:
The next challenging part was to integrate that with the frontend to complete the authentication process. Since the backend dev had some prior experience with that process, he was able to support the frontend dev in this regard. The integration between the backend and frontend was a success..
Filtering data was another challenging obstacle our developer team had to overcome. Due to the amount of data points within the table; we needed a way for users to filter through data seamlessly, getting the information as they choose their desired filters. Thus we went with a front -end filtering approach rather than filtering within the back-end then making a get request. To accomplish this we simply attached a filter method to the array we pulled from the back-end. From here we used a variety of conditional statements and array methods to ensure our filter toggles and select options matched our data point values.
Since our project is WIP, front end issues such as page pagination & responsive design as well as form validation still exist though these issues will be ironed out over the coming days.
Currently our app does not have any scaling issues as the number of users we have are small. We will need to further evaluate probable challenges that we would face as the user base grows in size where our back-end and front-end could not support the amount of information being generated.
Product Success Metrics
For the MVP, below are some of the success metrics we would be interested in tracking to assess the overall value our product delivers to our users. This will also be an avenue to get customer feedback and incorporate the learnings in future iterations and product improvement.
We will be continuing the project with a focus on expanding features as a value add for users to help them level up in their careers. Our team is excited by the opportunity to integrate web analytics, targeted messaging, and automated scraping to retain user anonymity for uploaded documents.
As a team, some of the key learnings we discovered were the following: