Matheus Donangelo

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Freelancer Journey Management App

The Brazilian market for outsourcing digital product professionals was growing, and we wanted to ride this wave with a new approach: creating a delightful experience for the outsourced, improving their well-being and reducing turnover.

So Libri was born as a talent-first platform with the aim of connecting talented workers to amazing job opportunities in big companies such as Bosch, Board of Innovation, Ambev, Novo Nordisk, and so on.

My role

Product Designer.

Responsabilities

Define the design process.

Test hypotheses.

Continuos Discovery process.

Year

Q3 2022 to Q4 2023.

Team

Julio Barros, Full Stack Developer.

Mauricio Bueno, Product Manager.

Paulo Vitor Araujo, Product Designer.

Matheus Donangelo, Product Designer.

01 Main Goals

  1. 1Improve the connection between companies and freelancers (increase NPS on both sides).
  2. 2Reduce talent turnover.
  3. 3Increase the satisfaction of talents in the companies where they are allocated.

Our main challange

How might we deliver a talent-first web app platform for the full journey (from preboarding to offboarding) of the contractors' talents?

02 Design Process

Research and initial alignments

We start by understanding the market through benchmarks and interviews, conducting over 50 interviews with users and alignments with HR experts.

Using this knowledge and the key business drivers, we built protopersonas, user journeys, value proposition canvas, and strategic alignments to guide our efforts.

MVP construction

Based on our learnings, we created an MVP with five core functionalities for the outsourced workers: rewards, invoices, missions, journey metrics, and profile.

Evaluation of the MVP implementation

We analyzed weekly and monthly active users, heatmaps, and conducted quick user interviews to identify which features provided the most value.

Metrics feature and invoice feature performed well, revealing an opportunity to enhance the talent experience for our contractors.

Product expansion

With solid features like wallet, invoice, and metrics, we recognized the need for new experiences to support different stages of the contractor journey.

Therefore, we initiated a new discovery process with benchmarks, market research, and user interviews to understand how we could make onboarding, candidate selection, and other aspects delightful.

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03 Final Results

What worked?

  • Our clients saw and valued our effort to give a better experience to their contractors.
  • We have observed the growth of Pulse and eNPS metrics. In addition, we have become able to act quickly on issues reported in the talent journey.
  • A The connection between the Invoice sending functionality and the collection of eNPS and Pulse allowed for consistent metric collection. Although we knew it wouldn't be the least biased way of collection, but it provided us with a great baseline.
  • Talents felt motivated and excited about the flexible rewards.

What didn't work?

  • We were a small product team making decisions based on business drivers, technical feasibility, and user pains and needs. However, sometimes we came across decisions that addressed user pains and were technically feasible, but when delving into the business modeling flow, we encountered financial barriers. E.g: we wanted to create a data structure based on the LinkedIn API application, but the previous API was discontinued, and the new API introduced a cost structure that made the implementation of the solution unfeasible.
  • The digital product worked very well for the company and for the talents. However, we didn't find a strong thesis to create a scalable product that justified the continuation of investment in the product team.

04 Key Learnings

  1. 1

    We can learn deeply by reducing the MVP

    Our first MVP was full of features. While it helped us prioritize what worked, it made our work slow. Therefore, that process didn't pay off.

  2. 2

    A good product for users may not be scalable

    We created a good product for our HR team and contractors, and we saw metrics increase over time. However, the product did not prove to be scalable.

  3. 3

    Value of in-depth user interviews

    At our startup stage, we needed more evidence and quality responses than what Useberry or Maze provided.

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