Case study by Attila Miklya, Pine Digital Studio

  1. Context. Why?

On the fall of 2020, Pine Digital Studio CEO was approached by a group of young startuppers planning to run a pre-seed crowdfunding campaign for their student e-learning solution. Although the founders, Marci, Bálint and Andris made the impression of a perfect team, the product itself was a bit premature. Students could post study topics they were stuck with and receive help from peers. Although the domain (study issues) was quite hot at the time due to covid, the functionality of the service wasn’t unique enough — and I wasn’t the only one thinking so.

The original name was Prepaired - the new Value Proposition brought a new name as well.

They received feedback from potential investors that the unique value proposition of the service wasn't quite clear and so many witheld investment. In response to this feedback, we decided to test and re-discover the Value Proposition of the service and see if we can find a job-to-be-done that emerged for students at a global scale, in response to covid and at a mass level.

2. Methodologies. How?

In this project, I worked as a solo researcher, in close collaboration with the Bindr product team - this meant close co-creation with the product manager, Marci and Bálint for a month. The role of the research was quite strategic and as such, they had given close attention to this project.

Research framework: Value Proposition Canvas

Scoping, data collection and analysis was done using the Value Proposition Canvas framework. The VPC was the main artefact of this project, and was used to document each step of the research process.

  1. An as-is version of the VPC
  2. The assumptions VPC: Jobs-to-be-done we want to explore.
  3. Gains, Pains and validated JTBD's from the research results.
  4. Product pain relievers and gain creators tailored to the findings.

Source: Feedough

  1. Scoping, desk research.

    We drafted an as-is version of the product with the VPC. The persona was rough, JTBD's and pain points very general, such as "Wants to get a diploma". We made a long-list of more fine-grained JTBD's and came up with a shortlist together. One of the fine-grained JTBD's: "Wants to keep up-to-date with the study material every week to avoid being overwhelmed".

    We decided to explore these assumed JTBD's in the hope of finding one that is unique, unsolved, severe and also close to the already existing functionality of the product.

    Based on a heuristic competitor analysis, we also identified market-defining product strategy axes, such as:

    We set these axes up to help us map the competitors and find Bindr's unique place. When we began, we had uncertainties about some of these axes and aimed to clear those up during the research project.

2. Recruitment and data collection

In-depth interviewing was an appropriate choice of methodology for data collection because the JTBD's are relatively important in the lives of the student. As the team was aiming at the british market, we recruited students of british universities, locals and expats both. We managed to involve a "connector" during recruitment, for whom it took just a few hours to get 17 interviewees from the target group (see Malcolm Gladwell's story about connectors and market mavens).

Each in-depth interview took 45-90 mins. We intervieweed 17 end-users in 4 batches, iterating the research questions and topics to avoid saturation and "zoom into" opportunities. We intervieweed students with one of the founders, took recordings and later I transcribed them into a steck of 650 insights.

As a different source of validation for the assumptions and product strategy, we also contacted edTech experts and investors to learn more about the market entry of similar products. These interviews were less research-y, less formalized: they lead to mentoring and later investment agreements.

3. Data analysis, delivery.

We used Miro to categorize the research insights. We collected positive and negative signals for each of our assumptions and distilled in-depth patterns abouth each JTBD to find the ones worth targeting (read more about my affinity diagramming methodology here).

This process took several days and was done with the constant involvement of the product manager and occasional involvement of the CEO and developer of the team. Co-creation unlocked the true value of this vast qualitative data because they had a chance to apply their own knowledge and understanding to it. If the research goals are so strategic, I like to push for a similar level of involvement. Summaries and research reports are very dense, high-level pieces of understanding. Co-creation helps the product team come to its own conclusions about the data. Spending time with the raw data is like taking an empathy bath.

However, a research report was delivered as well, reflecting back to the original assumptions, explaining the results of validation. We also built new versions of the Value Proposition Canvas which helped the product team through a series of pivots.

Data analysis in co-creation

Data analysis in co-creation

3. Results, impact.

The research project successfully validated and invalidated JTBD's and set the psychological landscape for the Bindr team to work with. The value proposition was tweaked a few times after the delivery of this research projects. After a few consultations and their own internal product strategy meetings, they pivoted successfully to a JTBD well-defined by the original research. The final pivot required them to shift their thinking into the emotional plane of studying, taking a step away from mentoring and study materials.

Many students use libraries during the year and the examination period to boost their commitment towards learning. The shared space, the agreement on the timeframe and the secluded space all help students focus on learning. Being held accountable by peers was one of the top strategies students used to stop wasting time and this habit was radically endangered by the closing of libraries and similar learning spaces. Today hundreds of students share a virtual space on the Bindr platform each week, studying together silently.

The JTBD this final VP built on was only a fragment of the data we generated during this research project. The topic makes up less than 1 page in the 10 page final delivery and the same delivery also had 9 other, equally interesting pages with equally interesting product directions. Accountability just happened to resonate with all the other inputs of the team. For me the biggest impact of this project, was that the team kept the habit of interacting with end-users ever since. They adapted a routine of talking to 5-10 users every week to validate the designs and functionality they come up with. My proudest achievement so far is that user-centric product discovery became the engine of the product.