Value Proposition
The Jobs to be Done Framework is a model that allows you to identify opportunities without wasting resources
Strategies to craft value propositions out of interviews There's a fine line between an interview and a sales pitch. In many cases there's no distinction, just that scipreneurs feel a bit more at unease about being salesy.
However, before we can even try to sell, we must identify how we can deliver value to someone.
I always found the Jobs to be Done Framework a great model to lead interviews and to focus on identifying areas were we can deliver the most value, mostly because they are underserved by the alternatives.
One aspect we must always consider is that scipreneurs have a smaller range of action in the solutions-space compared to, for example, people working on web-based products. On the one hand we are bound by what science allows. We can't promise unrealistic resolutions or acquisition times. On the other, we are bound by the range of techniques we are comfortable working with.
People are always trying to perform jobs. It is our job to find out which ones are currently underserved or impossible, that's where opportunities lie.
Jobs Theory identifies three key roles: The job executor, the lifecycle support team, and the Purchase decision maker.
I bet that in your own context, you can give names to the people behind each role. From the PhD, to the technician, and the professor. They all have different jobs associated with the instruments around you.
That already means that depending on who you are talking to, the focus will be slightly different. How you prepare for a discussion will
Professors perform many different tasks, which may include submitting grants to get funding, arranging training for new equipment that arrives to the lab, setting up data management, disposing of equipment once it no longer works. And those are just the ones I can come up with before I actually talk to someone.
And that's how I would prepare before I talk to them. How much time do they spend on grant-writing, how successful they are, how do they generate preliminary data to increase their chances. I could also check whether they have instruments they can't use because no one is trained on them, and how they go about it.
More importantly, I would ask about the broader scientific ambitions they have, and how they plan to solve them.