I had even more fun doing this experiment a second time; the feedback continues to be encouraging, and now I think I have enough information to keep going with the project and move on the next step.
It will remain important to keep running and iterating on this experiment, but now it seems appropriate to think about scaling up. Doing it manually, I have only been able to accommodate 3 – 4 users at a time so far. I’d like to be able to do the experiment with more people than that, and with a longer timeframe than 7 days; this means working to automate the recommendation/curation process somehow, which will save me a lot of time.
Over the last week, I settled on a precise series of steps that I followed as much as possible to find conversations to recommend to my users. Given that I observed that my users mostly followed the recommendations, I would say it’s a decent start. Contingent on access to Twitter data, a more experienced developer at our company should be able to translate it into an algorithm that takes any Twitter user as an input and returns a list of conversations relevant to that user.
The other side to all of this is marketing: who are our customers, what do they want, and why should they even care about what we’re trying to offer? The second experiment has given me a chance to get to know more users, from which to make a few generalizations, but this continues to be something for me to work on carefully. Hopefully, being able to iterate on the experiment while including a larger number of participants will help us further along this goal.
It has been highly enjoyable seeing this project sprout from nothing to what it is at the moment—still little more than a seed of an idea, but continually growing week by week. It can be difficult at any given moment to feel like I’m making any real progress, but with each new milestone reached the path becomes a little clearer.