Hear Matt describe the automatic outreach research process he has implemented at Coda.

Set up automatic outreach to book calls at scale. This can be as simple as attaching a Calendly link to existing NPS or lifecycle emails.

Set up a doc to automatically “set the table” and follow-through for each new call booked. Create your notes, send reminders, pull in context about the user’s attributes, etc.

Use a consistent format for each call — and hyperfocus. Most of my calls are only ~10 mins because I’m reviewing existing data about the user before the call, focus on a broad + high-signal opening question, then dive deep to understand 1 key aspect.

Summarize insights immediately and pair with verbatims. I use Grain’s highlights + story feature for this during the call and it saves tons of time, but still provides an instant 1-4 minute highlight reel from the call.

Share insights so the entire team learns, but keep your source of truth in the doc for long-term review. The doc makes it easy to share the handful of key highlights directly to Slack to spark discussion, but still surface the entire call notes + recording if needed.

I’ve fine-tuned this process over time, so that the doc removes just all of the things that made research time-consuming, and it’s removed a lot of the friction to doing it all. I could have good intentions to talk to users, but it's still a lot of friction if you're not super intentional about it.

Since I introduced this process, it has sparked a lot of insights that we use across the product team. It makes research a low-friction, constant habit. This can massively build your product sense and intuition for customer pains, and it can do it quickly especially depending on how much you scale the calls up or down. 

It’s helped me ramp up my user intuition. Now I can contribute to product conversations with high-signal insights backed by video clips from real users. It’s really helpful to be able to reach into my catalog of dozens of calls and pluck a specific clip related to a feature or a problem that a teammate has mentioned. It feels a little bit like magic when it works really well.

Another benefit of doing research this way is that you can proactively pinpoint important (but not always urgent) insights that feel obvious to your users but might be easily overlooked by your product team’s “official” research initiatives.

I think this tracker is deceptively useful. One of the big unlocks is that, instead of chasing down users to solicit feedback, this approach means you always have a steady stream of user research coming to you. This can be a really impactful style of research that I don’t see a lot of people doing. But I’ve had a lot of people ask me about this and how to emulate it.

When someone asks for advice setting up a process like this, I tell them to focus on your #1 user segment and pain point. Make sure to keep the calls simple but to carefully craft your opening question.

And while reducing friction is great, I wouldn’t automate or over-streamline the most important part of the research process: listening 

carefully to the user and letting them guide me to the most important feedback. These bite-sized calls don’t work as well if you’re following a rigid script or falling back to overly narrow opening questions.

When a call is set up, the table (from the image above) alerts the team over Slack.

Another important part of these interviews is making sure I have any of the context that I can scrape about the interviewee from their user attributes and data. Otherwise, it’s just 10 minutes of asking them, “how did you find out about us?” or “why did you sign up?” or “how deeply have you been in the product?” You know, I can just look at the data to tell me all that stuff. Instead, I make sure to ask a really good opening question, like, hey, can you walk me through this and how you’re feeling. Then I can really understand their experiences.

I can take notes right here. I have a process where I’m actually actually recording time stamped notes that directly attach to the video. And that means that – after the call – it only takes a few minutes to stitch together the highlight reel, almost like an Instagram story of just the most important parts. I try to knock that out while it’s still fresh.

I think it’s important to summarize the highlights so the rest of the team can get that context, and so we can make product decisions on it. But there’s still the option for others to go deep if they want.

Matt Woods

I use this approach to automate the process of reaching out to a steady stream of research participants and tracking what he learns from them.

Automatic outreach research process and tracker at Coda

Overview of research "on tap" system

Table of scheduled calls

Automatic Slack notification about booked call

Time-stamped call notes and takeaways

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