In the midst of insight overload, feelings can be a data source

Data-driven insights, optimized features, there’s diamonds in your data if only you could find it (you’re missing out as you’re reading this instead of analyzing!) has been the narrative that’s been all the rage the last few years.

But how does this work if you’re a growing startup, with sort of clean data and not that much of it? Is there really a crazy nirvana of value? And how does your gut fit into all of this?

For early-stage founders in the idea stage iteration squiggle, this experience is probably amplified. You probably don’t have much data, and the data you do have leans heavily qualitative in the form of sound bites, Zoom recordings, and iPhone notes app drafts from in-person meetings. There are looming cognitive biases that can influence reactions and decision-making — over-indexing on your most recent conversation, talking to folks that aren’t your target market, etc.

Even on a larger company scale, most people don’t want to seem emotional or impulsive. The reality is, though, you can easily subconsciously pull out the right data to back up what you already think. You can also just as easily bury the gut feelings and follow the data map blindly — and there’s plenty of small and large instances where the philosophy “in God we trust, all others bring data” didn’t pan out successfully for one reason or another.

So how can you balance valuable but sometimes off gut reactions with your data? Proposition: by taking feelings and energy (and not just your own) seriously as a data source and quantifying something that’s traditionally considered qualitative.

1. Structured interview framework to get enough data points

   • The same set of prescriptive questions front-loaded in the beginning of a conversation establish structure, enable you to see patterns and differences emerge.

2. Triangulate different data sources and start segmenting

   • Consolidate discovery information over months, quarters, years, and see if patterns emerge across persona, startup size, sector, etc.

3. Iterate and watch reactions carefully

   • Which points of the product do people fixate on? Do they interrupt you to get a point across — if yes, why? Where do they have no feedback (because they don’t care)? What points in the conversation do they feel frustrated or excitable?

4. Look for the points of biggest failure

   • Where are all the things that could prove whatever you think wrong? Do you still have conviction after going through all the pieces of lukewarm/negative feedback?

Productboard’s cofounder and CEO, when reflecting back on the building days, talks about how he relied on intuition developed through hundreds of conversations with customers — not necessarily what they said, but how they felt, and how he felt about how they felt.

Funnily enough, although his own moods fluctuated from “Super depressed” to “Great convo with Dan” in one day and were probably not particularly reliable (real), it was focusing on the emotional reactions of his target market as they collectively gravitated from one of indifference to one of delight that enabled them to pivot and refine their product to a holy grail.

In conversation with friends that run startups that employ over two hundred and mentors that lead teams at companies that are thousands-large, this stays somewhat consistent. Numbers are a good start, but when they come in deep conflict with your gut, it’s probably worth taking a deeper dive.