(Nick Noreña, a Lean Startup Coach at Kromatic, works with teams and organizations to help them implement Lean techniques in their daily business. An entrepreneur at heart, his favorite thing to do is work with early stage startups. If he’s not in his office in San Francisco, you can probably find him on a long bike ride, or on Twitter and LinkedIn.)
This is Part 1 of a two part blog post on effectively using the data you gather from customer discovery interviews. Check out Part 2 here.
As a Lean Startup coach, I get the opportunity to hear a lot of questions about the challenges of implementing Lean methodologies. One question I hear a lot is: “How do I use the data I get from customer discovery interviews?” This post is the first in a two part series on making sense of the qualitative data we get from talking with customers.
Part 1 focuses on what you can do before and during a customer discovery interview to make life easier for yourself after the conversation is done. In part 2, I will focus on finding patterns in the data you have collected.
Before the interview
Alright, so you’ve figured out that you should be talking to your users to learn something about your product/service. But what are you doing to prep for those conversations? How are you spending your time during those conversations? Well, the good news is that even if you don’t prep for or take notes during your customer conversation, you are still doing more than the entrepreneur who is sitting at her desk all day trying to figure out what is wrong with her product. But here’s how you can do more:
Focus your conversation
Before beginning a customer conversation, you should have a learning goal(s) associated with it. This learning goal(s) will help you keep your conversation on track, and as long as you stay focused in the conversation, the feedback and data you receive from the user will be focused as well. An example of a learning goal for customer discovery interviews might be:
“I want to learn how university professors currently distribute their presentations and other classroom material with their students.”
“I want to learn how much time and money first time employees in San Francisco spend commuting to work.”
If you walk into a conversation with a focused learning goal, you are more likely to walk out with focused learnings. I like to keep the learning goal front and center in any of my conversations by writing it out in big letters at the top of each page I’m going to take notes on. That way, each time I glance down at my notes, I’m reminded of what I’m there to accomplish.
During the interview
Limit your note taking
Before I talk about how I take notes, I have to credit David Bland for helping me think differently about my note taking habits in customer discovery interviews. I used to just try and write down everything I heard so I wouldn’t miss anything (sound familiar?). But that all changed when I started focusing on taking only four different types of notes:
This type of note is pretty self explanatory. The reason that direct quotes are so important is that it prevents you from trying to gather insight while you are note taking. The direct quote is a piece of objective data that you can analyze after you’re done with the interview. It helps you treat note taking as its own activity (an activity you get better at with practice).
Often I’ll record my customer discovery interviews so that I can make sure I got the direct quotes down accurately. Just to be clear, I don’t use the recording to replace or supplement the interview itself. I just use it to double check my notes and make sure my direct quotes are accurate.
Much like the direct quotes, the purpose of writing down observations is to remain objective while taking notes. Observations are different from insights, and when you try and draw insights during note taking, you run the risk of missing something else that a user/customer says.
Let’s say you are interviewing Jimmy about his lunch time eating habits. He tells you about going to McDonalds five times last week, and also what he ordered when he was there. It’s the difference between writing down that note as:
Jimmy went to McDonalds for lunch 5 times last week. [Observation]
Jimmy loves McDonalds, he goes there as much as he can. [Insight]
It’s a subtle difference, but an important one. In the above example, you might very well be right about Jimmy. But the riskiest thing here is being wrong about that assumption. What if Jimmy went to McDonalds against his will?! After you have completed the interview, you will have a more complete data set to help you limit the risk of being wrong about your insights.
This is one of my favorite note types because of how powerful non-verbal communication can be. Admittedly, it’s a tricky note type to record. Often the signals you are looking for happen in a split second: a little smile, eyes widening, someone shying away, etc. But when you recognize it, body language provides a powerful signal. Watching someone react to what you are telling them is like taking the filter out of the equation. So whether it’s positive or negative body language, make note of it. You might just find more truth in that than in the words of your customers!
With every customer discovery interview I run, I always have a section for taking down miscellaneous notes. Sometimes they’re notes that have to do with the participant, and other times they are just ideas that pop into my head that the participant might be making me think. You’ll figure out what to put in this section when you actually do the interviews!
After the interview
In my next post, I’ll be writing about what do with your data after you have conducted a customer discovery interview. But in the interest of identifying our riskiest assumption, it behooves us to get the “before” and “during” parts down before worrying about the “after” part. We need to set ourselves up for success by:
- Focusing our conversation with a learning goal
- Limiting our note taking, focusing on 1) Direct Quotes, 2) Observations, and 3) Body Language
Once we have done that, then we can be confident with the data we are collecting, and know that we are using our time wisely when we gather insight from that data.