Crossing the Desert: Customer Observations to Insights

How do you turn customer observations into insights?

You turn observations into insights by asking “why” once you have gathered the “what.” Observations are what customers say, do, think, and feel. Insights don’t come straight from them. They come when the team overlays its own knowledge of the world around the customer and synthesizes a new picture of that customer’s motivations. Great insights surprise you, they point to opportunity areas, and they hand you assumptions worth testing.

It separates the strong from the weak. It humbles and tempers the traveler like cleansing fire. And it focuses those who survive in a way that they never imagined possible. Crossing the desert and living to tell the story is an important part of any hero's journey.

It is also a pivotal moment in your innovation journey.

You have gathered the data. There are piles of pdfs, interview notes, videos, and recordings. On the wall, you've distilled that data into an archipelago of Post-it observations. The big islands seem to want to tell you something; you strain to hear, but can't quite make it out.

That was the easy part. Now what does it all mean?

Good question. Observations are just that: observations. They are what you observe from the data, taking the form of things people say, do, think, and feel; answering the question of "what". Insights answer the question of "why" and don't come directly from the observations. This is where the team works together to overlay their knowledge of the world around the customer to synthesize a new picture of the customer and their motivations. A story or two might help illustrate.

From Observation to Insight: Real-World Examples

In the classic jobs-to-be done example, a fast food chain observed that a lot of adults were buying shakes in the drive-thru lane every morning. This was a menu item marketed as an evening treat for children. Through asking very specific questions of these customers and observing the circumstances of their orders, the insight was that these were busy people, on their way to work, using the shake as a filling meal replacement that could eaten with one hand, so that they could consume it without slowing their commute. Boom! A meal category is born.

In another case closer to home, a business partner and I saw that some employees were passionate about referring friends and family to the company, and did it regularly, while others did only sporadically, or not at all. Through a couple of dozen interviews, we were about to build a formula to predict a person's propensity to refer. It was a groundbreaking insight that helped to shed light on why different employees behave the way they do.

The Value of Great Insights

Great insights surprise you. They are a reflection of your success in shedding your own biases during the research. While not directly actionable, insights usually point to opportunity areas where you could deliver value to your customer. Actually driving that value, however, is rife with risk. Those risks take the form of behavioral assumptions that you can test.

For example: an insight from the employee referral work referenced above was that a lack of open jobs or knowledge of those jobs was keeping employees from referring. So, the opportunity area was to give these people an avenue to get more of what they are passionate about—referring! But without specific jobs open, would employees still refer?

To be able to move forward, we have to know if employees will engage in the behavior of referring, to get the value of the great feeling they get when they help out a friend or family member. We could spend a lot of money and build a platform, or we could do something simpler. We sent a form to the group of passionate referrers that we had studied. The form gave the employee the opportunity to refer up to three people, tell us what kinds of jobs they'd be good at, and why that person is so great. The result of the experiment? Over 80% returned the form, and asked what the next step was. We showed that there was an untapped resource hidden in our midst.

Insights, like lenses, both narrow and sharpen our focus on areas of potential value and help us to understand the assumptions we should test. Great insights energize teams and demonstrate progress to stakeholders. And they lie just out of sight on the other side of the desert.

Observation vs. Insight: What’s the Difference?

An observation is something you pulled straight from the research — what a customer said, did, thought, or felt. It answers “what.” An insight is the team’s best explanation of the motivation sitting underneath that fact. It answers “why,” and nobody hands it to you. You build it.

Six Steps Across the Desert

1. Gather the raw material. PDFs, interview notes, videos, recordings — everything the research threw off.
2. Distill it into observations. Put what people say, do, think, and feel up on the wall, and let the big islands form.
3. Ask why, together. Overlay what the team knows about the world around this customer. Specific questions about the circumstances of an order are what turned “adults buy shakes in the morning” into “commuters need a filling meal they can eat with one hand.”
4. Look for the surprise. If nothing surprises you, odds are you are reading your own biases back off the wall.
5. Name the opportunity area. Insights aren’t directly actionable. They show you where you could deliver value, and every one of those places carries risk.
6. Write the risk as a behavior, then test it cheaply. We could have built a platform to learn whether employees would refer with no jobs open. We sent a form.

So here is a question worth putting to your team on Monday: which of our insights did we actually cross the desert for, and which ones are observations wearing a better hat?

Frequently asked questions

What's the difference between an observation and an insight?

An observation is what you pull straight from the data: the things people say, do, think, and feel. It answers "what." An insight answers "why," and it never comes directly out of the observations. The team has to overlay what it knows about the world around the customer and synthesize a new picture of that customer and their motivations.

How do I know if an insight is any good?

Great insights surprise you. That surprise is the proof that you shed your own biases while you were doing the research. Insights aren't directly actionable on their own, but they point to opportunity areas where you could deliver value to your customer.

What's an example of turning a customer observation into an insight?

A fast food chain noticed that a lot of adults were buying shakes in the drive-thru lane every morning, on a menu item it had marketed as an evening treat for children. Specific questions about the circumstances of those orders produced the insight: these were busy people on their way to work, using the shake as a filling meal replacement they could eat with one hand without slowing the commute. A whole meal category came out of that.

What do you do with an insight once you have one?

You find the behavioral assumption buried inside it and test that. An insight points to an opportunity area, and actually delivering value there is rife with risk. The risk lives in what you are assuming people will do. Name the assumption, then run the cheapest thing that will answer it.

Do you have to build something to test an insight?

No. We wanted to know whether employees would refer friends and family with no specific jobs open, and we could have spent a lot of money building a platform to find out. We sent a form instead: refer up to three people, tell us what kinds of jobs they'd be good at, and why that person is so great. Over 80% returned it and asked what the next step was.

Why is it so hard to get from customer observations to insights?

It's hard because the data doesn't hand you the answer. You finish the research with piles of PDFs, interview notes, videos and recordings, and an archipelago of Post-it observations whose big islands seem to want to tell you something you can't quite make out. Gathering all of it was the easy part. Making meaning of it is the crossing.