Knowcast, Diary Studies, and Behaviour Science 101: “watch what I do, not what I say”

Key insights from the Knowcast diary study focusing on existing behaviour, prior to even giving them a solution

Remember the classic ad?

“9 out of 10 dentists recommend <brand>”

Can we do better? What about:

“9 out of 10 dentists use <brand> regularly”

The best way to design a product is to deeply understand where it might be used, and by who. Unfortunately for survey tools, there are whole disciplines (including behaviour science, and behavioural economics) that provide evidence that opinions are not enough: you must look at behaviour.

Recap: Knowcast POC 1 gave me some early insights but they weren’t enough

In my previous post I shared my experiences doing some early prototyping trying to solve a problem I had personally with consuming web content on the go. The insights from that prototype included:

• An ultra-low-noise consumption experience (just text) helps focus, cognitive load and consumption quality
• Sentence-level highlighting and chunking: 500%+ more efficient
• Audio playback of web content works well

This was my own experience: but what about other potential users?

Enter the dark swamp of behaviour change inertia

When looking to get some validation of this prototype in the field from my target audience, I started thinking about behaviour change.

Key insight: the less users have to change their existing behaviours to use your product, the easier it will be for them to adopt it. It’s why, for example, Microsoft Excel offered a full emulation of Lotus 123 — type “/” in Excel and suddenly all the Lotus 123 commands worked.

Originally, I wanted to listen to and take notes from these types:

• Podcasts
• Web pages
• PDFs

I chose web pages because they were easier to implement technically. But through the behaviour lens, my rationale changed. All it took was to compare these questions:

“Tell me about your podcast consumption in the last 3 months.”

vs

“Tell me about your web page audio consumption in the last 3 months.”

No guesses as to which would have a wider audience! Even anecdotal research strongly suggested podcast consumption was a vastly more common behaviour than web page audio consumption.

Fundamentally, I decided to plant this simple founding assumption: optimise my product roadmap by minimising behaviour change. Moving from “listen to podcasts” to “take notes from podcasts” is less effort than moving from “reading web pages” to “listening to web pages”. Here’s my rationale:

• Listening to podcasts already has people in the same context of use (broadly “on the go” is what I settled on), at the same time, and in many cases (backed up later), the same job to be done.
• Reading web pages is a very different behaviour to listening to web pages, in a different context, with a different job to be done. It is always done when people are somewhere they can concentrate on a screen, which might be handheld, or on a desktop, but the context of use is also different, almost exclusively the opposite.

Back to basics: how do we understand podcast needs?

The best way to understand the true nature of the problem you’re trying to solve is to observe users experiencing the problem.

A powerful way to do this is to run a diary study that gives you direct access to people’s behaviour: when they experience the problem, where, how they address it today, and how important it is to solve.

Diary studies used to demand quite a lot from participants: hand-written journals, paper forms and often a big gap between the experience of the problem during the day and a written recollection later on.

Knowcast used a diary study to capture rich in-field insights into user behaviour and their needs, before any solution was built.

I worked around a lot of the traditional issues and costs by using WhatsApp for the Knowcast diary study.

WhatsApp meant participants used a familiar tool already on their phone and sent us rich video clips captured often at the exact moment they experienced something relevant to the study.

It saved us thousands of pounds over specialist research tools, and being on basically every phone, requires no training or support either and was still very easy to analyse.

My journey with a diary study went like this:

• Plan
• Execute
• Identify Insights

On to the next post: planning the diary study.