Finding insights, not platitudes

Design has a particular competence–divining insight from people’s experiences, needs, and worldview. This is paramount to good design, as insight is the basis for any concepts an organisation might entertain for implementation. Get it right, everyone is a rockstar. Get it wrong, the organisation will waste time and money, and become increasingly irrelevant to their customers.

However it is all too common that the process that should lead to insight results in platitudes instead. They are essentially “motherhood statements” that sound nice, but are actually meaningless. Examples of some motherhood statements:

“People want things to be simple and easy.” 
“I want to be in control of my finances.” 
“Family is at the centre of people’s lives.” 

These are just a few of the very real “insights” I have seen from experienced researches, designers, and marketing professionals. And these same insights have been the basis for multi-million dollar projects. Even if these were insightful statements, how does one design to ideas like “simple and easy”? Were the previous products and services designed to be opaque and difficult, take control away, or ignore the importance of family? Of course not.

The correct approach to gaining insight actually starts much earlier. Strong insight begins with rigorous problem framing, research, analysis, and synthesis. When these phases are done well, insight becomes almost obvious. When practitioners shortcut the process, either due to time constraints, budgets, resources, or poor design practices, the result will almost always be a platitude. Unfortunately, to the uninitiated, a platitude can seem just as meaningful as an insight.

So what leads to such a lack of meaningful insight within both design and organisations? Long story short, insights are extremely hard to uncover. They require four steps that must be carried out with intense rigour and critical thinking:


Proper problem framing. Your problem frame forms the basis of your research protocol. Problem framing defines who you will interview and what areas of their lives and experiences are most relevant to your initial hypothesis.

If your framing is inherently organisation-centred, it will likely boil down to one of two goals: cut cost or raise profits. It is impossible to develop a meaningful research protocol based on these or any organisational needs. Proper framing is always about finding relevance for users. Once you know what is relevant, it is relatively easy to make a business out of it. Not true for the reverse. So, by definition, problem framing is from the perspective of your user.

With a well defined frame it is possible to determine how many people to interview, whether gender matters, they type of age groups and spread, if cultural background will have an impact, or if being single, married or having children plays an important role. And a myriad of other qualities that either need to be accounted for or can be determined as not having a meaningful impact on your research findings.


Qualitative and desktop research. Research is a starting point; it is about going out in the spirit of discovery and gathering the knowledge needed to fuel a robust creative process. Research must include a special focus on the customers or end users—those who will be on the receiving end of value creation. This exploration includes both desktop research and observation; It is necessary to be both an analyst and anthropologist if one is to a.) understand the present market situation, b.) reframe the current situation in new ways, and c.) get predictive about where future opportunities might lie. It’s also important to see that, while assembling historical data is an important part of expanding our knowledge, data analysis alone is a passive and backward-looking activity. It must be framed by immersive and active research into people’s lives and experiences right now (ethnography).

The key output of the research phase is an intelligent representation of the current market context, including a lively description of customers (or potential customers) and their needs. Research is a divergent activity, in that we are not immediately seeking the closure of solving problems (which comes later in our process), but rather opening ourselves up to a wide landscape of contexts and possibilities. The wider we are able to push our research, the more we increase our ability to generate imaginative new concepts. 

It’s important to note that it is common for a fairly pedestrian research phase to create over a thousand discrete data points: verbatims from participants, observations of context and customer behaviours, as well as market and industry data. Post-Its are commonly used to capture each data point, as they are easily group and resorted.

At this stage, it is also common to reframe your problem statement. This can require another round of research based on your new hypothesis. If you are fortunate, you'll become aware of the need to reframe after the first several interviews.


Pattern analysis. The typical approach to problem solving often collapses analysis and synthesis into a single fluid activity. In design, we are careful to distinguish between our answers to the questions “What?” and “So What?” This is because the starting point for idea-generation is often mysterious. It comes more from intuition than from any kind of logical thought process (which is why we can’t analyse our way into innovation). Developing good intuition is a direct result of deep immersion into our research.

Analysis allows us to replicate or extrapolate. We are not trying to make conclusions about why, we are just looking for the basis for how people behave and what unmet or inarticulate needs they’ve revealed. What is unsaid can play as much a role in what users have explicitly stated. 

The activity of analysis requires that we become intimately familiar with all of our data (each Post-It) so that we can leverage our own innate ability to see patterns across a diverse range of information. This requires weeks of sorting, creating clusters, challenging assumptions, and exploring all potentials within our research data. When we first begin the process of pattern finding, our brains take shortcuts. We see clichés and our own biases. This is often where design and market research stops. However the key to understanding is to fatigue our brains sufficiently that we begin to freely associate. At a minimum, this takes a week of effort, and depending on the amount of data, up to several weeks.


Synthesising meaning. This is where we gain actual insight. An good rule of thumb is that an insight is a non-obvious behaviour that makes us say “aha!”.

Synthesis is the spark that allows us to see and identify opportunities. It comes when we ask ourselves, “so what does this all mean?” At this stage, we are making connections, looking through patterns to draw new conclusions. We look for things we didn’t see when we review our research as discrete data points. We look for the ways in which the experiences we observe in the world reflects or refutes our data. Meaning emerges when we draw connections between the range of research and observation.

We want to formulate insight based on people’s actions, needs, and desires. We look for those “aha!” behaviours that allow us to define the shape of our design space. Each insight helps us describe the bounds of our concept generation phase. All potential solutions must be in support our insights. Without these limits, concepts will tend to focus on “me-too” products and service or overly broad ideas such as “apps” or “micro-sites”. Well defined insights provide clarity and helpful limits on “what could be”.


But what does a good insight look like?

Remember, they are “aha!” observations. When you hear them, you say to yourself “of course!”. They form the very basis of how we behave and their importance gives us direction on how we might design a solution.

As one example, I led a design project within the financial services industry. The organisation’s framing was “how do we make people consolidate their super with us?”. And, yes, it was how do we “make” them consolidate. The reframe was to look into what people valued in their lives. I interviewed 26 Australians that covered a spread of demographics. In my favour, the target group for superannuation was effectively anyone between 18 and dead. Segmentation was used to ensure that results were not inadvertently biased towards any one age, gender, income, or location.

One important insight that emerged was related to how people think about money. They acted as if it had physical weight. I’ll give you an example of what I mean. Imagine that you have two piles of logs. One pile has 5 logs and the other has 100 logs. If I asked you to consolidate them. What do you do? Well, if you are like most people, you will toss the 5 logs on to the pile of 100. It’s easier.

What if you had two superannuation accounts, one with $5,000 and the other with $100,000. If I ask you to consolidate them, what is your first instinct? If it was to move the $5,000 over to the larger super, congratulations, you are like 95% of Australians. If you thought “well, I’d consider which had the better return and fees”, you are a rare outlier (even those who said they would consider fees often ended up moving the smaller amount because it was deemed to be easier).

So, if you are a superannuation company and you spend a significant amount of marketing trying to convince your customers to consolidate their supers, what is the likely outcome? If you have their big pile of money, you are likely to get their other funds. But if you aren’t the big pile, that same customer will move their fund away from your company (note, superannuation companies don’t have visibility of customer’s other balances). Customers won’t even think twice, as they will be very confident that it is the right thing to do because it is easier. Of course that’s not accurate, it’s all digital and there is no difference in effort. But people’s behaviour is so tied into the fact that they treat money like it has weight, they will make their decision based on less perceived effort, namely moving the smaller fund.

You can see how this insight drastically changes the conversation for a superannuation company. If they speak to super consolidation as a pile of cash, they are just as likely to lose someone’s funds as to gain their other super. If they engage the topic of consolidation such that other considerations take precedent, there is a much higher likelihood of keeping their customers funds in-house and increasing overall balances through consolidation.

This is just one example. But it’s important to note that if your insights don’t provide actionable direction on people’s behaviour, they’re likely platitudes that sound nice but are meaningless. The only way to address this is to go back to where your understanding lacks the necessary rigour: revisit your problem frame, revise and conduct your research again, spend sufficient time pattern finding, or stay with your analysis until you’ve been able to synthesis real understanding into people’s behaviours.

I can’t overemphasise how important it is to have meaningful insight, as these become the foundation for the concepts you and your peers will be developing. Without real insight, your solutions will be irrelevant and clichéd. And as a bonus, real insights are one of the few defences against the opinions of those who have “been in this business 20 years and know what customers want.” Do yourself a favour and don’t shortcut the process of determining what it means to be relevant to your users. Your customers and your business will thank you.