For the last couple of years, the U1 team has been using generative research to inform complex customer design solutions. It has resulted in some great experiences and projects that I want to share in this article. Before we plunge into the detail, here’s an overview:
- Generative research “pulls” product and service inspiration from your audience, increasing the likelihood they will meet a true need and be successful
- Digging into motivations, behaviours and attitudes in your research can help you to reach a deeper understanding of your customers
- Keep an open mind and allow research insights to inform your decisions, rather than seeking to validate pre-existing assumptions, it can save time and money in the long run
Generative vs market research
Best before starting (or at the very beginning of) a project, generative research helps to identify potential problems or questions and informs design solutions. Somewhat the opposite of traditional market research, in which the researcher explores potential markets to push a product out to, this type of research pulls from participants in order to inspire the design of a new product or solution.
Where to start?
I want to provide some insight into how this type of research can be employed to address business goals and inform experience design decisions. I’ll draw on experiences from some recent projects with the hope that you’ll gain some understanding of the ways in which this research can assist you or your team with decision-making. These projects either have an end result that is unknown or undefined or require a revisit of the current approach to ensure continued relevance. Often based on a question, the work in this area attempts to ensure we are designing the right thing before verifying whether or not we are designing the thing right.
The most common outputs we provide are insights that inform strategies, personas and future state user journeys. Each project begins with a goal to understand a group of people better, usually behaviours, experiences, motivations and needs regarding a particular topic, such as “energy use in the home”.
We have done quite a bit of work now with Australian energy consumers as an example, getting to know them via interviews, workshops and a very fun method we refer to as mobile ethnography. One of the things we were looking for with the energy consumers was how they made decisions about which energy company and offer to go with and how engaged they were with the service once they had signed up. As you can guess, not everyone is highly engaged during this process, however there were some surprising findings regarding what contributed to their decision process and how they wanted to receive information. I can’t reveal these findings as this was paid client work, however I wanted to use this group of participants as an example to show how we might go about uncovering useful information.
There were some surprising findings about what contributed to energy consumers’ decision process and how they wanted to receive information.
One particular activity we found effective as a way to learn about how much participants engaged with their energy usage was asking them to draw or explain their bill to us. In workshops where we had the participants sitting in a room with us, we asked them to draw their bill from memory for us and then walk us through it. For participants in the mobile ethnography, we asked them to send a video walkthrough of their most recent bill, explaining the things that they look at each time and what they understand all of the other information to be. This gave us a baseline understanding of how much they knew (or thought they knew) about energy usage. Additional activities covered previous experiences and explored topics like influences and brand loyalty.
Applying insights to decision making
As a result of the research, we were able to uncover distinct types of energy consumers based on key behaviours and perceptions. For one project, this turned into a set of personas with a strategic framework to accompany them, indicating unique needs for each different type of consumer and offering suggestions as to how to customise design solutions based on key identifiable behaviours. For another project, we offered two variations for an energy comparator tool based on two distinct ideal user journeys.
By leaving the solution space open at this stage, generative research can reveal unexpected insights that lead to a complete direction change or cancellation of the proposed design altogether, saving money and time in the long run. One project I did a few years ago with a university ended in the decision to halt the build of a new internal website as it was found to be an unnecessary channel for the desired audience. Another project we tackled more recently explored digital opportunities for senior residents and produced results counter to current beliefs about what content was needed and where it was sought out, significantly altering the intended solution.
Perhaps the best result of this type of research is the readjustment of an organisation’s understanding of its customers or staff. Where at one time, business stakeholders would have made assumptions to fill in gaps in their understanding, they are now able to better empathise with the intended user of a new product or service and make more accurate design decisions with a whole lot more confidence.
By leaving the solution space open, generative research can reveal unexpected insights.
There is a substantial element of co-design in this area and it is expected that the client and audiences be engaged in each project, participating as much as possible in the research. This means that clients learn with us and understand where our insights and recommendations are coming from, usually contributing a bunch of their own to the final report. I wrapped up a project late last year with a law firm in which the client was able to create their own persona set after participating in all of the interviews and a couple of whiteboard sessions with our team.
All of the projects mentioned above have benefited from an open approach in which the research goal was to understand people and look for inspiration for new solutions and services. Rather than designing something based on assumptions and then testing with the intended audience, we reached out to the audience first to get to know them better.
We uncover from this approach that there is often more than one solution and we are therefore able to choose a direction that is most accurate for the audience group and the needs of the business. Generative research is most effective when there is flexibility within the business to explore multiple solutions or when the problem has not been completely defined. Once a solution has been set and prototyped, a more evaluative research approach would be best.