{"id":11850,"date":"2023-01-13T10:06:41","date_gmt":"2023-01-13T10:06:41","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=11850"},"modified":"2024-12-10T00:34:58","modified_gmt":"2024-12-10T00:34:58","slug":"how-to-generative-research-steps","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/our-thinking\/how-to-generative-research-steps\/","title":{"rendered":"Using generative research to build the right thing"},"content":{"rendered":"

What is generative research?<\/h2>\n

Generative research is an exploratory technique used to understand users\u2019 experiences, needs and behaviours and generate insights to support design and delivery. The goal is to identify problems to be solved first, rather than jumping straight into solutions and validating the idea with users afterwards, which risks teams building the wrong thing. By gaining an in-depth understanding of users’ motivations and challenges, teams can build empathy with their user base and explore solutions that meet user needs.<\/p>\n

Generative discovery can take different forms:<\/p>\n

Interviews:<\/strong> At the Department for Health and Social Care we conducted video interviews with overweight people with comorbid health conditions to understand barriers to accessing weight management services. This helped us to understand the needs of a wide range of users and make recommendations for improved signposting into digital services.<\/p>\n

Contextual interviews:<\/strong> At the Ministry of Justice, we visited prisons to see how staff accessed and used data, to help understand how to replace a legacy reporting service. This allowed us to see user challenges first hand whilst using existing tools, and understand the environment. Is it noisy, what technology are people using, what workarounds do people use, and how could they be turned into opportunities for a new service?<\/p>\n

Observations:<\/strong> At Pret a Manger, we visited coffee shops to observe the flow of customers, and to help understand how new digital services would intersect with in-store processes. If your prospective service has an offline touchpoint or will be used in a specific environment, immersing yourself in the environment and trying out services can help you to design a service that solves a whole problem for users, on and offline. In this example, seeing the flow of customers in store meant that we were able to make recommendations for how collection points could work for a new online ordering and digital messaging service.<\/p>\n

Ethnographic research:<\/strong> At Jio we shadowed broadband installers in Mumbai to understand staff and customer experiences of broadband installation. This helped us to identify opportunities for new digital tools that track installation. We created customer journey maps to better understand the challenges faced by end users, and support the design of a new app for installers.<\/p>\n

Surveys: <\/strong>One to one conversations are the preferred method for generative research so you can dig in with follow up questions and see context. However, surveys can be used to supplement this information and gain insight from a broader range of users. At the Department for Health and Social Care we also sent out a questionnaire with exploratory questions, which enabled us to gain insights from a broad range of users that were harder to reach through interviews.<\/p>\n

Common questions<\/h2>\n

I\u2019m replacing a legacy service, do I need to do this?<\/p>\n

Yes, often working practices have evolved around systems rather than actually supporting users to do what they need to do. Investing time in some formative research helps to uncover unmet user needs and pain points, ensuring existing inefficiencies are not baked into a new system.<\/p>\n

Can\u2019t we just do prototype testing?<\/p>\n

It\u2019s tempting to jump ahead to prototyping solutions but there is a risk of making assumptions about the right problem to solve and partially deciding the solution. Once you start down this road it can feel much harder to go back from here once money has been spent and stakeholders are invested in an idea. It\u2019s easy to come up with ideas; the trick is selecting the good ones that solve real problems and add value. Generative research will give you the evidence and confidence you need to choose the right solution.<\/p>\n

Even if you have a couple of different ideas to solve an assumed problem, there are risks in showing users multiple prototypes and asking which they want most. Cognitive psychology <\/a>has taught us that humans don\u2019t have reliable insight into why they make the choices they make, so we can have little confidence in asking users what they want and why.<\/p>\n

We have a few ideas already – can\u2019t we just get started?<\/p>\n

David Travis and Philip Hodgson share a useful anecdote illustrating this issue in a brilliant book called \u2018Think like a UX researcher<\/a>\u2019. In a consumer study, people were presented with four pairs of identical ladies\u2019 tights labelled A, B, C or D. All of the tights were identical but the majority of users chose option D. This was because of a known position effect, where people have a tendency to choose things from the right hand side. More interesting is the fact that people were able to give reasons for why they had chosen pair D, for example that they were better quality or they had more elasticity.<\/p>\n

By exploring needs, rather than wants, through carefully curated open questions and observations we can start to uncover users\u2019 challenges. This helps us to be confident that the solutions we\u2019re proposing will solve real problems and delight users. In the example above, if tights are our \u2018product\u2019, we haven\u2019t learned if people actually need to buy tights, because we forced a decision upon them in the research. We can conclude that if people want to buy tights, they want them to be good quality with good elasticity, but what if the solution people needed wasn\u2019t actually tights? Perhaps the real problem is something else: keeping legs warm, dressing modestly or making a fashion statement. We won\u2019t know unless we ask open questions. By giving people a solution before we fully understand the problem and asking them to choose A, B, C or D, we limit our opportunity to identify the right thing.<\/p>\n

How to conduct generative research<\/h2>\n

Successful generative research should follow the steps below. You can read about these steps in more detail in this blog post<\/a>.<\/p>\n

    \n
  1. Identify your goals – what are you trying to achieve?<\/li>\n
  2. Create an interview plan to meet these goals<\/li>\n
  3. Identify users from each subsection of your target audience<\/li>\n
  4. Run the research including interviews and observations<\/li>\n
  5. Analyse and synthesise the data into themes<\/li>\n
  6. Create actionable insights from the analysis<\/li>\n<\/ol>\n

    How to make sense of the data<\/h2>\n

    You\u2019re going to generate a lot of qualitative data through the research and this can feel overwhelming. Avoid analysis paralysis by remembering the goals you defined in step one. To make sense of data, you can run a \u2018thematic\u2019 analysis. You\u2019re looking for:<\/p>\n

    Commonalities in the kinds of things users say – this can help you identify the biggest pain points or opportunities.<\/p>\n

    Differences in what different groups of users say – this can help to establish different personas for your target product or service. There are a number of practical ways to collate and sort this information, including:<\/p>\n