With SenseMaker®, we can create deeper, more inclusive, and more far-reaching engagement to advance our democracies. We use real-time feedback to create more strategic and evidence-informed decision making. We empower citizens and communities to co-create on a local scale so they can rewrite the global story.

SenseMaker® allows us to hear ordinary peoples’ stories. In understanding their dinner-table conversations; their experiences, observations, and perceptions, we can decode the patterns that are really driving behaviours and attitudes. The stories we tell are a fundamental patterning device through which we understand the world and, therefore, capturing these stories and underpinning them with quantitative data creates contextualised insights into what is happening and why, from country to community level at any given time.

The power of interpretation always remains with the person who told the story; they decide what it means (self-signification) by completing quantitative questions about their story. This contrasts with the traditional questionnaires and qualitative studies, in which participants often feel they need to give the ‘right answer’ and the data is mediated by experts or researchers.

Our approach democratises the process, reveals unexpected insights decision-makers weren’t even looking for (in complexity theory, this is called ‘unknown unknowns’), and prevents misunderstanding due to context, experience, and/or language – we start with exploring, not with assuming.

Based on 20 years of research and situated learning, it is the world’s first scalable ethnographic research tool.

SenseMaker® can empower citizens to become experts and problem solvers in their own communities. The insights gained from SenseMaker® can not only inform intervention design, implementation, and when action should be taken, but also enables these steps to be taken in collaboration with communities. SenseMaker®’s capability to create a tailored approach to policy formation and evaluation enables accommodation of a diversity of needs across different / differing contexts. We use real-time feedback to create more strategic and evidence-informed decision-making.

•  In standard surveys respondents offer their opinions or answer based on their beliefs about their values. This is risky; what people believe drives them is often quite different from what actually drives them. By contrast, SenseMaker respondents describe a real experience. For example, a well-known coffee company found that their survey data differed radically from their sales data: customers said they liked espressos, but in reality ordered flat whites. Collecting stories about peoples’ actual experiences with a product, organisation, workplace or community yields far more reliable data.

•  In surveys people will often say what they believe their interviewer wants to hear (‘social desirability bias’). SenseMaker questionnaires eliminate this risk by beginning with a prompt or inquiry that is open-ended. Additionally, whereas surveys test existing theories, SenseMaker provides a system-wide perspective and may radically shift understanding of a problem.

•  Lastly, ‘messy’ qualitative data from surveys must be categorised to make it easier to filter and examine. Researchers thus interpret the meaning of the responses themselves, and invariably introduce unintentional biases. With SenseMaker, respondents perform the initial interpretation task themselves. Because they clarify the meaning of their own stories, the resulting data is more credible. In addition, we first examine patterns emerging from the SenseMaker data, only then do we read any stories.

Since its launch in 2004, SenseMaker’s anthro-complexity approach has made an impact in business, government, development and research sectors around the world. You can find more about the Use Cases here