Structure and clarity for your strategic decisions
The goals of your organization and your customers may not be aligned. If you offer an inadequate solution, you fail. How people think, feel, and make decisions is your window into their goals.
Align your goals with people’s cognition, and you succeed.
1. Stabilize Your Strategy
People’s cognition changes slowly. A mental model skyline means your decisions will always be on solid ground.
- The problem space prioritizes people’s cognition and goals (rather than your solutions) as a framework.
- This means your strategy space can include more thinking styles with less cognitive bias, providing stakeholders with empiric data science.
- And your solution space will blossom with creativity, variety, and solid tracking .
Solid data science like this an investment that pays back year after year.
2. Measure Value to People
A mental model skyline accurately tracks the value you provide to people.
- Use familiar evaluative studies, but framed by skyline tower and recruited by thinking style.
- Watch spark lines lift over time as your solutions improve.
- See surprising opportunities to help people do things their way.
Strategy is about people.
3. Recognize Who Is Missing
Strategy defined by demographic assumptions pushes most people into cracks. Roles don’t describe your audience in a useful way.
- Build for the variety of hidden patterns of thinking styles.
- Stop using generalizing narratives.
- Cognitive empathy is scalable (where emotional empathy is 1-to-1).
The average user is a myth.
4. Innovate for people
Innovation focused on people is rare because orgs find it easier to innovate by technology.
- Find a whole new path your competition hasn’t even looked for.
- See the problem your solution is looking for.
- Build technology that supports people’s cognition.
Do something very different. There is power in that.
5. Teach your AI to recognize humanity
We can finally do it! We can support more people’s intents and approaches with designed solutions. And ask AI to recognize who is after what, cognitively, and guide them there.
Data Science That Listens provides the foundation for adaptive experiences that are deeply nuanced to the people you intend to serve.
AI won’t replace listening deeply and it won’t replace strategic thinking. But it will help us achieve a world of solutions matching people’s thinking styles.
6. Strengthen current methods
Elevate outcomes of the framework you already use with components of Data Science That Listens. It plays well with others.
Jobs To Be Done
(Tony Ulwick)
Empathy Mapping
Tie Quant to a Qual Structure
Behavioral Science Frameworks
(Robert Meza)
Design Thinking
Continuous Discovery
(Teresa Torres)
7. See work fitting together
Innovation is not the work of a single genius, but of a team.
Catalyze your collaboration with a common understanding of how things fit together from a customer point of view.
The mental model skyline holds layers and layers of your data, structured by cognition.
Testimonials
I was designing for dispatchers of windmill mechanics. The existing dispatch system could cover huge geographical areas, but it didn’t take into account the pure stress of 12 hour shifts and working with high voltage high above the ground. The listening sessions helped me understand how the different dispatchers approach their processes and their pain points.
Huge benefit to learning this method: a consistent approach, from recruiting to coding, to ensure you get empirical qualitative data.
I came back from vacation to discover our team had presented a draft redesign to stakeholders, who were less than pleased. So we used mental models to understand each stakeholder’s concerns, context, and lens. The effect this had was amazing. The resulting presentation was so well-received we were asked to give it to the executive team. My team has used this approach over and over since.
Mental model skylines fill a giant gap. It’s easy to jump right to designing a solution when a need is found. Instead, by focusing on interior cognition and purpose the team can design the solution in abstract of its implementation. Beginning with mental models fosters incremental change and establishes a benchmark to measure against.
I have been tasked with several technical projects at Utah State. Mental models have been essential in the planning stage to determine what functionality is the most crucial and what “would be nice.” To come back weekly and look at our initial goal has proved helpful in keeping us working in the proper direction.
The most attractive thing about this knowledge repository is that it’s not at the solution level. Many teams can use it. Unlike scenarios and research notes, it’s way more organized. It’s a visual way to see the whole picture in the data. We can grow it over time, and keep track of our innovation space.
We used mental models to determine how to categorise the vast amounts of information needed by residents, business owners, and visitors. One of the challenging issues was where to show what to do with a dead animal found on one’s lawn. We followed their mental model. Pets (dogs, cats) were part of Home section, while wild animals (raccoons, squirrels) went in the Parks section.
Mental model diagrams are a quicker path to team building and team alignment.
I joined as an ethnographer to help a Zen buddhist meditation center figure out what it takes for people feel supported. Mental models allowed us to see clear steps along an individual’s relationship with the Center, mindful moments involving pivotal steps – getting calm, ringing a bell, sitting in silence. With these, we constructed a prototype for finding spiritual peer-mentors for meditation practice.
While designing software for professional tax preparers I used a number of UX methods, but none helped me more than Mental Modeling. The internal engineering team had lots of ambiguity around how to approach the many problems to solve. Consulting government SMEs clarified how they saw their work and the design process for us.
The frameworks Indi teaches are truly remarkable. While there may be initial resistance from some managers, I believe that over time your methodologies will become standard practice in every product design process.
We made a process for learning design that incorporates experience design. Most educational organisations are stuck using bureacratic processes meant for compliance. These days, learning crosses face-to-face and digital, centering around mobile phones. To see where we could add most value, we built a mental model, mapping the work of learning designers against supports we could put in place.