Create confidence and clarity in your strategic decisions
Revealing people’s cognition lays foundations for strategic decision makers to creatively navigate choices and lead effectively.
People’s cognition is the term we use to summarize how people think, feel and make decisions as they address their goal.
This is what we mean by Data Science That Listens.
Data Science That Listens provides strong, flexible strategy visualization for years
People’s cognition changes slowly. This 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, supporting stakeholders with empiric data science.
- And your solution space will blossom with creativity, variety, and solid tracking back to people.
Solid data science like this an investment that pays back year after year.
“Are we doing everything we can do?”
A mental model skyline accurately measures the value you provide to people, using data science that listens.
- Frame your evaluative work using the skyline to see how well you help people do things their way.
- See abundant opportunities to provide surprising value.
Strategy is about people.
The average user is a myth.
Don’t let people stay stuck in cracks. Revealing cognitive detail allows you to see the variety of thinking styles hidden when you only use quantitative data.
- Reduce churn. Reduce complaints. Reduce unpaid labor.
- Increase your capacity to give people what they expect.
- Build variety. Recognize patterns of thinking. Guide people to solutions designed for them.
Find the assumptions your org is making about people.
Human innovation is more valuable than a pure technology play
Innovation focused on people is rare, because orgs rarely have a data science method like this to reveal cognition.
A mental model skyline reveals multiple areas where your team can experiment with innovation.
You can build technology and look for problems it solves. … Or you can understand people’s cognition and build technology to support it.
Humanize your AI solutions
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, and guide them there.
Data Science That Listens provides the foundation for adaptive experiences that are not shallow, but 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.
Strengthen your 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
AI Guides Users toward solution that fits them
Behavioral Science Frameworks
(Robert Meza)
Design Thinking
Existing Research: Don’t Waste It
See how your work fits together
Innovation is not the work of a single genius, but of a team.
Simplify your collaboration with a common understanding of the people you are creating for.
The mental model skyline holds layers and layers of your data, structured by cognition. Data science that listens is the lasting foundation you can depend upon.
Testimonials
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.
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.
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.
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.
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.
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.