Structure and clarity for your strategic decisions
The goals 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.
Data Science That Listens is the strategic basis for your org to help customers, communities, and employees 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 shift 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. Demographics don’t describe your audience in a wholesome way.
- Reveal the variety of hidden thinking styles.
- Reduce risk, churn, complaints, and the unpaid customer labor that causes it.
- Build variety. Recognize patterns of thinking.
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.
- Skirt the expense of building a solution then looking for problems it might solve.
- Understand people’s cognition and build technology to support it.
Do something very different. There is power in that.
5. Humanize your AI
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.
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
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 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.