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KleinHaus Technologies
The FIRST EVER User-oriented Decision-making and Prioritizing Tool.
Over 2,000 Company worldwide rely on MasteryKit
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KleinHaus Technologies
See our research on Decision making and Prioritizing
This tool takes your thoughts and emotions about a goal and provides an unbiased and accurate response.
Scope Of the Tech:
Improve Work/Life Balance
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Improve Big Decisions
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Accurately Prioritize
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Research:
Introduction
Do people struggle to make decisions? Of course. We all do. Everyone makes hundreds of decisions daily, and with that many choices, there’s bound to be a bit of struggle along the way. So, which decisions do people struggle with the most? How often do people find themselves stuck in the decision making process? In this world of technology, innovation, and advanced systems, what kind of tools or techniques do people use to assist them in decision making? What decision making issues brought you here?
In this report, we’ll explore the feedback we received through performing live questionnaires pointed at higher-level decision making. We analyzed the tools, systems, and processes people and organizations are using, how they’re going about the process with these resources, and what successes and downfalls they have from them. It’s clear that decisions are being assisted with these tools and techniques — so the question becomes, are there better options? We believe from this research that we have a tool, rooted in deep mathematical science, that will optimize decisions and priorities – expanding on or replacing tools that are already in use.
Background
Several years ago, I had difficulty gaining influence with an employer to develop some tools I believed were fundamental to the growth of our organization.After months of trying to make a positive impact, it was evident that I would have to use a different method to show them my thoughts. So, I built a decision making and prioritizing tool based on some well researched, but under-utilized, mathematical principles. After using the tool myself and having my employer use it separately, we came to the same conclusion on how to proceed toward our goal.
After that, the tool sat in my back pocket until I found the right opportunity to build it into something greater. I brought it up with an extremely skilled and bright friend of mine who was incredibly interested in the application’s potential. We determined that we would build this tool out for ourselves, if nothing else, and use start-up and innovation engineering techniques to test viability for other users.
What We Did
We started listing assumptions related to the concept of decision making and utilizing decision making tools. We ranked them according to risk level, and then we built questions to test those assumptions with phone-driven market research. To preserve integrity, we avoided creating leading questions (as much as possible) and focused on making them open-ended. This is when we reached out to ask for input and referrals. In almost all cases, we expressed our interest in researching decision making and prioritizing, leaving out that there was a potential product in the works.
We built a tool to record interviews in note format, turning qualitative opinions into usable quantitative data. Our goal was to sort each response into high-level categories that we could tabulate for or against our hypotheses. We also collected demographic data, ranging from age and gender to position and experience. After around 50 interviews, we analyzed the statistics for a predefined go/no-go test to proceed, pivot, or perish the project.
Thrilled with the results, we went forward with our plan for a second round of research. We changed our method for data collection to an online questionnaire that would automatically systematize the results and provide analytical data. Then, to gain deeper insight into the validity of our assumptions, we updated the questions to test more refined assumptions. While the questionnaire continued to gather data, we started to build out the tool.
Demographic Results:
Using the methods described above, we present the results below. We started with the interviewee demographics, and then we proceeded to the assumptions in order from highest to lowest risk level. The assumption is stated first, followed by the question we asked, and then we share the data and conclusion. Additionally, some sections have explanations or arguments for, or against, the information and/or method.
Of the 50 initial participants, we broke the demographics down as shown below:
Gender
Gender | Count
Male | 29
Female | 20
Age
Age | Count
18-24 | 0
25-34 | 18
35-44 | 16
45-59 | 8
60+ | 7
Position
Position | Count
Owner/Partner | 17
President | 1
VP/Chair | 3
Manager | 7
Director | 9
Consultant | 3
Engineer | 1
Therapist | 1
Salesman | 4
Operations | 1
Artist | 1
Medical Doctor | 1
Race
Race | Count
White | 39
Black | 6
Asian | 2
Indian | 1
Mexican / Latin American | 1
American Indian / Alaskan Native | 0
Mixed | 0
Industry
Industry | Count
Financial | 5
Consulting | 4
Food/Bev | 4
Information Technology | 3
Marketing | 2
Agricultural | 1
Software | 4
Aerospace | 1
Education | 3
Healthcare | 6
Security | 1
Construction | 1
Power | 1
Judicial Reform | 1
Design | 1
Community Development | 2
Publishing | 2
Local Government | 1
Manufacturing | 1
Logistics | 1
Oil | 1
Art | 1
Communications | 1
Real Estate | 1
Then, we categorized the demographics further as shown to the right (note: we broke it down further to include the industries but omitted them here, as it is a huge data set):
Female Age & Role
Age & Role | Count
25-34 Owner/Partner | 3
35-44 Owner/Partner | 0
45-59 Owner/Partner1 | 2
60+ Owner/Partner | 1
45-59 President | 1
35-44 VP / Chair | 0
45-59 VP / Chair | 0
60+ VP / Chair | 0
25-34 Manager | 2
35-44 Manager | 1
25-34 Director | 2
35-44 Director | 3
45-59 Director | 0
60+ Director | 1
45-59 Consultant | 1
60+ Consultant | 0
25-34 Engineer | 0
25-34 Therapist | 1
25-34 Salesperson | 2
25-34 Operations | 0
25-34 Artist | 0
25-34 Medical Doctor | 0
Male Age & Role
Age & Role | Count
25-34 Owner/Partner | 1
35-44 Owner/Partner | 8
45-59 Owner/Partner1 | 1
60+ Owner/Partner | 1
45-59 President | 0
35-44 VP / Chair | 1
45-59 VP / Chair | 1
60+ VP / Chair | 1
25-34 Manager | 1
35-44 Manager | 3
25-34 Director | 0
35-44 Director | 0
45-59 Director | 1
60+ Director | 2
45-59 Consultant | 1
60+ Consultant | 1
25-34 Engineer | 1
25-34 Therapist | 0
25-34 Salesperson | 2
25-34 Operations | 1
25-34 Artist | 1
25-34 Medical Doctor | 1
List of Assumptions:
Assumption 1
People struggle to make difficult decisions/priorities.
Assumption 2
People will use a decision making tool.
Assumption 3
A decision making tool would be trusted (for validity and that it isn't being used nefariously).
Assumption 4
People make bad decisions.
Assumption 5
Initially we wanted to test whether “people will use a decision making tool outside of a work setting”. Instead, our testing was geared more towards the hypothesis that, “in their personal life, people struggle most with making financial and/or big life decisions”.
Assumption 6
People struggle to make emotional decisions.
Results:
Assumption 1
People struggle to make difficult decisions/priorities.
Question 1:
“What kind of decisions, or priorities, are the most difficult to make for you and/or your team?” This question was intended to determine the percentage of our participants that struggle to make difficult decisions or priorities.
Result 1:
Almost 96% of people are able to identify types of decisions and/or priorities that are difficult for them and/or their team.
Assumption 2
People will use a decision making tool.
Question 2:
“What kind of tools, systems, or processes are you, or your team, using/utilizing to make big decisions or to prioritize?”
This question was intended to give us insight into the methods that people currently use in order to project whether or not they would use a tool to make decisions/priorities
Result 2:
Almost 70% of people use tools to assist in making big decisions or prioritizing. We assumed that if a person currently used some type of tool, they also would in the future. We determined if someone mentioned using processes, lists, or projections/modeling, then they were likely to use a tool to assist with decision making/prioritizing.
Assumption 3
A decision making tool would be trusted (for validity and that it isn’t being used nefariously).
Question 3:
This hypothesis was tested with a combination of three separate questions:
A) “What kind of tools, systems, or processes are you, or your team, using/utilizing to make big decisions or to prioritize?”
We only wanted to count data points from participants who indicated that they were currently using some sort of tool or process for decision making.
B) “What are some of the benefits of these?”
We wanted to test how many people got positive, trust-related benefits from using a tool to make decisions.
C) “What are some of the issues you’ve had with these?”
We wanted to know how many people had negative experiences with the tool(s) they were using in the context of trust.
We wanted to know what strategies/tools people used to aid in making decisions and determining priorities, and then we wanted to examine the trust people have in those tools, systems, and processes. We only counted participants whose answer to question A indicated that they currently used tools to make decisions. Then, we further categorized responses according to whether question B indicated many benefits AND question C indicated few or minor issues as supporting our hypothesis. Conversely, we categorized responses to question B indicating few benefits AND question C indicating many issues as disproving our hypothesis. All other responses were added to a “maybe” category, as they could neither support nor disprove this hypothesis.
Result 3:
Over 30 percent of responses indicated our hypothesis was correct, while none of the responses indicated that our hypothesis was incorrect. Nearly 70 percent of responses showed potential for participants to trust a decision making tool.
Assumption 4
People make bad decisions.
Question 4:
“Can you tell me about a time when you made a bad decision and how it affected you/your team?”.
We hoped to keep questions as open as possible, but we included this in order to gather more information about the kinds of decision making that people struggle with.
Result 4:
This question was thrown out as all respondents (but one) reported to have made bad decisions. This indicated that the question was too biased and of little use. We gathered a lot of great information about the types of difficult decisions people make, but unfortunately, we do not believe the context could prove or disprove our hypothesis.
Assumption 5
Initially we wanted to test whether “people will use a decision making tool outside of a work setting”.
Instead, our testing was geared more towards the hypothesis that, “in their personal life, people struggle most with making financial and/or big life decisions”.
Question 5:
“What kind of decisions, or priorities, are the most difficult to make in your personal life?”
We wanted to know if people may be interested in using a tool outside of a professional setting.
Result 5:
Over 70% of respondents admitted that their hardest decisions were not monetary or life-changing decisions. This indicated that emotionally-driven decisions were harder to make— however this did not technically disprove the original hypothesis. In fact, two participants claimed to never struggle with personal decisions.
Assumption 6
People struggle to make emotional decisions.
Question 6:
“What kind of decisions, or priorities, are the most difficult to make for you and/or your team?”
This is the same question used to test hypothesis 1, but was analyzed separately according to categories assigned to each individual response.
Result 6:
Over 67% of people struggle to make emotional decisions. Emotional decisions were categorized as hire/fire, mission/vision, advertising — anything that dealt with personal interaction.
We are still collecting data from the automated questionnaire, so the results are not shown here. Instead, these are the updated demographic fields which respondents enter themselves. Note the significant difference in requesting “Decision Type(s)” rather than “Title” for each of the respondents. Additionally, the “Industry” category was standardized with US government codes.
Gender:
-Male
-Female
-Decline to Answer
-Other
Age:
0-17
18-24
25-34
35-44
45-59
60+
Race:
-American Indian / Alaskan Native
-Asian
-Black
-Decline to answer
-Indian
-Mexican / Latin American
-Other
-White
Decision Types:
-Task Level (for oneself, or directly involving the task-at-hand. For example, a task your boss asked you to do)
-Management Level (leading a team or project planning)
-Executive Level (organizational strategizing)
Age
-Accommodation and Food Services
-Administrative and Support and Waste Management
-Agriculture, Forestry, Fishing & Hunting
-Arts, Entertainment and Recreation
-Construction
-Central Administrative Office Activity
-Educational Services
-Finance and Insurance
-Health Care and Social Assistance
-Information
-Management of Companies and Enterprises
-Manufacturing
-Mining
-Professional, Scientific and Technical Services
-Real Estate and Rental and Leasing
-Retail Trade
-Transportation and Warehousing
-Utilities
-Wholesale Trade
-Other Services (except Public Administration)
-Unemployed
Thoughts:
The results presented here, gained through interviews and the testing of our assumptions, gave us data-driven support to move forward with building our application. It also gave us insight as to what issues people highly value and how they consider different situations. For instance, as an aside hypothesis, we assumed that people would express monetary decisions as being the most difficult. In fact, we found that the majority of responders struggled most with work-life balance and hire/fire situations – both of which take into consideration an emotional aspect, which our tool is great at capturing and filtering. This may be a causal effect from the timing of these interviews, which coincided with the peak of the Covid-19 pandemic. However, we believe that difficult situations intensify and amplify people’s values and personalities, so we believe in the validity of the results.
More research is needed to clearly define the demographic and decision-making level/industry of a target audience for this type of tool to be most effective. However, it appears that tools are already used extensively in different capacities, and our research shows great potential for our targeted tool to revolutionize all types of industries.
We will continue to build our tool, gather data for more research, and present you with cutting edge technology to ensure you’re making the most informed decisions.
Contact Us:
Us
Jason Kleinhenz
Brian Bailey
