Sustainable success in business is the dutiful combination of data-informed opportunity multiplied by the investibility of the founders.
by Lance G. Douglas
Summary
Many books have been written, and many great frameworks exist, to create a business, it’s products, it’s branding and marking, it’s processes, it’s business model, it’s financing and IPO, etc. I feel like I’ve read most of them, but I’ve certainly read enough to know that they commonly fail to culminate the end-to-end story into how to build a great business, which is sustainable for customers, staff, investors, and founders.
I’ve been working on an understanding, through study, experience, failures, and prayer of what makes success possible, likely, achieved, and sustained for a little over 10 years. The following is the summary of my thesis that has, only as recent as a couple days ago, become finally tangible and resembling complete.
Why, and How, Now?
The lenses that I collected and gained along the way through life have given me a lot of diverse perspective that keeps me well grounded, empathetic, and hopeful. I spent a lot of my life solving problems; I considered myself to be an outstanding disruptive innovation architect (before ever even learning of Christensen), that could deliver solutions extremely quickly and with far too few resources to ever achieve anything and my health at the same time.
I attribute my successes to the fact that I could really understand the solution being asked for and dive right in and know of the simplest, more direct route to a solution that was both relevant and future-architected for growth and extensibility. I look back and can tell you that 110% of the times, I was pulling a solution towards a problem, and that is insatiable.
Recently, I’ve been lucky enough to have some downtime to self-reflect and rejuvenate near the mountains. During the past couple months, I’ve come to realize that I’m not intrinsically motivated by being a “solution architect”, which is the implementation of the solution, even though I had thought was the design and strategy of solutions.
I am intrinsically motivated by
“problem science followed by solution science”
I am intrinsically motivated by “problem science followed by solution science”. The main difference being that and the former, is that Solution Architecting pulls solutions towards the problem, and the latter sciences are pushing a problem towards a solution. It’s subtle, but this paper explains it in detail.Combine the current situation with my passion of helping others achieve their passions, and that has driven me to re-evaluating all of my perspectives through this new lens. I began formally studying Product Management at ProductSchool.com and that really put everything within the correct sets of lenses for me.
Finally, I set out to validate a hypothesis that angel and seed investors have a low seed hit-rate of only 10% and would be interested in paying for a product that I could deliver them: 2x-10x improvement on their early stage investments hit-rate, and with a second side of the market being those founders wanting to become both funded and sustainably successful. I summarized this into a complex well working system of the combination of data-informed opportunity multiplied investable founders.
I spoke with several family, VC, and angel fund managers during my problem science phase.
The MVP: a start-up development process that resulted in the combination of data-informed opportunities, at revenue, with investable founders, with a standardized scoring system tuned to the investors’ risk preferences.
Problem Science followed by Solution Science
The combination of the two, to me, is the fundamental definition of Product Management. More importantly, Problem Science is only ever followed by Solution Science.
The outcome of this combined process is only ever:
A problem not worth pursuing; or
A problem worth pursuing + a solution not worth pursuing; or
A problem worth pursuing + solution worth pursuing.
It is critical at this point is to understand that a solution is purely an equation defining what changes were tested to move the metrics, to what impact and value, and via which methods.
Problem Science is the process of gaining insight until you feel confident that you understand:
the hypothesis;
the problem’s root cause(s) and for whom;
the impact of the problem and to whom;
quantification of how problem manifests and to whom;
qualification of the value of the problem being solved and for whom;
assumptions taken and/or unknowns;
SWOT analysis of understanding of root cause(s), impact, and value;
the metrics needed to measure the understanding on a continuous basis; and
one or more prioritized equations that clearly articulate the conclusion(s), favourably or otherwise.
Solution Science is the process of attempting to discover a favourable means to transform favourable problem-insight into targeted value(s) by:
a hypothesis of the simplest possible introductions into the Problem Science equation(s) will move the metrics towards the desired value, only to the degree of a minimum lovable product, for the best persona’s to target right now;
highly targeted test employing the simplest approaches to exemplify, quantify, and qualify those introductions;
SWOT/GAP analysis of total set of problem equations; and
revisiting of the Problem Science process with all gained data; or
one or more prioritized equations that clearly articulate the conclusion(s), favourably or otherwise.
Start with Why - Simon Sinek
Sinek’s decade-old video is still just as inspiring and informed for me.
His discovery on how the world works, and his codification of the biological aspects which drive great leaders, is greatly simply. However, in it’s simplicity, it is not a shortcut.
Knowing your why, how, and what is not a recipe for success. Sinek’s circle drawing is a summation of the reflection of what can be a masterfully crafted opportunity helmed by focused leaders.
The Problem with Why
In the same way that Sinek states that “profit is always a result, never a goal”, I believe that a company achieving the simplicity portrayed in his codification is only a result of dutiful of design, construction, measurement, and management of an opportunity, and only when combined with founders well prepared for their part in the journey to success. What I’ve come to realize is that the journey to the clean, simple, concentric “ Why, How, What” that Sinek depicts is not created in the same outward progression as drawn.
That direct approach is where companies and founders make their core mistake: taking a inspiring “why”, coming up with a great “how”, and then making a “what”.
You can sum up the common journey as often witnessed:
Why (Vision): We believe in making this big impact in the world.
How (Mission): We’ll demonstrate our beliefs by this powerful approach.
What (Product/Service): Here is what we do to convey our how to you.
The problem with this approach is that it is far to simple to fake it. Fake the dutiful efforts required to properly organize both the founders and the opportunity for success, and more importantly, perpetual success.
I believe that this problem is firmly exemplified in the statistics of early-stage investments. Approximately 10% of seed rounds return the investment, and the horizon is 7-10 years.
When talking with angel, family foundation, and seed fund managers, the average rate for the post mortem cause of failure being “limitations of the founders” was an astonishingly high 67%. While that is still a bit subjective at this point, you have to take into consideration that the founders’ past successes barely moved the needle on future success. Was it more likely just plain luck to be a successful early stage investor? Maybe, but why? I wanted to understand the root.
Investable Founders & Data-Informed Opportunities
I set out recently to understand the root cause of the early-stage hit rate being a measly 10%, with a hypothesis that I would need to increase that hit rate to somewhere above 20% to create value. I have been through many startups, as well as fostered transformational changes in large enterprises, so I broke down the positive, negative, and missing pieces in each, the shortcuts if you will, which lead to the shortcomings that I witnessed (or performed). I then began researching well known failures and successes. I was looking for a common thread, and I found one.
Ideas are worthless, it’s execution that matters; but execution without dutiful attention to opportunity stability will inevitably result in failure. This rings loud and clear as a nod to John Gall’s famous axiom.
“A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over, beginning with a working simple system.”
My first realization was that often the shortcut taken is a lack of good appreciation and execution of Problem and Solution Science. It may be a lack of knowledge of product management practices, or simply a disdain for process. So, how do we counteract that? What is the root cause and factors that drive people to short-cutting their way through execution, inevitably to achieve failure?
In my research, I laid out the simple well working systems that combine to make the complex well working systems of Sinek’s “What. and “How”. That is to suggest, I discovered the Sinek’s diagram is not simple, it is complexity cubed. It’s a well working complex system made up of no less than three well working complex systems, each of which are made up of their own complex well working systems, and so on. I sought to unravel the complexity down to it’s core simple well working systems.
Hint: the resulting Solution Science hypothesis was I could automate the execution and gating process of evolving the simple systems into the necessarily effective approach that Sinek arrived at. The designed MLP was to use an automated set of scales to guide founders into problem interrogation and discovery and then force their solution to be derived from the qualified value (both corporately as well as to customers) that the Problem Science has isolated. This was nearly identical to the SCORM-delivered logic-tunneling system I architected and engineered two decades ago, so I had an advantage and/or blindspot to the approach that I had to watch out for.
Next, I shared my premise of the problem (not solution) with a successful investor friend of mine and the response was positive, but they gave a hint to an additional complementary factor in the structure of the root cause; founders’ limitations of getting out of the way of a good opportunity, where those founders are anchoring down the ability to achieve the next stage of growth and/or sustainable success.
Reading between the lines of this new information, I returned to the Problem Science to iterate some more seeking to quantify and qualifying what makes founders not investable, and what was the root cause of that. Perhaps, as my hypothesis now indicated, if I could solve for the combined root causes of opportunity and founder inadequacies, that would move the needle for customers, measured by the increased rate of success of both founders and investors.
I was able to isolate five key measures that investors use for qualification of an early-stage investment, and also build an algorithm to match their fund’s risk profiles to a customized “investibility score”:
DScore: Defensibly
FScore: Founding Team
GScore: GTM Strategy
MScore: Market Situation
RScore: Regulatory/Privacy/Social Complexity
IScore: Investor-Specific Holistic Risk Profile Matching
While this is a great notion of what problems, and value, that Investors could realize by the isolated problems I’ve interrogated, I still had to figure out a way to quantify and measure those factors in founders and their ideas. So I set out to test the underlying simple well working systems that I could use to gain the necessary insight into driving those metrics (scores).
Iterating Through Solution Science
All to often, experts and novices alike have a passion for a solution that they’ve been conjuring up. Then, once some hint of a problem is witnessed by these well-wishing founders, one which looks like a target that they could throw their solution at, they work to hone the problem to look more and more like “thee target of their solution”. They are actually trying to justify the solution that they love, and their passion for the solution can often be confused with passion for the problem.
That situation is a purely emotional one, and easily primed for overindulgence, and unfortunately a real possibility for unethical decisions[1], consciously or unconsciously. How do we counteract that? I set out to find out, and my General Hypothesis was that core drivers of the founders’ “Why” could be unraveled methodically and reasonably to rebuild it back up with simple well working systems. This would be measurable by the compounding value realized of founders, and their companies, that are better stewards of a problem, investible, and long-term sustainably successful.
The latest iteration of Solution Science explored the manipulatable levers of the root causes, and value impacts, and began with hypotheses of:
organize the process of capturing and objectifying data via simple well working systems into measurable equations of causation, state, influences, and value, but founders are typically dealing with relatively subjective and/or highly-biased data;
realign founders’ complex unmeasurable motivations, positions, and situation into simple well working systems that support the complex well working system of Problem Science followed by Solution Science, but we’re dealing with humans; and
protect from organic erosion of success that is caused by a lack of sustained focus on the levers in the simple well working systems, but the two above equations do not perform within a bidirectional and continuous feedback loop.
Armed with those three solving equations, I ran some initial paper tests and discovered the remaining Problem hypothesis:
4. Creating equations that solve for the first three problems [of “Why”, “What”, and sustainable protection] still doesn’t make any difference in deriving sustainable value for customers, staff, investors, or founders.
So, another round of Problem interrogation was able to reduce the complexity of Sinek’s “What” down to a few layers of complex well working systems to finally arrive at their simple well working systems. The outcome was astonishing, and refreshingly simple to explain. To make as simple as possible, I further codified the overall set of systems against Sinek’s Why, How, What.
Conclusion
Holistically, Simon Sinek’s codification is brilliant, but it is nothing close to simple. It is a well working complex system made up of several tiers of complex well working systems, all inevitably built up from simple well working systems.
I reached an equation for solving hypotheses #1, #2, and #3, using Knowledge Graphs and Logic-Tunneling, with the future potential to automate using AI and both Supervised and Unsupervised Neural Networks combined to act as the world’s best executive coach as a truly objective executive assistant able to process billions of possible combinations of biases, inputs, levers, and outcomes. I’ll decide whether or not to disclose a little bit more about this as I build a few more iterations of it.
The equation for #4, the “How” is the overall process of building a successful business and implementing the simplest operating procedures to deliver the “Why+What combination”.
What I discovered is that there is literally nothing new here, every simple well working system that I used is very well documented in books. What I did was read a lot of those books, take courses from some of the greatest minds in their areas, phone/email/visit with many industry leaders and found either the necessary problems that each simple system was defining, or relate all their stories across the different narratives.
Then I’ve quantified and qualified where they each fit, or don’t, in the overall levers of value. With that understanding, it became clear that there is a component of Why, How, and What layered below each of Sinek’s.
In the below diagram, I depict the necessary flow, which follows an extremely simple narrative:
there are no shortcuts
In walking through the diagram, you must follow from top left down and avoid all red-dotted lines, which exemplify common shortcuts that I found various failed businesses have taken.
Everything is a compounding factor, so mush be measurable and monitored and reacted to.
Founders:
Wants: Founders have to want to self-actuate.
The Why:
Experience: The founders’ experience in creativity and leadership (not in the problem domain) need to be qualified for both improvement and maintainability.
Perspective: The founders’ need to have humble perspective that gains from their experience which drives the source of their passion.
Passion: The founders’ passion, the root of the why, the inspirational combination of the above.
Why this why?: the combination of the above components are critical, else it’s a hobby and the self-actuation will collapse or be ill-directed in future.
Timing: even with a perfect why, if the timing isn’t right, the project will achieve failure
Situation: the situation of all of the prior components and reality must align
How this how? the combination of all the previous components must be a positive yes
Trigger: even with the why and how of the founders’ self-actuation, there needs to be a trigger to cause it all to align into a completed “why”
Exactly what is this Why!: the actual visionary and meaningful battle cry based on at least some form of all the components. If a founder can’t answer the clear details behind his why, it is not sustainable.
The What:
Why this what?: is a combination of a general hypothesis that ties Thy Why to all levers, metrics, and solutions.
Problem Science: output as equations, SWOT, levers, personas, assumptions
Metrics - Continuous Improvement: the metrics discovered here, measure the components within the equation
How this What?: without the detailed analysis, resulting equations, and metrics to continuously monitor and improve, this what will fail
Solution Science: output as prioritized equations of change agents that result in value directly corresponding to the equations and metrics of the previous step
Exactly what is this What!: the actual product/service to be delivered, to whom, and to what value, measured by which metrics, with what risks
The How:
Only after a clearly articulated What is completed, that is based on a clearly articulated Why, can you begin on the how
Strategy: how to realize The What and The Why, including defensibility options, GTM, positioning, pricing
Advantages: the advantages the founders, partners, situation, etc have or can create in achieving and defending The What and The Why
Why this how: is a culmination of all all preceded components filtered through strategy and advantage development; many folks actually being to fail by starting at this step feeling that their technical or business expertise is an advantage worth carrying backwards to The What and the Why.
Simplicity Science: now armed with the beginnings of a viable business, tear it back down to “As simple as possible, but no simpler - Einstein”. I’ll document out this process like the others shortly, but simply, it is taking the “Why this how” and removing absolutely everything that isn’t absolutely necessary to deliver a Minimum Lovable Product.
Business Model: whip out your business model canvas and only now begin the process of choosing the key attributes, costs, channels, etc, else you’ll fall into the trap of using your business model canvas as a solution and trying to make every step prior to this one fit into it, and spur failure.
JourneyMap with Continuous Improvement: at this point you need to document every step of every participant, customer, vendor, metric, and process involved and carrying The Why to the markets, AARRR’ing your customers, and feeding your continuous feedback loop
How this how?: without the simple well working systems and their combinations being now complete and ready to actuate and be measured, you are able to move to the final stage of business science
Value Science: the final step, and the one that trips up most established enterprises from valuable change, is only now taking the culmination of all the preparation and clearly defining the internal operational processes to onboard, deliver, support, and monitor your business. At first this can be fairly simple, but before you can grow beyond a few customers and staff, you must articulate how every process receives, works, and outputs the tenants of Your Why, Your What, and Your How
Exactly what is this How!: having combined all previous systems, only at this point would I consider a business worth considering viable.
This may seem overwhelming, but most of these simple well working systems are either already common, absolutely necessary, and/or just polished definitions of what can be measured. Ultimately, if you think of a time that you had to write corporate and cascading SMART Goals, OKRs, and/or Strategy Maps, you’ll quickly realize that any struggle that you had in those tasks, actuating change, and measuring the success was a direct and quantifiable result of one of these simple well working systems missing from the organization.
“The will to win is not nearly as important as the will to prepare to win.”
An interesting discovery that I found along this journey was that you should never create your How without first clearly defining the What with equations of both the Problem and Solution sciences. But in depicting the end result, Simon Sinek’s diagram still holds true.
In other words “People don’t buy what you do, they buy why you do it.” is still true, but “Success is driven by what you do, sustained by how you do it.”
… more to come… please comment and help me test this.
Seeing green: Mere exposure to money triggers a business decision frame and unethical outcomes http://fm.cnbc.com/applications/cnbc.com/resources/editorialfiles/2013/06/12/Kouchaki%20et%20al%20%20OBHDP%202013.pdf
