Showing posts with label startup. Show all posts
Showing posts with label startup. Show all posts

A roadmap for accelerators

Mike's Notes

It's getting busy, so I need a roadmap for accelerators now.

Resources

References

  • Reference

Repository

  • Home > Ajabbi Research > Library >
  • Home > Handbook > 

Last Updated

02/06/2026

A roadmap for accelerators

By: Mike Peters
On a Sandy Beach: 27/03/2026

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

Lots of opportunities are coming in.

This is a roadmap for using coaching, workshops, incubators and accelerators to develop, test and validate the Ajabbi Mission Business Model and the Pipi closed-core and Pipi open-source applications.

Ultimately, it's a record of what is learned, so it doesn't include missed opportunities or declined applications. There are a few missing items from some time back that are yet to be added.

Free is good (cloud credits, bro bono, software, training), but no funding or investment is being sought.

The roadmap is sorted by deadline, so that I remember to do them. The first row of the table is a key.


Date Roadmap

deadline

Start-End

Status

Title

Description

What

To come.

  • To come

Learned

  • To come

To do/done

  • To come

Resources


deadline

2017-2020

Completed

Steve Blank

"Steve Blank (born 1953) is an American entrepreneur, educator, author and speaker. He created the customer development method that launched the lean startup movement. His work has influenced modern entrepreneurship through the creation of tools and processes for new ventures, which differ from those used in large companies."

What

Learning from the very best.

  • Reading his books and blog
  • Watching videos
  • Using all the free courses and tools

Learned

  • How to use a Business Model Canvas
  • How to use a Mission Model Canvas
  • How to do customer discovery
  • How to run experiments to validate assumptions

Done

    • Read and tried everything
    • Build Pipi Experiment Engine

    Resources

    deadline

    2020-2023

    Completed

    KiwiSaaS

    "Our community is free to join, and it's where we can safely share our knowledge and experiences with each other. Paying it forward is what drives kiwiSaaS growth."

    What

    Online workshops and random monthly one-on-one meetings with other founders.

      Learned

      • To keep going and when to change course
      • It is OK to make mistakes
      • Will get lots of insights from being open
      • The importance of listening to others

      Done

      • Get stuck in

      Resources

      January 2024

      January - October 2024

      Completed

      Startup Aotearoa

      "Startup Aotearoa ignites New Zealand’s entrepreneurial spirit by providing personalised one-to-one coaching to early-stage startup founders. Delivered nationwide through local regional providers,"

      What

      Mentoring from Mr G led to testing the ICP at Waimumu Southern Field Days 2024 on

      • Developers at Agritech companies
      • Agricultural suppliers

      Learned

      • Developers are the ICP
      • There is a real problem to solve
      • Find a teaching customer

      Done

        • Pivot ajabbi.com to developers
        • Host a teaching customer requiring 3 languages

        Resources

        April 2024

        May - November 2024

        Completed

        Creative HQ's On the Business workshop series

        "This 'On the Business' workshop series gives you the dedicated time and resource to help you grow your business. We'll provide tools, frameworks and hands-on..."

        What

        Remote workshops using Miro canvas.

        Learned

        • To come

        Done

        • To come

        Resources

        February 2025

        February 2025 - March 2025

        Completed

        NZTE Export Essentials SaaS 4-part workshop.

        "Learn what best-practise exporting involves when you sell SaaS offshore."

        What

        Workshops with individual follow-up sessions.

        Learned

        • To use the tools available to test assumptions.

        Done

        • To come

        Resources

        February 2025

        February 2025 - March 2025

        Completed

        NZTE Position for Growth workshop.

        "Our Position for Growth workshops help you define what problem you solve for"

        What

        Workshops with individual follow-up sessions.

        Learned

        • To use the tools available to test assumptions.

        Done

          • To come

          Resources

          25/03/2026

          April 2026 - March 2028

          Application Withdrawn

          Google AI Accelerator

          "With this program, you can get access to startup experts, your Google Cloud and Firebase costs covered up to $200,000 USD (up to $350,000 USD for AI startups) over 2 years, technical training, business support, and Google-wide offers."

          What

          Collaborate with DeepMind to run wild ML integration experiments to go where no developer has gone before.

          • Pipi > IaC > GCP
          • Pipi > VM > BoxLang > Workspaces
          • Pipi > MCP > DeepMind Gemini
          • Pipi > Scientific Workflows > TPU

          Learned

          • Invited to apply by a Google chap who was assisting behind the scenes using an unlisted pathway. I then discovered that free credits begin on the day of application approval, so I will reapply when ready to start in July to make the most of the 24-month window of opportunity.

          To do

          • Increase Pipi DevOps speed (x1000) by completing work on automating the data centre (x10), workspace rendering (x10), and IaC to GCP free tier (x10). This will enable fast, multiple automated experiments.

          Resources

            26/05/2026

            July - November 2026

            Application underway

            Sprout Accelerator

            "The Sprout Accelerator takes a cohort of agrifood innovators on a 3-month adventure to discover, articulate and refine the foundations to grow global startups."

            What

            Test farm management workspace using HTML Mockups on

            • Dairy farmer-led catchment group
            • Agritech wait list from Waimumu

            Learned

            • To come

            To do

            • To come

            Resources

            June 2026

            July 2026 - June 2028

            To apply

            Google AI Accelerator

            "With this program, you can get access to startup experts, your Google Cloud and Firebase costs covered up to $200,000 USD (up to $350,000 USD for AI startups) over 2 years, technical training, business support, and Google-wide offers."

            What

            Collaborate with DeepMind to run wild ML integration experiments to go where no developer has gone before.

            • Pipi > IaC > GCP
            • Pipi > VM > BoxLang > Workspaces
            • Pipi > MCP > DeepMind Gemini
            • Pipi > Scientific Workflows > TPU

            Learned

            • To come

            To do

            • To come

            Resources


             


             

            What Founders Want

            Mike's Notes

            This is a useful resource for startups in NZ. Organised like a structured catalogue. It has a weekly mailing list for frequent updates.

            Below is an index.

            Resources

            References

            • Reference

            Repository

            • Home > Ajabbi Research > Library >
            • Home > Handbook > 

            Last Updated

            21/11/2025

            What Founders Want

            By: 
            What Founders Want: 21/11/2025

            The single source of truth for New Zealand startup founders.

            From raising capital and government grants to legal templates and hiring tools — built on what founders are actively searching for, but can’t find.

            What’s New

            • Expert Editions (Updated Monthly)
            • Case Studies (Real Founder Journeys)

            Save Money

            • NZ legal & financial templates 
            • Grants, R&D credits, co-funding
            • $1M+ in startup perks & discounts

            Raise Money

            • Complete NZ capital directory
            • Angels, VCs, and alt-finance options
            • Curated contacts to save weeks of research

            Make Money

            • AI Power Stack for NZ founders
            • Hiring & automation tools
            • Overseas easy-entry sales channels

            Dead Startups

            • Hiring or being hired

            What Founders Want Directories

            • Get Funded
            • Government Support for Startups in NZ
            • Startup Accelerators in New Zealand
            • NZ Startup Ecosystem Directory
            • Startup Perks & Credits (NZ-Verified)
            • Start a Startup in New Zealand — Step by Step
            • Free Legal & Financial Templates

            Bootstrapped CPC rule of thumb: ARPU/25

            Mike's Notes

            Ajabbi is a bootstrapping social enterprise, but the useful measures outlined in this excellent article still apply.

            Resources

            References

            • Reference

            Repository

            • Home > Ajabbi Research > Library > Subscriptions > A Smart Bear
            • Home > Handbook > 

            Last Updated

            09/07/2025

            Bootstrapped CPC rule of thumb: ARPU/25

            By: Jason Cohen
            A Smart Bear 01/07/2025

            In the first year of business, you have no data for decision-making.

            Even after the first hundred customers, half of those were serendipitous one-offs, not representative of repeatable, predictable customer acquisition, and the scale of the data isn’t statistically significant.

            One of the fundamental data-driven questions (but you don’t have data) is: What’s the maximum I should bid for CPC (cost-per-click) campaigns like Google AdWords?

            The answer for a funded startup is “Bid as much as possible, to get as many customers—and data!—as you can, as quickly as you can, then rapidly iterate from there in the presence of that data.”

            That’s a smart use of money: To “pay to find out.” But what about a bootstrapped, profit-driven business? You don’t have that budget, and you’re keen on getting a reasonable return on investment reasonably quickly.

            Here’s my way.

            (Tune the exact numbers if you disagree with my assumptions!)

            LTV = ARPU x 20

            ARPU (Average Revenue Per User) is the amount you charge the average customer every month, which is typically a mixture of different quantities of customers at different tiers, special add-ons, etc..

            LTV (Life-Time Value) is the total amount of money you expect to collect from a customer over their entire tenure. A simple version [1] is ARPU ✕ [expected months] meaning the average number of months a customer sticks with you.

            [1] The correct version also includes multiplying by Gross Profit Margin, i.e. the cost to serve customers, which for SaaS is tech support, server infrastructure, and payment fees. You should include this for a more accurate calculation; small bootstrapped companies often have very high GPMs, so ignoring it for this back-of-the-envelop calculation was simpler.

            Some customers cancel in one month, some cancel in a year, some in five years, and some never cancel! So it can be difficult to compute LTV accurately for small companies, and impossible to know for young companies (where five years hasn’t elapsed yet to see how many customer stuck it out that long). These are among the reasons that I dislike the LTV metric, but it’s common to use it in this context.

            If you do have data, the simplistic calculation is [expected months] = 1/c where c is your monthly cancellation rate.

            But since you don’t, in my experience (and in a non-scientific survey of some of the 100 startups currently officed at the fabulously Capital Factory co-working space in Austin), a good pre-data rule of thumb is 20 months.

            If you have an average customer lifetime smaller than 20 months (i.e. cancellation rate higher than 5%/mo), that’s a dangerously high cancellation rate for almost any SaaS business, and you need to focus on addressing the business issues before acquiring more unsatisfied customers. Use surveys and one-on-ones to try to understand whether it’s technical failings, lack of features, missed expectations, bad service, doesn’t hit pain points, or what.

            A healthy SaaS company will have a higher number of expected months, but at the start you also will have lots of mis-steps with weird early-adopters and non-ICPs where your product is at its worst—least features, least quality, etc—so it’s good to assume a low LTV instead of inflating it to where it might be in future.

            CAC = LTV / 5

            CAC (Cost to Acquire a Customer) is your average total cost to get a new customer, which includes direct costs (AdWords spend, affiliate payouts, the fees your affiliate system charges to process them) and indirect costs (consultants and your own time). So to compute CAC, take your total costs to acquire new customers and divide by the number of customers you acquired.

            In general of course CAC needs to be less than LTV, otherwise it costs so much to get the customer that you will never make money. A surprising number of startups have CAC > LTV. Many justify this either by not correctly computing CAC (e.g. ignoring indirect costs) or saying they’ll “fix that later” by raising prices or finding other channels of revenue. Others justify by saying they’re doing a “land-grab” for customers, and just having a customer at all has intrinsic value.

            Profit-seeking bootstrapped companies cannot afford those delusions. Also you need something far stronger than CAC = LTV, because you need to pay for other business expenses and still produce a profit. So how big can CAC be before it’s “too big?”

            Growing, funded SaaS companies who treat CAC with respect often commonly target CAC = LTV / 3.

            Back at my second startup IT WatchDogs, my co-founder Gerry Cullen used to say “A third to built it, a third to get rid of it, and a third to keep,” meaning a third of revenue goes to pay for hardware/inventory/shipping costs of the sale, a third goes to what I’m calling “CAC” here, and a third for the overhead costs, development costs, and profit.

            That’s a good model, and I think a bootstrapped company can copy it, but I urge profit-seekers to instead adopt an even more strict model of CAC = LTV / 5. The reason is that at the start you should be able to find a few efficient ways of acquiring customers, even if those get saturated over time.

            CAC = ARPU x 4

            If you combine the previous two results, you see that the cost to acquire a customer should be no more than four months of revenue.

            Another good way to think about it is: “The payback-period for my cost to acquire a customer is four months.” Also, ideally you’re getting the first month of revenue back immediately, so it’s really three months of cash-float.

            Companies with large budgets to deploy at scale will often be happy with 12 month payback periods; some very high volume businesses like shared hosting will accept 24 or 36 months! But a bootstrapped company’s cash-flow won’t allow it, even if the math would work in the long run.

            Conversion Rate = 1%

            Conversion Rate is the percentage of visitors to your website who convert to a paying customer.

            This is another step which in practice should be completely data-driven, segmented by customer type and marketing channel, segmented by landing page, A/B tested and iterated, blah blah blah. But since you don’t have data, and you don’t have enough visitors to have real ratios, you have to take a swag at this number.

            In that same informal survey I ran, and bolstered by other formal surveys, a huge number of bootstrapped SaaS companies report a 1% conversion rate.

            Another way of saying the same thing is “You need 100 visitors to make 1 sale.”

            And since you need to incur no more than CAC dollars in the making of that sale, you need to incur no more than CAC/100 dollars in the making of each of those visitors.

            And if you’re running a CPC campaign, that means you can pay up to CAC/100 dollars per click.

            And since CAC is ARPU x 4, we can substitute and get the end result:

            CPC = ARPU / 25

            So for example if your average customer generates $50/mo, you can spend $2/click.

            Indeed, this is a great way to prove one of my main arguments for all bootstrapped companies, which is that you should charge a lot more than you think, in part because it enables you to pay quite a lot per click, which enables a wide number of marketing channels, and out-bidding parsimonious competitors whose paltry LTVs preclude them from competitive marketing spend.

            Customized

            “But my numbers are different!” Of course, but now you have a formula you can plug them into, to arrive at the answer:

            CPC = (ARPU) r/5c

            Where:

            • c = monthly cancellation rate
            • r = visitor → purchase conversion rate from the paid marketing source in question

            Ruthless prioritization while the dog pees on the floor

            Mike's Notes

            Some handy insights from Jason Cohen from his weekly newsletter, A Smart Bear. Much of Jason's approach also applies to Ajabbi, a non-profit organisation established to support socially necessary critical infrastructure and culture, including;

            • Hospitals
            • Rail
            • Ports
            • Public transport
            • Drinking water
            • Museums
            • Electricity distribution
            • etc

            No Innovation Theatre

            Jason Cohen has a lot of sensible things to say without the bullshit. Kromatic and IT Revolution also have excellent articles.

            His articles always include humorous cartoons from Andertoons.

            I left out 95% of his reference links to other articles on his website. See the original article to follow them.

            Resources

            References

            • Reference

            Repository

            • Home > Ajabbi Research > Library > Subscriptions > A Smart Bear
            • Home > Handbook > 

            Last Updated

            28/06/2025

            Ruthless prioritization while the dog pees on the floor

            By: Jason Cohen
            A Smart Bear: 22/06/2025

            The Chief Innovation Officer of WP Engine.

            Because time is zero-sum, prioritization is mandatory. This is an index of purpose-built prioritization frameworks, and an overarching one to optimize your life.

            The prioritization mandate

            Time is a zero-sum resource: An hour spent on one thing necessarily means not spending an hour on the entire universe of alternative things. Every minute is a choice. Every choice is a trade-off.

            Time is a hard limit. We can maximize productive time through better habits: good sleep, sensible diet, reasonable exercise, restorative rest, reading books instead of doom-scrolling Twitter, sleeker task-management, fewer meetings, shorter meetings. But even here we’re prioritizing within 24 hours each day, choosing how we labor and rest to maximize our effectiveness. There isn’t enough time for more than two big things in your life.

            All we have to decide is what to do with the time that is given us.

            —Gandalf the Wizard

            We need more time. Not 30% more, but 3000% more.

            Every company proves that statement with a data repository called “the issue tracker”. We accumulate thousands of valid items over the years: Little ideas that would be nice to do; big features that would create differentiation; bugs that paying customers actually experience; design tweaks that make the team proud of their craft. You will never complete all these; completing even 10% would be a miracle, the ratio worsening every week as more items are added than removed. The better you listen to customers and the more creative your teams, the worse the ratio becomes. You hire more people to get more done, but new talent have new ideas, and the ratio worsens again. Inspiring, and maddening.

            So, time is a fixed constraint that limits us to 1%-10% of what we “need” to execute. Unintuitively, the fraction diminishes with scale; we will never defeat it.

            The inescapable conclusion is the trite statement that “We must prioritize”—intelligently determining which precious few things we will actually do.

            In fact we can’t help but prioritize, even if mindlessly. Since we can only do one thing at a time, whatever we’re doing now is definitionally our “highest priority.” Reading this sentence is currently your highest priority. While “prioritizing” doom-scrolling is obviously faulty decision-making, there’s the more insidious cases of prioritizing things that are fun but only somewhat useful, or of prioritizing things due to necessity but not importance (e.g. paying taxes on time). Indeed, a common complaint of prioritization is that we’re only doing things that happen to have deadlines, instead of things that matter. “Urgent, but not important” tends to win over “Important, but not urgent,” if we’re not paying attention.

            Prioritization is a choice, and more often than we’d like to admit, the choice was thoughtless. We’ve all lost a few hours to doom-scrolling or YouTube or TV (if you were born before 1990). Maybe that activity was useful for “restorative rest.” Maybe it was just a poor choice. Maybe we should give ourselves grace. Maybe.

            Prior art

            There are, of course, myriad prioritization frameworks. On this site alone I’ve detailed many of my own:

            • Fermi ROI: Replacing rubrics, especially “ROI”-style
            • Binstack: Making significant choices with incomparable dimensions
            • Adjacency Matrix: How to expand an existing product
            • Investment Criteria: When to invest significant time and money
            • Rocks, Pebbles, Sand: Analyzing and prioritizing three sizes of work
            • Satisficing vs Maximizing: Prioritizing some things as “good enough,” others as “never good enough”
            • Leverage Points: Where incremental change yields large results
            • Cleaving: Separating upside from downside, treating each differently
            • Fairytale Quarterly Planning: Prioritizing work against strategic objectives and the obstacles that are preventing us from winning
            • JIT streams: Handling multiple, incomparable inputs, separating prioritization from work-planning

            I don’t like anything with a rubric or a computed score (this is why). I don’t like anything with a “confidence level” (because you can’t discuss it accurately). I don’t like anything that is built to produce symmetry for consultants’ slides rather than reflecting the messiness of the real world. I don’t like anything that purports to compare incomparable things (e.g. scoring “more growth” with the same made-up number as we score “don’t run out of money” or “make employees happy”).

            Beyond the built-for-purpose frameworks above, there’s a simple overarching framework that applies to every type of prioritization, foisted upon us through the observation that we have time for less than 10% of what we’d like to accomplish.

            Here is that framework.

            10x / 0.1x Prioritization

            10x tasks

            Despite how precious time is, some tasks are so valuable, so impactful, that the return on your investment is an order of magnitude more than what you put in, even if you valued your time at (say) $1000/hour.

            You must seek out these “10x things” that can transform the company. Examples:

            • The few features that win the majority of sales, whether through pure delight or because of a combination of utility and uniqueness among the competition. Rule of thumb: Impactful features are actively used by at least 40% of customers, or are a critical reason-to-buy or reason-to-stay for at least 15%.
            • Finding the ideal marketing positioning and wording where advertisements convert 2x higher and people landing on the home page buy 2x more often. (Not 10% higher—you can’t actually measure that.)
            • Finding the pricing model that maximizes profitable growth while maintaining fairness for customers.
            • Taking the time to correctly identify the next strategic objective or biggest obstacle, so everyone can prioritize their own time towards this most-important thing; working on the wrong thing is a 100% waste of time, even if efficiently- and perfectly-executed.
            • Hiring the next critical employee who dramatically increases the company’s throughput and work-quality and decision-making-quality, while adding a skill-set that was previously missing, a skill-set needed to overcome the current obstacle or the next strategic objective.
            • Applying energy to a Leverage Point—an area where even small changes have a large impact on growth or profitability.
            • Addressing the single biggest drag on growth.
            • Deciding how to expand the business into the next adjacency.

            If you don’t know what one or two 10x tasks you should be working on, then identifying that is your highest priority. Otherwise you are certainly not prioritizing properly; the entire company is misusing their time. This is my method for determining what those things are.

            If you have too many 10x choices, the Binstack prioritization framework is designed for this type of decision, where the inputs are incomparable, and you want to maximal impact.

            It’s rare, however, to actually have too many 10x possibilities. If you think you do, it’s likely that you are being too generous in declaring things “10x”. They need to literally “10x” a key metric, or be the difference between survival or bankruptcy. Their upside must be so massive, that even when it inevitably takes 2x longer to implement and is \frac{1}{2} as impactful as you thought, it was still worth it. To take that 4x hit and still have a great outcome requires starting at 10x.

            To further refine your thinking, treats these are investments—i.e. applying significant time, expecting an out-sized return, but with uncertainty. Here is a guide for making good investments.

            0.1x tasks

            Most activities are worth far less than $1000/hour, even if they are mandatory. These are the “0.1x tasks.” You must minimize these through several strategies:

            Eliminate them completely by structuring your life, product, target-customer-selection, or company strategy to avoid them. Here is a guide for inventing ways to avoid them.

            Delegate, even if the result is worse than what you’d do yourself (e.g. grocery delivery being both more expensive, and not picking the same apples you would have picked). It’s not worth the time to do everything (your definition of) “perfect.” You need to be in command instead of in control.

            Batch or automate, accepting minor penalties (e.g. paying bills only once per month and risking occasional late fees, or batching security patches, since you can’t ignore them forever but each one is unlikely to be exploited in the next few weeks).

            Archive loose tickets that are older than 100 days. Because of the rule that more than 90% of our ideas will never be done, realize that these are already among that 90%; deleting them will help you prioritize the remainder. If something is truly important, it will come up again in future. (Hence “archive”, not “delete”.)

            It’s tempting to assume that small, easy tasks are automatically 0.1x tasks, but that is a fallacy of conflating “impact” with “effort”. Easy things can have a large impact (making them 10x almost definitionally), and complex things can have no impact on revenue or employee happiness.

            In some regimes, many small tasks add up to a 10x impact; I call this “life by a thousand sparks”.[1] A common example is “great design”. While amazing design is not required for success, there are many examples of products winning primarily because of beloved design. Great design is not “one thing,” however. There are macro-scale architectural decisions, but also it emerges from thousand details: subtle color choices, pixel-perfect layout, font and word selection, aspect ratios, play of white space, satisfyingly snappy interactivity, a design system for comprehensive consistency, fixing every last bug that is aesthetic rather than functional, and harmony between the website, the product, the emails, and the material for marketing, sales, and support. These myriad tasks should not be dismissed as individually 0.1x, if they are specifically in service to a 10x concept that is also the primary way you win customers in a competitive market.

            [1] A facetious inverse of “death by a thousand cuts”.

            What if there were no 1x tasks?

            Prioritization isn’t a dichotomy, but I encourage you to act is if it were, forcing yourself to make clearer decisions. Idea-abundance is beautiful, but we must be ruthless, final, and precious with our time.

            You will inevitably label things as “10x” which are really 1x. You will spend too much time on 0.1x things; after all, there are so many. Forcing this binary choice is reductive, but helps us be ruthless.

            Of course in reality there is a spectrum of ideas, sizes, impacts, risks, and confidence. Our ability to measure any of that even post facto is laughably poor, and our ability to predict any of them is even more pitiful. If you insist on the existence of 1x tasks,2 use the Rocks, Pebbles, Sand Framework which explains how to segment by size, how to prioritize each size differently, and how to resolve the common conflicts that arise as you schedule work in the messy real world.

            [2] Another exponent of 10x / 1x / 0.1x is the great Product Management teacher Shreyas Doshi with his excellent LNO framework.

            Dealing with the fallout: Dogs peeing on the floor

            In a well-lit living room, a dog is peeing on the floor while a man sits in a chair, reading a book. The man doesn’t react. You’re watching from a window, concluding that this man must be ignorant, crazy, or at least a poor decision-maker. Put down the book and take that dog for a walk, idiot!

            Except, you don’t know the full story.

            In one hour, the man has the most important meeting of his life. His performance in this meeting will dictate the next ten years of his career. Everything he needs to know to be successful in this meeting, is in that book. Yes the dog should have been taken for a walk, but the penalty of having to clean up the pee is worth it, because the call is that important.

            The decision is rational. Yet the observer sees only irrationality. This is caused by two things:

            1. The observer doesn’t know the complete story.
            2. The observer has trouble accepting that something bad/stupid can nevertheless be the right decision, because the alternative is even worse.

            This inevitably happens at your company when you ruthlessly prioritize. Because you’re so focused on the most important thing, other things lay fallow. Important things. Fires burning that you’re intentionally ignoring because although they are fires, they still aren’t as important as the Most Important Thing. Dogs peeing on the floor. On purpose.

            bear working while fire burns

            But others see the fires burning, the dogs peeing, for months, for years, and then lose faith in leadership. They complain—understandably, and accurately—and lose morale. Worse, they start believing that The Deciders must be crazy, just as the window-peeker believed. Loss of trust and respect leads to talent leaving, which leads to the death of an organization.

            “They never listen to my ideas,” the most prolific idea-creators complain. They’re right, too; after all, mathematically you could never implement more than a few percent of those ideas. Not because you’re “not listening,” but because of inexorable math. You have to focus on the peeing dogs, and you can’t do most other things.

            The way you combat this natural progression is to address the two points above. You have to transmit the complete story, not just the few priorities, but also why they are the top ones. You have to acknowledge the twenty things other that also deserve attention, explaining why we’re intentionally letting those fires burn, those dogs pee, because the top priorities are even more important. This is why the “not doing” list is such an important part of the plan.

            And you can retell the story of the dog peeing on the floor, acknowledging that it feels bad watching the dogs pee, especially when you know how to prevent it. I don’t remember the origin of this particular parable, but I’ve retold it many times at WP Engine, and it sticks. It works.

            This is what prioritization actually looks like. The full picture, not just the social media admonitions that you “have to focus!”.

            The full story requires constant, repetitive communication. Because it is bad to see the pee; we can rationalize it only when we see that the pee buys us time to do the most important things—the things that leverage our precious, woefully limited time, for out-sized results.

            No one said it would be easy. Good luck.

            Innovation Accounting in Practice

            Mike's Notes

            Ajabbi is a bootstrapped not-for-profit startup. Which is a tough way to go. Innovation accounting will help. I had an online meeting earlier this year with Tristan Kromer, and I gained valuable insights from him. Kenny was originally a musician.

            The Kromatic resources are testable and strongly maths-based. They also don't make a fetish of Business Canvases like the innovation theatre crowd. The focus is on finding tools that are actually useful in a specific context. If they don't quite work, tweak them so they do, or invent one.

            The Monte Carlo simulation is fantastic.

            Ajabbi will pay for support from Kromatic once it has the financial resources. 

            Resources

            References

            • Reference

            Repository

            • Home > Ajabbi Research > Library > Subscriptions > Kromatic
            • Home > Handbook > 

            Last Updated

            27/06/2025

            Innovation Accounting in Practice

            By: Tristan Kromer & Elijah Eilert
            Kromatic: Copied 25/06/2025

            It is not enough to say, “We’re early stage and we shouldn’t focus on a business plan or metrics.” It is not enough to say, “We’re focusing on qualitative data.” And it is certainly not enough to say, “We’ll figure out how to monetize later.”

            As an early-stage venture, you don’t need a business plan, but you do need a business model from Day Zero.

            You don’t need a financial plan projecting cash flows 4 years out, but you do need a financial model on Day Zero.

            Pointing to examples like Twitter and Facebook and how they found their financial model much later is not a good excuse. Even social media products have clear metrics that they measure in the early stages. Social media and game companies count on the fact that they are acquiring a user’s attention and data, and those are valuable assets that can be quantified and monetized later. But we can measure the user’s attention and willingness to relinquish data right away.

            Social media companies are similar to a mining operation. If you were digging up gold from a mine shaft, no one would complain that you didn’t have a detailed plan and metrics to sell it in the market. We know gold is valuable and we can figure out how to sell it later. The value is clear. We just need to know if there is gold down there and how much.

            To say, “we’re an early-stage mining operation and we don’t need to focus on a business plan or metrics,” would be an absurd statement. We can quantify how deep, far, and fast we’re digging. We can quantify the mineral content of the soil and the geology of the area. No one would accept the qualitative data of a dowsing rod to make a serious mining investment.

            Startups, more than ever, should start with a hypothesis-driven financial model from Day Zero. That is why we use innovation accounting.

            In the last article, we discussed why standard business cases don’t work in an innovation context, and the three principles we need in order to replace the standard business case with something better. In this article, we’ll go through how to actually do it. Using innovation accounting, we can build a financial model that is accurate, true, and testable.

            How to Solve the Problem

            To implement innovation accounting for an early-stage project we need to:

            • Identify assumptions
            • Construct a visual model
            • Build a hypothesis-driven financial model
            • Integrate uncertainty


            Four steps innovation accounting method

            1. Identify Assumptions

            step one - innovation accounting method

            There are a ton of assumptions that we make when starting a new innovation project. Fortunately, there are a number of different templates and frameworks that capture those assumptions, such as the business model canvas and customer personas. But none are as useful to innovation accounting as a Storyboard.

            A storyboard, similar to a user-journey map, maps each step of the user journey from start to finish. This includes hearing about the product or service, actually using it, renewing their subscription, inviting friends, or simply finishing their use and throwing it in the trash.

            Storyboards can be used to organize our assumptions into a clear series of actions that the user must take in order for us to both provide value and capture the revenue (or impact if you are a non-profit organization.) The advantage of a storyboard is that it represents observable moments that we can measure. Frame to frame, step to step, each moment in the user journey transitions into another — and we can measure that conversion rate from moment to moment.

            If the first step of the story is downloading an app, and the second step is signing up for an account, that is a conversion rate we can measure — the % of people who sign up for an account after downloading the app. If the next step is applying a filter to a photo, then we can measure the % of users that apply a filter. From qualitative data about what the customer wants (to take beautiful pictures) we can map out our ideal story to deliver that value on quantitative metrics.

            Even from Day Zero with just a nascent idea, we can create the step by step measurable process by which a person becomes a customer. We may not know the actual conversion rates, but we know what we need to estimate and measure. From there, it is tempting to go straight to a spreadsheet, but sometimes a quick detour will help.

            2. Visual Business Model


            step two - innovation accounting method

            Once you have the basic story down, it’s useful to abstract this into a visual financial model. This really is the same thing as a storyboard where the user’s journey from acquisition to purchase is mapped out. However, we will want to simplify some aspects and include retention (if and how customers buy again) and virality (if and how customers refer their friends to become new customers) which are often left out of the storyboard.

            A storyboard or user-journey map is often too detailed for what we need in our financial model. We don’t need to know what % of users apply a filter to a photo, we need to know how many users upgrade, stick around after four weeks, or purchase something so we can zoom out to the bigger picture and only use the most critical metrics that signify important progress towards our business model.

            Startup Metrics for Pirates is a widely adopted framework with the right level of simplification for the purposes of innovation accounting. The five components of this framework are Acquisition, Activation, Revenue, Retention and Referral (AARRR, hence the pirate name.)

            Acquisition (getting a user to your service or product), Activation (getting the user to have a great first experience and recognize the value), and revenue (getting the user to pay something) should already be on your storyboard. It is only a matter of identifying the step in the storyboard that represents the critical points in your business and thus represent the most useful metrics. This simplified, three-step user journey is often represented as a vertical funnel (although representing it horizontally makes no difference).

            However, Retention and Referral are usually not included. That’s just because a storyboard or user-journey map are typically linear. Assembled with yellow sticky notes, it’s hard to represent retaining a customer or referring a friend (although we’ve seen some creative uses of blue sticky tape). But with a journey simplified into a shorter conversion funnel, loops can be more easily added to show where a user retains or refers a friend from a later stage (such add Revenue) back to Acquisition.

            With an easy-to-understand visual model and these last two Pirate Metrics in place, we’re ready to make the leap to a spreadsheet.

            3. Hypothesis-Driven Financial Model


            step three - innovation accounting method

            A hypothesis-driven financial model sounds complex, but it is not. It can and should be as simple as your visual model. Each step can be converted into a row in a spreadsheet, starting with Acquisition for the top line to represent the consistent number of organic visitors to your website or storefront.

            However, unlike a traditional financial model, the number of visitors is not guessed from month to month and hard coded. Instead, a single assumption sets the number for that variable, and a formula varies the value from month to month in the spreadsheet. That way, if the assumption turns out to be wrong, changing a single cell in the spreadsheet will correct it throughout the model.

            Each subsequent row applies the same logic as the visual model. The % of visitors that activate in your user journey becomes a variable that is held constant from month to month, changing visitors into users. The next row might convert users into paying customers who have taken a trial of your product and decided to buy based on another variable, the % of customers who purchase after trial.

            Referral and Retention loops require a bit more thought as they impact Month 2 based on Month 1, but still only require a couple of additional rows of calculation.

            With minimal effort – most teams take 1-2 hours to do this under guidance – a simple spreadsheet is constructed which is driven by a few variables. Those variables can be updated as more information is available.

            Of course, this is a wild oversimplification. But with startups, start simple. We can add complexity over time.

            With a tech startup, we often don’t even model costs on Day 1 because user growth might be all that matters for a social media app or game. However, costs can be introduced and more complexity added as the company grows.

            This basic model allows us to do some basic scenario testing. We can immediately start testing out different acquisition and retention rates to see what the impact on our growth will be. We can even set certain conditions our business must reach in order to meet our growth targets.

            With a limited number of variables, we can see that if our actual retention rate falls below 20%, our referral rate must increase accordingly if we are to continue to grow. This sort of scenario analysis is basic, but effective for helping early-stage innovation tests set pivot-or-persevere thresholds for their business and start designing tests to establish the actual numbers.

            Although entrepreneurs can dictate the shape of their business model, reality will ultimately dictate what numbers go into the variables.

            Here is a financial modeling template for startups if you would like to try it out.

            4. Integrate Uncertainty

            step four - innovation accounting method

            Lastly, we have to integrate uncertainty.

            Although the basic hypothesis-driven financial model allows us to play around and try out different scenarios, it doesn’t actually tell us what is going to happen or the likelihood of success. But we can do this if we get a little data and integrate uncertainty.

            Statisticians have a few tricks we can adopt here. Hurricane forecasts, baseball games, and even startups can use a technique called the Monte Carlo Method to predict outcomes based on uncertainty.

            Instead of entering a single number into each of our variables, we enter two numbers to represent the range and a distribution curve which tells us the likelihood of any individual outcome within that range. This is not easy.

            For example, I may not know the outcome of rolling two six-sided dice and adding the numbers, but I know for certain that it is between 2 and 12. It’s most likely that it’s 7, but 50% of the time the number will be between X & Y.

            We can make the same estimations with our business model variables. We may not know what price the customer is willing to pay, but we should be able to say that they will pay between $10 and $100. That’s all the information we need to start building a Monte Carlo simulation.

            The math behind choosing the right distribution curve is tricky, and the art of choosing the right range requires a bit of training. But both can be accomplished with a little effort. Building the right spreadsheet is even more complicated, but more and more tools are being created that allow teams to run Monte Carlo simulations right in their spreadsheet or in a specialized application.

            Here is a Monte Carlo simulation example if you would like to try it out.

            In practice, this means that innovation teams and executives can create go / no go criteria for their pivot / persevere decisions. You want your project to be at least 10% likely to reach 1B in revenue? The Monte Carlo simulation can tell you if your project has reached that threshold. If not, you can stop the project with confidence that it would not achieve your goals and move on to test the next idea.

            The output of the Monte Carlo simulation is a chart that shows a range of possible outcomes at any given point in the future and allows you to calculate the likelihood of any individual outcome.

            Lessons Learned

            This process is repeatable and is applicable to all types of business models. It doesn’t matter if it’s B2B, B2C, B2G, a network, a platform, or anything else not yet invented. Building a model from Day One allows innovation projects to make better decisions, make useful predictions, and demonstrate real progress to stakeholders.

            Start by:

            • Identifying assumptions
            • Constructing a visual model
            • Building a hypothesis-driven financial model
            • Integrating uncertainty