Move Into Advisory Services Work

I’d like to help you learn how to generate leads for advisory services

My Indie Experts email list is a place where I do that. If the transition from getting paid for implementation to getting paid for your advice is interesting to you, please join. Two ways to get this insight:

    [PMC Weekly Insight] It’s a grind-grind

    “It’s a grind-grind
    It’s a grind
    It’s a grind-grind”

    — “Bus to Beelzebub”, Soul Coughing

    That’s Soul Coughing, singing about the act of coding survey responses.

    I’m kidding, but yet… it can be a grind. The coding part, I mean.

    I’m continuing to work on my survey marketing experiment1 I’m not done coding the list sample responses.

    In particular, the list sample is some real work to code because y’all were much more verbose in your responses than the LinkedIn sample. My hot take on this difference: this is because y’all are much more actively investing in your careers. That’s probably why you’re on this list in the first place! So you simply have more to say on the subject.

    Getting help with the grind

    What about getting help with the high-effort parts of a research project like I’m conducting? Does that make sense? Is it worth doing? If yes, how would you go about getting help?

    Let’s start with the how question and then get to the should you question.

    Partner with a researcher

    You can partner with someone who has research experience. You’ll most easily find this type of experience in academia.

    Graduate-level students are one option. They bring the research rigor while you bring the business context and connections needed for the project. The collaboration may help them with their progress towards a degree, or with their publication needs, or with something else that’s important to them. And the collaboration helps you with your client work or marketing. So there’s a shared incentive in this arrangement.

    Professors or departments are another option, though they may be more selective because — at a departmental level — they’d be committing more resources to the project and so need to be more discerning about what they say yes to. At the professor or department level, you may gain worthwhile credibility because you’re involving a greater level of research rigor in your project, along with the brand of the professor or department’s institution.


    You can find freelance researchers outside of academia. They might be able to help you by taking on high effort work.

    By outsourcing parts of your study to a freelance researcher, you can buy back some of your time, but at what cost? Yes, there’s the financial cost, which is fine. But there’s also the cost of you being at least partially removed from parts of the process, and this might cost you insight and confidence in the outcome.

    Should you get help with the grind?

    This is all good stuff, but you need to evaluate whether the following costs are worth it:

    • Loss of control and flexibilty. In embracing a greater level of research rigor, you will be giving up certain forms of control and flexibility. You might be committing to a larger sample size, or a more expensive recruitment process, for example. For a high profile important study, this could be worth it. For others, it could not be, especially combined with the potential loss of flexibility. More on this below.
    • Loss of insight. In getting outside help, you’ll necessarily be less involved in all aspects of the study. This might cause you to feel less confident in the insight your results generate. To be clear, it might not cause this outcome, depending on how you handle it. But the risk is there.
    • Collaboration. In so many contexts, teamwork is presented as an unalloyed good, but in some contexts it is a cost that doesn’t pay off. In innovation work, the value of a collaborative team needs to be closely scrutinized. Yes, the team approach might produce value. “Many hands make for light work.” This is true. But also, many hands make for a lack of agility, additional expensive communication overhead, and a potential lack of focus and clarity. So specifically in the context of innovation work, a collaborative approach may be less effective.

    In a large, high-profile research project, the benefits of putting together a team are really worth considering. But in a small research project like mine, it’s possible to nearly ruin the whole thing by building an unnecessary team in order to avoid a few hours of unpleasant work. Much better to just do the effing work myself and avoid all those costs that would come from assembling a team.

    Mixed methods and qualitative/inductive flexibility

    I’ve been reading a freaking fantastic book on research, and now I feel like I have a foundational reading list for you if you’re interested in doing research in a business context. The list:

    1. “Mixed Methods: A short guide to applied mixed methods research”, by Sam Ladner
    2. “How to Measure Anything: Finding the Value of Intangibles in Business”, by Douglas Hubbard

    The book I’ve been reading recently is the first on the above list. It’s a short, highly readable, largely jargon-free book. And it’s just excellent. It helps you understand the inherent contradiction — and the resulting power — that comes from blending quantitative and qualitative methods in the same study.

    One of the points Dr. Ladner makes is that qualitative methods — which are inductive in nature (generating new theories) rather than deductive (attempting to test the truth of a theory) — are also more agile and usually involve less up-front cost.

    Something I believe but can’t prove: academics default to quantitative/deductive approaches rather than qualitative/inductive approaches. This might result in a mismatch if you partner with an academic on your research.

    You may begin with an ill-defined question, a strong but vague sense of what you want to learn, or what simply amounts to the wrong question to ask2. So if you use a high-up-front-cost method to answer a question like this, what you really have is an expensive boondoggle. A lean, iterative approach might have been much better, and starting with a small qualitative-dominated study might be a much better match between the maturity of your question and the method you use to get answers. In other words, a small qualitative-dominated study is the better tool to help improve your question.


    All this to say, I’m an advocate for the following process:

    1. Do your best to define a good question for your research project.
    2. Embrace the grunt work you’re about to deal with. Begin with a small-scale study that uses agile, flexible methods. This might mean avoiding getting outside help.
    3. Use the results from your initial small-scale study to refine your question. Also use these results as assets that help you connect and build trust with prospective clients. In other words, use the results of your study as marketing material.
    4. (Possibly) cycle through a few more small-scale iterations as you refine your question.
    5. Only once you’ve proven the value and clarity of your question would you consider scaling it up, and at this point you could benefit quite a lot from partnering with an academic or freelance researcher.


    Responses to these emails in this series about research have been, like, crickets, except for a few folks. This suggests I’m talking about stuff that’s relevant to only a small amount of my list.

    Your thoughts?


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    1. If you want to read up on this experiment:
    2. Douglas Hubbard talks a lot about how common and disastrous this is. It’s a simplification of his posiiton, but a pretty fair one, to say that we are excellent at choosing the wrong things to measure, and some lean iteration helps us to arrive at better things to measure.

    [PMC Weekly Insight] Survey marketing: Coding and counting

    My survey marketing experiment1 continues. As a quick reminder, I’m experimenting with using a survey to connect and build trust with a group of people.

    Today, we arrive at some further simple data munching. The first round of simple data munching was made up of counts and averages of the quantitative data the survey contains. This was both conceptually and mechanically simple.

    The next round of data munching is also conceptually simple, but a bit more mechanically involved. This part involves:

    1. Coding the survey open-ended responses so that they are more standardized and easy to analyze.
    2. Counting the frequency of the resulting coded responses. For example, once I did the coding of the LinkedIn sample, the action of networking showed up quite a lot in the open-ended responses. But how often? And where does this activity show up in a sorted list of all activities mentioned? This is why I want to count the frequency of the coded responses.

    Doing this was not difficult, and was all accomplished using Google Sheets2. The coding involved making some judgement calls. For example, when a survey respondent says “I do a lot of experimenting with programming patterns to find alternate solutions”, how do I best code that? That’s the kind of judgment call I’m talking about. (I went with “a-coding-learning” in this case.)

    Then I used a pivot table to count the frequency of the coded responses. Then I sorted the results of the pivot table by the count of each response.

    Here’s what I came up with (get ready to do some SCROLLIN’!):

    In case it’s not easy to read, the above is: “Coded responses to ‘2. Please list ways you have you spent time and money for career development.'”

    The above is: “Coded responses to ‘4. Please list ways you have you spent time and money for developing your technical skills.'”

    The above is: “Coded responses to ‘6. Please list ways you have you spent time and money for business or self-employment skills?'”

    The above is: “Coded responses to ‘7. Consider your entire career as a self-employed software developer and times you have gotten new opportunities, better projects, or other forms of career improvement. What do you think led to these improvements in your career?'”

    I experimented with putting the un-summarized list of coded responses into a word cloud generator, which was fun and something I might include in the report I write up about this, but I think the word cloud obscures more than it reveals when compared to a simple table.

    Elevator pitch summary of your research

    If I had just 20 seconds or so to summarize what this research is teaching me, I’d say the following:

    The self-employed software developers I’ve surveyed “in the wild” invest in career development with a heavy usage of online learning platforms like Pluralsight and IRL events, and they find new or better opportunities primarily through networking and experimenting with their own business. They invest about 300% more in cultivating technical skills than they do in cultivating business skills. In my study, they used the word “marketing” exactly zero times.

    I want to point out that the next-to-last sentence is purposefully constructed to be provocative, but its support in my data is questionable. Or rather, “they invest about 300% more in cultivating technical skills than they do in cultivating business skills” is one of several possible framings for the underlying data. Here are a few other possible framings:

    • Super factual: “When asked about how they invest in technical skills, respondents are about three times more verbose than when asked about how they invest in business or self-employment skills.”
    • Less provocative, still attempting to be factual: “I don’t have data on exactly how much time or money self-employed devs invest in technical vs. business skills, but my data does show that they clearly emphasize investing in technical skills over business skills.”
    • Simple, but suggesting a motive that the data might not support: “Self-employed devs seem way more interested in technical skills than business or self-employment skills like marketing.”

    To be clear: I’m not talking about how I’d interpret or frame this data in the body of a report, but rather in a time-compressed situation where being memorable and somewhat provocative is more important than being accurate or nuanced. In that time-compressed situation, impact is created differently than it is in a less time-bound situation.

    This all points to the actually difficult part: interpreting this data. Some of the key difficulties include:

    • Attributing intent or motive. What do I make of the fact that my respondents are more verbose in responding to questions about technical skills?
    • Interpreting importance. When I code and summarize the responses to open-ended questions, my list of codes for the “technical skills” and “where does opportunity come from” questions are much longer lists than the list of codes for the “business/self-employment skills” question. Maybe this does not mean that respondents actually emphasize the technical skills. Maybe it means they get lower ROI from that investment, so there’s more of that investment but less result from it. Maybe it means it simply requires more words to describe how they invest in tech skills, but if we look at the investment in terms of ROI or something else, a different picture would emerge.

    Interviews with some of my respondents would help clarify the questions above. In fact, I think it would be irresponsible of me to draw firm conclusions from this data without conducting around 5 interviews to better understand my respondents motive and thinking.

    Next week: I’ll have had time to code and summarize the responses to the other sample, which is folks from this very email list, and so I’ll be able to compare the two samples.

    Questions on this stuff? I’d love to hear ’em. Hit REPLY 🙂


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    1. If you want to read up on this experiment:
    2. There are better tools, like Delve, for doing this, but Google Sheets is good enough for my needs here.

    [PMC Daily Insight] Some useful market research tools

    I’ve been meaning to share a few useful tools for getting a quick and easy market size.

    First, this from Rob in the UK (shared with his permission):

    I’m really getting a lot of value from your work. Thank you.

    Thought I’d share this with you. It’s the UK industry classification framework which might be a useful alternative to the US version for some of your clients.

    The summary of the structure is an easy way to access the real meat…

    Adding on to what Rob shared, if you’re doing market sizing research in the UK, this is worth bookmarking:

    It’s a giant, very useful spreadsheet with recent data on the number of establishments within each vertical and sub-vertical, and then for each sub-vertical it breaks companies down by employee headcount, which is a very useful way to derive a more accurate market size.

    For example:

    Let’s imagine that your market is forestry products companies (listed as “02 : Forestry and logging” in this spreadsheet), and what you do for them is build software that helps them reduce the cost of complying with regulations, primarily by reducing the rework or fines that come from paperwork errors and oversights. Let’s further imagine that you have two ways of selling your software. The first is big, custom projects where you start with a generic codebase and completely customize it for the client in question. The second is where you license access to the un-customized generic version because even without extensive customization it provides real value.

    As you’re thinking about the two markets for this software, you’re possibly thinking in terms of client headcount, and targeting the fully custom work at those much larger companies, and the un-customized software at the smaller ones. Knowing the number of companies within each segment is really useful!

    You can get the same information here in the US, but dear God is it not nearly as easy. Here’s the step-by-step process I provide my clients:

    1. Make an educated guess about the size business that is right for your services.
    2. Make sure you know the NAICS code for the vertical you are researching. Use to look it up if you don’t.
    3. Use the American Fact Finder to identify how many companies in this vertical are the right size for your services:
      1. In your browser, navigate to
      2. Click Advanced Search, then click Show Me All
      3. In the left sidebar, click Industry Codes
      4. In the field labeled Enter an industry, product, or commodity name or code, or use the Industry Code Filter Options below, type in the NAICS code for the market vertical you are investigating, and then click the Go button.
      5. The search results in the Select Industry Codes pane will update. Click the item with the name that begins with All available codes…, then click the Close button at the top right of the pane.
      6. In the left sidebar, click Topics, click Business and Industry, click Business Characteristics, and the click Employment size of establishment/firm. This will narrow down the number of search results you need to look through.
      7. In the left sidebar, click Topics, click Year, and then click the most recent year that has data associated with it. This will further reduce the amount of sifting you need to do.
      8. In the search results click Geography Area Series: County Business Patterns by Employment Size Class.
      9. The table that loads will help you understand the distribution of business size within the vertical.

    I suppose the search UI for the American FactFinder1 is useful in some situations, but I’d much rather be able to pull down a spreadsheet with everything I need, just like the UK government provides.

    A final market research tip: If you’re trying to get a sense of the demand for a specific skill, the above won’t help you, but job boards will. Job boards can be a really useful proxy signal about what’s happening in a market.

    Quick caveat: I don’t recommend specializing in a single skill unless it’s the right moment in the shifting landscape of technology and you’re doing so as a transitional move towards something more evergreen. That said, understanding the demand for a specific skill can be a useful part of the decision making around a horizontal specialization or developing a service offering that accomplishes the same job-to-be-done as the skill in question.

    In situations like this, your options for measuring the demand for the skill are 1) a direct measurement or 2) a proxy measurement.

    A direct measurement would involve surveying or interviewing a probability sample of hiring managers or HR people, finding out how many are hiring for that skill, and then extrapolating from that sample to the entire population.

    A proxy measurement would involve searching job boards for the skill in question and noticing:

    1. How many companies/recruiters are seeking that specific skill.
    2. What type of companies are seeking that skill. This helps you understand where in the market the demand for this skill is coming from.

    The proxy measurement won’t be as exact, but it will still be very useful if you’re starting from a position of not knowing much at all about the market in question.

    I hope these tools come in handy at some point for you.



    1. The site that’s replacing the American FactFinder seems more modern and usable, but still has the same sort of underlying complexity.

    [PMC Daily Insight] The 2019 Meeker Report, interpreted for consultants

    Here’s what struck me from an initial read of Mary Meeker’s 2019 Internet Trends Report, seen from the lens of how these trends might be interesting or useful to the indie consultant.

    Slides 28 – 30: “Effective + Efficient Marketing = One’s Own Product + Happy Customers + Recommendations”

    This section tells a story of generally rising cost of acquiring new customers (CAC), which is really a larger story of rising cost per click for online paid advertising1. This is my main concern with over-relying on super-aggregators for online lead generation, because super-aggregators start out by making things very attractive for the supply side (that’s you, as someone who is supplying content to Google for their search engine, for example, or supplying content to LinkedIn to use another example) and then once the supply side has critical mass they start making things incrementally less and less attractive for the supply side as they seek to grow and retain their real asset, which is the demand side (their users/customer base, which is often an awkward and unholy combination of free users and paying advertisers).

    Mary highlights a few digital product companies (Zoom, for one) that aren’t currently suffering from rising CAC driven by the rising cost of paid advertising, and notes that they use a freemium model, which leverages a delightful free experience + network effects to acquire new customers at lower cost than paid advertising could. She goes deeper into this later in the deck.

    Consulting Takeaway: The first principle of my Expertise Incubator program applies here: “Anything you create in this program should be good enough to spread by word of mouth alone”. What could you create that is so good that it spreads by word of mouth alone? If you try to answer this question with the whole market you serve in mind, you’ll drive yourself mad. Instead, think of one specific really good client and think about what you could create for them specifically that they would find so useful, so valuable, that they might spontaneously share it with two other people.

    Slide 98: “Interactive gaming might be recreating reality around play + problem solving”

    Wow, this is a great way to think of consulting. I know the “play” part might be surprising, but how far away is “play” from “flow state” or other more “consultant-ey/business-ey” concepts?

    If we think of play as a lighthearted, enjoyable state of being, might not we have defined a very helpful context in which to do problem solving? Perhaps even an ideal context in which to do problem solving?

    Of course, not every moment of play is fun (cue memories of skinned knees or broken bones from childhood), nor is every moment of problem solving. But if the larger context in which we solve problems looks more like play than anything else, might be have created the most effective context in which to solve problems?

    Here are some common problem solving techniques, seen through the lens of play:

    • “Rubber ducking”. Isn’t the main thing you’re doing here communicating or connecting, while the secondary result is gaining clarity or solving a problem? Seems more like play than problem solving, yet problem solving is a very desirable secondary outcome, no?
    • Brainstorming. This can be fun! My experience with brainstorming is that it starts out kind of stiff and forced, and then if you stick with the intent, it becomes more fluid, veering at times into wacky stuff, and sometimes yields the kernel of good ideas. Along the way, it can be fun, and at some point you can enter a flow state. Sort of like play!
    • Design thinking. When I was doing copywriting work ages ago, I’d occasionally subcontract for an agency that was heavy on design thinking, and they’d do all sorts of play-like exercises with clients. Really, they were facilitating problem solving, but often it looked like some variation of a McDonalds Playland but with whiteboards and oversized post it notes and adults trying not to feel awkward.
    • Exercising as a way to get unstuck. This is another pretty common recommendation for problemsolving. This is often prescribed as literally playing–going for a fun run, or a bike ride, or working out–in order to problem solve. It gets your mind in a different state, which opens you up to different solutions.

    Consulting Takeaway: Even if you think your clients would hate it, how could you incorporate some element of play or fun into the problem solving work you do with clients?

    Slides 100 – 120: The Freemium model

    This section of Mary’s presentation is just really interesting stuff, period.

    One way to think about the lead generation you do is along similar lines to a freemium model: what free experience could you create for your market that is so delightful and compelling and valuable that the next natural step is to either spread the word or “upgrade” by becoming a client?

    My friend and podcast partner Liston Witherill is experimenting with this right now, though I don’t yet have a website to point you to so you can see it in action yet. Liston sells sales training, and he’s creating a strong differentiator by giving away the training content instead of hiding it behind a veil of mystery. In so doing, he’s hoping to (and I think he will) create a freemium dynamic, where there’s strong value in the free thing, and even more strong value in the paid upgrade to the free thing. He’s also creating real differentiation, since most of his competitors would blanche at the idea of giving away their content.

    Consulting Takeaway: Again, ask yourself: what free experience could you create for your market that is so delightful and compelling and valuable that the next natural step is to either spread the word or “upgrade” by becoming a client?

    Slides 123 – 151: How data collection and its usage in product development is changing

    This is a sea change in the tech world that trickles down into stuff like marketing automation, then the way these products are marketed starts to influence how the people I care about think about connecting and building trust with prospective clients, and this concerns me. The data that gets thrown off when we do stuff (lead gen, etc) online is potentially interesting, useful, and valuable. And it is also potentially a great way to systematize and bake inefficiencies and a sort of “Brazil”-like horribleness into your business. This deserves a longer treatment, but there are ways to structure your business development to accomplish the promise of data and personalization and marketing automation without any of the complexity of that stuff.

    Continuing this idea: you are less malleable than a digital product, so you must play by different rules. Using a bunch of data you’ve collected plus design thinking to create a “different you” doesn’t work the same way using design thinking to optimize product market fit or move a product into a better market position does. You can easily change how you dress to fit into a C-suite setting, but once you open your mouth it’ll be obvious whether you “belong” there or not. This is part of why I’m suspicious of caring much at all about the data that your online interaction with website visitors generates. The prospect of you thinking that you can crowdsource your business strategy to random internet traffic actually terrifies me.

    Consulting Takeaway: There’s currently a massive gravitational pull towards collecting and using data. The general idea might not be bad, but the particulars of how it’s done for digital products or big tech platforms have almost no relevance for you, and if you forget this, you do so at your peril. What data about your prospects and clients is actually meaningful to your business? What data helps you make better decisions for your own business and what helps you move the needle more powerfully for your clients? At most, that’s the data you should care about. And then you need to ask how best to collect and understand it. There might actually be no software tools that are needed to collect it, and the software tools that claim to collect it might encourage you to collect low value or flat out useless data.

    Slide 171: Expressed vs. demonstrated preference

    This slide talks about how despite our expressed preference for news that’s not negative, what we humans actually do is choose to consume negative news instead.

    I wonder if this explains why Direct Response marketing that focuses on problems is so brutally efficient and fast-acting while brand marketing that focuses on aspirational outcomes is more expensive and slow-acting.

    The niche famous direct marketer Gary Bencivenga once said: “Problems are markets”. This is a succinct expression of the way that direct response marketing tends to focus on problems moreso than general improvements, or rather the improvements are usually contextualized in the context of a painful, urgent problem.

    Slides 229 & 231

    These slides discuss the rise of “remote workers” from 3% to 5% from 2000 to 2016. This is an increase, but jeebus, are we still a small minority!

    Slide 231 mentions the most-cited benefit of remote work is Flexible Work Hours. Cool, but notice how that’s not a hard ROI-defined benefit, it’s a “squishy” “lifestyle” benefit.

    Consulting Takeaway: How often are your clients operating from “squishy” decision drivers, ones where the outcome/benefit is hard to measure and defined more in emotional terms than measurable, objective terms? If so, how could you get better at selling to these desires?

    Slide 233

    Ah! Now we have our first non-tech sector called out. I’m leaning in as I read this, because this is (maybe) where we start to see the power of specialization, where you as a technologist focus on a non-tech vertical in order to help that vertical create new value using technology.

    Incidentally, if you want to see a nice sea change narrative in action, this section of Mary’s deck is a good place for that. We have:

    • The sea changes themselves: presented as a curated collection of data points from a collection of sources that point to one simple conclusion: decline in traditional education delivery.
    • The general opportunity: presented as quantitive graphs depicting the growth of the new opportunity and qualitative quotes from founders reinforcing this growth story.
    • A key complicating factor: completion rates vary.
    • A key benefit: lower cost to consumers.
    • If this were a proposal you would go on to present the last part of the narrative, which would be: The specific opportunity for you, dear client.

    Slides 269 – 292

    The next non-tech sector that’s called out. Healthcare! Not a surprise to me, probably not to you either.

    The fact that Mary called out education and healthcare is interesting to me. It would be fair to think of education and healthcare2 as non-tech sectors that tech has “colonized” first, though you could certainly include others in this shortlist (finance would be another).

    Consulting Takeaway: If you fit my mental model of an ideal member of this list, you’re a technologist of some stripe looking to move into advisory services. As you do so, the tech sector is the last place I’d suggest looking for opportunity to create value as an advisor. I wouldn’t suggest avoiding it outright, but I’d suggest starting elsewhere to find opportunities to create value because as tech “colonizes” other sectors and verticals, it creates all this disruption and change and uncertainty where you can get in the “side door” to consulting more easily.


    Of course, the Mary Meeker whole deck is well worth a read. Again, here’s a link:


    1. This is probably related to or caused by “The Law of Shitty Clickthroughs”
    2. I’m kind of ignoring that previously in this deck, Mary called out e-commerce. That is an interesting sector, and I often get questions about whether it’s a vertical of its own, a subset of retail, or something else.
      It’s also interesting to wonder if it’s a subset of the tech sector, a subset of retail, or a completely different animal.
      In one sense, it’s a retail business enabled by tech.
      In another sense, it can be a tech business that just happens to use products as the basis for forming a customer relationship where some of the value is the delivery of a product but much of it lies elsewhere.
      This almost chimeric quality of e-commerce is why I’m not listing it as a non-tech sector that Mary calls out in her report, even though if you think of e-commerce as retail enabled by tech, it would be a non-tech sector.

    [PMC Daily Insight] Unfortunately, you’re in a relationship business

    This is really interesting, and although I’m linking you to what appears to be the full academic paper, you can read the whole thing in probably 3 to 5 minutes:

    I’ll summarize the key points if you’d rather not read the paper right now (even if you don’t read the paper, please look at the data visualization that’s embedded in it):

    1. Stack Overflow (SO) makes its entire content archive available here: This presents an interesting opportunity to study and visualize the patterns of communication between SO users. The paper’s authors take advantage of this opportunity.
    2. They ask the question: how centralized vs. distributed is collaborative knowledge creation? They choose to think of multiple contributions to a single SO question (either on the question or answer side) as a collaboration, and they draw lines on a world map between the geographical location (if available) of SO users who collaborate.
    3. The paper’s authors use the resulting map, which compares SO activity in the year 2009 to the year 2017 using a random census of 1% of all activity, to advance a hypothesis that the Internet has increased rather than decreased centralization, at least to the extent that Stack Overflow can reveal these patterns.

    The paper’s authors definition of “collaboration” is OK but also debatable. They define collaboration thusly: “we constructed a network: similar to scientific collaborations, two cities are connected if users from the cities jointly contribute to a question, either in posing the question or in providing an answer.” That’s not how I think of collaboration, but also, it’s a useful way of viewing patterns of sharing knowledge.

    If your worldview of the Internet is similar to mine, then this research presents a piece of evidence that contradicts your worldview, which can feel upsetting. Or alternately, this new evidence provides an opportunity to increase the fidelity of your worldview, which is a good thing.

    Either way, it’s somewhat of a surprise to see the Internet — something you think of as a democratizing force — host more centralization. It’s surprising to see something — again I’m talking about the Internet here — you view as “wanting” to increase inclusiveness actually generate a less inclusive conversation.

    Is this because of toxic, Silicon valley “bro culture”? I and this email list aren’t here to dig into those issues. Rather, this is my opportunity to remind you that, as I often like to say: unfortunately, you’re in a relationship business.

    I say this with a grin because many of us hate this reality, or resist it in some way. I’ve lost count of the number of times I’ve politely interrupted a client who is 5 minutes into detailing how they plan to use an email to respond to a complex, difficult situation with their client and said, “Just call them. I can help you prepare for the call, but please just call them. This will turn into a dumpster fire email thread and a mass grave of misunderstood intentions if you don’t.” And I’ve lost count of how many times I’ve been right to give this advice.

    Unfortunately, you’re in a relationship business. :->

    Relationships vs. expertise

    There’s an interesting tension between relationships and expertise.

    The platonic ideal of the expert is1:

    • Their expertise has value.
    • This value increases the demand for their time and access to their expertise.
    • This increased demand causes them to put in place various filters: high price for access to the expertise, gatekeepers for access to the person and their time, habits and norms that maintain or improve the expertise asset at the expense of availability to the public or other assets.

    Experts tend to be inaccessible, except if you pay a high price for access or you make it through a dense layer of intentional filters.

    On the other hand, building relationships often requires a generous investment of time. The trust that enables relationships takes time to build. Furthermore, someone usually has to “go first”, meaning one side of the relationship has to take a risk by trusting the other side more than the relationship would seem to merit. Relationships grow with investments of time and risk.

    If you’re an expert conforming to the platonic ideal of an expert, how do you build relationships?

    I’ll be brief here, since this is not the main point of this article:

    • A relationship with an expert is not a peer relationship. The expert has a form of power you do not, but you want access to. This power reduces the time it takes to build trust in proportion to the magnitude of the power differential. I can mow my own lawn, so the lawn service has relatively little power in the relationship with me, and so it takes a relatively long time for me to come to trust them. I cannot diagnose some major illness I might be suffering, so the physician has relatively a lot of power in the relationship, and so trust (in a limited sense) can be built much more rapidly between my and the physician.
      • Consulting takeway: Find a way to deliver a quick, impressive expertise-based win early in your relationship with clients or qualified prospects. A common way to do this is to throw the previous consultant under the bus by criticizing some obvious flaw in their work. Unsolicited teardowns have the same flaw. I fucking hate these approaches to trust-building because Consultant B doesn’t know what constraints Consultant A was operating under, so it’s almost always a cheap shot at the expense of a fellow professional. Please find a different way to deliver a quick, impressive expertise-based win.
    • Experts have more degrees of freedom in their personal life that allow them to invest in relationships in powerful ways. For example, a successful expert can take a risk by trusting the other side more than the relationship would seem to merit by saying, “Hey, why don’t you spend a week at our vacation cabin in the mountains some time this summer?” This is an example of a relationship-building mode that might be more available to experts than other folks.
      • Consulting takeaway: What ways of rapidly building trust with clients or qualified prospects might your expert position afford you? This is less about using the expertise itself and more about using the by-products of that expertise (money, discretionary time) to be generous in interesting ways. Ex: This hasn’t happened yet, but now that my daily emails are switching to a paid subscription, I plan to compile 3 to 4 topical anthologies every 2 months and send them to qualified prospects and clients. These anthologies would be made up of the best few paid emails from the last 2 months on the topics of specialization, lead generation, and cultivating IP, made into 1 e-book per topic, and distributed to prospects/clients based on what specific challenge they happen to be working on. This is an example (hopefully!) of building trust using the discretionary time that I have because of my expert position.

    Experts can build relationships, of course, but how they do it looks different because of the power differential, and because they have different degrees of freedom for investing in relationships.

    What the SO map tells us about relationships

    Let’s weave these threads together.

    One take on the Internet is: most of the world’s population is connected and accessible in some way, either directly through messaging (email, etc.) or indirectly through an aggregated platform (social media, etc.). For quite a while, this has been my operating worldview. It’s very much a techno-utopian worldview, and certainly a simplified way of understanding the world, as all worldviews necessarily are.

    The paper I linked to at the top of this article puts a significant kink in my operating worldview. Over time, the actual patterns of connection between SO users are looking less like my worldview, not more. One of the following things might be happening:

    • Yes, the Internet connects in a universal, democratizing way, but few people make use of this capability. In other words, perhaps we just prefer to not connect in a universal way, and instead prefer to connect with others like us.
    • The Internet does not actually connect the world in a universal, democratizing way. Access is more centralized in reality than it is in my techno-utopian worldview. I have evidence that this is true from my 2 years at the Oregon coast from 2011 to 2013, where the best Internet connection available to me was 1.5 Mbit DSL or somewhat faster satellite, both completely unsuitable for even a low-quality video call.
    • Reality is more complex, and cannot be explained by simple single-factor explanations.

    If you think of that data visualization from the above-linked paper as a map of relationships rather than as a map of the Internet’s democratizing capacity, it makes a lot more sense.

    Said differently, the map seems to contradict view #1 and support view #2 below:

    1. View #1: The internet broadens access to information and connection between people.
    2. View #2: Unfortunately, you’re in a relationship business, and this is visible even when you look at SO activity.

    I don’t want to dilute my point too much, but I’m compelled to point out that single-variable answers/solutions are rarely effective or complete answers/solutions. People are migrating to cities, and the tipping point where more than 50% of us will live in cities is not far in the future at all. So maybe this migration to cities does more to explain things than anything else?

    The world is a big complex place. Resist oversimplified answers and solutions!

    And, invest in relationships. Because after all, you’re in a relationship business whether you like it or not.


    1. Please read David C. Baker’s “The Business of Expertise” for more on this.

    [PMC Weekly Insight] Survey marketing: Qualitative analysis of the de-biasing survey

    My survey marketing experiment1 continues. As a quick reminder, I’m experimenting with using a survey to connect and build trust with a group of people.

    It’s been a few weeks since I’ve updated you on this2, so a quick recap seems helpful. As I previously wrote:

    The three things that must simultaneously be true for survey marketing to work:

    1. Question my audience can answer about themselves

    2. Question is one the audience is curious about RE: their peers/the industry

    3. Question is one I am very interested in the answer to

    This confluence of factors makes it possible for me to serve my audience by answering a question both they and I care about, then sharing the answer back with them using a permission mechanism that was created using the same means I used to generate the answer in the first place: a survey.

    I started this project with a very open-ended survey that I am referring to as my “de-biasing survey”. The purpose of this survey is to align my thinking with how my sample group thinks about the question of investing in their career.

    I distributed this survey to a sample I recruited from LinkedIn using scrappy, inexpensive methods. I also forked the survey and sent the fork to a sample recruited from my email list.

    I shared some quantitative results here, and in that data there’s a pretty clear difference between my LinkedIn sample and my email list sample, with the email list sample seemingly much more interested in and active in career development. And younger, too, you good-looking lot!

    That pretty much brings us current. Now to dig into the qualitative data this research has yielded.

    The qualitative data

    For this project, I’m thinking of the responses to my open-ended questions as qualitative data. It’s not as rich or nuanced a qualitative dataset as realtime audio or video or IRL interviews would yield, but it’s still useful because it adds context to the quantitative data.

    Here’s an example of a few responses to one of my open-ended questions. The responses come from the LinkedIn sample, and the question was:

    Consider your entire career as a self-employed software developer and times you have gotten new opportunities, better projects, or other forms of career improvement. What do you think led to these improvements in your career?

    • Coincidence. It’s much harder now because the applicant pool is overloaded.
    • taking many shots
    • capacity to focus and deal with problems
    • How to get more customers
    • “Being curious and open. I tell people about the things I’m interested in and the projects I hack together in my own time. Everytime that has come up in a “”9-to-5″” work environment it has led to me getting more money and interesting conversations (e.g. would you like to work here, would you like this project)”
    • Networking & experience.
    • I am not a full-time software developer. I started because my place of work needed certain applications not commercially available.
    • I haven’t been very successful in finding good projects.
    • longevity
    • Being friendly, honest, hard-working and producing quality results.

    This list is the first 10 responses to that question, listed in the chronological order the responses showed up. You really get a sense of the range here, from quick 1-word responses–some seeming to be nonsequiturs or misreadings of the question–to more lengthy, seemingly more thoughtful responses. This is totally normal in the context of a survey like this one.

    Bias alert!

    At this point, I’m on the lookout for a subtle bias in myself, which would be to discount the shorter responses in some way. To assume they’re less valuable, less thoughtful, or less meaningful to my question. Remember, when you are starting with no data, the marginal value of additional data is huge until you get to about 30 data points, then it starts tapering off pretty quickly. I’m referencing Douglas Hubbard here, who has said that beyond about 30 samples you need to quadruple the sample size to reduce error–which we can think of as uncertainty–by half. I can’t find online this nifty graph that Douglas shared in a recent webinar for the Military Operations Research Society, but the graph below, from a different source, conveys the same idea. Notice how the curve pretty quickly goes asymptotic around the 30 sample mark:

    This shows the decreasing marginal value of additional data. The biggest gains happen between 0 and ~30 samples.

    Anyway! I think it would be a mistake for me to discount the value of any of my responses here, even if the responses are super short or don’t make a lot of grammatical sense. They’re still data, and they’re still moving me from massive uncertainty to greatly-reduced uncertainty.

    Cleaning the qual data

    In order to address this bias in myself, and to make this qual data more useful, I need to normalize the responses to open-ended questions. This is coding the responses. I’ll do this right here, as an example, for a few of the above responses.

    1. First example:
      1. Actual response: “Coincidence. It’s much harder now because the applicant pool is overloaded.”
      2. Coded to: “s-chance, s-competition”
    2. Second example:
      1. Actual response: “taking many shots”
      2. Coded to: “a-volume”
    3. Third example:
      1. Actual response: “capacity to focus and deal with problems”
      2. Coded to: “a-problemsolving”

    You’ll notice each of my codes begins with a letter, which is a shorthand for one of two things: “a-” means action/activity, and “s-” means sentiment, or a sort of feeling/worldview being expressed. This allows me to sort and filter more easily, and it’s a meaningful distinction here.

    Any open-ended tagging or categorization system, such as my coding system here, presents a challenge because you can invent an infinite number of categories and become highly granular in your categorization. This is why almost every time I’ve ever set up a CRM for myself, I abandon using it. It collapses under the weight of its own complexity which, ironically, I created by creating a too-granular category/tagging system!

    So… be careful with your coding system. 🙂 You want it to be expressive and not conceal too much granularity and nuance, but you also want it to be useful, which means avoiding excessive complexity which means avoiding excessive detail and granularity.

    What I’ll be doing next with this research is coding the qualitative answers, and I’ll do so in an iterative way. I’ll read through each column of responses to open-ended questions, and set up an adjacent column in the spreadsheet that contains the responses and that’s where I’ll put in the coded responses. If column C contains responses to an open-ended question, I’ll add a column D for my codes, and so on. Theoretically a RDBMS would be better here–or perhaps Airtable–but I’m sticking with a spreadsheet at this point because it’s good enough.

    For each new action or sentiment I find in the qual data, I’ll create a new code. Then, I’ll pull out a list of all the codes and look for opportunities to simplify the coding schema by collapsing sufficiently similar codes into one, and then search for the old codes in my spreadsheet and replace them with the new codes based on the now-simplified schema. This is where the process becomes more art than science.

    Next steps

    Here’s my list of next steps for this research project:

    • Code the open-ended responses into one of two categories:
      • Crisply defined activities (somewhat objective on my end. More normalizing than interpreting.)
      • Sentiments (quite subjective on my end. More interpreting than normalizing. This is where I can skew objective or skew towards “rack the shotgun style filtering” where I apply my own worldview.)
        • Interesting to note my personal emotional reaction to some of the sentiments expressed. Judgey! 🙁
    • Analyze the coded responses:
      • Word cloud to facilitate easy, “cotton candy” sharing of results like those shitty infographics everywhere online do. 🙂
      • Simple quant analysis (“what % of respondents list this activity/sentiment?”)
      • Really, really think about what the patterns I see with the above analysis methods might be saying. Is there a story in the data?
    • Compare the LinkedIn sample vs. the list sample
    • And of course, write up my findings into a report to share back with those who left their email address for me.
    • Decide whether to extend or pivot based on what I’ve learned.

    Interesting derivative questions

    Thus far, this research–even though I’m nowhere near “done”–has raised some very interesting questions for me:

    • Wow, the responses from my email list sample are so immediately strikingly different. “Better” in my view. What filters for these kinds of people? What “racks the shotgun” for them? Where do they hang out in an already-filtered group?
    • Do I create two reports–one per sample group–or just one?
    • Was the structure of my survey questions redundant? I got a few comments to the effect that it was. I saw the later survey questions as drill-downs going deeper on earlier questions, but a few participants saw them as redundant. I need to be sensitive to this if I design a second survey to go bigger with this research.


    I think I’ll use the next few free Weekly Insight articles to update you on the continuation of this research and, incidentally, give myself helpful deadlines to keep it going. 🙂

    Looking forward to sharing my method and what I learn with you.


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    1. If you want to read up on this experiment:
    2. Moving is hella disruptive! 🙂 Cheryl and I are partially moved into our long-term rental, waiting for the moving company to deliver our furniture and the possessions we didn’t bring with us by car.

    [PMC Daily Insight] The second ideal

    In my small group program, The Expertise Incubator, participants face 5 challenges. As we do so, we are guided by three ideals:

    1. Anything you create in this program should be good enough to spread by word of mouth alone.
    2. Anything you create in this program–if you give it away for free–should be good enough that some would gladly pay real money for it.
    3. You should be willing to work daily for 2 to 3 years to make ideals 1 and 2 become true in your work. In other words, you should be OK with not living up to ideals #1 and #2 at first so that you can build up the skills you need to ultimately achieve those ideals.

    I would never ask anyone to live out these ideals if I wasn’t also doing my level best to live them out myself. And I wouldn’t have articulated these ideals at all if I didn’t think they are powerfuly transformative. I do, on both counts.

    Since January 2016, I’ve been publishing something I hope is worth reading 5 to 7 times a week. I have evidence that what I publish does spread by word of mouth alone (the first ideal), although I do use other ways to spread it (podcast guesting, for one example).

    I also have some evidence that my thinking alone is worth money to others. Thus far, that evidence looks like successfully selling advisory services and my books. A few people have told me that they would pay to subscribe to my email list. Not a lot, mind you, but a few.

    I’d like to see if I can make the second Expertise Incubator ideal true of my email list. I’d like to see if my thinking as expressed in that format is worth paying for.

    This is me putting even more pressure on myself (productive discomfort) to live up to the second Expertise Incubator ideal.

    So… my daily emails are now a paid subscription. I’ll publish a free article once a week, on Tue or Wed. If you’re subscribed to my list now, you’ll get these free once-weekly articles with no additional action on your part. I’ll publish an additional 3 emails per week, and to receive those or view them on my website, you’ll need to subscribe, and that costs $10/month or $100/year. You’re on a free 2-week “trial” right now, but after the 2 weeks (or before, if you like) you’ll need to subscribe to continue getting those 3 additional emails per week. You can do that at

    Those of you that are familiar with Ben Thompson’s Stratechery will recognize this model, because I’ve emulated him almost 100% in defining the price, publication schedule, and so forth.

    Those of you that are unfamiliar with his model can get an excellent overview straight from the horse’s mouth from 15:23 to 25:40 here:

    Ben’s serving a somewhat different audience and talking about different things than I do, but I think the fundamentals of his model are quite relevant for me and my audience, so why not use what he’s figured out as a starting point for what I’m doing?

    I imagine some of you will have questions. Please mash REPLY and let me know what they are. The ones I would have if I were you are answered here:

    Why are you doing this?

    Again, the desire to more fully live up to my own ideals is a big part of this decision. The precipitating event was a few clients wrapping up their work with me around the same time and me wanting to broaden my revenue base, and the fertilizer was a combination of my ongoing admiration for what Ben Thompson is doing with his business and this podcast episode:

    Do you think this will succeed?

    There are two answers here: 1) let’s find out!

    2) For an experiment like this, what would constitute success?

    One number that’s lodged in my brain is 1,000 paying subscribers, a-la Kevin Kelly’s “1000 True Fans”. That wouldn’t be a living for me, but it would be a nice leg in a 3-legged “revenue stool” that’s complemented by services and teaching revenue.

    What was this decision like on an emotional level?

    Not easy. It made me physically ill for about a day, and then it settled into part of the new status quo over the next week or so. It brought up imposter syndrome and other shitty feelings, and then those resolved into a sort of “eh, fuck it, let’s give this a go!” feeling.

    It’s one thing to say, “your thinking should be so valuable that clients would pay for access to your thinking alone”. It’s next level to take something you’ve been giving away for free for years and put it behind a paywall. But it’s hypocritical for me to say the former and avoid the latter. Not that I’ve been totally avoiding the latter. Again, I sell my thinking through my services and my books, and that works well. But I can take this further and test more deeply and push myself more, and that’s what I’m doing here. Doing this is not emotionally comfortable, but it’s necessary. Mandatory, almost.

    Will the tone or content of the daily emails change?

    We’ll see! I’ll probably joke around approximately 20% less, but still aim to write the emails in a way that’s enjoyable to read. I’ll still be pursuing the same core themes, and operating from the same points of view, but I hope with even more force and focus.

    Happy Monday!


    [PMC] The Opportunity Early Warning System

    List member Ben pointed me to this, and it’s quote interesting.It’s a short piece from AngelList on the opportunity in the funeral business and how some startups are moving on that opportunity.

    It causes one to think: what creates opportunity in the first place?

    Not what created this specific opportunity, but what creates opportunity in general?

    In the case of the ~20 Billion funeral market, two things had to happen: 1) broad changes in customer sentiment and 2) changes in legislation. It’s possible the former drove the latter, but even if not, changes in sentiment and legislation both had to happen in order to create the kind of opportunity we’re seeing today.

    That opportunity, by the way, can be very simply thought of as “green burials”; burials done without as much or any preservation and durable hardware.

    I’m getting very speculative here, but it’s interesting to try to imagine how one might have seen this opportunity coming and moved on it just early enough to build up a leading market position, but not too early, because that often doesn’t work out well. To build a model of how one might spot opportunity coming, let’s first think about market awareness.

    The more aware a market is, the less education cost you’ll bear.

    Circa 2006, the consumer market was largely unaware of the value of a $600 pocket-sized computer that could also make phone calls. This meant that Apple and other smartphone pioneers had a lot of expensive education and awareness-building work to do. You could think of this as an investment that lowered their future cost of sale, you could think of it as a cost that early movers have to bear in order to earn their first-mover advantage, or you could simply think of it as a cost that’s unavoidably bundled with innovation. But what you can’t do is ignore this cost, because it can be significant enough to kill innovations that are too early. The short 5-year lifespan of Apple Newton MessagePad was certainly not a single-factor failure, but the state of awareness of the circa 1993 market certainly played a role. Even at an inflation-adjusted $1129 price, it would have been extremely expensive to educate the market on the value of the Newton and acquire customers beyond those who already “got it”.

    So this first part of our model is market awareness. This is critical.

    The second part of our model for spotting opportunity coming at the right time is… well, I don’t have a great term for it, so let’s think of it as adjacency or context.

    Sometimes new opportunity in Industry X is created by changes that originated in… Industry X. And sometimes, it’s not.

    Sometimes new opportunity in Industry X is created by changes that originated outside Industry X. This is what I mean by adjacency/context. Sometimes the changes that create opportunity originate elsewhere, in either an adjacent industry or in the larger cultural context.

    The reading I’ve done on the opportunity in the burial business suggests that it’s broader customer sentiment about “green” issues that’s driving the current opportunity in the burials business. This is a change in the larger cultural context. It effects not just the burials business but also retail, auto, CPG, and a multitude of other sectors. And this change did not originate within the burials business. It came from the outside.

    A quick recap of our model:

    1. Market awareness: Increases in customer awareness (awareness of the value of a new innovation) drive down the cost of selling that innovation.
    2. Adjacency/Context: Change that creates opportunity can come from within an industry, but–quite importantly–it can also come from adjacent industries or the larger cultural context.
    3. Curiosity: Your ongoing curiosity drives a recurring process of inquiry into the above 2 factors.

    Back to our larger thought experiment here: How might one have seen this funeral business opportunity coming and moved on it just early enough to build up a leading market position, but not too early?

    In a general sense, and following our model here, I think you might have paid attention to broad changes in the culture and repeatedly asked of each change: “I wonder how this might effect my customers?”

    It might seem like I’ve made an argument against specialization here, but I don’t see it that way because specialization is not myopia, it’s strategic focus. This kind of focus doesn’t magically deprive you of the ability to pay attention to the big picture.

    In fact, specialization makes this “opportunity early warning system” work better. If you don’t have deep insight into a specific vertical or horizontal, you won’t see the full potential of adjacent or broad-based cultural changes to impact your area of specialized expertise. Specialization gives you the ability predict or imagine how external changes could effect your area of specialization. Without the specialized expertise, you might see the external changes just fine, but you won’t fully grasp the implications of those changes for your area of focus (because you haven’t chosen and pursued a single area of focus!). [1]

    This process of repeated inquiry from our innovative funerla business person might have looked something like this:

    1. “Huh, seems like every third article I read about these days is about the green movement.”
    2. “What would the funeral business look like if our customers insisted we were ‘green’ also?”
    3. “I suppose they might want fewer chemicals and less hardware involved in the process. I wonder what that might look like?”
    4. “OK, I’ve taken this as far as I can inside my head. I need to talk to customers and get their perspective on this. I especially need to understand their state of awareness and whether they need to be educated about the value of a burial with fewer chemicals and less hardware, or whether they just ‘get it’ already because of the broader cultural green movement.”

    Is this process economically efficient? Not in the short term. You might burn through quite a few non-starter ideas because the adjacent/cultural change is insufficiently relevant to your clients, or because the market awareness isn’t high enough.

    But in the long term, I can’t imagine running a great expertise-driven business without regularly investing in this process. Because this kind of thing is what keeps you creating new, exceptional value over the long haul.


    1: I didn’t have time here to go into the notion that as a more profitable specialist, you can simply afford more time to pay attention to adjacent/contextual changes. In other words, you’re not working so damn much, so you can read and learn and inquire more broadly.

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    [PMC] Social risk, and dating models

    Let’s imagine you’re a person interested in dating someone who works as a model.

    Quora has advice for you. Here’s one of the better answers:

    (source:, please note there’s stuff at that link some might find sexist or otherwise objectionable.)

    I spent a good long time trying to figure out what part of the list above doesn’t apply directly to lead generation for your services and couldn’t come up with anything. Here’s that list, adapted to answer the question: “How do you generate leads you find more desirable for some reason? Better clients, more valuable work, advisory instead of implementation, etc.”

    1. Find employment in a business that deals with the kind of leads you want to generate, intentionally build up a network (access) and credibility (trust), and then move into generating leads from that network as an indie.
    2. Hang out in places where the leads you want to attract socialize with each other. Your “hanging out” will be intentional and oriented around helpful service.
    3. Become close friends with the kind of person you want to have as a lead and have them introduce you to friends and co-workers.
    4. Become a celebrity and then generate leads through the fame and attention brought on by that. (This is the fundamental idea behind content marketing and thought leadership.)
    5. Become the kind of person you want to attract as leads, and use your insider status and insight into this community to attract leads from within the community.

    Notice what’s not on this list: place an ad in Craigslist or set up a profile on a dating site and troll (in the fishing sense, not in the internet hater sense) for the kind of person you’re looking for.

    Ads for sure can work. But their effectiveness is limited compared to other approaches that do more to build trust and social access.

    The bottom line: Ads limit your risk exposure to financial risk only.

    Lead generation approaches that include an element of social risk work better for attracting more desirable clients–or advisory services leads–because they build trust and social access.


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    [PMC] Pareto, Power Laws, Patterns, and Onions

    The Pareto Principle article on Wikipedia is a really valuable read.

    Unless you’ve been living under a rock and not reading the hundreds (thousands, perhaps?) of articles online that over-apply the idea of the Pareto Principle, you already get the main idea.

    20% of something is responsible for 80% of something else.

    That’s the core idea, expressed in the most general possible terms.

    Here’s the actual principle as described by Wikipedia:

    “The Pareto principle (also known as the 80/20 rule, the law of the vital few, or the principle of factor sparsity) states that, for many events, roughly 80% of the effects come from 20% of the causes.”

    The critical caveat there is: “for many events”. Not for all events. Not for all phenomena.

    That said, the Pareto Principle is impressively explanatory. It fits lots of phenomena, and in so doing provides a reassuringly simple sense of order to the world.

    It, however, does not explain why 80% of the effects come from 20% of the causes. And it does not predict causation; meaning it does not predict which causes will have that outsized contribution to effects.

    The Pareto Principle is not truly universal either. You can’t just pick any pair of cause and effect and say 20% of this cause will be responsible for 80% of this other effect. I mean you could say that, but you would often be wrong. 🙂

    If you read the Wikipedia article (and I think you should), you’ll see lots of examples of situations where the Pareto Principle does and does not apply. Here is one interesting place where it does not:

    “However, it is important to note that while there have been associations of such with meritocracy, the principle should not be confused with farther reaching implications. As Alessandro Pluchino at the University of Catania in Italy points out, other attributes do not necessarily correlate. Using talent as an example, he and other researchers state, “The maximum success never coincides with the maximum talent, and vice-versa.”, and that such factors are the result of chance.”

    Digging deeper into Pluchino’s research is fascinating, and we find this helpful summary of their research from the MIT Technology Review.

    Quick aside: it was amusingly ironic to me that the abovelinked MIT Tech review article was published in early 2018, around the same time that one of the biggest recent “black swan” events involving complex systems was kicking the crap out of the world economy. The Pluchino paper’s authors talk about how luck is the determining factor in success, and they were doing so at a time when something like luck was an outsized factor in the health of the world economy, but in a negative–rather than positive–direction.

    From the MIT Tech Review summary:

    “The results are something of an eye-opener. Their simulations accurately reproduce the wealth distribution in the real world. But the wealthiest individuals are not the most talented (although they must have a certain level of talent). They are the luckiest. And this has significant implications for the way societies can optimize the returns they get for investments in everything from business to science.”

    Just like with the Pareto principle, there’s a certain sense of comfort we get from ideas that are simple to express, but in this case the explanation is: “They’re wealthy because they’re lucky”, not “They’re getting outsized results because they know what 20% of inputs to focus on”. Both ideas suggest a simple correlation between inputs and results.

    Here’s the actual paper Pluchino and his team published, BTW. The MIT Tech Review summary (necessarily, I imagine) omits lots of juicy, interesting detail from the source paper. For example, the following:

    “In fact, from the micro point of view, following the dynamical rules of the TvL model, a talented individual has a greater a priori probability to reach a high level of success than a moderately gifted one, since she has a greater ability to grasp any opportunity will come. Of course, luck has to help her in yielding those opportunities. Therefore, from the point of view of a single individual, we should therefore conclude that, being impossible (by definition) to control the occurrence of lucky events, the best strategy to increase the probability of success (at any talent level) is to broaden the personal activity, the production of ideas, the communication with other people, seeking for diversity and mutual enrichment. In other words, to be an openminded person, ready to be in contact with others, exposes to the highest probability of lucky events (to be exploited by means of the personal talent).”

    There’s a whole series of articles begging to elaborate on just that one paragraph! And there are echos of the widely distributed quote: “The harder I work, the luckier I get.”

    What’s our bottom line here?

    I hope you’re a little less quick to apply the Pareto Principle to any ole situation, and more thoughtful about it when you do. Going forward, I certainly will be.

    There’s a deeper question I’m grappling with here:

    If you’re curious what contributes to success, who do you model?

    I mean sure, you can just throw up your hands and say: “Luck matters. So…. good luck! See ya!”

    But if you’re in the business of advising clients, surely you’re interested in more systematic ways of helping them improve their condition, right? Surely they’d like to get a little more for their money than an elaborately fashioned statement of: “Good luck, kid!”.

    So that raises the question: who do you study and attempt to model as you’re looking for the patterns that contribute to success?

    I don’t mean who specifically do you look at to understand what contributes to success. I mean where in the distribution of outcomes do you look?

    Let’s think in Pareto terms, and let’s think about 3 layers of an “onion”:

    • In the outer layer of the “onion”, 80% of the effects come from 20% of the causes. Ex: 80% of dollars of global wealth come from 20% of people.
    • In the middle layer of this onion, we apply the same 80/20 distribution to the top 20% from the previous layer of the onion. Ex: 64% of global wealth comes from 4% of people.
    • In the innermost layer of this onion, we go again with the 80/20 applied to the previous layer and get: 41% of the global wealth comes from 1% of the people (and in this we hear echoes of Occupy Wallstreet’s “the 1%”).

    If you’re wanting to understand what contributes to success in some endeavor, which layer of this onion do you look at to discover patterns?

    It’s tempting to look at the innermost layer–the 41/1 distribution. It’s tempting because this is the smallest group, so, questions of access and transparency aside, it should be the easiest to study. It’s also the group with the most emotionally impressive ratio between inputs and outcomes. After all, these people have the same 24 hours in a day and many of the same fundamental constraints we all do. If they need to fly from New York to London for business, they can’t do it 10 times faster on a $50 million Learjet than someone who buys a $500 coach ticket on Norwegian Air. Other aspects of their experience might be 10 or 100 times better, but they won’t get there 10x faster. Yet despite sharing many constraints with the rest of us, they get such outsized results!

    This innermost layer of the Pareto Onion is a seductive place to look for patterns that contribute to success, but I am unconvinced that it’s the best place for such lessons.

    My hunch is that, like Pluchino’s team claims, idiosyncratic factors like luck–but not limited to luck–play an outsized role in what gets you to that inner layer of the Pareto Onion.

    I am respectful of the role that idiosyncratic factors play in your life and career. And like the Pluchino paper says, you can (and should!) do things that increase the chances that these idiosyncratic factors work in your favor.

    But I’m more interested in understanding systematic factors that contribute to success for small expertise-driven businesses. And that means I’m not looking at the inner layer of the Pareto Onion. In fact, I’m not sure I’m even looking at any layer of this onion. In other words, the best lessons for systematic success might not lie in the top 20% at all!

    I hope this is at least a little bit shocking to you.

    I can’t prove this yet. It’s just a hunch.

    But stay tuned as I follow this thread. I think it’s important.

    On a personal note, is anyone else psyched that Apple is adding mouse support to iPaDoS 13 or whatever they’re calling it? When my wife and I hit the road 2 months ago I committed to using an iPad as my primary work computer (and quickly upgraded to the larger size iPad Pro).

    Working primarily on an iPad has added productive friction that I’ve appreciated. And I’ve been surprised how quickly I’ve habituated to touching the screen. Even when I pull out my laptop for some thing I can only do there, I find myself instinctively reaching to touch the screen.

    And yet, I’ll be super happy to have mouse support on the upgraded iPad this fall. I hope it supports this SwiftpointGT mouse I have.