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  1. How can self-employed professionals use specialization to create outsized value?
  2. How can ordinary folks cultivate and sell extremely valuable self-made expertise?
  3. How can average firms build a systematic innovation capacity to--over time--become exceptional firms?

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--Philip.

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

By Philip | June 11, 2019

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.

Wrap-up

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.

-P


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  1. If you want to read up on this experiment:
    1. https://philipmorganconsulting.com/pmc-survey-marketing/
    2. https://philipmorganconsulting.com/pmc-the-de-biasing-survey/
    3. https://philipmorganconsulting.com/pmc-survey-marketing-recruitment/
    4. philipmorganconsulting.com/pmc-survey-marketing-initial-data-from-the-de-biasing-survey/
  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] The Opportunity Early Warning System

By Philip | June 7, 2019

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.

-P


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

By Philip | June 6, 2019

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: www.quora.com/How-does-one-date-a-model, 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.

-P

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

By Philip | June 5, 2019

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.

-P

[PMC] Survey marketing: initial data from the de-biasing survey

By Philip | May 30, 2019

(Readin’ time: 3m 36s)

My survey marketing experiment [1] continues!

Here’s the initial “de-biasing” survey summary data from my two samples.

The LinkedIn Sample

My LinkedIn sample was a convenience opt-in sample, recruited using a Sales Navigator search for:

  • Self-employed
  • In United States
  • More than 10 years in current position
  • Keyword “software developer” in LI profile

I invited these folks to connect with me, and in my LI connection request message, I said the following:

Hi @firstname, my name is Philip. I am working to better understand how self-employed devs improve their career. Would you be willing to spare 3m for a survey? It will mean the world to me.

-> https://www.getfeedback.com/r/4B6uBroa

I’m not selling anything; you have my NO SALES PITCH GUARANTEE.

The automation tool I used–LinkedProspect–sent 1537 connection requests. 364 (23.68%) of them accepted, 38 (10.44%) of those started the survey, and 22 (57.9%) of those completed the survey, which means 1.43% of the sample I attempted to recruit from LinkedIn actually completed the survey.

The list sample

I also recruited a sample from my email list. I sent this sample to a fork of the survey so I could compare the two samples.

This sample was also a convenience opt-in sample. Across three daily emails, I invited this sample using the following text:

Quick tophat: I am working to better understand how self-employed devs improve their career. Would you be willing to spare 3m for a survey? It will mean the world to me.

-> https://www.getfeedback.com/r/fNWSDcfj

I’m not selling anything; you have my NO SALES PITCH GUARANTEE.

An average of 1898 people received this invitation 3 times. There’s no way for me to know how many actually saw, noticed, or really thought about this invitation, but let’s use the same funnel math as with the LinkedIn sample. So from 1898 “connection requests”, 56 (2.96%) of those started the survey, 33 (58.93%) of those completed the survey, which means that 1.75% of the sample I attempted to recruit from my email list actually completed the survey.

Summary data

Here’s the first picture of the data I’ve collected, looking at it through a quantitative lens:

The next step is to dig through the responses to open-ended question and look for patterns there, but even at this summary level there seems to be a distinct difference between y’all (if you’re reading this in your inbox) and my LinkedIn sample.

This is what I would have guessed. Members of an email list that’s about marketing and expertise is likely to filter for self-employed people who are interested in investing in their career. This is a biased sample.

Having a LinkedIn profile and responding to connection request there is also a filtering mechanism that also gives me a biased sample.

These biases are totally OK! I’m trying to understand how self-employed devs think about investing in their career, but implicit in that question is an assumption: I only really care about the subset of this group that I can actually reach! So the bias in my sample matches the bias I’d experience everywhere else in my business, so this sampling bias isn’t going to skew my results in a way that undermines my decision-making (and yes, I do have a decision I need to make about how I message what I do).

How statistically valid is my data?

Wrong question!

Before I started this project, I had zero quantitive data on my question. I was operating on intuition and the sense of the market I gained through lots of conversations and casual, qualitative research. This is not low-value information at all. But! It’s not quantitive.

So even a small amount of not-exactly-rigorously-collected quantitive data represents a significant increase in what I know. And that’s super valuable.

Next steps on this project, which I’ll report back to you on as I complete them:

  1. Analyze the qualitative data I’ve collected.
  2. (Likely, but not 100% sure) Ask the respondents who provided an email address for a brief interview. Aim for 5 such interviews, with the goal of going deeper on the dataset and getting even more qualitative data.
  3. Write and deliver the promised report.
  4. Decide if I want to invest in writing the “real” survey and recruiting a broader sample, or instead pivot to an improved question and start the process over again. [2]

-P


Notes

1: If you want to read up on this experiment:

  1. https://philipmorganconsulting.com/pmc-survey-marketing/
  2. https://philipmorganconsulting.com/pmc-the-de-biasing-survey/
  3. https://philipmorganconsulting.com/pmc-survey-marketing-recruitment/

2: I’m working hard to resist having a strong, emotional reaction to this initial collection of data. One hot-take that wouldn’t be totally unreasonable would be: “Crap!! Only 36% of my LinkedIn survey respondents even care about the superset of services that my business lives in!!” But 36% of a massive market is… still massive. So I’m working to temper my initial emotional reaction to the data so that I can take an objective look at the qualitative data that accompanies this quant data.

[PMC] Survey marketing recruitment

By Philip | May 15, 2019

Quick tophat: I am working to better understand how self-employed devs improve their career. Would you be willing to spare 3m for a survey? It will mean the world to me.

-> https://www.getfeedback.com/r/fNWSDcfj

I’m not selling anything; you have my NO SALES PITCH GUARANTEE.

(Readin’ time: 8m 54s)

“If a survey falls in the forest and no one fills it out, does the survey produce data?” — The Senile Prophet

Let’s talk about recruitment. This is the fancy word for finding folks who might respond to my survey.

Recently I hired Heather Creek, a PhD andinternal surveys consultant at the Pew Charitable Trust, to give a presentation to TEI on surveying. It was fantastically informative.

We learned from Heather that there are 8 distinctly different kinds of sample populations you might survey, grouped into 3 categories:

  • Probability samples
    • Random general public
    • Random from a curated list (like a customer list)
    • Intercept
    • Panel
  • Convenience or opt-in samples
    • Non-probability panel
    • Convenience intercept
    • Snowball
  • Census
    • Every member of a population

For my research project on how self-employed devs invest in their career, I’m recruiting a convenience intercept sample.

I asked the folks at Qualtrics, which has a very robust survey and survey analytics platform and a research services department, what they would charge to do this kind of recruiting. I believe they would recruit from a bunch of panels they have access to, meaning the sample they recruit for me might be a probability panel which is considered a more rigorous type of sample.

They quoted me something like $40 per recruit, and said the range of cost per recruit ranges from small (like $10/per) to much greater at over $100 per recruit for people that are hard to find or have very specific characteristics.

Is one sampling method better than the others? Are the more rigorous (probabilistic and census) sampling methods more desirable? You can’t answer that without knowing what your research question and other parameters are.

For my purposes (reducing uncertainty in a business context), my less rigorous and less probabilistic method is fine. But my approach would not work for other research projects with different questions being asked or greater uncertainty reduction needs.

Chances are, if you’re doing research to benefit your own business or help a client make better decisions or help all your future clients make better decisions, you can assemble a sample using less rigorous methods just like I am. Your question is likely to be very focused (and if it’s not, that’s a problem you need to fix first before surveying or interviewing) and you can recruit from a small but pretty homogenous group to assemble your sample. Both of these things help you produce more impactful findings.

To expand on this, what question you choose is certainly the most impactful variable in this whole process! No amount of rigor in your survey design, recruitment, and sampling methodology can compensate for asking the wrong question.

Last week I sat in on a webinar hosted by the Military Operations Research Society, where Douglas Hubbard gave a really fascinating almost-2-hour-long presentation. Douglas offered numerous examples of asking the wrong question. He made the general claim–and I have no reason to doubt this–that in most business situations, the economic value of measuring a variable is usually inversely proportional to the measurement attention it typically gets.

In other words, we are reliably bad at choosing what things to study (or reliably good at misplacing our investigative effort)! Here’s one example he gave, specific to IT projects:

  1. Initial cost
  2. Long-term costs
  3. Cost saving benefit other than labor productivity
  4. Labor productivity
  5. Revenue enhancement
  6. Technology adoption rate
  7. Project completion

This list is ordered from lowest to highest information value. Meaning the value of knowing #1 on this list is significantly lower than the value of knowing #7. So want to guess what most folks will spend the most effort on measuring?

You guessed it. Not #7. The effort is focused on the first few items on this list, meaning the effort is focused on the lowest impact stuff.

I tell you this to contextualize the discussion of recruiting a sample for my survey.

Me asking the right question is so dramatically much more important than using highly rigorous methods downstream in my research.

We generally use the phrase “good enough for government work” in a somewhat pejorative way, but it fits here in a more neutral way. In other words, there’s no need to strive for extremely high levels of rigor in the context of research for business purposes. Neither should we be sloppy. Horses for courses.

How I’m recruiting

I’m recruiting the sample for my de-biasing survey in two ways.

The first is a method I learned from Ari Zelmanow. This approach uses LinkedIn connection requests to ask a group of people to fill out my survey. I honestly didn’t think this would work at all, much less work well.

Here are some numbers I captured mid-project for a recent recruitment project:

  • Connection requests sent: 155
  • Connections accepted: 55 (35.48% of connection requests)
  • Surveys completed: 17 (10.97% of connection requests)

If you have some experience recruiting for surveys, you know those numbers are very, very good. Like, eyebrow-raising good.

I can’t take credit for this; I was simply running Ari’s playbook here.

I will note that the numbers I’m seeing for my current project (understanding how self-employed devs invest in their career) are much less impressive. 🙂

  • Connection requests sent: 1537
  • Connections accepted: 21.28% of connection requests
  • Surveys completed: 20 (1.301%)

Those last two numbers will climb a bit over the next week or two, but you can see they’re much lower than the previous set (and unlikely to ever close the 10x gap in performance). The previous recruitment outreach was for a client project investigating developer sentiment around a specific platform. Again, the question you’re investigating matters. A lot!

The LinkedIn connection message I’m using is not just your standard “Hi, let’s connect” message. Instead, it’s a message that explains the purpose of my research and asks folks to fill out my survey. So the connection message is not really about connecting on LinkedIn, it’s about recruiting for my survey.

You’ve seen the message before. It’s almost identical to the message at the top of this and yesterday’s email:

Hi @firstname. I am working to better understand how self-employed devs improve their career. Would you be willing to spare 3m for a survey? It will mean the world to me.

-> https://www.getfeedback.com/r/fNWSDcfj

I’m not selling anything; you have my NO SALES PITCH GUARANTEE.

The @firstname field is a variable that’s personalized at runtime by LinkedProspect, the tool I’m using to automate my outreach. I like LinkedProspect over Dux-Soup because LinkedProspect runs in the cloud and doesn’t require me to babysit the connection automation process the way Dux-Soup does.

I’m identifying candidates for my recruitment pool using a LinkedIn Sales Navigator search. I’ve put in time making sure the results of this search are as relevant as possible to the survey. This is another important variable in this process. If I define a pool of candidates that doesn’t find my research question relevant or interesting, it will effect my results.

In fact, if I wasn’t able to continue this research project for some reason, I already have data that supports a low-confidence conclusion: the question of career development or investing in one’s own career is less interesting or relevant to self-employed software developers than a question about a technology platform is to developers with experience in that platform. Even if I couldn’t look at the results of the survey for some reason, I could still reasonably (again, with low confidence) draw this conclusion based on the response rate I’m seeing.

As you also know, I’m recruiting for this survey from a second pool of candidates. What I haven’t mentioned yet is that I’m pointing this second pool of candidates to a fork of the survey. It’s identical: same questions, same delivery platform (GetFeedback). But I forked the survey so I could compare the two candidate pools and hopefully answer this question: is my email list different from self-employed devs who have LinkedIn profiles?

In more colloquial terms: I suspect y’all are special. Will the survey data support this belief?

You’ll remember that I’m using a convenience intercept sampling method. This is not a probabilistic sampling method, which means… probably not much in the context of this research. But a more rigorous research project would suffer from this less rigorous recruiting method.

Let’s look at how my email list as a group is performing in terms of response to my survey. I had to think a bit about the question of which number to use as my “top of funnel” number. Is it the total number of people on my list who are sent these daily emails, or is it that number multiplied by my global average 27.36% open rate?

Well, for the LinkedIn outreach I’m using the total number of people I reached out to, so for a fair comparison I should use the total number of people each of the last two emails got sent out to.

  • Email addresses exposed to my survey request: 1,906
  • Surveys completed: 23 (1.207%)

Again, that last number will climb over time as I repeat my CTA to take the survey for the rest of this week. It’s surprisingly close to my LinkedIn recruitment numbers with one notable difference: It’s taken me about 2 weeks to get 20 responses from the LinkedIn candidate pool. It took me 2 days to get 23 responses from my email list.

Another fundamental difference between these two recruitment methods is the LinkedIn method gives me one shot at getting a response, while my email list gives me multiple opportunities to get a response.

On that webinar I mentioned earlier, Douglas Hubbard shared some info about the Student-T method that I don’t really understand yet, but he boiled it down to this easier to understand takeaway:

As your number of samples increases, it gets easier to reach a 90% confidence level. Beyond about 30 samples, you need to quadruple the sample size to cut error in half.

Remember that we’re talking about my de-biasing survey here, which is not really measuring anything. It’s using open-ended questions to explore the problem space and make sure my thinking about the question aligns with the way my sample population thinks about the question.

All that to say that at this stage of my research, I’m less interested in confidence level in my findings and more interested in having enough data to do a good job of de-biasing myself. In other words, the de-biasing survey’s purpose is to make sure I ask the right question(s) in the second survey I’ll use in this project. The de-biasing survey is less of a measuring tool and more of a making-sure-I-don’t-screw-up-the-measurement-question tool. 🙂

When I get to the second survey in this project, I’ll be more interested in error and confidence and sample size.

I’ll end with this:

This is the only dick-ish response I’ve ever gotten to LinkedIn outreach, and I’ve reached out to thousands of people using the method described above.

So I’m way over that 30 sample size threshold, which gives me an extremely high confidence level when I say this: almost every human will either want to help with my research (at best) or ignore my request (at worst). It’s exceedingly rare to encounter hostile jerks, and such people are extreme outliers.

I think I’ve got you up-to-the-minute with this research project! I haven’t looked at the survey responses yet, so I don’t think there’s anything more for me to say about this, unless y’all have questions. Please do hit REPLY and let me know.

This email series will continue as I have more to share about this project. I’m on a plane to SFO tomorrow to participate in a money mindset workshop and then supervise the movers packing up our house (we’re moving to Taos!!), so I won’t have a ton of time for this research project until next week anyway. I’ll keep repeating my “take the survey” CTA to this list for the remainder of this week, and then turn off the de-biasing survey, work through the results, construct my measuring survey, and then update you.

-P

[PMC] Survey marketing

By Philip | May 13, 2019

(Readin’ time: 3m)

I wanna let y’all in on a lil’ experiment I’m running, and also see if I can sucker you into helping with the experiment.

Recently I realized that if 3 things are simultaneously true, I can do research and marketing at the same time with the same mechanism, and I think that’s super cool.

First, I must remind you what I mean when I say “do marketing”.

To me, marketing is connecting with prospective clients and earning trust from some of those I connect with.

Marketing is not persuading or pressuring.

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. Let me unpack this a bit.

Let’s say that my audience invests in improving their careers. This satisfies the first two of the three criteria above. My audience can answer the question “How are you investing in your career?” about themselves because they have firsthand knowledge of how they are improving their careers. And it’s reasonable for me to believe my audience might be curious how their peers are answering this question as well.

And–critically–I am very interested in the answer to this question! If I’m not then, frankly, I won’t be sufficiently motivated to do my part, which is:

  • Building a survey (actually two surveys, for reasons I’ll get to soon)
  • Using scrappy methods to recruit survey participants
  • Writing a brief, engaging report containing my findings (this is the part I’d underperform on if I’m not interested in the question this research is addressing)
  • Distributing this report back to survey participants who asked to receive it

That last part is the “permission mechanism” I mentioned earlier. The final question on the first of my two surveys is as follows:

Would you like me to share the 100% anonymous results of this survey with you? If so, please leave your email address here and I will use it only to communicate with you about the results of this survey.

This question is optional. Respondents don’t have to leave an email address, or type anything at all in this field on the survey.

But if they do leave their email address, I have permission to contact them for one purpose: to share back the results of this survey with them.

On the advice of my research coach Ari Zelmanow, I plan to reach out to a few respondents who left their email address and ask them for a brief interview before I write up the results of this survey. So I suppose this is stretching the permission a bit, but it feels fine to me because I’m not doing something fundamentally misaligned with what the permission represents.

The report I share back with respondents who have left me an email address will be succinct and descriptive, but it’s actually an intermediate report. It’s not the final thing.

The first survey is what I think of as a de-biasing survey. More on this tomorrow.

I said I was going to try to sucker you into helping with this research. Here goes!

Hi there, my name is Philip. I am working to better understand how self-employed devs improve their career. Would you be willing to spare 3m for a survey? It will mean the world to me.

-> https://www.getfeedback.com/r/fNWSDcfj

I’m not selling anything; you have my NO SALES PITCH GUARANTEE.

This is the same message I used in my survey recruitment. More on this soon.

-P

PS – When I talk about having an innovation budget, this is the kind of stuff I’m talking about. You might do it yourself using scrappy methods like I am, or you might partner with a researcher, or you might outsource it, but either way you budget for and regularly invest in stuff that can help you innovate.

[PMC] 50mg of Zoloft

By Philip | April 15, 2019

(Readin’ time: 2m 12s)

50mg a day of Zoloft has made me a better self-employed pseudo-entrepreneur [1].

Mental health is one hell of a hot potato because, at least in the cultural context I came up in, any deviation from a narrow mental health “norm” is tightly coupled with secrecy and shame. I’ve long wanted to write about this, but see aforementioned secrecy and shame. Let’s just rip the bandaid off anyway.

My physical brain, my mind, my emotions, my sapped and impurified precious bodily fluids, or some combination of the above get occasional depressive episodes. It’s been that way since I was bullied in the 7th and 8th grades, and as an adult, anxiety and depression have been recurring dance partners that sometimes get the spotlight in the big tent of my emotional circus.

The worst depressive episode started on July 6, 2012. I was driving across the southern I5 bridge in Portland, OR and I had a huge panic attack. My body started to go numb, and my mind freaked out, thinking I would lose control of the car and crash, probably over the side of the bridge. The rest of that drive to the Oregon Coast was a looooong drive. 🙂

I was deeply depressed for the next ~18 months. Or rather, cycling between periodic intense panic attacks, social anxiety episodes, and longer depressive episodes until I made some significant changes in my life that started to help. I’m not kidding when I say that ditching hourly billing was one of the changes that actually helped.

I still deal with anxiety, panic, and depression.

Accepting help with these debilitating issues has been a bitter pill to swallow. I do ask others for help, but it’s usually a last resort for me. I spent a long time deeply suspicious of pharmaceutical tools (see the earlier tongue in cheek reference to Jack D. Ripper’s monologue). So setting up an appointment via DoctorOnDemand and picking up a prescription for 50mg of Zoloft was not an easy, natural move for me. I had to swallow a lot of pride, set aside a few weak opinions I strongly hold, and find other ways to be kind to myself.

And it’s made me a better pseudo-entrepreneur, I think. The Zoloft, I mean. Or maybe it’s not the Zoloft. Maybe it’s the willingness to get help with this shit plus a blue colored placebo pill.

Either way, I’m less driven by the emotional froth of business and life. And I’m more driven by what I deeply want, even if the emotional froth tries to interfere.

I normally don’t write such purely self-focused emails, but I know I’m not the only one out there who struggles with the emotional froth in the context of self-employment or entrepreneurship. And maybe in some way it helps to know you’re not alone?

Cause you’re not. <3

-P

1: I say pseudo-entrepreneur half tongue in cheek and half-seriously because a) so many people who do nothing that could ever be objectively called entrepreneurial call themselves entrepreneurs and I like teasing them b) I myself am not a super impressive entrepreneur and c) despite my self-deprecating humor I actually definitely objectively am achieving the scalable impact that entrepreneurs achieve, albeit through intellectual property/Internet Business(tm) scalability rather than people scalability.

Product Positioning vs. Services Positioning

By Philip | November 24, 2018

If you’d really like to understand the idea of positioning, it’s useful to compare positioning in the world of products to positioning in the world of services.

They’re both based on the same fundamental idea, which is moving into a position from which you can take advantage of some marketplace feature, need, or desire. We could shorten this to: moving into a position from which you can act on an opportunity in the market. Once you get past that similarity, though, they become different.

Product positioning is based on the inherent objective observability of a product, and the ability of advertising to amplify a message about those observable features.

The superior build quality of Apple’s first generation unibody aluminum MacBooks was easily observable when compared to Windows PCs of the same generation. Almost anybody paying any level of attention would notice the sturdy yet smooth movement of the hinge that supported the MacBook screen. That person could pick up the computer and try to flex or twist it and see how strong it was. They could bang on the keyboard and feel how little give there was. And side by side with a same-generation Windows PC, they would notice a distinct difference in build quality.

This is what I mean when I say products have an inherent objective observability. You can see, feel, smell, taste, and measure the differences between products. This lends a sense of objectivity to how we compare products. Products also tend to have fixed, known prices, which further lends a sense of objectivity to how we compare them. “Apple is expensive, but very high quality. PCs are cheaper but lower quality.” Reasonable people can disagree on those bottom line assessments, but at least you can explain why you arrive at that conclusion. “See! When you press on the MacBook’s case it doesn’t flex at all! When you press on this PC’s case, it flexes 2 or 3 millimeters. That’s why the Apple is more expensive!”

Services have no such objective observability.

Because most services are custom scope, custom price, and delivered under various forms of secrecy, they lack this quality of objective observability. We try to compensate for this with case studies, testimonials, and other post hoc artifacts that come from successful engagements. This is how we try to make our services seem more objectively observable, but ultimately they are not. They’re like any human relationship: there’s the reality of the relationship, there’s what your close friends know about it, and there’s what everybody else thinks they know about it. Those are three distinctly different categories of knowledge about the actual thing.

So services is positioning is actually all about developing a reputation. This makes it very close to branding in practice. Certain public figures (presidents, leaders of nations and multi-national companies, entertainers, etc.) will have a global reputation or brand. That is to say, those people will have a reputation among lots of people all over the plant. You, almost certainly, will not.

Instead, your reputation will exist within the context of an industry, audience, or community, and you will be relatively unknown outside of that group. So your choice of where or by whom to be known is very consequential to your marketplace position.

Remember that for both services and products, positioning is moving into a position from which you can take advantage of some marketplace feature, need, or desire.

Let’s clarify some terminology using a few examples:

  • A feature of the market for Sitecore expertise is that the software itself is massively expensive and most service providers offer high ticket, long-term contracts because that matches the needs of the typical Sitecore user. The need for Sitecore expertise delivered with more flexibility is therefore underserved. More on this: http://consultingpipelinepodcast.com/083
  • A temporary feature of the market for React skills is that demand currently exceeds supply. This (temporarily) simplifies lead generation and increases the price buyers are willing to pay.
  • A temporary feature of the AWS market is the complexity of understanding and managing the AWS bill. This creates a need for specialized expertise in this area. More on this: http://consultingpipelinepodcast.com/119
  • A need of many businesses is drive down costs. Technology and custom software can address this need in an ongoing way. Shunting support delivery to self-service forums is one simple example of businesses satisfying this cost-reduction need.
  • A desire of some businesses is to innovate using software before others in the market do. Technology and custom software can address this desire.

How might you cultivate a reputation that helps you take advantage of some marketplace feature, need, or desire?

I can think of 7 ways:

  • Effectively leverage a platform
  • Start with a microscopically small group
  • Standardize/productize an offer
  • Piggyback on an audience or community
  • Find and fill an information deficit
  • Play the expertise long game
  • Identify an entrepreneurial opportunity early on and get involved while the signal/noise ratio is favorable

These 7 approaches break break down into 2 groups: those you can do almost anytime (evergreen) and those that are dependent on external factors that have to do with luck, timing, and the lifecycle of tech (I refer to these as temporal):

  • Temporal
    • Effectively leverage a platform
    • Find and fill an information deficit
    • Identify an entrepreneurial opportunity early on and get involved while the signal/noise ratio is favorable
    • Standardize/productize an offer
  • Evergreen
    • Start with a microscopically small group
    • Piggyback on an audience or community
    • Play the expertise long game

I’m not opposed to the temporal approaches to cultivating a reputation, but I’m more interested in the evergreen approaches because they tend to deliver better ROI when viewed across the length of a career.

A bit more detail on each of those approaches to cultivating a reputation:

Effectively leverage a platform

The popularity, complexity, or value of a platform can help you cultivate a reputation. Platform users are often actively searching for expertise in using or optimizing the platform, so you can place yourself in front of their search traffic, within the physical and online venues where they seek support, or on the stages where they seek leadership and inspiration.

Find and fill an information deficit

Change creates new needs for information, and like a hole dug in the sand some distance from the ocean will eventually fill with water, this information deficit will eventually be filled with varying forms of expertise. If the timing is good, you can fill an information deficit and thereby cultivate an expert reputation.

Identify an entrepreneurial opportunity early on and get involved while the signal/noise ratio is favorable

Change creates new opportunities; new needs for expertise. Before social media was a dominant force, there was no need for experts in social media addiction, experts in advertising on social platforms, or experts in social media strategy. Now there are. Change always opens up entrepreneurial opportunities, but those opportunities attract risk-takers, and so timing plays an important role in identifying and acting on these kinds of opportunities before they become saturated.

Standardize/productize an offer

Developing a specific, standardized offering that addresses a specific need can help you cultivate a reputation. The hopefully near-perfect alignment between the need/problem and your offer drives word of mouth which helps build a reputation.

Now we get into the methods that are more evergreen in nature. By the way, none of these 7 approaches to cultivating a reputation are mutually exclusive. In fact, you can often “stack” several of them for better results.

Start with a microscopically small group

Among your family or friends you probably have a reputation you didn’t really work to cultivate. It just kind of happened. That’s the power of small social groups: it’s easier to meet, remember, and feel like we understand other members of a small social group. It’s also easier to intentionally cultivate a reputation within a small industry, audience, or community, even if you start out as an unknown to them. It’s simple but not necessarily easy: you start small, show up consistently, and contribute thoughtfully & generously.

Piggyback on an audience or community

This is a slightly different take on the “start microscopically small” approach. Finding an existing audience or community–so that you don’t have to build your own–accelerates your ability to cultivate a reputation. It’s not the only way, of course, but it gives you a head start.

Play the expertise long game

Finally, we reach my favorite approach, which is to cultivate economically valuable expertise over 2, 5, 10, or more years. Share freely what you learn as you are cultivating this expertise, and you will develop a reputation that scales in direct proportion to the value and impact of your expertise. The long timeframe I specify mandates that you choose expertise that has a long “shelf life” and that you go very deep with this expertise in order to credibly claim world class levels of expertise. The sharing as you go creates productive discomfort and provides plenty of quality marketing fodder so you’re freed from cheap, manipulative, or less effective forms of marketing.

With services, for better or for worse, time combined with disciplined focus is your best ally in cultivating the expertise that builds a reputation that positions you to take advantage of an opportunity in the marketplace. In other words:

Focus + time + discipline -> expertise + sharing as you go -> reputation -> ability to take advantage of opportunity in a market

That’s the most powerful version of positioning for a services business.

There are other ways to use the positioning concept in your business, but by comparison they are superficial in their impact on your career.

Application

If you have any questions about applying this guidance to your business, please feel free to contact me at philip@philipmorganconsulting.com.

If you’d like my direct help navigating the transition from coder to consultant, my services may be a fit: https://philipmorganconsulting.com/services/

Evaluating the Strategic Value of Prospective Work

By Philip | November 24, 2018

Businesses are often money-making enterprises. This generally encourages a short-term orientation in our decision making.

Strategy, however, is the relationship between decisions you make now and possibilities those decision bring within reach later. There is a real–but often unclear–relationship between the decisions about now and the future possibilities those now-focused decisions unlock.

In other words, if you’re only focused on the short term, your decisions may not be very strategic. They may not unlock the most desirable future possibilities for your business.

I’d like to explore this tension a bit, with a focus on the non-monetary value that some work can have for you.

If your current situation is such that you truly cannot afford to think about anything other than the short term, this paper is not for you. And you have my sympathy. I spent years in that very same situation.

But if you’re starting to think about how you might build some short-term success you’re current realizing into something more valuable over the long term, this paper is definitely for you.

Strategic Value

If you decide to do something because doing that thing might create value in the future or give you better options in the future, you have done two things.

First, you have taken a risk. Your decision might not work out the way you thought! That future value might not materialize. Those future better options might not become available to you. Or they might! That is the nature of risk-taking. You are less than 100% certain about the cost and benefit of doing a thing. You can not guarantee a particular outcome. This comes with the territory of risk-taking. But smart risk-taking can be a very profitable activity, as many wealthy \0x10people will tell you.

Second, you have made a strategic decision. You have made a specific choice to do something now because of the future benefit of that decision.

When I’m working with coaching clients, I often ask them: “What is the strategic value of this opportunity?”

When I ask this question, I’m asking them to focus on what this opportunity might lead to, or what future value they might create by saying “yes” to this opportunity.

If the opportunity is client work that is likely to be very profitable and might lead to very desirable future opportunities or significant future value (beyond just the revenue from the engagement), then it’s an easy decision. This is a “have your cake and eat it too” opportunity. These are wonderful, but earlier in our careers, few of us get these kinds of opportunities.

Instead, when you’re earlier in your career or trying to transition from implementation to advice work, you’re more often getting opportunities that are comprised in some way. They might meet short-term revenue needs but not offer any strategic value. Or they might offer strategic value but generate only very modest immediate revenue.

I think you should at least consider taking that immediate revenue-strategic value tradeoff when it shows up. And if these kinds of lower revenue-higher strategic value opportunities don’t show up for you, I think you should actively cultivate them in order to move your career more assertively towards cultivating exceptionally valuable expertise.

There’s an implicit point of view I’ll make explicit here: It might be just my personal experience, or it might be generally true, but if you’re just starting to move out of a generalist market position, I believe that it’s easier to get access to opportunities that are primarily strategically valuable than it is opportunities that are both lucrative and strategically valuable. Said differently, if you can delay financial gratification a bit–maybe 2 or 3 years–I think you can spend that time optimizing for the future value of your expertise at the expense of short-term revenue. I think this is an awesomely desirable tradeoff if you can afford to make it. Graduate degree students do it all the time. I hope you can, too.

So let’s talk about how to evaluate the strategic value of opportunities.

Evaluating Strategic Value

I’m going to focus on the strategic value of client-facing opportunities. In doing this, I’m ignoring the strategic value of things like writing a book, doing research, and that kind of thing. That stuff has strategic value too, but I’m specifically focusing on the strategic value of paid client work.

Earned credibility

The most fundamental future benefit of strategically valuable work is the credibility it can create. The project goes well and you walk away with a logo for your website, a case study, a happy client, referrals from that client, greater expertise, and greater confidence in future sales conversations. Maybe not all those things, but the chance to acquire all those things.

Those things have utility in your business development. They can unlock opportunities in the form of access to better, more desirable, or more profitable work. They can contribute to an ability to speak more authoritatively on your expertise using real-life examples.

Earned access

Access is my shorthand for your ability to gain access to buyers in a market. Access is not a binary thing that you do or don’t have. It’s more like a network that’s built over time and measured in terms of reach, density, and strength of connection.

Strategically valuable work can pay you back in the form of greater access. This naturally raises the question: access to what or who?

It could be access to a certain type of decision-maker or buyer. Maybe you’ve never worked much with large publicly-held companies, and you don’t really understand how their buying process works. A strategically valuable project could help you learn how to find and engage with decision-makers in that kind of environment.

It could be access to a market. Your first project in a desirable market you’ve never worked in before could help you learn the landscape of that market. Who are the players. What are the watering holes? Which conferences are the must-attend events?

Again, access is not a binary all or nothing thing. It’s the result of building multiple connections, or a network of connections. So one single strategically valuable project is unlikely to transform your access to a market or type of buyer from zero-to-amazing overnight.

Even so, multiple carefully chosen strategically valuable projects can pay significant dividends in terms of both credibility and access.

Paid learning

As a generalist, I was constantly learning on the job. At first, this was exciting, and towards the end it was very problematic.

One of the core promises of an expert’s value proposition is that they won’t be learning on the job, or if they are it’ll be clearly disclosed and handled as a R&D or a proof of concept where the risk of the unknown is acknowledged by both sides.

But as a generalist becoming a self-made expert, you will still be learning on the job. And as a result, you will probably discount your fees to account for the risk inherent in this on-the-job learning.

This situation should be temporary. The narrow focus you’ve willingly embraced lets you “stack” closely-related on-the-job learning quickly, which helps you move out of the learning-on-the-job mode into genuine expert mode within a year or three.

It’s not that you’ll stop learning, but you’ll stop having to discount your fees because of the risk of on-the-job learning. You’ll stack experience and find other ways to learn using the downtime offered by the greater profitability that comes with higher fees and less labor-intensive work.

Another form the strategic value of certain opportunities can take is on the job learning. Some examples I’ve seen in my clients:

  • A coaching client was a very good PHP developer. He’d tired of doing dev work and wanted to use what he’d learned about process, workflow, and quality to help other dev teams level up. He proposed doing this for a previous client. They balked at the price, he circled back later with some scope changes and a modestly lower price and a realization that the experience would give him credibility and on-the-job learning that he could use later down the line to justify higher prices.
  • Another coaching client helps companies gain competitive advantage from open source practices. He has experience and access to the tech world, but wants to apply his expertise to the Fortune 500 world. This will involve on-the-job learning. Not so much in his area of expertise, but in how to apply that expertise in a different business environment (tech vs. F500). He will manage the risk of this on-the-job learning with initial fee discounts, carefully managed scope, or a combination of the two. While he may leave money on the table now, with future clients he will be in a stronger position to charge super-premium fees.

Proving a concept

Related to on-the-job learning, you may have opportunities to prove a concept, and this can have strategic value to your business. With on-the-job learning, you are often applying existing expertise in an environment that’s new to you. When proving a concept, you are experimenting with an un-tested concept with a client. Obviously, you do this with their full knowledge and permission, and they choose to accept the risk because of the potential upside.

Somebody had to be the human that got the first artificial heart tried out on them. Barney Clark was that somebody in 1982. I presume he knew the risk, and judged it better than the alternative.

Proving a concept is innovation. It’s pretty well established that the cost and risk of innovation will often not turn into immediate payback. For this reason, you may have opportunities to prove a concept with a client, and you may accept a reduced fee in exchange for the risk and delayed reward this implies for your client.

Going upmarket

Going upmarket may mean working with a new segment of your existing market, or it may mean seeking a new but more desirable market for your services. Either way, your lack of credibility in the new, more desirable market may mean that you say yes to work under terms you would normally reject. This can be strategically valuable because you can gain credibility and access you might otherwise not.

Sawdust

Finally, we have the concept of “sawdust”, which I reluctantly attribute first hearing about to Gary Vaynerchuk. The idea is that your paid work may produce “by-products” that are useful in your marketing. These by-products can be thought of as the sawdust in a woodshop. A factory can convert sawdust into secondary products like medium density fiberboard.

You might take on work that is not all that attractive or lucrative on its face but offers you strategically valuable sawdust you can use in your marketing or elsewhere in your business.

Conclusion

I’ll conclude by saying that trading short-term profitability for longer-term value creation is always a nuanced judgement call. It’s never easy to sacrifice relatively certain revenue for relatively uncertain future value. Yet, I see this tradeoff as a critical part of the self-made expert’s path. If you’re optimizing for the future value of your future expertise, I think you–like me–will choose to make these kinds of tradeoffs.

You won’t have to make them forever because you’re making them in service of an asset–your future expertise–that will, as it grows, make it increasingly unnecessary to sacrifice revenue. It’s fundamentally no different than someone investing in schooling or training, with the exception that you’re doing it this way because no curriculum exists for the expertise you’re trying to create.

So… you build the curriculum yourself out of strategically valuable opportunities, and you fund your own education by trading short-term revenue for long-term expertise.

Application

If you have any questions about applying this guidance to your business, please feel free to contact me at philip@philipmorganconsulting.com.

If you’d like my direct help identifying and capitalizing on strategically valuable opportunities, my services may be a fit: https://philipmorganconsulting.com/services/