Marketing & Data Priorities for a New Business

Note: Apologies, some hiccup with Blogger caused an old, unfinished version of this to be posted.  I think I captured the gist of it, and filled in the missing words.

In my career, I have worked with some very large, mature companies while on the agency side, and in-house with a small-to-medium sized business, though closer to small at the time when I joined.  I bring this up because a colleague of mine recently decided to leave our thriving enterprise to join a start-up, as one of the 'first five.'  I think that everyone considers what it would be like to work for a company from the very beginning, no matter his or her profession, and so it got me thinking about what I would do in that position.

I would guess that for many people, myself included, the attraction of working for a start-up as an early employee isn't about the beer cart, the foosball Fridays, or even the stock options, but about the ability to come in and practice our particular craft on a tabula rasa.  Deep down, almost everyone who is good at what they do thinks that they could be a little bit better if not burdened by the ghosts of business past.  Every company evolves over time, but like a city, new growth is inevitably on top of the old, no matter the lengths that you go to clear the site.  I've never met a developer who hasn't been frustrated by old code, an SEO who understands why a website was built a certain way, or a DBA who would design a database the way he found it.

Obviously, I think that it probably goes without saying that most of these legacy problems are not the result of incompetence, but rather a combination of a lack of foresight, and the normal "things that happen" over time.  We can assume that most of the people who build the legacies that the rest of us inherit have good intentions, but lack the luxury of building a foundation that will stand the test of time.  The key, for our hypothetical selves at start-ups as it was for our real-life predecessors, is to wed the long-term concerns with the immediate business needs of the fledgling company.

So as a thought exercise, here are some considerations and strategies that I would prioritize if I found myself moving to a brand new company:

Advertising:
Think about your target audience, and how they gather information.  Chances are, budget will be an issue, so the most important things will be efficiency and extremely narrow targeting.  There is always a natural progression of one platform at a time, but that's a mistake.  The evidence all points to a multi-platform strategy from the beginning.  I hate to fall back on a cliche like "synergy," but it is easier than actually explaining the math.  The point is, launch your content, social, paid social, and paid search all at once, even if you only do a limited amount of each one.  Coordinate them, because they amplify one another, and you will maximize the effect of your spend.

• Know your audience
• What need does your product fill, who has that need?
• If you had that need, how would you go about satisfying it? Try it.

• Make sure that you have a keyword strategy that looks beyond CPC to CPA/CPE or whatever your target user action is
• Note that this means you will have to track everything FROM THE START

• Know your budget, and what will fit into it
• Set some money aside for testing, maybe 10% at first
• Start with very tight keyword groups, be proactive with matchtypes and negative keywords, and watch any GDN or YouTube spend carefully
• Careful targeting is better for conversions and budget, so start with only your own country

Content:
Produce as much of it as possible, make it valuable, don't make it sell your own product.  Mix your mediums, make sure that you think about the life cycle of each piece, and how you can distribute it.

• Ensure that you have a place for content to live permanently, preferably on your site.  You want a link that can live forever
• Use your content as a source of advertising material, from keywords to messaging.  If you find yourself saying something a lot in your content, it's probably important in your industry

SEO:
This is one is simple on the face of it, but also one of the easiest matters for a new company to overlook and one of the hardest things to plan for as it grows.  Generally speaking, your best bet is to everything right, but here are a few ideas that you can start with:

• Make sure that every page that has clear focus
• that you explain that focus on that page with at least a few hundred words worth of text
• and that you repeat that focus very concisely in the metadata  
• Make sure that your URL structure is logical 
• clear hierarchy in the sitemap based on importance of the page
• hyperlink deeper pages to appropriate higher-level nav pages using relevant anchor text

Data Collection:
Set yourself up with a Google Analytics account immediately.  It doesn't matter if you plan to use another web tracking platform down the road, you want to make sure that you are measuring out of the gate.  This is free, and it will join to your AdWords account to give you significant targeting benefits.

• Don't just stick to the out-of-the-box defaults with GA, spend the extra time to make it fit your needs
• Activate ecommerce tracking if need be, and add the little bit of code (seriously, like one line) needed to gather demographic data
• Add events and goals to customize your map of the customer journey
• are there key pages you want to track? forms? videos?
• think as you build your site, about every key interaction you will have with visitors from discovery through whatever signifies success, and be ready to track each step

Don't feel limited to Google Analytics for your data collection and housing, either.  It's great for one part of the interactions you will have, but there is more to your business than your website, and you will want information from other sources as well.  MS Access will do if you have loads of CSV files or similar data that will overwhelm Excel, but eventually you may need to move to a SQL database, or other RDB.  Maybe you want to skip that, and go straight to NoSQL, given the direction that the business work is moving in.  With AWS and Google Cloud, storing data in a non-relational manner and then accessing it freely with MapReduce jobs is becoming easier and cheaper every day.

The point is, track everything.  Collect all of the information that you can from the get go, because you will always think of things down the line and wish that you had tracked them all along.  Starting with day one, you will be asking questions about your customers and your business that will have profound effects on your marketing strategy and execution.  Give yourself every chance to make informed decisions whenever possible.

There may be more to follow this, but I wanted to get it out before the new year.  Good luck in 2015, especially if you work at a new business!

Don't limit yourself

Ad Viewability Matters, But Let's Not Overreact

The issue of display impressions, both how they are measured after delivery (in terms of tracking effectiveness for marketers) and how they are measured at the point of delivery (in terms of accountability for publishers), is whether anyone actually sees them.  There are really two problems here, both of which the IAB (Internet Advertising Bureau) has tried to address recently, though it remains to be seen if they can have any effect.

The first issue, and one that has been well-publicized over the last year or so, is the prevalence of non-human traffic hitting website servers.  Depending on who you believe, this traffic, whether from deliberately fraudulent bot networks or innocent (though equally irrelevant) spiders and other web crawlers, makes up anywhere from 15% to 50% of server requests, the implement by which impressions are "served."

The second issue is that even if the site visit is from a real person, there has historically been little or no attempt made to determine if the user was ever able to see the ad itself.  For instance, if a page loads but the particular inventory that was purchased is below the fold, and the user then clicks a link in the header to leave the page, an ad impression would be recorded despite the fact that it never appeared on the screen.  The New York Times ran an article about this not long ago, which helped bring attention to a wider audience, but largely covered existing topics of discussion.

The IAB's attempt to define "viewable" impressions and instruct the marketplace to deal only in those impressions that fit the description (“must be in the viewable portion of an internet browser for a minimum of one continuous second to qualify as a viewable display impression”) is well-meaning, but unfortunately fraught with problems.  Basically, if those conditions either aren't met or can't be guaranteed, they recommend that publishers don't sell that inventory and that media buyers don't pay for it.  And it is here that we marketers run into a bit of a conundrum.

As digital marketers, one of our big selling points for years, in terms of justifying budgets, was the fact that digital is just so much more measurable than traditional advertising channels, which in turn meant that we were comfortable being more accountable.  Over time, it became clear that display ROI wasn't quite so cut-and-dry as search and other direct response marketing, but we were quick to assure those who control the purse strings that it was ok, because we could still measure the impact.  Using a variety of modeling techniques and brand lift studies, we claimed that we could show the benefit of display advertising on other channels, whether it was increasing searches on our brand terms or improving conversion rates on page for those exposed to a display ad.

So now that we find ourselves balking at the fact that we may, in fact, have been paying for bogus traffic all along, we are in something of a bind.  Some marketers are suggesting that we should simply not purchase inventory from those publishers and networks that can't promise "viewable" impressions now, but I don't see that as feasible.  For one thing, a lot of people selling inventory, especially targeting niche audiences, simply don't have the technology built into their ad serving to track and guarantee such things.  More importantly, however, the idea of cutting off a channel for this reason has a real logical flaw to it.

Here's the deal, the product (display impressions) hasn't changed significantly, we are simply more aware of shortcomings than we were before.  So, if we are going to suddenly tell those to whom we marketers are accountable (boss or client) that we should no longer be buying certain inventory that we used to be, we are forced to acknowledge one of two things:

1.) The product has always not been worth what we have been paying for it, and thus all of our past attempts to demonstrate its value through various methods have been either inept (bad), or deliberately deceitful (very bad).

or

2.) The product always has always been worth purchasing for all of the reasons that we said it was, and still is, but we are recommending shutting off that valuable channel for what essentially amounts to moral reasons.

Since the whole point of most measurements for display had to do with the results, not the delivery itself, what we should be able to say is something like the following:

"Even with what we now know to be only 65% viewable delivery of purchased impressions, we have still been able to achieve [X, Y, and Z beneficial results] on our display campaigns.  While we will continue to work with our publishers and providers and encourage them to improve their delivery methods, recent industry findings have done nothing to change the core value returned by past campaigns, nor do they suggest any reduction in returns on future campaigns, even if circumstances remain the same.  As such, we do not recommend any change in current investment in this channel."

Some marketers seem to be tempted to cut off their noses to spite their own faces, but this is a wrong-headed approach.  There is a solution here, and it is a market solution.  In the same way that demographic information and services like comScore have allowed inventory sellers to specify their audience and separate into tiers that are reflected in pricing, so too will these provably "viewable" impressions command a premium.  This new information may give media buyers a little more leverage over pricing with some publishers/networks, but it doesn't give us the ability, or need, to re-write the history of our past results.  Think about it this way: if display has been as effective as it seems despite so many served impressions never being seen, there is basically nowhere to go but up.

I for one, however, will certainly be looking for a lower CPM next month from those providers who can't make the '50 %on screen for one continuous second' guarantee.

Online Continuing Education & The Modern Professional Profile

There has been a recent explosion in online educational options, from prestigious universities offering degree programs online and dubious for-profit colleges pumping out graduates, to more specific micro-focus courses on just a single topic.  Some are free, like Coursera (in exchange for labor) or Khan Academy, while others charge for access to a library, such as Lynda.com.  The platforms can be broad-ranging in content and tied to traditional learning institutions, like edX, or provide in-depth material on a niche topic, like CBT Nuggets* (IT training) or Code Academy (programming).  This does not even take into account the learning portals created by brands to aid users of their products, like the Google Analytics Academy.  The point is not to provide an exhaustive list of online education platforms (because that would be a hefty task on its own), but merely to illustrate that the recent vogue of MOOCs and self-directed learning has given everyone (or at least those lucky enough to have computers, high-speed internet, and the free time to use them) the opportunity to enhance their knowledge bases outside of the traditional classroom setting.  With this opportunity, however, come unexpected pitfalls.

The advantages and disadvantages of this type of learning have been debated extensively elsewhere, and I won't attempt to weigh in with any authority on the subject.  The issue that I am writing about today is regarding how these educational platforms are treated by us as individuals and by corporate entities, and specifically how this interaction plays out in the realm of the professional digital profile.  In this context, the "professional digital profile" that I am referring to comprises a person's LinkedIn profile, resume (at this point resumes exist almost entirely in the digital realm, whether as an attachment to an email or uploaded to Monster.com), github portfolio, blog, etc..  These profiles are how our respective industries and peers judge us, and how we are able to present ourselves to audiences that are interested in us as human capital.  Given their importance in today's professional world, however, there is also an inherent gravity to the manner in which we represent our skills and experiences on these platforms, and like all new frontiers, the world of online education is fraught with grey areas and unwritten rules.

Let's pretend that my own example is illustrative.  I have a four-year degree from a state university, which obviously gets included in all of my professional profiles, as do the classes I took while still working at an academic institution later on, though I was not in a degree program (and even those I barely mention, as the classes were not in a relevant field to my current work).  Now though, things get trickier.  I have a degree in history, which has nothing to do with marketing or data analysis (my particular lines of work), but it goes into my professional digital profile, because it is a degree earned from a traditional institution in a classical classroom setting.  Since that time however, I have been certified through the Google AdWords official program, at both the introductory and advanced levels, and yet I wouldn't list that under the "education" section of my resume, or on LinkedIn.  I list AdWords as a skill, but anyone can do that, and it doesn't mention that I ever achieved the certification.

I have been considering getting an MS in Predictive Analytics from Northwestern, because it could be done entirely online, but it would take a substantial time commitment over the next few years and cost over $40,000.  I took a look at the course requirements, then started to explore other options, knowing what the curriculum looked like and what knowledge I could expect to gain.  I realized that I could essentially take the equivalent of all of the same classes on other platforms, with substantially more flexibility and at a significantly lower cost.  Knowing that, I just finished my first course on edX, having signed up for the ID verified, graded version of the class (and paid the fee), which means that I received a certificate upon completion that I can print out.  And hang on the refrigerator?  The same goes for the Google Analytics Academy eCommerce certificate.

And there is the crux of the biscuit, so to speak.  These online courses are very relevant to my professional experience, and I have satisfied the requirements to verify that I in fact participated and learned the material enough to pass the given assessments.  However, what do I do with that information?  On edX, I could take classes from elite universities representing all of the knowledge that I would be expected to gain from the Northwestern program, in a virtually identical format, with the same amount of faculty interaction.  In one case, though, I would pay $40k and get an MS at the end, and in the other I would pay a few hundred dollars and get a series of PDF certificates.  Obviously, the weight that these respective tracks would lend to my professional digital profile would be remarkably uneven, but based primarily on cost and accredidation rather than the actual knowledge acquired.

Is this a flaw, or a feature?  Is the Northwestern program actually SO much more rigorous and effective, or does it simply benefit from the entrenched reputation of a legacy mode of thought?  Or is the answer somewhere in between?

Really, what matters in the context of this piece, is the question of what to do with these certifications issued by the new educational platforms.  Can I list a Google Analytics course alongside a degree from UMass?  Is a certification from a class on edX equal to a class taken at Harvard Extension in person?  How do we, as a generation of professionals in an age where the locus of learning shifts from the classroom to the livingroom, acknowledge the skills that we gain under the new system without undermining the old?  Should I tuck them under "Other Skills/Interests" on my resume?  Where do they fit in a LinkedIn profile?  In the summary? Are they "specialties?"  Do they go in "Skills & Endorsements," or under education?

Despite the proliferation of the educational options available to professionals today, the codification and weighting thereof has lagged behind.  As more and more platforms and institutions issue their own certifications like so many currencies, the relative valuation only becomes muddier, for both the student and the employer.  A consensus needs to be reached, or at least a convention, and LinkedIn is in a perfect spot to lead that movement.  All they need to do is add a new section to the profile, for "Additional Education and Certification" or something along those lines.

So what do you do?  Please take the poll on the right and let us know, or leave comments below!

Top 5 Skills for the Modern Marketer/Data Analyst

[Skip to the bottom if you just want the top 5 list]

Over the years that I have been in digital marketing and analysis, I have been constantly shocked by the gaps and deficiencies that I have found in not only my own, but the entire industry's skill set.  When I first began as a lowly search specialist, I came in with nothing more than a decent understanding of how organic search engines worked, a basic familiarity with Excel, and a passionate, though amateurish, interest in statistical theory.  Within three months, my relevant knowledge base had expanded exponentially, but still I felt that I lacked useful skills, and frankly, that most people in the industry did as well.  I have been trying to rectify that situation ever since.

Right off the bat, I was amazed at how little rigorous statistical analysis was being applied to SEM and other digital media buying and planning channels, given the volume of available data that was being (or could be) collected.  This was manifest not only in the proportion of data analysts to account team members (which was very low at the time), but also in the absence of fundamental conceptual understanding of statistics held by the marketers themselves.  It was naive of me to think that every account team would have a dedicated analyst (though I had assumed as much before my first day), but even at the time I thought that some rudimentary education on the theory and practice of utilizing data sets should be a prerequisite for a digital marketer.

Even more simply, I realized quickly that my Excel proficiency was not where it needed to be, or at least not at a point that my work couldn't be substantially improved by getting better with spreadsheet applications.  What I thought I had known about Excel (still one of my all-time favorite human inventions) was a drop in the bucket compared to what I felt like I ended up needing, but as I developed those skills I was once again shocked by their conspicuous absence from the average marketer's tool kit.  The number of people in our office who really knew Excel, and could maximize the efficiency of its capabilities, was limited to single digits, even though it is the bread and butter of any search marketer.  From there, overly large data sets led me to need to use MS Access, a program which even fewer people were qualified to use, causing all kinds of missed opportunities and bottlenecks.  While most people in every office that I have ever worked in tend to just seek out those who have that knowledge when they need it, very few companies require, or even encourage, widespread acquisition of information and skills that are borderline critical to the work their employees do.

When tagging and tracking issues came up (and they always do), I found myself frustrated by the gate-keepers and communication disconnects that exist between marketers and IT/website maintenance teams, so I realized that I would have to understand (at least at a rudimentary level) HTML, and then JavaScript.  I had to learn more principles of SEO at times, which also required understanding of those basic web development languages.  I had to understand other marketing channels to really see interactions, I had to understand offline sales processes to gain insights into lead generation marketing, which meant that I had to first learn about CRM pipelines, and then CRM platforms like Salesforce and Hubspot.  As the lines between social, paid social, content, and SEO blurred, I had to approach each subject in turn; in order to understand any one of them I had to understand all of them.  To understand what my data meant I needed to know all of the data that was collected, so I had to learn about databases.  In order to make use of the databases, I had to learn SQL.  I'm so far from where I started, and yet still so much further still from where I need to be.  I will never have enough knowledge and understanding to do my job as well as I think I should.

But at every step in my career I have been surprised to see just how many people in the industry lack not only the skills that I have been seeking, but even the awareness of the roles that they should play, within the agency world and without.  For so many years everything was siloed in terms of labor division that marketers (and really, everyone in business) came to believe that the world outside of their specific responsibility was segmented this way as well.  There is this common theme in the industry today that those walls are finally breaking down, that channels are at long last interacting and that the ecosystem has finally become diverse and highly dependent, but this is a false concept.  The ecosystem has always been complex, and the fact that we are finally starting to recognize it doesn't excuse us from responsibility for the gaps in the past, nor the continuing specification of skills moving forward.

A search marketer can't get away with simply knowing the AdWords and Bing platforms anymore, or at least shouldn't be able to in your workplace.  Would you want someone in charge of a campaign that doesn't understand how the tracking codes work in a jquery library?  Do you want someone presenting to clients or superiors not only raw information, but conclusions and insights, who doesn't understand sampling concepts, or how to differentiate between correlation and cause?  How can a marketer assess the value of a user action without understanding the offline sales process, or the difference in the consumer journey for B to C versus B to B?

For so long digital marketers were like Oz, we claimed to be wizards and got away with it because no one looked behind the curtain.  People finally looked behind curtain and found that in fact, it was all done with machines, and they were actually fine with that, because we said we were running the machines expertly.  The problem is that now digital marketers are often demonstrated to simply be the people standing next to the machine, with no more understanding of how it works than those who were on the other side of the curtain.  In order to stay relevant, we all need to not only be able to read the outputs, but understand and interact with the inputs as well.  The world is changing fast, and education, in any form, is the only path to relevance.

So to sum this up into a top-five list (because that's what the internet wants), here we go:

Top 5 Skills for Every Data-Driven Marketer

1.) Microsoft Excel (custom sorting, formulas, pivot tables)

2.) Basic Statistical Theory (samples size & significance, correlation vs causation, variance & standard deviation)

3.) CRM Process/Offsite Interaction (digital is not a separate realm, it is part of the broader business we engage in)

4.) Minimal HTML, JavaScript knowledge (metadata tags, H1s, how API calls work, tagging intricacies & common problems)

5.) SQL/RDB Querying (pick one, MYSQL, PostgreSQL, even NOSQL, it doesn't matter; maybe learn R or Hadoop if you want to get fancy)

Krugman Bitcoin Dilemma Pt. 2: How to Discuss Economics

So, the plan laid out in Part 1 of this series was, and still is, to discuss the idea of an inflationary vs deflationary currency, especially as it pertains to Bitcoin, and the larger Keynesian/Austrian debate.  However, as this touches on one of the most controversial and fundamental arguments between traditional economists as well as Bitcoin enthusiasts, I need to touch on one point first, one that applies to most economic conversations.  

(The ferocity in what should be a philosophical/intellectual argument is stunning)

Spend a few minutes on Reddit, and you will see that the emotional and rhetorical levels seen in this debate are off the charts, to the detriment of all involved.  Outside of religious fundamentalism (and possibly not even there), the dogmatic approaches to economic issues are unrivaled in their violence and fervor.  You are more likely to see the pope accept Buddah as his savior than you are to see a Keynesian embrace methodological individualism, or an Austrian to accept that macroeconomic forces can be modeled based on observable statistical data.  This is not an argument that is going to be settled, ever, but that does not mandate or excuse the level of venom from both sides that is injected into the conversation.

So here is the biggest point to take away from everything that follows here, and anywhere else you choose to read about economics:

We are all wrong.  And we are all right, insofar as none of us are provably wrong.

The real problem is that economics (the field, and those who study it) still suffers from the delusion that this is not the case.  While it can be approached “scientifically,” it is not a science in the way that physics is (and for the record, that field still has huge fundamental differences in interpretation as well).  Economics, despite the use of so many numbers in the mainstream practice now, is not math, but a combination of political theory, social psychology, game theory, and philosophy.  Every school, every law, and every theory in the field of economics, is basically just a really fancy opinion.  Now, they can be really well thought out, and based on a plethora of examples and solid theoretical underpinnings, but that doesn’t make any of them predictive, and that’s the problem.

Think of an economist the way you do a meteorologist.  They have a lot of data about what has happened in the past, and a lot of theories about why they happened, and yet the prediction of the weather, even over as short a time period as a few days, remains almost impossible.  There are just too many variables that can’t be measured and accounted for, and too much complexity in the system even if we could have values for all of them.  So what they can do is one of two things:

1.) They can look at how weather patterns work and the forces that they know are involved, from convection to gulf streams to the condensation points at various altitudes, and try to synthesize certain principles that make logical sense based on this information (Austrian approach).

or

2.) They can collate as much of the data as possible into a huge database, and create a “dumb” model that says, “in the 100 observable situations we have measured in which case variables X, Y, and Z were found at the same time, there resulted n instances of O1 outcome, n instances of O2, outcome, and n instances of O3 outcome” (Keynesian approach).

Both of these are very reasonable ways to go about this process, and yet both contain extremely troubling methodological problems from a predictive standpoint.  Each attempts to deal with the “known unknowns” by either reasoning them away (Austrian), or modeling/regressing them away (Keynesian), but neither is capable of addressing the “unknown unknowns” despite seeing their effects everywhere (In physics, this is basically where the dark matter theory came from, but the lack of absolute laws in economics prevent you from working backwards to missing data in equations).

Again, think of this in terms of weather prediction.  I used to live in the midwest, and often times in the summer, we would have hot muggy air coming up from the south and cooler, drier air coming from the northwest.  Every so often they would come together just right, and we would be warned that circumstances were ripe for a tornado.  Weather people who favored one approach would look at what they know of how tornadoes form, then work back from there to figure out why the weather patterns that were converging on us would interact in such a way that logically we should get a tornado.  Other weather people would look back at their data and say, “over the past 70 years that we have been collecting data, the combination of conditions today were present most frequently on days when tornadoes were observed, so we are most likely to get a tornado.  Both are sound approaches, that arrive at the same conclusion, that there was a good chance a tornado was going to form.

Then we would usually not have a tornado.  

There is a similar situation where I live now, in Oregon.  Being in a valley in the Pacific Northwest, with some mountains to the east and a big warm ocean to the west, conditions are pretty perfect for rain a lot of the time.  Both logically based on what we know of meteorology, and observationally based on what has happened in the past, you can often count on seeing Weather.com show you a 30-60% chance of rain at any given time here.  But like all of the cliches about predicting the weather, and if you don’t like it just wait an hour, etc., etc., it’s pretty much a coin-flip whether or not they are correct in their assessment just days or hours ahead of time.

And yet, I never see people fighting so viciously over weather prediction as I do economics.

The primary reason for that is that we can’t entirely separate our own political and moral beliefs from economics.  We just can’t.  Due to the fact that an economic argument is tied to a policy argument (i.e. I believe in X theory/school, so I want the government to do Y), we all have an agenda, from the ivory tower economics to the professional bloggers to the people fighting on Reddit, including me.  By suggesting that our opponents, whoever they might be, have some bias that underlies their analysis, the implication is that we ourselves are free from such a problem, and therefore our analysis is superior, but that’s simply not the case.  Everyone has an agenda when it comes to economics, without exception.  It doesn’t necessarily invalidate anyone’s opinions, but it can’t be ignored either, simply because this is not a context-neutral environment.

Another big impediment to having a reasonable discussion between followers of different schools of economic thought is that very rarely are people even talking about the same thing, and if they are it is probably by chance.  Let’s take the relevant concern here, in this case ‘Is it better for the economy to have an inflationary or deflationary currency?’  This is usually how the debate is phrased, or something similar to this.  

What does “better” mean?  How are we defining success or failure?  Per capita GDP?  Real wages

Further more, what is “the economy,” exactly?  Is it some autonomous, ethereal collection of statistics?  Is it made up of actual people who participate in commerce?  Is it the entire population of a place that uses one currency?  In this era of globalization, can one country really even have an economy that is separate from the global economy?

Without even defining the parameters of the debate, how can we hope to answer the central question?  And yet, it’s not easy to do.

So, what’s the solution?  

This is discourse, and that means that words matter.  There are no absolute truths in economics, which means that we can’t speak in absolutes.  We also too often use words with a moral or religious overtones, such as “good” or “bad” that have no real-world meaning in this context, as economies are non-sentient and have no agency, so they cannot recognize or ‘behave’ in a manner that is positive or negative morally.  Above all, we need to explain our thought process to reveal our preconceptions and acknowledge that we are expressing opinions, rather than just shouting at eachother.

So, here is an example of a terrible way of saying something that might have value:

“Federal deficits are bad and so is anyone who disagrees!”  (Absolutism, moralistic, no context)

Here is a way to say the same thing that has a great deal of value in a conversation:

“I believe that growing government deficit spending can potentially have a negative impact on future economic growth and social prosperity because increasing debt liability, combined with expansion of the monetary supply in order to cover servicing the interest payments thereof, can force governments to allocate resources in a sub-optimal manner in the long run and prevent capital from being put to its most efficient and logical use.”

See what happens there?  I said everything that was in the first example, but fleshed it out and worded it in a way that is productive rather than combative.

Normal Krugman/mainstream economist response:

“You’re dumb and don’t understand modern macroeconomics.” (Absolutism, moralistic, no context)

Better response:

“I see what you are saying, and I understand the need to be cautious with deficits to prevent the cost of servicing that debt to become unmanageable (acknowledging historical examples like Weimar republic), however, in looking at the past 130 years worth of data from the 20 most developed economies in the world, sovereign debt/GDP ratios have been more than one standard deviation above where the current US ratio X times without a corresponding drastic rise in inflation or correlation to negative adjusted GDP growth.” (numbers are made up, it’s just an example argument)

Now, I realize that I am asking a lot here, especially for the internet.  My point though, is that while we may disagree on an issue, if one person (Krugman, in this case) takes the time to think about a position and write several hundred words on the issue, while we (Bitcoin supporters) simply respond with a variant of “you’re wrong and I hate you,” he essentially wins by default.

You have to play to win, and most people will (correctly) assume that if you can’t articulate your position, then you don’t really have one.

Ok, NOW the next post is on to the potential effects of a deflationary vs. inflationary currency!

The Krugman Bitcoin Dilemma, Pt. 1: Is it Good Money?

Happy New Year, Everyone.

It’s hard to believe that I went all of 2012 and 2013 without writing a post about Bitcoin, considering how much I was talking about it in the analog world.  As the calendar turns over though, I think I’m finally ready to put some thoughts on paper, in part to address some of the more persistent misconceptions and logical fallacies that still seem to infect the discourse, from both supporters and detractors.  This will probably have to be a series, so I will try not to disappear for weeks between posts, as I often have in the past.

Since I haven’t written about the subject in this forum yet (given the very slim connection to marketing), I think that a little context on my own backgrounds and biases is certainly in order, in the interest of full disclosure.  So a few stipulations:

1.) I am a Bitcoin supporter, an enthusiast, even.  I own some, so that bias exists.

2.) Unlike many of the most vocal Bitcoin enthusiasts however, I have some reservations, and my skepticism will not allow me to overlook either current or potential flaws.

3.) I am not a programmer.  I can read/write a very little JS and HTML and have some fundamental understanding of programming, but that’s it, so I can’t look at the code itself and get anything from it, despite it being open source.  I trust the community for my information there.

4.) I am not a cryptographer, and while I have a pretty good grasp of the fundamentals of how Bitcoin and the SHA-256 implementation work together, and how the hashing function works, etc., it is an imperfect understanding even after a year or two of study by a novice.  I like to think that I am a pretty smart guy, but this stuff is complicated, so once again, I tend to trust smarter people than myself on the details here.

5.) I am not an economist by training or trade officially, but I am on more solid ground here than programming or cryptography.  I have made a long-time hobby of the subject, have read a fair bit of the major primary and secondary sources, and keep up with the various schools of thought.  

6.) I like liberty, but I am not a libertarian.  This also sets me apart from a lot of Bitcoiners.

That should do it, but if I think of anything else as I write this I will make sure to include it.  I just want everyone to go into this knowing approximately how large a grain of salt they will need at various times.  

One of the issues that has been at the forefront of the public discussion lately has been the opinion of the popular economist Paul Krugman, who writes for the New York Times, which is generally negative towards Bitcoin.  While we mainly focus on him, and his back and forth with the Bitcoin community, he serves as the face of what is a broader group of traditional public economists, which tends to share a number of his views on the matter.  Now for a bit more disclosure, I will say that unlike the vast majority of Bitcoiners, I often like Krugman’s work and columns, and respect his intellect and the body of both scholarly and popular writing on recessionary/depressionary monetary policy that he has produced in particular.  I don’t hate quantitative easing, I don’t fear the imminent collapse of the US fiscal system due to rampant inflation, and I generally subscribe to what are considered “liberal” economic theories, so we have all of that in common.  That said, I respectfully disagree with him on the subject of Bitcoin, for a number of reasons.

I think that he is in part unable to separate, despite his claims to the contrary, his own political and moral beliefs from the debate, which has often lacked in civility from both sides.  In discussing the “normative economics” of Bitcoin here, he allows a logical leap to be made from the features of an inherently apolitical computing process to the aims of its users relying on no more than assumption.  He sees the Bitcoin community at large as a single mindset dominated by libertarian motives and thought process.  While that is undoubtedly describes a large number of Bitcoin enthusiasts (spend any time in the forums and you will see it), I am living proof that there is a more diverse ecosystem below the surface, and that it is possible to believe in the protocol without espousing a particular set of political beliefs.  To paint with such a broad brush is damaging, and seems to cloud his judgement a bit at times.  To be fair, the abuse heaped upon him would ruffle almost anyone’s feathers, but that’s the unfortunate reality of a debate played out entirely over the internet between one public individual and a legion of anonymous opponents.  The price you pay for the vast reach of the digital realm is the lack of a filter, so you have to take the polite, well-reasoned argument with the… less so.

There does seem to be a disconnect with traditional economists and the source of Bitcoin’s “intrinsic” value, which ignores the fact that this is largely an irrelevant argument.  The comparison to gold which is often drawn, while overly simplistic and imperfect, is apt enough to be commonly used in this debate, so for now I will gloss over those imperfections and keep to the analogue that everyone is comfortable with.  For those who aren’t familiar with the article that I linked above, I’m going to quote a relevant section:

Underpinning the value of gold is that if all else fails you can use it to make pretty things. Underpinning the value of the dollar is a combination of (a) the fact that you can use them to pay your taxes to the U.S. government, and (b) that the Federal Reserve is a potential dollar sink and has promised to buy them back and extinguish them if their real value starts to sink at (much) more than 2%/year (yes, I know).

Placing a ceiling on the value of gold is mining technology, and the prospect that if its price gets out of whack for long on the upside a great deal more of it will be created. Placing a ceiling on the value of the dollar is the Federal Reserve’s role as actual dollar source, and its commitment not to allow deflation to happen.

Placing a ceiling on the value of bitcoins is computer technology and the form of the hash function… until the limit of 21 million bitcoins is reached. Placing a floor on the value of bitcoins is… what, exactly?

I have had and am continuing to have a dialogue with smart technologists who are very high on BitCoin — but when I try to get them to explain to me why BitCoin is a reliable store of value, they always seem to come back with explanations about how it’s a terrific medium of exchange. Even if I buy this (which I don’t, entirely), it doesn’t solve my problem. And I haven’t been able to get my correspondents to recognize that these are different questions.

So the argument being made by Krugman here is about Bitcoin’s usefulness as a store of value (or lack thereof), and comparing it to gold as a counter-example.  However, citing the ceiling value of gold says nothing about the floor value of Bitcoins.  Additionally, there seems to be some conflation here with ceiling on supply and ceiling on value, and they are not the same thing, which begs the question of whether he really doesn’t see the distinction, or whether he is just being sloppy.

Regardless, the factors that affect the upside price opportunity (and make no mistake, the factors that he cites are simply several of many that determine price, not actual fixed limits), are not the same as qualities that make a good store of value, nor do they confer a special “intrinsic” value upon the medium.  What he is getting at with that mining business is scarcity, which IS one of the qualities you look for in a store of value, along with fungibility, divisibility, etc., all things that bitcoins have in common with gold.  However, when push comes to shove critics of BTC always come back to a.) gold has value because people have long agreed that it does, so basically credibility, b.) gold has some industrial applications that make it useful and will provide a minimum of demand (it is a good semi-conductor), and c.) that gold is pretty and jewelry/aesthetics will also provide a minimum level of demand.

Here is where the argument that Krugman is making falls apart.  He specifically complains that supporters of BTC use its utility as a medium of exchange to explain why it is a good store of value, and he is saying that they are different questions.  But in fact, when trying to explain something like “intrinsic value” (which we shouldn’t bother with, but the critics insist), that utility is exactly the same as citing gold’s utility as a semi-conductor or jewelry material as reasons that it is a good store of value.  You can’t have it both ways.

As to the credibility argument, this in part gets back to why any discussion of “intrinsic” value is a waste of time.  Quite frankly, Bitcoin is a good store of value because right now tens of thousands of people in the world are willing to pay upwards of $700 for a bitcoin.  As long as some people think that they are worth more than nothing, they can store value.  Not to mention the fact that price stability isn’t a valid argument in a year that has seen gold price fall so substantially.  You can say that this means that gold is also not a good store of value, but it’s both or nothing.  Now the argument of gold/bitcoins vs the dollar is one worth having, because there you are basically trading stability with slow depreciation vs volatility with a chance of appreciation.  Like bonds vs equities, it is simply a matter of personal risk tolerance, but that’s finance, not economics.

Another part of the issue here, is that Krugman keeps thinking about what makes Bitcoin viable/valuable as “money,” which is near-sighted when looking at Bitcoin as a whole.  It can function as money, certainly, but in viewing it as an equity or a commodity, you have to look at the whole, rather than specific parts.  When someone talks about, or invests in, Google, they aren’t simply referring to the search engine.  There is an entire suite of products and services that make up Google, from the ubiquitous e-mail service to analytics, social media to hardware like Google Glass, and a million other things.  Sure, the search engine was the foundation for their empire, but it is not the only facet that a valuation is based upon, and the same is true of Bitcoin and it’s function as money.  The features that make it a great payment protocol are part of what make it a good medium of exchange, and that utility in turn provides some basis for its “floor” as a store of value.

Up Next, BTC Deflation & the Economic Schools at Play: Keynes vs. Hayek!