Product Operations: do you need it, or is it just a fad?

Product Operations: do you need it, or is it just a fad?

Recently, I’ve been hearing about more and more companies building “Product Operations” functions.  This appears to be especially true of startups with high operational-complexity; particularly marketplaces.

So, what is Product Operations? Do you need it? Or, is it just another fad?

I spoke with several founders/leaders at a number of high-growth and operationally complex startups who have invested in Product Operations functions, including Uber, Thumbtack, and Zipline.

So, what is it?

The definition of “Product Operations” varies somewhat between companies but here are the main themes I hear:

  • can be part of the Product organization, but distinct from Product Managers
  • can also report into the Operations organization
  • works very closely with operational teams
  • very detail-oriented and data-driven, especially as it relates to process optimization
  • provides the bridge between the operations teams and Product Managers, solving problems and being supportive, and ensuring communication is effective

The simplistic distinction between Product Management and Product Operations seems to be as follows:

  • Product Management – “what should we build?”
  • Product Operations – “is what we’ve built working?”

So, is it a fad?

Product Operations vs Product Management

This is where my own cynicism started and where I got the first whiff of a possible fad.

To me, being very close to customers, even if internal customers, and having a great finger on the pulse of whether what you’ve built is working is a major aspect of any Product Manager’s job.  If a Product Manager is not doing that, they’re not a good Product Manager, right?

Pragmatism Rules

Everyone I spoke to agrees with me…in theory.

However, the reality is that it’s very hard to find Product Managers who have a natural affinity for what’s involved in operating a complex business “at the coal face”.  This makes sense because most Product Managers come up through the ranks of product and engineering organizations.

Although a Product Manager’s job is to understand and empathize with users, it seems it can be more pragmatic to hire people with an operational background who just more naturally “get it” and put them in a Product Operations role as the go between operation users and the product team.

Put simply, one person I spoke to said that Product Operations “makes sure operations are getting respected” by the Product organization.

What to look for

So, who makes a great Product Operations person?  Here’s a summary of what I’ve heard:

  • have an operational background (vs a product/engineering background)
  • adaptive
  • empathetic
  • data-driven
  • strong personalities willing to fight for what they think operations needs

Side effects

Are there any undesirable side-effects of introducing a Product Operations function? The consensus seems to be that two problems can occur:

  • there is some duplication of effort/ownership between the Product Management and Product Operations and therefore some potential ambiguity and politics that needs to be carefully managed.  To mitigate this, the distinction between the two functions summarized above must be made very clear to both sides.
  • the introduction of Product Operations risks Product Managers retreating further to their ivory towers, allowing them to get more divorced from the (internal) customers they serve.

Marketplaces: Scaling with Operations vs Engineering?

There are many things that make building and scaling marketplace businesses hard: for example, there’s the quintessential chicken-and-egg problem of building and balancing supply and demand, and there’s the need to build two or more products in parallel to serve the needs of the different participants in your marketplace.

There is also the question of how you scale your marketplace once you’ve got product-market fit established and some unit economics that seem to work.

Electrons vs Atoms

Most marketplaces have to deal with the tangible, real world: unlike pure software/SaaS companies, marketplaces have to deal with whole atoms, rather than just electrons.

Those atoms might make up people, or cars, or meals, or apartments but they are physical resources that have to be managed. This is why marketplaces tend to need significant operational headcount.

However, most marketplace companies aspire to be, and actively position themselves as, technology platform companies.  This of course requires an ongoing investment in product/engineering.

Given finite resources, how do you choose between scaling a market place through operation headcount versus product/engineering investment?  How do you strike the right balance?

The Comparables

I did some quick research to look at what other marketplace businesses are doing.

I took a basket of marketplace companies at varying funding stages and looked at their employee counts on LinkedIn by role.

Firstly, let’s set the scene by looking at the absolute number of engineers that various marketplaces have and compare that to their funding stage:

Perhaps no surprises here: as marketplaces develop, they hire more engineers. I am struck, though, by the widely varying number of engineers that the earlier stages marketplaces seem to have.

Now let’s look at the ratio between operational headcount and engineering headcount in these same companies:

This is also what you might expect.  Although the data is noisy*, it seems that as marketplaces grow, they become less dependent on operational headcount. Presumably, their investments in product/engineering payoff in terms of automations and efficiencies.

Of course there’s also survivorship bias here – these are only the marketplaces that are still around. Perhaps the ones that didn’t make it had wildly different ratios.

What would be great is to get historical data on these ratios and see how that correlates with outcomes. Unfortunately, I don’t have that data (if you do, let me know!).

My bet would be that a higher ratio of operational to engineering headcount is hard for marketplaces to wean themselves off – i.e. it’s hard to change the ratio over time.  If you organization gets accustomed to scaling and solving problems by hiring ops people rather than hiring engineers to automate, that just gets amplified over time.

* my methodology here was simply to search on LinkedIn for people with “engineer” and people with “operations” in their job title. This is obviously error prone for a number of reasons.  For example, some “engineering” roles have “operations” in their job titles, and not all headcount are necessarily on LinkedIn, especially if a company outsources or off-shores some functions. However, given a sufficiently large sample set, one would hope that these effects blend out.

Brute Force Growth vs Long-term Value

Like any business, marketplaces have to continue to show top-line revenue growth in order to maintain the faith of investors and employees and be able to continue to raise money.  The first, second, and third rules of business are “don’t run out of money“.

However, while it’s possible to “brute force” growth of many marketplaces through reliance on operational headcount in the short to medium-term, I believe this strategy has large associated dangers in the longer term.

In a perfect world, you could scale both operational and product/engineering headcount as needed but, in reality, you will be forced to choose between spending each $1 on one or the other. Here’s my quick take on the pros and cons:

In summary, the biggest danger with scaling by adding operational headcount is that it works…in the short-term.  It’s also cheaper.  But, the danger is that you win the battle but not the war.

Agree or disagree, please leave a comment.

Are you confusing Optimization with Growth?

Are you confusing Optimization with Growth?

In a startup, there are always many things that aren’t working as efficiently as they could be – acquisition funnel conversion, manual processes, customer acquisition costs, etc.  This may be incredibly frustrating, especially for the team members who have to deal with it on a day-to-day basis.

It’s very tempting to direct precious money, time, and energy to resolving these frustrations, especially as its your team’s tired faces that you have to look at every day.  It’s always tempting to give the squeaky wheel some oil.

However, it’s vital that you remain focused on growth and don’t confuse growth with optimization. Burning lots of time optimizing at the expense of growing is not a recipe for success for early- to mid-stage, venture-backed startups.

Of course, there is some nuance here: if things are so broken that your team starts to leave, you have to address that – no team; no company.

Also, the smart investors (i.e. the ones you want) realize that, if your unit economics fundamentally don’t work, you will simply lose more money as you grow.

However, conversely, it’s unlikely that a Tier 1 investor will invest in the also-ran, #3 player in any category in terms of growth rate and/or absolute revenue, however optimized and healthy the acquisition funnels, gross margins, etc. Investors are in the business of selecting for the biggest return on their capital, not the best run or most efficient business.  The biggest return comes from the biggest exit and the biggest exit goes to the category winners.

As a venture-backed startup, the most important thing is to stay as one of the leaders in your category – this is what allows you to maintain team confidence and morale, attract the best talent and investors, and continue to raise money when you need it.  Note: there are usually only 1 or 2 “leaders” in any category.

Let’s take two startups:  to start with, Company A and Company B are neck-and-neck.  Both have a $5M in gross revenue, with a average revenue of $5,000 per customer per year and a customer acquisition cost (CAC) of $2,000.  Both have revenue that is doubling each year.  Both are mid-stage startups – they’re not yet profitable and don’t expect to be any time soon.

Both companies also know that their CAC is too high and, by some optimizations, the CAC can be reduced significantly.  The high CAC drives some members of the team crazy – so many opportunities lost, so many wasted marketing dollars.

So, the CEO of Company A directs the team to work on CAC.  Over 6 months, they manage to effect a series of changes process and product changes in their customer-acquisition funnel, through A/B testing, cost reduction, etc.  These compound and end up halving the CAC to $1,000 – that’s a huge improvement.  Company A’s gross margin has significantly improved.

Meanwhile, the CEO of Company B ignores the CAC for now and instead directs the team to focus on increasing the size of the sales and marketing teams significantly and filling the top of the sales funnel with as many leads as possible.

One year later, Company A’s revenue has doubled again and they’re netting an average of $4,000 per customer per year – 33% more.  Not bad.

However, by focusing on growth, one year later, Company B’s revenue has tripled rather than just doubling.  They still net an average of $3,000 per customer per year but there are 3 times more customers.

Both Company A and Company B need to raise more money.  So does a 3rd player in the category; Company C.  Company C is going gang-busters, beating both Company A and Company B on growth rate and total revenue.

You know how this story ends:  Company B and Company C are able to raise giant C-rounds from Tier 1 investors at great valuations.  Meanwhile, Company A has fallen behind – its unit economics are better than Company B’s but it’s now an also-ran and struggles to raise money.  Without that money, it cannot continue to grow and falls further and further behind Company B and Company C.  Perhaps it’s acquired by Company C at a fire-sale valuation or perhaps it’s a giant smoking crater.

Of course, this is a contrived story.  In reality, you can probably achieve growth and some optimization in parallel.  But, the key is not to confuse one with the other.

So, grow and optimize as you go, as long as that optimization doesn’t slow your growth.  Don’t optimize hoping that it will deliver meaningful growth.

tl;dr – in a startup, you can’t optimize your way to success – you must out-grow your competitors.

Are You Hiring in Your Own Image? Avoid Your Blindside

Are You Hiring in Your Own Image? Avoid Your Blindside

You’re sitting in a quiet meeting when a stranger suddenly bursts into the room, screaming and ranting – what is your immediate reaction?

Your answer might say something about your personality. If your first instinct is to act – perhaps to tackle the person, or run and hide – you’re likely a “doer”.  If your first reaction is empathy; to wonder why the person is so upset, you’re probably a “feeler”.  Lastly, if your first reaction is purely internal – to consider why the person is so mad  – you’re a “thinker”.

This simple personality model – doer, feeler, thinker – is of course just one of many. Like any model, none of us fits perfectly.  No one is purely a doer, feeler or thinker (we’d be a weird bunch if we were), but we do tend to have a primary or dominant characteristic.  Further, it’s important to be aware of the weaknesses of each of these dominant characteristics.

One of the complimentary comments that several members of the team at Wonolo have said to us over the past 4 years is that they think Yong (CEO), AJ (COO) and yours truly (CTO) are a good exec team because we are all very different.

It’s of course very nice to hear and I think the truth here is that, by compensating for each others’ weaknesses, we achieve more than the sum of our parts.

AJ (COO) is a doer; a man of action. His catchphrase might be “Just Do It”.

Last year, when he heard that the US government recommends people walk 10,000 steps per day,  he set himself a goal of doing it every single day.  At the time of writing,  he’s not missed 1 day in 326 – despite weather, holidays, travel, vacation, etc – not one!   It’s hard for me to imagine having the consistency and commitment to achieve this.

Yong is a feeler. We use Slack for internal communication and Yong’s handle is “sobstory” (handles are generally chosen by the team).

I think it’s only right and proper that we have a feeler as a CEO, given that we are in the people business.  Yong uses his “sob stories” to motivate and inspire the team and to bring empathy and humanity to our business.

That’s not to say that Yong’s not a thinker and a doer too – as well as being one of the nicest guys I’ve ever met, he’s also one of the hardest working. AJ, too, is one of the smartest people I know.  But, again, we’re talking about the the dominant aspect of their personalities.

On the flip side, one weaknesses of “doers” tends to be that their bias to action or impatience can make them act before necessary analysis of all the options is done – doers have a low tolerance for long discussions and theory.

Doers also tend to be highly competitive. AJ’s Slack handle is “bookie” owing to his tendency to bet on anything he thinks he can win.

Being competitive is of course a positive quality in many situations.   But, for doers, it can also mean that needing to win the argument is more important than making the right decision, and that achieving the goal can become more important than considering whether the goal in question is actually valuable or important.

As to feelers, their empathy can mean they tend to focus too much on how something feels rather than how it is. It means they struggle with decisions they know are right but which negatively impact people.  They can be subject to emotional manipulation by others who know they can exploit the feels.

I myself am a thinker. My Slack handle is “dirtyprofessor” (“knowitall” was another candidate).

As a thinker, one of my weaknesses is that, once I’ve worked out how to do something, I’m less interested in actually doing it. I’m more interested in the theory than the practice; the abstract over the concrete.  Learning for the sake of learning is fun for me.

Another weakness is that by being very analytical and data-driven, I can tend to get disconnected from the real-world, human impact.

AJ and Yong, respectively, definitely help counter these weaknesses.

I first met AJ and Yong in the summer of 2014 when they were working on Wonolo  inside Coca-Cola. I’d love to say that we immediately saw this complementary set of personal styles and that’s why we decided to join forces but the reality, like many things in startup life, is that we simply Got Lucky.

As I’ve aged and learned, I believe I’ve managed to compensate for the weaknesses of being a “thinker” and become a more rounded person but it’s nice to know that Yong and AJ have my back.

What this experience reminds me of is the importance of recognizing your own weaknesses and not hiring solely in your own image.  By hiring people who are different to you, you can compensate for our own weaknesses; your blindside. There are no “right” personality types and no one fits precisely in one bucket but, by having a well-rounded team, you will avoid many pitfalls.

Please leave a comment if you have one.

Conducting an Effective Postmortem

Conducting an Effective Postmortem

Wouldn’t life would be great if nothing ever went wrong and technology never broke?

Unfortunately, the reality is that things break all the time.

Your product probably breaks a little bit all the time and sometimes breaks big time (hopefully, much more occasionally).

Perhaps your website goes down for 4 hours during your busiest season and you miss $10M in potential revenue. Or, you discover that no payments have been processing for 2 days before anyone noticed.

In these serious to catastrophic cases, you really want to get to the bottom of why the problem occurred, with a view to making sure it never happens again.

That’s where conducting an effective Postmortem becomes vital.

Avoid Blame

The ultimate purpose of the Postmortem is to make sure the problem experienced never occurs again.

In order to do this, a Postmortem involves ferreting out root causes (more on this below). Bringing blame into the Postmortem process itself risks causing defensiveness and “ass-covering”.  This defensiveness tends to obscure the true root causes.

This is not to say that blame isn’t important or can be avoided. Perhaps someone needs to be fired.  But, keep the “blame” part separate from the Postmortem itself in order to get to the true root causes.

The Postmortem Process

I like to conduct the Postmortem process as a group, with all the stakeholders in a room in front of a whiteboard.  It’s important that everyone impacted and/or responsible for the problem is involved and has a voice.

At a high-level, my process for conducting a Postmortem is as follows:

  1. agree and define the impact of the problem to the business – e.g. “we lost $10M in potential revenue”
  2. flush out all the causes of the problem down to their root, as far as possible
  3. agree a set of recommendations aimed at ensuring the problem never occurs again

Let’s use a contrived example for illustration.  Imagine that I fell off my bike and broke my wrist.  We start on the whiteboard with the impact – i.e. I broke my wrist.

Why-Because

My preferred method to analyze causes (step #2 above) is Why-because Analysis.

Why-because is a formalized process but don’t be put off – it can be used more casually with great success and you can add rigor as you become more familiar.

Why-because essentially involves repeatedly asking “Why?” and repeatedly answering with the “because” part.  My 5-year old son is also great at this.

e.g. “Why did I fall off my bike?” “…because I hit a pothole.”
“Why did you hit a pothole?” “…because I wasn’t looking where I was going.”
“Why weren’t you looking where you were going?” “…because I was distracted”

…you get the idea.

Why-because is similar to other processes you may be familiar with like “5 Whys”.  (I have found 5 whys to be insufficient because big problems typically have complex causes and the causal chains are often more than 5 levels deep.)

What you end up with at the end of the Why-because Analysis is a graph that shows you all the contributory causes that caused the impact on your business. More formally, when complete, the Why-because graph should include all the necessary and sufficient causes.

Continuing our example, here’s our Why-because analysis of why I broke my wrist:

Of course, you need to decide when you’ve gone deep enough and can stop asking Why? There is no hard-and-fast rule here – just use your judgement – but you don’t want to end up drilling down to “because the big bang happened” in every case.

One great thing about Why-because graphs like the one above is that you can test them to make sure they’re complete:

  • for each box on the chart, you can ask, had this not occurred, would the problem still have occurred? If the answer is no, it’s a necessary condition.
  • looking at all the boxes on the chart, you can ask, if all of these happened again, would the problem occur again? If the answer is no,  your conditions are not sufficient and you’re not done yet.

Generally, big problems tend to have complex causes. This is because any reasonably mature organization will have checks and balances in place to avoid obvious and predictable failures.

Therefore, you will likely end up with a complex graph that includes a mixture of technical, operational and human contributory factors. It’s particularly important not to overlook or underplay the human factors since fixing the technical and operational issues alone will not avoid the problem recurring.

You can read more about Why-Because on Wikipedia.

Recommendations

The most important part of the process is to create a list of recommendations to act on, informed by the detailed understanding of the causes from the Why-because analysis.

Don’t forget the human factors – these are often the most important to address, e.g. additional training, more staff or better process.

Again, you can test your recommendations by saying, if we do all these things, is it highly likely to prevent this problem from recurring again?  If the answer is no, you’ve not got the right recommendations.

Lastly, give each recommendation an owner who is responsible for taking action and be sure to follow-up.

12 Cognitive Biases that Will Kill your Startup

If you asked me to choose the 3 most important things that determine success or failure of a startup, I would say:

  1. company culture – it has a profound and pervasive effect and it’s essential to consciously and deliberately nurture it.
  2. focus – doing many things badly rather than a few things well is surefire startup suicide.
  3. cognitive biases – the cognitive weaknesses of the human brain can if, unrecognized, wreak havoc.

I’d argue that luck is an even bigger factor than any of these 3 but, by definition, luck is outside your control.

So, let’s talk about #3 – Cognitive Biases…

Definition

“A systematic pattern of deviation from rationality in judgment and decision making.”

Despite what you might like to think, your brain is not a rational, logical computer – you’re a human being.  Your human brain has limitations.  It has bugs.

Cognitive Biases have been experimentally proven (again and again) to exist.

So What?

Cognitive Biases are potentially dangerous to all people and organizations but I think are particularly deadly to startups because:

  • Startups involve making a series of low-data, high-risk decisions. With limited data, Cognitive Biases have a stronger sway.
  • People working on startups are often stressed and working long hours, making them more susceptible.
  • The room for error is often very small because of limited funding runway.
  • Startups are commonly started and staffed by younger people who have less prior knowledge to counteract the impact of Cognitive Biases.

I believe that understanding and being aware of Cognitive Biases leads to better decision making and that better decision making, on balance, leads to better outcomes.

So, let’s look at 12 Cognitive Biases that I have seen are particularly prevalent and particularly dangerous to startups, and how you might spot them and counter them:

1. Correlation vs Causation

Did you know that sleeping with your shoes on is strongly correlated with waking up with a headache?…

…therefore, sleeping with your shoes on causes headaches!

Of course it doesn’t; this is a classic example of confusing correlation with causation.

For some more amusing examples, Tyler Vigen created a great series of charts over at tylervigen.com.  Here’s just one for illustration:

In my experience, confusion between correlation and causation is rife in startups.

Partly it’s the limited availability of data…but I’ve also seen that more data can actually make the problem worse.  With so many metrics tools available, people tend to get lost in the data and miss the bigger context.

Startups run at high speed and many things are changing in parallel, with little ability to control variables.  For example, on several occasions I’ve worked to optimize conversion in a product funnel and been happy to see that the optimization worked, only to discover later that conversion improved due to an unrelated change elsewhere – e.g. positive media coverage.

Remember to always ask yourself, does A really cause B?  Always consider that, perhaps:

  • B causes A
  • both A and B are caused by C
  • A and B effect each other
  • it’s a coincidence

2. Confirmation Bias

Confirmation Bias is the tendency to search for, interpret, favor and recall information in a way that confirms one’s pre-existing beliefs or hypotheses.

More commonly, you might call it “cherry picking”.

I think that Confirmation Bias is a particular problem for startups because they generally have a lot invested in a particular view of how the world should be (or will be) but have very little solid data to go on, especially at an early stage.

Life at a startup is ambiguous; startups struggle and go through hard-times. Therefore, belief is often what carries a team through the tough times.

Startup founders tend to be “true believers”, with a tendency to get high on their own supply.  One aspect of Confirmation Bias is that it can maintain or strengthen beliefs in the face of contrary evidence.

Always ask yourself whether you’re truly open to evidence that contradicts your existing views and beliefs.  Startups regularly pivot but often pivot too late.

3. Overconfidence & the Planning Fallacy

“the most pervasive and potentially catastrophic of all the cognitive biases to which human beings fall victim” – Svenson (1981)
sydney_opera_house_-_construction_-_phase_2_1966

Sydney Opera House:

  • Planned Completion Date: 1963, Planned Cost: $7M
  • Actual Completion Date: 1973, Actual Cost: $102M

Unfortunately, your confidence in your judgments is reliably greater than the accuracy of those judgments.  You are overconfident.

The real kicker is that this is especially true when your confidence is relatively high.

Read that again: you’re probably wrong and the more confident you are that you’re right, the more likely you are to be wrong.

One particular aspect of Overconfidence is what is referred to as the “Planning Fallacy” – most people who are involved in software development are probably familiar with it.  The Planning Fallacy is the primary reason that software development projects are almost always late.

The Planning Fallacy is the tendency for people to be overly optimistic in how much time will be needed to achieve a task.  Counterintuitively, experience doesn’t seem to eliminate the problem – i.e. knowing that similar tasks have taken longer than expected doesn’t solve the problem.

The good news is that the bias behind the Planning Fallacy can be mitigated with a couple of relatively simple “hacks”:

  1. ask someone else – overconfidence generally only occurs when people are estimating their own tasks and disappears when people are estimating for others. So, never ask the person who will be performing the work how long it will take – ask someone else.  Better still, ask a number of other people.
    In software development, a common technique is “Planning Poker” where a group of people provide blind estimates (so they don’t influence each other) of how long a task will take and the median is used.
  2. break tasks into smaller chunks – experiments have shown that the estimation for how long it will take to do a task is generally always less that the sum of the sub-tasks once they are broken out.

4. Group Think

life-of-brian

“Yes; we are all different!”

“Group Think” is probably a term that most people are familiar with.

My experience is that, in startups, it’s closely linked to the Confirmation Bias problem.  As discussed above, startups are carried forward by true believers (founders) and dissent is often considered heretical.

Perhaps the best way to understand Group Think is to look at what people do that makes it happen:

  • Minimize conflict – with a few exceptions, most people don’t want to fight.  Hey, startups are stressful enough. However, there are sometimes necessary conflicts.
  • Suppress dissenting viewpoints – startup founders are usually, almost by definition, very strongly opinionated and convincing. The flip side of this is that they tend – consciously or otherwise – to stifle viewpoints that contradict their worldview.
  • Isolate outside influences – some startups have company cultures that are cult-like. While this may be very useful in getting everyone aligned on an objective, taken too far it is dangerous, leading to “not invented here” syndrome and hubris.
  • Appeal to authority – startup teams must be encouraged to “speak truth to power” and call out the elephant in the room.

5. The Curse of Knowledge

It’s extremely hard to imagine what it’s like to not know what you know.  i.e. you can’t “unknow” something.  This is the Curse of Knowledge.

In the context of a startup, one of the biggest problems is trying to put yourself in the shoes of your user or customer.  The reality is that you can’t.  You’re so deep into the problem that you’re trying to solve that it’s impossible to see it in the same way that an outsider would.

This really underlines the importance of doing User Testing on your product using real users, not internal team members.  Watching people unfamiliar with your business and the problem you are trying to solve use your product is always extremely revealing.  There are always many implicit assumptions that you’ve made and these are only exposed through contact with real users.

6. Anchoring

Anchoring is the tendency to rely too heavily on the first piece of information you get and a tendency not to adjust your position based on further evidence that contradicts it.

Anchoring is what skillful negotiators – e.g. car salespeople – exploit to get the deal they want.  The first price discussed tends to anchor the subsequent negotiation.

In a startup, your first customer deal, your first hire, your first customer loss, etc tend to set a mental template for “how things work” with your business.  It’s important to periodically ask yourself if you’ve become anchored in a world view that isn’t necessarily correct.

7. Sunk Cost Fallacy

You might call this “flogging a dead horse” or “throwing good money after bad”.

Sunk Cost Fallacy is a tendency to continue to rationalize decisions and actions when faced with increasingly negative outcomes.  A “sunk cost” is a cost that you’ve already paid and can’t get back – money that is already spent whether you continue or not.

Sunk Cost Fallacy is, I think, one of the primary reasons that many startups pivot too late and end up running out of money.

Being rigorously data-driven is probably the best antidote to Sunk Cost Fallacy (and other Cognitive Biases).  Set specific metrics that need to be achieved by specific dates in order to assess whether a particular initiative or direction is working and hold yourself and your team to them.

8. Attribution Bias

bro

Attribution Bias is the tendency, when evaluating the causes of the behaviors of a person you dislike, to attribute their positive behaviors to the environment and their negative behaviors to the person’s inherent nature.

We all have to work with people we don’t necessarily like.  It’s important to realize that we probably can’t accurately understand others’ internal motivations and try not to take personally the behaviors that we consider negative.

9. Ostrich Effect / “The Elephant in the Room”

ostrich

The Ostrich Effect is the avoidance of risky/difficult situations by pretending they do not exist.

The “elephant in the room” is an obvious truth that is being ignored or going unaddressed.

It’s critical to build a company culture in which people are encourage to call out the elephant in the room.  If you don’t, you will be trampled by the elephant.

10. Hindsight Bias

tswift

“I knew it all along!” …actually, you didn’t.

Hindsight Bias is our tendency to see an event, after it has occurred, as having been predictable.

Hindsight Bias is a large and fascinating topic.  I can’t possibly do it justice here.

It’s unfortunately one of the hardest to counteract. The only known way is to ask whether or not alternate hypotheses and predictions would have been equally believable ahead of time.

11. Survivorship Bias

Ever had a conversation like this?  “Uber did X therefore we should be doing X!”

In reality, it’s likely that there were many other companies that did X but which don’t exist anymore…so you won’t be hearing from them.

Survivorship Bias is concentrating on the people or things that “survived” some process and overlooking those that didn’t because of their lack of visibility.

Survivorship Bias is one of my favorites simply because it is extremely common in Silicon Valley.  Beware of people claiming that companies succeeded because of specific reasons without data showing that those reasons were in fact the reasons they succeeded.

12. Bias Blindspot

Lastly, we all tend to think of our own perceptions and judgments as being rational, accurate, and free of bias.

In a sample of more than 600 residents of the United States, more than 85% believed they were less biased than the average American.

This is despite the overwhelming amount of experimental evidence that they are not.

Summary

Firstly, forget any idea that you can eliminate these biases – you can’t.  However, you can educate yourself about them, build awareness in your team and encourage people to question themselves and call them out when they see them.

Additionally, the #1 thing you can do to help counteract these biases in your startup is be Data Driven.  Data of course does not solve all problems but, by asking the right questions and getting accurate answers, you can cut through many Cognitive Biases.

Lastly, it’s important to note that these Cognitive Biases are subtle and pernicious. Companies do not usually fail because of one Cognitive Bias affecting one decision. Instead, the impact Cognitive Biases have is across a series of decisions over time.  Be mindful.

Further Reading

There are a few other Cognitive Biases that didn’t quite make the list but which I still think are hugely relevant and I’ve observed in startups:

  • illusion of validity – belief that additional information generates additional relevant data for predictions, even when it evidently does not
  • information bias – the tendency to seek information even when it cannot affect action
  • zero-risk bias – preference for reducing a small risk to zero over a greater reduction in a larger risk
  • loss aversion – the tendency to focus more on what you might lose from a particular decision than what you might gain

The best place to start for these and many others is the List of Cognitive Biases on Wikipedia.

I Crave Your Feedback

Good, bad or indifferent, please leave a comment.  Thanks.

The Coder’s Oath

Doctors take the Hippocratic Oath.  We need a Coder’s Oath.  Will you take it?

I am a software professional.  I swear to fulfill, to the best of my ability and judgment, this covenant:

I build on the shoulders of the software professionals that have gone before me, recognizing that rarely are truly new programming paradigms invented. I therefore commit myself to fully understanding existing solutions before I reinvent the wheel.

I recognize that the simplest solution is almost always the best solution.  I will not over-engineer or prematurely optimize.

I will always seek out the root causes of problems. I understand that the time taken to seek out and address root causes will yield savings in all but the very shortest term.

I will work to understand my cognitive biases but recognize that I can never fully overcome them. In assessing the effort and time required to complete a task, I will consult with my peers to understand the true scope before making a commitment.

While I always strive to increase my skills and knowledge, I recognize that my work, and the work of my peers, will never be without errors. I accept that all software has bugs and that I myself will write many bugs.  I will allow my work to be scrutinized and critiqued by my peers without taking it personally.  I have the courage to say “I don’t know”.

I do not build software in a vacuum or create software for my own glorification or for technology’s sake. Instead, I create software that is valuable to users.

I accept that users are human beings and that human beings often do not behave rationally. I understand that if I build software expecting people to behave rationally, I will be forever frustrated.

While I may have entered into the software field because I am introverted and/or prefer computers to people, I commit to trying to understand users and the reality of how they use my software.

Frustrating though it may be to me, I understand and accept that most users will lack the time or inclination to understand how software works or why it was built the way it was. I accept that, to users, my software is just a tool to get a job done as quickly and easily as possible.

If I do not violate this oath, may I enjoy life and art, respected while I live and remembered with affection thereafter. May I always act so as to further the software craft and produce software that delivers true value to users.

So, will you take it?  Let me know in the comments.