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.

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How Startups can work with Big Companies and not get Killed

(Note: this post was originally published on the Wonolo blog under the title “Collaborate. Innovate. Top Tips for How Large Enterprises and Startups Can Have a Winning Partnership”)

Recently, I was invited by Unum, one of our FORTUNE 500 customers, to participate in a panel session about corporate innovation at Maine Startup and Create Week. At the heart of our discussion was how large companies like Unum can be more innovative and how startups and large companies can work together toward this goal.

It’s a topic that’s close to my heart: I spent the first part of my career in the wireless industry, and back in 1998, I was a part of the founding team of Symbian, one of the first operating system platforms for smartphones (although they weren’t yet called “smartphones” at that point).

Symbian was a joint-venture between Nokia, Ericsson, Motorola, Psion, Panasonic and, later, several others. Their rationale for investing in Symbian was a desire to have a common software platform for smartphones. However, what these companies actually had in common was that they were large, bureaucratic, and they were arch-competitors.

Getting these large companies in the Symbian joint-venture to work together was somewhere between very hard and impossible. (For the full story, see David Wood’s excellent book.) The term of art at the time was term “coopetition,” and it didn’t work. None of the participants in Symbian really had any desire to share their product plans with their competitors.

So, the irony was that, while Symbian was arguably at the spearhead of technology innovation, it was frequently stymied from actually being innovative by the inertia and culture of its participants. This left the market open to more focused, agile and independent companies like Google and Apple to dominate the smartphone market of today. In contrast, Nokia had an ignominious end – broken up and sold off, with billions of dollars in market value destroyed.

So, fast-forward to 2016 and my panel discussion at Maine Startup and Create Week…How can big companies be more innovative, and how can startups and large companies work together to the benefit of both?

Designer, Builder or Maintainer?

First, let’s take a look at the kinds of people that tend to work at startups versus larger companies: I have a simplistic but hopefully powerful model that divides people into three groups – “designers,” “builders” and “maintainers.”

Let’s use an analogy: here in San Francisco, arguably our best-known symbol is the Golden Gate Bridge – just look at any tourist tchotchke.

If we think about the Golden Gate Bridge, first there were the designers. In our culture, the designers generally have the “sexy” job – they are the visionaries.

GG_Bridge_Plans.png

Next come the “builders” who actually constructed the Golden Gate Bridge.

GG_Bridge_Maintainers.png

Last come the “maintainers.”  These are the workers who hang on ropes off the bridge, scraping off rust and continuously repainting it in International Orange.

International_Orange.png

This is the least sexy job in most people’s eyes: which would you rather be – the visionary designer of the Golden Gate Bridge or someone who hangs off it on a rope, scraping rust?

Now, in a startup, what you need for success are just a few designers – these are typically the founders.  You can’t have too many because they tend to butt heads.

What you really need for a startup is a boat-load of builders: these are the doers – people that create and Get Shit Done (GSD). Builders are the backbone of any startup.

What you don’t need in a startup is maintainers: everything in a startup is being created anew so there isn’t anything to maintain. You’re also focused on growth rather than optimization.

Contrast that to a big, established company: there, most people are maintainers. Their job is to ensure that an already successful business continues to be more successful. They are there to grease the wheels and optimize.

So What?

What this means is that there is a cultural mismatch between a large company and a startup.

At the core of the startup mindset is a willingness to fail and an acceptance of it. In a startup, failure is the norm – as the cliché goes, you fail your way to success. Since “failure” has negative connotations, I think it better to simply reframe it as “learning.”

Another important aspect of building a startup is understanding the art of the “good enough.” Because you’re bandwidth-constrained, you are forced to be very selective and very efficient in how you do things. You have to get them done quickly. You have to not let the great be the enemy of the good. You have to focus on delivering 80% of perfection for 20% of the effort.

Naively, when large companies aspire to become more innovative, they trot out clichés like “we reward risk-takers.” This is a lie. The last thing you want when you have a large company generating billions in revenue it to have some cowboy risk-taker come in and break it. What you want are maintainers to keep it working and keep it generating billions of dollars.

To take it back to the Golden Gate Bridge example, would you want a maintenance worker who said, “Let’s see what happens if we take all the bolts out”?

I think it would be better to rephrase it as, “We reward people who make small, smart bets.” Making a series of small, smart bets to test various hypotheses is the basis of iteration, and iteration is the how you build great products and great companies.

Recognizing these problems, many large companies have started to take a different approach – they have created specific initiatives intended to foster and drive innovation. Wonolo itself was created through The Coca-Cola Company’s innovation program.

Creating a Great Corporate Innovation Program

So, how can a large company create an innovation group and/or program likely to succeed? These initiatives can take various forms, but I think these are the most important elements:

  1. Set money aside – the budget for innovation can’t come out of the normal, operating budget for any existing business unit. If it does, it competes with the budget needed for maintenance of what’s already working.
  2. The innovation group must report directly into the CEO – this demonstrates genuine commitment to innovation and also helps unblock bureaucracy.
  3. Be clear with objectives – what specifically are you hoping that the innovation program does for your business? What are you looking to achieve? How does it positively impact your core business?
  4. Build the right team – a good mix is designers and builders from outside of the organization, along with some inside players who can help navigate the existing organization, as long as they carry enough weight. You will also need to reassure your best maintainers that they should stick to what they do well rather than trying to join the innovation program because it’s sexy.
  5. Accept failure – as discussed above, you must accept that failure is a vital part of the process. Not all initiatives you start or companies you fund will be successful, but you will learn something important from each.

How Can a Startup Engage with a Big Company and Win?

Big companies can kill startups. I’ve seen it happen.

Big companies can lead startups on and consume lots of their time and bandwidth with no pay-day. At the end of the process, the large company has perhaps lost a few hundred thousand dollars. Meanwhile, the startup has run out of funding and is dead.

Here’s what I’ve learned (the hard way) to avoid that outcome:

  1. Find your champion. Ted Reed is our champion at Unum. Not only is he an all-round great guy, but he also understands the need to be completely transparent with us. A great champion is your guide to the large company – its structure, how it makes decisions and the key players you’ll need to win over.
  2. Seek trust, honesty and transparency – any great relationship is built on mutual trust. Get feedback early and often (from your champion) on your likelihood to succeed.
  3. Don’t over-invest until you have clear commitment – be prepared to scale back or end the relationship if it’s not clear you are on a path to success. The opportunity cost of your time in a startup is huge. Don’t do anything for free – free means there’s no value, and it won’t be taken seriously.
  4. Ensure clarity in objectives, value and define success – if both sides are not clear on the business value that your product or service is providing to the large customer, be very cautious. Make sure both sides agree on what success means.
  5. Start with a small, well-defined trial – rather than trying to boil the ocean, it’s wise to start with a trial that demonstrates the value your product or service provides to the large company. This has less risk, requires less investment and has a higher likelihood of success. For more tips on how to best go about setting up a pilot, check out our related blog post.

How Can a Big Company Engage with a Startup and Win?

On the other side, how can big companies successfully engage with startups and win? Here’s my personal recipe:

  1. Be honest and transparent – don’t lead startups on. Be honest about chances of success and what it will take.
  2. Be respectful of bandwidth and provide funding, if possible – realize that a startup’s most precious commodities are bandwidth and funding. Do everything you can to reduce the sales cycle. Structure the deal to provide the funding and/or revenue necessary for the startup to succeed.
  3. Have realistic expectations in terms of maturity of a startup and its processes -don’t try to apply your pre-existing vendor onboarding process when engaging with a startup. For example, a 10-person startup won’t pass your 50-page IT security audit.
  4. Respect the need for independence – you may be providing a startup with revenue, funding and a great customer reference. However, a startup needs to be in control of its own destiny and own its own product roadmap. Don’t treat a startup like a consulting company or development shop, unless that’s how the startup sees themselves.

Overall, the relationship between a large company and a startup can be a marriage made in heaven. I would marry Ted Reed if I could.

Meaningful Metrics: 6 Steps to Metrics Heaven

Surely, one of the benefits of technology-based businesses is that it’s much easier to make data-based decisions – to “drive by the numbers”.

Superficially, this seems like it should be easy – just look at the data and decide what to do, right?  However, in every company I’ve seen, it’s always been painful.

Hopefully, this will help you avoid some of that pain.

Six Steps to Metrics Heaven

Six Steps to Metrics Heaven

I believe there are 6 steps to “metrics heaven”:

  1. Define
  2. Measure
  3. Present
  4. Discuss
  5. Action
  6. Iterate

Sorry, there are no short cuts; you have to get all 6 right to be successful.

Step 1 – Define

Ask the Right Questions

Your path to Metrics Heaven starts simply by asking the right questions.

Firstly, try to state the question behind each metric as unambiguously as possible.  Secondly, explain in plain English what this question means and why it’s important to your business – the context.

Example:
Question:  “Of the users that have signed up in the past 12 months, what percentage have logged in in the past 1 month?”

Context:  “This metric measures our ability to keep users coming back to our site.  This is vital to our core business model as we pay $40 on average to acquire each new user and can only make a profit on them if they keep coming back.”

Clear definition of the question is particularly important because the person responsible for actually answering the question – writing the database query, wrangling Google Analytics, etc –  is often not the person framing the question.  So, any ambiguity has the potential to result in an answer that is misleading or just plain wrong.

Fight Data Overload

If you try to measure, track, discuss and action too many metrics, you’ll lose track of what’s important.  You’ll lose sight of the forest for the trees.

Data overload is an insidious problem.  The addition of each new metric seems innocuous so it’s hard to say no.  Plus, there’s a tendency for people to want to get their group’s name “up in lights”.  But it’s vital that you do say no.  Otherwise, here’s what I’ve seen happen every time:

  • pulling and calculating all the metrics becomes a chore and takes forever
  • you end up with a big and unwieldy dashboard
  • the dashboard takes more than 30 minutes just to run through with everyone during the metrics meeting
  • people fall asleep or get distracted by their laptops/iPads/iPhones
  • you run out of time to discuss actions
  • people start to view the metrics meeting as a pain and find excuses to avoid it

Top 10 Metrics

To avoid overload, you need to identify a maximum of 10 metrics.  This “Top 10” constitutes your top-level dashboard.

By “top-level dashboard”, I mean the dashboard that is shared with the whole company, which the executive team uses to drive the business and the first – and maybe only – set of metrics you review when you meet.

Each group or department may have its own dashboard too.  There may be multiple levels of dashboards that dive deeper and deeper into the internals but don’t let those pollute your Top 10.

Your Top 10 metrics dashboard will probably be something you’ll want to work to automate so that it’s available live, displayed on a big flat-screen in your office, etc (more on that later).

To help ensure that you don’t grow beyond 10 metrics, use his rule:  if someone wants to add a metric to the top-level dashboard, they have to choose one to remove also and justify why the one they want to add is more important than the one they want to remove.

Now, let’s look at how to identify which metrics should be in your Top 10…

MAIN Metrics

I have come up with a handy-dandy acronym to help identify the metrics that you should choose, particularly your Top 10 metrics; “MAIN”:

  • Meaningful
  • Actionable
  • Isolatable
  • Not misleading

Let’s look at these 4 in more detail…

Meaningful

Is the metric a true and valid indicator of success in your core business?

A good way to ask this is to pose the following question:

“if this metric changes in the right direction, and nothing else changes, will my business be doing better?”

If the answer to this question is no, then you may have identified a useful metric to measure and try to impact, but it’s unlikely that you’ve identified a Top 10 metric.

Actionable

There’s not really much use measuring something if you can’t do anything about it.  You must be able to “move the needle”.

Metrics where you can’t move the needle are potentially interesting in terms of giving you a better understanding of what drives your business but almost certainly are not Top 10 metrics.

Isolatable

It’s vital that you choose and define metrics in such a way that they do not vary based on other, independent variables, whether those variables are in your control or not.

You need to be confident in measuring the effect of the actions you take to move the needle.  You need to avoid the age-old problem of confusing correlation with causation.

A classic example in web businesses is not insulating your metrics from the impact of fluctuations in traffic caused by other factors.  For example, you may have been measuring the number of user adds on a weekly basis and be working aggressively to drive up that number by reducing friction in your sign-up funnel.  The week after you launch a slew of sign-up funnel optimizations, you get 25% more users.  Yay for you – it worked – gold star.

Not so fast.  It turns out that what actually happened was that an article in a local style magazine that your PR agency setup some weeks back was finally published which drove 25% more traffic to your homepage.  Your conversion rate actually stayed the same.

To isolate a metric as far as possible, first frame the question in such a way that it’s not impacted by other variables.  The easiest approach to this is to always define metrics in terms of rates, not in terms of absolute numbers – e.g. “what % of users that started down the signup funnel completed it last week?” Not, “how many users signed up last week?”

Lastly, always be suspicious if it seems too good to be true.  Dig deeper to check whether your action really was the cause of the change.

Not Misleading

Lastly, even if a metric is isolatable, it may also be misleading in other ways.  It may give a false negative or false positive, or otherwise cause you to react in the wrong way.

It’s important to define metrics in such a way that they are hard to misinterpret.  Even if that’s not completely possible, it’s important to highlight the ways the metric could be misinterpreted when it is discussed.

Another classic example from the web is measuring the cost effectiveness of various CPC (cost-per-click) advertising campaigns.  You may find that certain keywords or certain channels allow you to buy traffic at a lower cost.  So, you decide to spend more money on those channels and reduce your spend on other channels.  However, not all traffic is created equally.  You may find that the users/customers brought in by the cheaper CPC rates actually generate proportionally less revenue for you so it’s worth paying more for higher quality traffic.

The danger of misleading metrics is one of the main reasons why stating the context of each metric (as discussed above) is so critical.

Step 2 – Measure

So, you’ve defined your Top-10 MAIN metrics in a precise and unambiguous way.  Now you just need to measure them.

Easy, right?

Um.  Mostly not.  Measuring is usually hard for a variety of reasons:

  • the relevant data is spread across multiple systems (your database, Google Analytics, MixPanel, AddThis, etc)
  • data is buried in the depths of your system and extracting it requires in-depth knowledge of your systems (and probably wicked SQL skills too)
  • the people with the know-how to extract the data are probably engineers who are busy with other things and consider extracting metrics beneath them
  • metrics were not really considered when the original system was built so the “instrumentation” has to be added retrospectively; this work contending with other product feature work
  • lots of manual steps are required, making it a chore

Tools

The good news is that there is a growing range of tools available to help.  The bad news is that any tool, if badly used, can be worse than no tool at all.

Let’s look at a few…

Google Analytics (aka “GA”)

I hate Google Analytics.  However, I can’t argue with the price.  And it’s incredibly powerful…if you can work out how to use it.  It’s unfortunately incredibly unintuitive.  It’s a camel (horse designed by committee).

The good news is that it’s fairly ubiquitous so you probably already have people on your team who have experience with it.

Mixpanel

I like Mixpanel.  I have no relationship with them other than as a happy customer.  Mixpanel does the 10% subset of Google Analytics that you actually need and does it well – the charts make sense, things are consistently referred to by terms that make sense.  It’s not free but it’s not expensive compared to the value it provides, in my opinion.

KissMetrics

Personally, I had a bad experience with KissMetrics but that’s just one data point.  They are probably Mixpanel’s main competitor.

SQL Queries

The data in your app is probably in a relational database.  That means you need at least one SQL ninja to write queries on that database.  SQL is a technology that is older than I am and therefore is definitely not cool but it does what it does better than anything else, if you know how to use it.

Unfortunately, modern web frameworks mostly insulate developers from having to use SQL directly.  Whilst this may be a good thing from the perspective of developer productivity and “separation of concerns”, it means that the number of engineers that can write complex SQL queries is reducing.

Microsoft Excel

There ain’t no shame in Excel, in my opinion.  Likewise, Google Sheets, although the graphs are much better in Excel.  Both have plugins to pull in data from various sources, including Google Analytics.

Hire Someone

You might want to consider hiring a “data analyst” – i.e. someone who runs the metrics process as their main job, rather than it being done badly as a part-time job by others.  I use parentheses around the job title because I’ve found that people who self-identify as Data Analysts have a tendency to be quite academic and unable to roll their sleeves up and actually grapple with Google Analytics to extract the numbers.

I would suggest you’re better off starting with a technically-minded digital marketing person.  They’re like unicorn poop in the current job market so good luck.

Step 3 – Present

Once you’ve got the actual data, you now have to present it in a clear way that can be digested by others.

Build a Dashboard

The first step in presenting metrics is to build a dashboard that quickly and clearly communicates your “Top 10” metrics.  Don’t worry about finessing the dashboard for metrics outside your Top 10;  it’s better to get the Top 10 dashboard as good as possible before you spend time elsewhere.

Excel is the most common tool used for this purpose and, to repeat myself, I believe there is no shame in Excel.  However, as discussed above, there are an increasing range of more automated options.

Use Charts

Some people are able to “see the matrix” – they can see patterns and trends in raw numbers.  But, most people can’t, including a lot of very technical people.

So, show charts in your dashboard, not numbers – they really help highlight trends over time, progress versus budget and other patterns.

“One Number” Nirvana

I already talked about limiting yourself to a maximum of 10 top-level metrics.  I’m going to go a stage further and say that every business has just ONE number that represents how successfully the business is performing.  This may seem impossible at the outset but but I bet you it’s not.

So, set yourself the stretch goal of identifying that One Number.  You’re unlikely to get to it immediately so start with your Top 10 and see if you can iterate towards your One Number.  It may be one of your Top 10 or it may be a calculation based on a number of metrics.

Simplistically, you might say that revenue or profit is the One Number for any business but absolute revenue or profit is driven as much by scale as anything else.  Before you can scale to maximize revenue or profit, you need to have a business model and product that is worth scaling and you’ll also likely need to persuade investors to give you the cash required to scale.  That’s where your One Number comes in.  So, your One Number is likely to be something like customer acquisition cost (CAC/CPGA) or profit per user add.

Build a Live Dashboard

Data junkies will argue that the ultimate nirvana is to have a live dashboard that shows your One Number and Top-10 metrics, with graphs, on a web page that anyone in the company can view at any time to see a real-time view of how the product and business is performing.

But, however easy it is to get view the metrics, that does not remove the need to meet to discuss them and to action them.  So, let’s talk about that.

Step 4 – Discuss

Having your metrics beautifully presented is no use if you don’t actually talk about them as a team.

Meeting to discuss the metrics regularly keeps them in the front of everyone’s minds, makes sure that everyone understands what actually drives your business and, most importantly, is the venue to come up with your action plan for pushing them in the right direction.

Meet Weekly

The only cadence that I have seen work when it comes to reviewing the metrics in an online business is weekly.  A month is an aeon in Internet time.  Daily is good for quota driven sales team but too much for a startup team that is trying to build and rapidly iterate a product.

So, set a fixed time to meet every week.

The most important people to have at the meeting to review metrics is the people in the company that can actually impact those metrics.  Others can attend but you don’t want too many cooks.

Manage the Time

It’s very easy for the weekly metrics meeting to turn into a mechanical run through of all the metrics.  This is especially true if you’ve let your dashboard grow beyond 10 metrics or if you spend time diving into lots of very detailed metrics below your Top 10.

Most important is that you spend at least as much time on actions as you do on reviewing the numbers.  People who attend the weekly metrics meeting should be expected to have reviewed the metrics ahead of the meeting and come to the meeting armed with questions and suggestions for actions.

Step 5 – Action

Having perfectly accurate and beautifully-presented metrics that you discuss weekly as a team is useless if you don’t actually do anything about them.

What’s surprising is how often this happens.  People come to the weekly data meeting, review the metrics, ask questions and then go back to their jobs.

If this is what’s happening, you’re wasting time preparing the metrics and might as well get a job rearranging deckchairs on a sinking cruise ship.

Set Targets

It’s way easier to move a metric in the right direction if you specify a target of where you’re trying to get to.

Frankly, any target is better than none but it’s of course better to have a target that is achievable.  It’s better to start with a small, achievable increment and then move the target as you get better and more sophisticated in impacting your metrics through action.

To learn more about this, read a good dieting book.  Trying to either “stop being fat” or “lose 40lbs” is much, much harder and more demotivating than setting an achievable target for each week.

Actions for every metric

Every metric in your Top 10 should have a corresponding list of actions aimed at moving the needle in the right direction, along with an owner for each metric.  These can be marketing efforts, new features, A-B tests, whatever.

If you repeatedly don’t come up with actions for a Top 10 metric, ask why.  It probably means you should remove that metric from your Top 10 and replace it with something you feel is more important and which you can actually impact.

Likewise, if agreed actions don’t get performed by their owners, you can keep people honest by asking whether people still think the metric is important in terms of making the business successful.  If it is, then ask what other tasks they are doing that are more important.  If it’s not, remove the metric.

Step 6 – Iterate

I would say that the chance that you’ll choose the right set of metrics, be able to extract all the data you need, present them clearly and  build an action plan to move the needle first time around is approaching zero.

Expect it to be painful initially.  Expect it to take time.  Expect that you’ll need to iterate.

Summary

If there are just 4 things I suggest you take away from this, they would be:

  • Ask the right questions
  • Fight data overload
  • Focus on action
  • Iterate and expect it to take time to get right

Agree/disagree?  Please leave a comment (link is top-right of the page).

Why We Fight – Sales vs Engineering

I am one of probably a relatively rare breed. I started my career as a software engineer, then moved over to the “dark-side” for a few years but more recently have come full-circle back to running a development organization. I’ve been a CEO, GM, CMO, CTO, VP Corp Dev and VP Bus Dev.

Honestly, I’ve never felt 100% at home on either “side” and I’ve come to accept that this part of the value that I can provide. I am a hybrid that can “translate” between engineers and sales people. Sometimes this causes me an enormous amount of “cognitive dissonance” but mostly it proves useful.

Fighting

Now and again people come to me asking me to help resolve disputes or to explain why engineers don’t respect sales people or sales people don’t respect engineers. Often, the person in question is hopping mad and simply cannot understand the other side’s behavior. This is a quintessential problem for any technology company and I’ve seen it in every company I’ve been involved in. It’s the basis of many a Dilbert strip.

The first step to resolving any dispute is for each side to understand the other. So, this post tries to explain what makes engineers and sales people tick, looking at the differences and similarities.

I have realistic expectations and you should too. I don’t think the “sales versus engineering” dispute will ever be completely resolved in any organization but I do think both sides can cut each other some slack as a result of better understanding.

We are all weirdos

So, at the risk of offending both sides at the outset, the first thing we have to accept is that both sales people and engineers are weirdos.

Sales people are weirdos because their job essentially involves talking to people who don’t want to talk to them, getting rejected again and again and repeating that process day after day. If they weren’t sales people, they’d probably be diagnosed as having a personality disorder. Most “normal” people would quickly become discouraged and demotivated and, once that happens, you can’t sell anymore.

Software engineers are weirdos because they have to talk to computers all day and get them to do what they want them to do. Computers aren’t like people. Computers have no common sense. Computers have no manners. Computers are unforgiving, humorless and pedantic to the extreme. Computers don’t get ambiguity and shades of grey. It’s either precisely right or it’s wrong and it won’t work.

This is partly selection bias – i.e. being a software engineer or sales person attracts a certain kind of person with certain characteristics to start with – but it’s also partly what the job does to you. When I’ve spent long periods writing software, I’ve found myself becoming more like the stereotype of a software engineer – more introverted, more cynical, more negative. If you talk to computers all day, and people much less, you start to think and communicate in a certain way. Likewise, when I’ve been on the front lines talking to customers and trying to close deals, I’ve become more like the sales stereotype – more outgoing, more predisposed to bend the truth.

Stereotypes

As a disclaimer, a lot of what I have to say here involves stereotyping. Stereotypes are useful but there are always exceptions to them.  There are extrovert, positive engineers that I’ve met. Honestly, I’ve never met a negative, introverted and cynical sales person but there’s always a first time…

Motivations

When it comes to motivations, let’s start by seeking some common ground:

I believe that both sales people and engineers are, like almost anyone, motivated by the respect of their peers.  They want to be respected by people they respect.

However, beyond that, engineers and sales people have fundamentally different motivations and different cultures. That’s where a big part of the conflict originates.

Engineers are generally motivated by learning new thingsunderstanding abstract ideas and doing smart things. In the eyes of their peers, they want to be regarded as being clever.

An important thing to realize here (and one of the perennial problems with managing development teams) is that being clever has nothing directly to do with getting things done. Software is never “done” and there are many extraordinarily clever software engineers  who are very bad at delivery and will happily tinker with any software indefinitely, if left to their own devices.

In terms of an engineer’s motivations, the means are often more interesting, and therefore more important, than the ends.

In contrast, sales people are generally motivated by building relationshipsachieving objectives and winning.  In the eyes of their peers, they want to be regarded as being successful.

For a sales person, the means only exist to serve the end – getting the deal done.  This leads to the perennial problem of sales people over-promising to get a deal done.

Personality Type

Engineers are generally introverts, sales people are generally extroverts.

The distinction between extrovert and introvert is commonly misunderstood and oversimplified as being simply whether you are socially outgoing or reserved. That is true to some extent but misses more fundamental differences between the way the two types think and interact with others.

Extroverts think out-loud. They find interaction with other people energizing. They solve problems and get things done by externalizing them – talking about them with others. To an extrovert, having a meeting to talk about something is getting the job done.

Introverts think internally. They find interaction with other people draining. They like to get their thoughts straight before they externalize them.  They solve problems by working on their own until they have a solution they’re happy with. To an introvert, having a meeting to talk about something gets in the way of getting the job done.

Beyond the simple introvert versus extrovert distinction, there are other common distinctions. For example, if you’re into Myers-Briggs personality types, the stereotype engineer is an INTP (Introverted, iNtuitive, Thinker, Perceiver). A fuller discussion of this topic is an attractive rat-hole but I will resist the temptation.

A typical day

A sales person’s typical day is very different to an engineer’s.

The sales person’s day is generally driven by a series of scheduled calls and meetings, each with a determined start and end time and, hopefully, clear objectives. A sales person’s typical day is largely focused on a lot of communication with other human beings. Outside of scheduled meetings, it is also commonly “interrupt driven” – e.g. a potential customer calls and you pick up the phone and talk to them.

The engineer’s day is generally driven by long periods (sometimes extremely long) of concentrated, heads-down thinking around solving a particular problem.

You can only be effective in this mode if you can be truly focused and able to get into a state of flow (also referred to as “being in the zone”). On any given day, it takes time to establish focus and it is very easily broken. Unfortunately, once its broken you can write off the rest of the hour, and sometimes the day. Different people have differing abilities to get themselves into the zone quickly and at will but it’s definitely a big issue – it’s not just a question of time lost but you’re also much more likely to introduce defects into software if your train of thought is interrupted.

I’ve referenced in previous posts Paul Graham’s excellent Maker’s Schedule, Manager’s Schedule which discusses this topic in more detail.

We’ve all seen this

This is where another very common source of conflict comes in.  You’ve probably witnessed something like this firsthand yourself:  the sales person gets a call from a potential customer that is close to signing a deal. The customer has one remaining technical question that the sales person cannot answer directly. Being an extrovert and being motivated to please the customer and get the deal closed, the sales person doesn’t send an email but rather calls or walks over to the engineer with the customer on the phone still to ask the question…

The engineer emerges from what seems like a dream like state and stares blankly at the sales person. The engineer is pissy and, if the sales person is lucky, answers the question, albeit in an extremely terse way.

The sales person doesn’t understand why the engineer is being so difficult and unpleasant, especially given that this is a question from a customer that is about to sign a big deal. On the other hand, the engineer is annoyed because the sales person has just interrupted their flow and they know they’ll find it hard to get back into what they were working on.

Furthermore, the engineer doesn’t understand why the sales person couldn’t answer the problem for himself. After all, the engineer’s motivation is to understand things and seem clever so they don’t get why the sales person wasn’t as interested as they would have been to work out the answer themselves on their own, rather than appearing dumb by having to ask someone else.

In contrast, the sales person doesn’t understand why the engineer doesn’t care about winning the deal.

However, once you understand the difference in motivations and cultures, it makes sense.

Culture

Along with the differences in personality type and motivation, come some big differences in culture.

If high school is a microcosm of “real life”, think of it this way:  sales people are the jocks and the engineers are the nerds that got pushed into their lockers in the hall every day by the jocks.

Digging a bit deeper, let’s look at what it takes for a sales person to be successful:

Sales is about building and finessing a story that sells. Contrary to what the other side may think, it’s not generally about telling out-and-out lies but it is about managing perception and, for the sales person, perception is reality.

That story and that perception is fragile and needs to be carefully massaged and continuously buoyed-up.

Likewise, because the sales person faces continual rejection, day after day, the sales people themselves need careful and continual massage. This makes sales people generally very sensitive to people who come along and burst their bubble by asking hard questions or being negative.

Unfortunately, this is exactly what engineers tend to do. Engineers are extremely suspicious, and often downright hostile, to the “story” that sales people are selling – they see it as artifice, fakery, puffery.

Is this simply because engineers are cynical, negative people? Sort of – but, the problem is confusing cause with effect.  Engineers are cynical and negative because it’s essential to doing their job well. Remember that, when you’re dealing with a computer, there is no perception, there are no shades of grey – it’s either exactly right or it’s wrong.

Therefore, engineering culture is deliberately extremely suspicious of, and aggressively hunts down and eliminates, artifice and “perception management” because it hides the truth and the right answer. It is kryptonite to the engineering process.

People who try to manage perception don’t survive very long as engineers because they have to tell computers what to do and computers aren’t having any of it. In contrast, managing perception is what sales people do for a living.

This all results in two very different cultures.  Sales culture requires being buoyed up on a cushion of positivity, whereas engineering culture requires negativity and challenging everything.

Religious Arguments

A side effect of the engineering culture is to spend many hours engaging in what are referred to as “religious arguments” – Python versus Ruby, Mac versus PC, etc, etc, etc.

Because engineers and engineering culture is wired to seek and find the one correct answer, engineers have trouble dealing with the concept of differences in opinion and equally valid choices.

Opinion simply doesn’t compute.

Agree, disagree? Please leave a comment.

Focus – what it really means and why it’s important

When I started my first company in the late 1990s, many people advised me on the importance of focus.

“Focus”, I’d repeat and nod sagely, not really knowing what they meant.

“Focus” seemed like an inherently Good Thing™ on the face of it – like happiness and democracy – but a pretty vague concept.

Since then, personal experience has shown me the critical importance of understanding what focus means when building a company.

Focus is absolutely essential when building a startup but it’s also important for companies at any stage so I hope this is useful and relevant to all.

Focus – a definition

So, how do we define “focus” in the context of building a company? I would define it like this:

Focus is maximizing the time spent on the essential complexity of the problem your company is trying to solve and minimizing the time spent on incidental complexity.

Every company and every product is trying to create some value for its customers and users. Without that, the company and product by definition has no value. Typically, creating that value can be thought of in terms of solving some problem for your customer.

The more time you can spend on understanding the value you are creating for your customer –  i.e. understanding the problem you are solving for them and solving it – the more you are increasing the value of your company.

Working out what the value is you are creating –  the problem you are solving for your customer and how to solve it in the best possible way – is the essential complexity of your company.

In contrast, time spent on incidental complexity does not increase the value of your company and product.  It simply takes away time and energy from working on the essential complexity.

In summary, you don’t build value by solving problems that others have already solved – you create value by solving problems not yet solved and, to a lesser extent, by solving problems in significantly better ways.

Incidental Complexities

So, what are incidental complexities?

Here are some examples of the most common ones I’ve seen (in no particular order).  I bet many readers will have seen all or most of these:

  • infighting between colleagues
  • “busy work” that could be automated easily
  • reinventing the wheel
  • failure to make decisions / trying to keep too many options open
  • early stages of recruitment – finding candidates, reviewing resumes
  • meetings with ill-defined agendas
  • waiting because of colleagues’ poor time-management
  • attending conferences without pre-scheduled meetings and clear objectives
  • over-analysis of vendor/technology selection
  • doing your own IT support
  • obsessing over your competition
  • pitching investors
  • trying to make money from things that are not part of the problem you’re solving for your Customer
  • frequently switching between multiple tasks
  • rewriting things that are good enough (see my post on Why You Should (Almost) Never Rewrite here)

Let’s look at some of these examples in more detail and try to  identify some general sources of incidental complexity.

Decisions

“We would rather suffer the visible costs of a few bad decisions than incur the many invisible costs that come from decisions made too slowly – or not at all – because of a stifling bureaucracy.” – Warren Buffett

“Commit to making decisions. Don’t wait for the perfect solution. Decide and move forward.” – 37signals

“A good decision is a made decision.” – Clayton Christensen

Many people have written about the art and importance of decision making.  I will not attempt to cover this topic in depth.

My view is simply that you don’t learn anything from a decision you don’t make.  Whereas, you learn from all decisions, especially bad ones.  Fail fast, move on.

Startups, in particular, are all about making high-risk, low-data decisions.  If you don’t have the stomach for those, you shouldn’t be running a startup.  Deferring hard decisions just creates ambiguity and saps time, morale and energy.

Choice is Bad

When it comes to moving quickly and focusing as much time and energy as possible on the essential complexity of your business, choices are a Bad Thing™.

This seems counterintuitive to many people, especially in the West where our culture is built on the importance of individualism and choice, and denial of such freedom is tantamount to treason. However, choice in areas outside of your essential complexity just slows you down.

In software development, one of the reasons that Ruby-on-Rails is so popular, and one of the reasons why we currently use it, is that it is built on the principle of “Convention over Configuration“.  This principle is about reducing the number of decisions a developer has to make – gaining simplicity without necessarily losing flexibility.

Think about every aspect of your business in these same terms.  Everyone likes to think that their business is special and unique but the reality is that, outside of the essential complexity of your business, the other challenges you face are common to 90-100% of businesses of a similar size and stage of growth. They are solved problems and therefore textbook incidental complexity.

Analyzing choices that don’t really have impact on the value you’re building isn’t time well spent.  In a typical Silicon Valley startup, these are decisions like which accounting system to use, which CRM system to use, which web development framework to use.

Of course, I’m not advocating making completely blind choices with no thought whatsoever. However, what I’ve seen is that people often err on the side of overanalysis. The return on this time diminishes very rapidly.

If it’s not core to your competitiveness or differentiation, why spend another hour or day or week analyzing to make a 5% better decision?

Outsource Everything You Can – Use Specialists and Don’t Reinvent the Wheel

Money spent taking away incidental complexity is money, time and energy saved to focus on essential complexity.

A classic case in point is recruitment – people hate recruiters.  With a few notable exceptions, I hate them too.  But, they are a necessary evil.  At the time of writing, hiring in Silicon Valley is as hard as it ever has been in my memory.

To be clear; recruitment is one of the most important things that you can do as a founder, manager or executive – controlling the quality of the people that you let in is one of the best controls you have over the quality of your company and its output.  Therefore, I would never advocate for outsourcing the process of choosing the best people.

But, that doesn’t mean you have to burn lots of hours of your own time on the front-end of the recruitment process – finding people, contacting them, reviewing resumes, screening candidates, etc is not a good use of your time – outsource it.  That’s what recruiters do. Their fees may seem outrageous but they’re not when you consider the real cost of the time you’d spend doing it yourself – not only the direct cost of your time, but the much bigger opportunity cost of not spending that time addressing your essential complexity.

Another big mistake I’ve seen is startups taking non-core parts of their customers’ business process and incorporating them into their own product – invoicing, online payments, backup, logging and monitoring, reporting, hosting, etc, etc, etc.  These aspects are peripheral to your essential complexity so they should be peripheral to your product.  They are solved problems.

Usually, what superficially seems like a simple problem to solve inevitably turns out to be more complex.  By implementing the functionality in your own product, not only have you lost that time you could be spending on essential complexity but you’ve also taken on the burden of supporting and maintaining a feature that doesn’t return anything for you in value and differentiation.  You’ve basically re-invented the wheel.

Don’t forget that the vendors of these systems understand their overall domain pretty well (it’s their essential complexity) and they are building products that cover 70%-100% of the most common requirements.  At the same time, you likely understand your own problem domain better than almost anyone so, if you happen to have unusual or unique requirements on invoicing, data backup, etc, you’ll know about it. If there is some aspect of these systems that you are differentiating on, that validly falls into your essential complexity.

These days, with SaaS companies proliferating and APIs available for more and more business systems and processes, it’s very simple to incorporate all of these important but non-core aspects into your overall offering with minimal effort.

Today, most companies wouldn’t imagine building their own data centers – most companies just use the Cloud (Amazon EC2 or similar).

Take that to it’s natural conclusion – what other aspects of your business can you out-source?

Beware False Economies

In a startup, money is always tight, and managing your cashflow is critical to success.  However, it’s easy to let this drive you into false economies.

In every business there is “busy work” that is no fun but just has to get done.  In startups, this is a bigger problem because people have to wear many hats so this busy work has to be done by someone who has many other things to do.  Some of those things are likely to be part of your essential complexity.

Every case is unique but always think through the true cost of having internal resources spend their time on busy work that could be easily automated and/or outsourced.  Again, there is a direct cost but a bigger opportunity cost.

Don’t Sweat the “Competition”

Another mistake I have seen is for early stage startups to waste time and energy worrying about their “competition”.

The reality is that, early on, most startups are in an ill-defined space and still in the Customer Discovery phase.  This means your “competition” is hard or impossible to define.  Your true competition will emerge over time, once you’re out of Customer Discovery and better understand your positioning.

In the meantime, just focus on creating value.  You’ll likely beat your “competition” simply by focusing more of your energy on the essential complexity of the problem you’re solving than others.  The faster you understand and solve the problem for your customer, the faster you create value.

Death by Meeting

So many people have written about meetings, that I will only give it a cursory discussion.

In my view, meetings are another necessary evil but are overused.  Here are my guidelines:

  • meetings should be schedule to 30 minutes by default, not 1 hour
  • only invite those people who need to be there.  The productivity of a meeting decreases with the square of the number of people in the meeting.  Those people that need to be in the loop can be updated by one of the people in the meeting afterwards.
  • every meeting has a clear objective – not a detailed agenda, necessarily – a clear objective of what is expected to have got done by the end of the meeting
  • every meeting starts on time

Beware the Cost of Context Switches

Another hidden cost of having insufficient focus, is the cost of continually having to switch between tasks.  This switching cost is often underestimated or ignored.

Paul Graham wrote a great post on the big impact of context switching to “makers” (software engineers, etc) versus managers, who are generally more used to scheduling their time in blocks.

Actually, the cost of context switch is there for  everyone, even the most adept executive with the most fastidiously managed calendar. Research shows that human brains are very poor at multi-tasking and, ironically, those that think they are best at multitasking are actually the worst.

Don’t Underestimate the Importance of Motivation and Energy

If you’re only looking at the time spent, you’re only seeing part of the picture.  You need to understand the importance of motivation, energy and morale on your own output and the output of the company as a whole.

Incidental Complexity not only takes time away from Essential Complexity but it also demotivates and saps energy and goodwill, especially when the people doing the work know they are working on Incidental Complexity.

Essential Complexity – What Should You Be Spending Your Time On?

So, what should you actually be spending your time on?  What is essential complexity?

It’s tough to give a simple, single answer to this question.

Firstly, it depends on the stage of your business.  At this point, I will pause to plug Steve Blank’s book “Four Steps to the Epiphany“.  I dont’ know Steve but if there is just one book you should read on how to build a startup, you should read this one.

Early on in a startup, the essential complexity is working out who your customer is, the problem you are solving for them, the value of that problem, what your product is and how you’re going to solve it.

Once you’ve solved that (and only once you’ve solved that), your essential complexity becomes understanding your customers’ problem in even more detail and making sure you are designing, building and deploying a product that best satisfies those customer needs.

Further down the line, the essential complexity will be to make sure you stay ahead of your competition, increase efficiency, etc.

Of course, you can’t also ignore the #1 rule of any business; don’t run out of money.  So, raising money is also essential complexity for most startups.

It also depends on your individual role in the business but this is easy to overstate.  In a startup, people always have to wear multiple hats.  The right things for you to do are the right things to do, whether  or not they are strictly speaking part of your “job description”.  People who have rigid ideas about job descriptions probably shouldn’t be working in startups.

Some Focused Suggestions

In a smaller company (i.e. a typical startup), the first step is to make sure that everyone – from the CEO down to the intern – knows what the critical success factors of the business are – i.e. what are the things that the business needs to get done in order to be successful.  This list will change over time but needs to be regularly communicated and repeated.  Achieving these critical success factors is the essential complexity for your business.

With that list in mind, every person in the company needs to get into the habit of asking the following question:

Of the things I could be doing, am I currently doing that task which most contributes towards the business achieving its critical success factors?

Of course, people won’t always get the answer right.  Also, very often, and particularly for those people lower in the organization, they may be several degrees of separation away from the overall success factor for the business.

However, don’t underestimate people too much – I think most people will answer this question correctly more often than not.  The trick is to get and keep the list of critical success factors at the front of everyone’s minds and to get people into the habit of asking the question of themselves, and of others.

The next part is about communication.  People need to communicate what they think their priorities are and why.  This helps flush out people’s dependencies on each other.

In a very small organization this can be done verbally.  Once you’re beyond about 10 people, it makes sense to write these down and communicate them – I’d recommend weekly, by email.

As the organization grows, you’ll have to get more formalized.  In a bigger organization I’ve seen large SIPOC exercises run at whole-company off-sites work very effectively.

Whatever the size of the organization, the most important thing is that you and everyone else understand the difference between essential complexity and incidental complexity and what the specific critical success factors are for your business.

Focus, focus, focus.

Agree, disagree?  Please leave a comment (link at top of article).

The Funnel is the Product, The Product is the Funnel

Others have made the important point that the critical word in the phrase “Software as a Service” (SaaS) is “Service” not “Software”.

Taking this a stage further, I believe that the traditional distinction between a company’s “Product” and the stuff that surrounds it needs to be completely eliminated.

In my view, this distinction is a hangover from the bygone days of software companies selling boxed software products.  In contrast, if you’re a SaaS company, you’re truly in the service business, not the software business, so your view of what constitutes your “product” has to evolve.

The Traditional View

The main building blocks of a “software company” have traditionally been:

  • the “Product” – this is the functionality that delivers on the company’s value proposition.  The “Product” is built by the development team and actively worked on.  For online software (i.e. most software these days), the “Product” is generally everything that’s behind the log-in stage.
  • “Marketing Material” – this is generally static content including the homepage and those other pages that are linked to from it.  It describes product features, benefits, case studies, etc.
  • “Sales” – unless your company relies solely on self-signups, these are people responsible for getting customers to pay for your product.  In contrast, the job of development is to create what people get after they’ve paid.  The Product does not directly help with “sales”, except in the obvious sense that it’s the promise made of what you’ll get when you pay and you might be allowed a free demo beforehand.
  • the “Funnel” – in most companies, there is typically the notion of a funnel – i.e. that series of steps that a person has to go through from first contact with your company to becoming a customer, be they on your website or on the phone with a sales rep. There may be people focused on increasing the conversion rate through your funnel (i.e. the % of people that end up being customers after first contact) and these people may even be active planning to reduce friction at each stage of the funnel.  However, the “Funnel” is generally still thought of as “marketing stuff” – not part of the “Product”.

What’s the Problem?

This is how the story of an Internet startup typically plays out:

After an initial launch, the rush is then on to keep evolving the product and keep delivering on a roadmap of features.  The development team thinks its job starts after the user is signed up and signed in and that its role is to give the user more and better stuff for their usage / money.

In contrast, the “marketing material” quickly becomes the poor step-child of the product and becomes stale as it increasingly gets out of sync with the product over time.

This happens for a few reasons:  partly because no one inside your company ever looks at it (out of sight, out of mind), partly because the marketing team is much smaller than the development team so there is little bandwidth for content changes and, often, because changing the static content is hard (it often requires involvement of developers who see it as busy work).

As for the Funnel; it may be actively measured by marketing and sales people. Also, noble efforts may be made to improve conversion each step along the way but the Funnel is still “marketing stuff” in the eyes of the development team and large scale changes to it are hard to make happen since the Funnel is not part of the “Product”.

Typically, the things that are deemed worthy of development attention and time are, in decreasing order:

#1 the platform
#2 the product
#3 the funnel
#4 the marketing material (static content)

Your Potential Customer’s View

Unfortunately, if you are in the shoes of a potential customer, it’s the marketing material you usually see first and which forms your first impression.

So, to the potential customer,  the order above is exactly the reverse order of importance.  It should be:

#1 the marketing material
#2 the funnel
#3 the product
(#4 the platform) – never really seen by the customer, except indirectly

You can lose huge numbers of potential customers on their first touch with your company if your marketing content is weak.  Inside your company, marketing and sales realize that first. Development often doesn’t realize it or does and doesn’t care.

This creates tension and all the required ingredients for a text-book “marketing versus development” internal feud seen in so many software companies.

The Funnel is the Product

The solution is to abandon the notion of “marketing stuff” vs “product”.

In this view, everything that a potential user sees is now your “product” and should be continually improved and finessed, starting from the stuff the potential user touches first – typically your homepage.  Because everything is now the “product”, development now owns it too.

For a lot of people, this is a pretty fundamental change so don’t expect to be able to make it over night.  It’s about seeing your product from an outside-in perspective, rather than an inside-out perspective.

However, this transition has a number of significant benefits:

  • everyone – including the development team – is engaged and feels responsible for driving usage and, if applicable to your business, sign-ups
  • much less, if any, “them versus us” mentality between marketing/sales and development
  • “marketing content” no longer the poor step-child and no longer out of date
  • the funnel is part of the product, usually resulting in a more innovative, more interactive and smoother funnel meaning less friction and, hence, more users

The Product Event Horizon

Making this change requires joined-up thinking.

As soon as someone has a slightest touch with your company, your product needs to act like a tractor-beam and gradually draw that person deeper and deeper in.  The product needs to progressively reveal more and more benefits and features and get the person to start using it without even realizing they are.

This is the “unconscious adoption of a product”.  Think of it as seducing your user.

At a certain point, the user crosses what I call the Product Event Horizon.  The Product Event Horizon is an invisible line that users hopefully won’t realize they’ve crossed but which they can never pass back across again.

(Read more about Event Horizons on Wikipedia if you want to know more about why I chose this term and geek-out on blackholes for a bit.)

Your company’s job is to push the Product Event Horizon out as close to possible to the first touch with your potential user and to make passing over it as unnoticeable as possible.  i.e. people start using your product without even realizing they are and before they can stop, they’re hooked.

To use a more plebeian analogy, the Pringles slogan had it right – “once you pop, you can’t stop”.

What to do

So, how do you actually make this change? Here are some pointers:

1.  Make sure everyone understands your funnel
Everyone in your company should understand the series of stages that a potential user goes through from first touch with your company to being an active user and/or paying customer.

More specifically, make sure everyone understands the conversion rate at each stage of the funnel – i.e. what people % of people advance to the next stage rather than drop off.

2.  Measure with tools
As the saying goes, you can’t manage what you can’t measure.  More specifically, you don’t know what changes help if you can’t measure their effects.

Another issue here is that SaaS companies are usually dominated by engineers and engineers generally hate “marketing stuff”.  They argue endlessly with sales and marketing over what’s important in the product.

However, engineers are generally suckers for facts and the scientific method.  If you show them actual data showing what users click on, how they make there way through the product from their first touch and where they drop off, there’ll be much less argument over what’s important to fix.

There are loads of tools out there but here are three of my favorites:

CrazyEgg – click-tracking – presents a visual “heatmap” of where people click and what they scroll to
Optimizely – predominantly an A-B testing tool but also tracks clicks
Google Analytics – the obvious choice

3.  Make sure you can track users all the way through your product
An important step in the joined-up thinking and elimination of the distinction between “product” and “marketing stuff” is the ability to track a user all the way through from their first touch to their ongoing use of your product over time.  Don’t let data about conversion rates through your funnel stay separated or get separated from data about usage of your product.

4.  Get your head around and use Cohort Analysis
Cohort Analysis allows you to compare the behavior of your users based on how long they’ve been using your product rather than when they started using your product.  For example, it allows you to easily answer the question, “during their 2nd month of usage, how much did users who joined in January use the product versus those who joined in August?”

There are many articles out there on Cohort Analysis which explain what it is and why it’s important. Try starting here:

http://themetricsystem.rjmetrics.com/2009/09/09/cohort-analysis-in-rjmetrics/

5.  Invest in a Content Management System
A good Content Management System (CMS) will allow tweaks to the text, images, videos and, to some extent, layout, throughout your site without having to dive into raw HTML or source control.

This significantly decreases the time to make such changes and increases the number of people who are able to make them.  In turn, this means they can be tweaked much more frequently.

Importantly, don’t fall into the trap of only using a CMS for the “marketing stuff” and not for the “product”.  It should be easy to tweak language and layout throughout your whole site.

6.  A-B test as a matter of course
A-B testing is a habit you have to get into, rather like test-driven development.  A-B testing lets you answer the question scientifically as to whether a change you make has a positive benefit.

One note of caution is that A-B testing can only be effective once you have a certain volume of hits/users – you need enough volume in order for the comparison to be statistically significant. Premature A-B testing is a form of premature optimization that can hinder rather than help.  A-B test with too few hits/users and you’re trying to read tea-leaves, rather than apply the scientific method.
Fortunately, good A-B testing tools also let you know when you have enough data for the comparison to be statistically significant.

7.  Have a regular data meeting
This is the meeting where you review the data on how your company is performing.  It’s the meeting that engages everyone in driving the company by the numbers.

Ideally, have this meeting weekly.  In a small company, make it an all-hands meeting.  In bigger companies, make sure that there are representatives from all areas including, importantly, actual developers.  Make sure that no one can stick their head in the sand and get disconnected from the reality of your business.

Make it the most important meeting of the week.  It’s the heartbeat of your company.

The raw inputs are all that data you are collecting through the approaches above – funnel conversion rates, hits, click-tracking, A-B testing results, etc.

However, identify a small number – some say less than 10, others say only 1 – of metrics that you focus as the true indicators of how your business is doing.  You can dive into others as required.  Try to review too many data points and you can’t see the wood for the trees, the meeting drags on and people start to think of it as a chore.

8.  Hire a marketing engineer
Or, because these people are hard to find and are highly prized, convince someone internal to become your marketing engineer.

Marketing engineers bridge the gap between the marketing activities and software engineering – they live and breathe meta-data, tags, SEO, viral co-efficients and conversion rates.

On the face of it, developers should naturally gravitate towards this work since it’s hard, involves tinkering and is data-driven.  However, many developers still shy away from it because it smells of “marketing stuff”.

Agree, disagree?  Please leave a comment. (Hint: the link to comments is hidden at the top of the article, underneath the title.)

Why You Should (Almost) Never Rewrite Code – A Graphical Guide

I’m by no means the first person to write about the dangers of rewriting code.  The definitive work for me is “Things You Should Never Do, Part I” written by Joel Spolsky in 2000.  If you haven’t read that, you should.

A recent discussion caused me to create a series of charts to graphically illustrate the dangers of rewriting.  I tend to think graphically – blame too much time in Powerpoint creating VC pitch decks.

I hope these charts help anyone considering rewriting code or being hectored by earnest, bright, young engineers and architects advocating a rewrite.  I’ve seen this movie before and I know how it ends.

The Status Quo

Firstly, here’s the status quo:

The more time and money you spend on an existing product, generally speaking, the more functionality you get.

Let’s then overlay the competitiveness of the product in its target market.  Of course, what you want to achieve is a continual improvement in the competitiveness of the product.  Competitiveness doesn’t increase as fast as functionality.  Your competitors don’t stand still so, typically, the best you can hope for is to increase competitiveness gradually over time – even staying flat is a big challenge.

Of course, this chart is idealized – functionality will increase in a more lumpy way as you release major new chunks of functionality and competitiveness will move up and down against your market. However, they demonstrate the point.

Note:  the Y axis on all these charts represents “functionality” which, for the purposes of this discussion, can be considered a blend of features and quality.  The distinction between the two is not really important in this analysis since you always want to be advancing on one or both and they both impact overall product competitiveness.

Let’s Rewrite!

Cue your software architect.  He’s just been reading about this great new application framework and language called Ban.an.as – it’s so much better than the way you’ve been doing things previously. Hell, Ban.an.as has built-in back-hashed inline quadroople integration comprehensions. Plus, all the cool kids are using it.

If you’re a non-technical manager or executive, this can become quickly overwhelming and hard to argue against.  They’re the experts, right, so they must know what they’re talking about.

[By the way, if you’re a non-engineer, there’s no such thing as “back-hashed inline quadroople integration comprehensions” – I made that up.  Sorry.
A little secret here: there really haven’t been any new programming paradigms invented since the 1970s.  Software folks are generally in their 20s and didn’t see them the first time around – they just get “re-discovered” and given new names. Sssh – don’t tell anyone.]

Just to be clear – I’m not saying that new languages, frameworks and technologies can’t create significant improvements in developer productivity – they can.  But, their introduction into an existing product has a big price, as we’ll see.

Lastly, lest I alienate or offend my fellow geeks, I have been that very software architect advocating for the rewrite.  I learned this the hard way.

So, this is how the rewrite is supposed to work in theory:

Let’s break this down:

The rework is expected to take some amount of time.  During this time, functionality won’t increase because developers are focusing on rebuilding the foundations.
In the graph, that’s the blue area you can see peeking through.  That blue area represents the cost of the rewrite.

But, once the rewrite is done, the idea is that progress will be massively greater than it was previously because the new technology used is inherently better than the old.  Developers will be more productive with the new.  There’s no need to work with the old spaghetti code – instead, there’s a beautiful new architecture free of all the baggage that came before.

The critical point in time is the break-even point – this is the point at which the functionality of your product starts to exceed where you would have been had you stuck with the original implementation and continued working on it.

During the rewrite period, the competitiveness of your product will typically decline since your competitors are not standing still and you can’t develop new functionality.

However, the claim typically made by the rewrite advocates is that the rework will be relatively easy, take a relatively short period of time and hence achieve break-even quickly, after which it’s non-stop to the moon…

What tends to go wrong – Part 1

So, what tends to go wrong?  The most common problem is arguably the most common problem in software development generally; the rewrite takes significantly longer that expected.

There’s a discussion of why this tends to happen below but, for now, trust me that this often happens – if you’ve had any experience with software development at all, it’s highly likely that you’ve seen this happen too.

The result is that the cost of your rewrite is significantly larger than originally claimed.  (The blue area showing through on the chart is now much larger.)  This means that the break-even point is also significantly pushed back in time.

The knock-on effect is that the competitiveness of your product drops for a much longer period of time.

If you are a big company, you can probably (hopefully) absorb the pushed out break-even point – maybe other products provide revenue, maybe good channel relationships continue to deliver sales of your product even though it’s falling versus the competition or maybe you’ve simply got lots of cash reserves.  Joel Spolsky references several big company rewrites that failed but did not kill the company.

However, if you are a startup or smaller company (or even a less fortunate bigger company), this can be – some might argue, is very likely to be – fatal.

What tends to go wrong – Part 2

It gets worse.

Not only does the rewrite often take longer than expected but functionality is not even flat at the end of it – functionality is actually lost as a side-effect of the rewrite.

That’s because all of those small but important features, tweaks and bug-fixes that were in the original product don’t get reimplemented during the rewrite.  (These are the “hairs” that Joel discusses.)

Remember that the main focus of the rewrite in the developer/architect’s mind is generally to build a better architecture and these small features don’t seem important and mess up this beautiful new architecture.

Plus, however good the developers are, they will introduce new bugs that will only get exposed and fixed through usage.

The net result is that the product after the rewrite is, in the eyes of the end-user and the market, worse than the product before the rewrite, even if it’s better in the eyes of the developer.

This further pushes out the break-even point in terms of functionality and competitiveness of your product will take a long time to recover.  If you listen carefully, you can probably hear your competitors wetting themselves laughing at your folly.

What tends to go wrong – Part 3

Oh dear.

The nail in the coffin is that, not only does the rewrite take longer than expected, and functionality get lost in the process, but the benefits of the new technology/language/framework turn out to not be nearly as great as claimed.  Meanwhile, if you’d stuck with the status quo, you would have got more productive due to normal learning effects – i.e. don’t forget that while your team is climbing the learning curve with the new technology, you would have been getting better with the old one anyway.

Any one of the 3 problems above is potentially fatal but all 3 together is definitely so.  Competitiveness of your product will likely never recover.

Why does this happen?

So, the above sections discuss what can go wrong.  It might help to understand why this happens.

In Joel’s seminal post cited above, he makes the point, “It’s harder to read code than to write it.”

Very true – it’s also more fun to write new code than learn someone else’s code.

Developers like to feel smart and, intellectually, learning someone else’s code can seem like a lose-lose scenario to a lot of developers – if the code is bad, it’s painful to learn and you’re going to have to fix it.  On the other hand, if it’s better than you would have written, it’s going to make you feel stupid.

Also, bear in mind that the fundamental motivation of most (not all) engineers is to Learn New Stuff ™.  They are always going to gravitate towards new things over old problems they feel they’ve already solved.  Again, I’m not faulting developers for this – it doesn’t make them bad people but, if you’re a non-developer, it’s critical you understand this motivation.

Put it this way: would you rather be the guy that architected the Golden Gate Bridge or one of the guys hanging off it on a rope, scraping off rust and repainting it?

Another problem is that when you rewrite, you typically make rapid progress early on which gives false validation to the decision to rewrite.  It makes you feel smart – what a beautiful cathedral I’m building.

That’s because you’re writing code in a vacuum.  No one is using the code yet; certainly no actual users.  But, as you start to reach launch date, all those small features, tweaks and bug-fixes – all that learning that was encapsulated in your old product – starts to become conspicuous by its absence.

The bottom-line is that, when the case to rewrite is being made, it is not comparing apples to apples – it is comparing theoretical benefits with actual benefits.  The actual benefit in question being, of course, that you have an existing product that works.

Comparing the pros and cons of writing a new application in language A versus B is not the same as comparing an application already written in language A versus rewriting that application in language B.  Why?  because any productivity gains of one language over the other are typically massively overridden by the loss of all the domain knowledge, testing and fixes encapsulated in the existing product.

Should you EVER rewrite?

So, are there ever circumstances when you should rewrite?  My answer here is an emphatic“maybe”.

Let’s consider some of the possible situations:

1.  An irretrievably sick code-base
The symptom here is that it takes exponentially longer to add each new feature – per the chart above.  Another symptom is that defect reports continue to come in as fast, or faster than you can fix them – you’re treading water.
However, an irretrievably sick development team is a much more likely culprit than an irretrievably sick product.
Don’t let developers convince you that they can’t maintain a codebase – what they most often mean is that they don’t want to maintain a codebase.

2.  The developers who wrote the code are not available
If you buy/find/inherit a codebase, make sure you have access to as many of the developers who wrote it as possible, for as long as possible.  If you didn’t…doh!
Again, don’t fall into the trap – developers will always prefer to write new code of their own than learn how the existing code works.

3.  A genuine change to a new problem domain
Sometimes, some of the fundamental assumptions and technology choices may no longer be valid for the problem domain that your product is addressing.  But, then I’d argue, you’re really talking about building a new product rather than rewriting an existing one.

4. Fundamentally incorrect or limiting technology choices
In some cases, the team that originally built the product may have made some poor choices in the technologies to use or the approaches to take.  They may simply not map well to the problem domain of your product.  However, in my experience, this is true much less often than developers claim it’s true.
Also, there are points where you start to hit fundamental scalability limits of certain technologies.  However, keep in mind that if you’re hitting those scalability limits, someone else probably has too before you, and solutions to the problem exist.

So, if you are even entertaining the suggestion of a rewrite, make sure you get the developers to give you a specific cost-benefit analysis of the rewrite.  Show them the charts above and get them to tell you why these problems won’t occur in this case, even though they occur in almost every case.

If and when you are convinced that this is one of those rare situations where a rewrite is the right approach, you need to make it surgical:  where will you make the incision?  How deep do you cut?  What are the risks?  What are the potential side-effects?  How can I make sure no functionality is lost? How can it be done incrementally?

Divide the rewrite into a series of smaller changes, during which functionality absolutely cannot be lost.  Rewrite parts of the system at a time with a well defined, understood and testable interface between old and new.

Better still, don’t rewrite.

Good luck.

Agree, disagree?  Please leave a comment.