Everyone wants more features.

It’s like a car that has a toaster and a rocket engine. Sometimes that can totally make sense.

Plenty of people have covered the dangers of new features and I’m not going to talk about that the cost of building new features. Let’s assume that the new features are important for your market. What I want to talk about is the cost of supporting new features. Say you have a development team of ten. What percentage of that team is available for new development and which need to hang back on bug fixes and minor improvements?  At GoReact we’ve been able to maintain a sub-25% bug allocation for the past two years and we’ve really felt free to work on functionality that’s good for new customers and partnerships. But even with 75% of our resources we haven’t churned out 75% new features. Pointy-haired boss is wondering why at this point, right? Let him wonder. You will always have work in a couple of areas and your ability to anticipate and manage those pipelines as the needs ebb and flow is a critical piece to continued success with development projects. For me this means balancing the bugs, new features, internal projects and changes.

Kudos to the Phoenix Project for making this approach available for the general masses. It took me a year to read this book after a friend and coworker recommended it, and I regret that I lost an entire year of this perspective. It’s now required reading for my team and I keep a fresh copy at my desk for any new-comers. The sooner you understand what is really happening and what is really needed to run year after year, the sooner you regain the reigns on your team’s ability to really make the business move.

Those of us who have been around the block a few times know the pitfalls that come with maxing out development on new functionality. Don’t get me wrong here, I’m talking about sustained focus, like years.*  After huge pushes like this you can turn around to find out that your existing customers are leaving you because their product–the one that you built last year–is falling apart. Almost all of the time this is a bad thing, so unless you’re pivoting your product to a new market and pretty much leaving the old one behind, you’d better run some quick calculations.

Now here’s where I get a little fuzzy, so you’ll have to finish these equations out for yourself

Your velocity for new development (Vn) is what’s left over after you subtract velocity wasted on bugs (Vb), spent on changes (Vc) and invested in internal projects (Vi). In my experience the internal projects are usually small and pretty easy to prioritize where they support Sales, Marketing or Support. Hint: one of those is a cost center. That leaves bugs and changes.

This simple little ditty came to me during a daydream I had while in a product meeting. I’ll walk you through it and then we can have a good laugh about it. Your change velocity (which is refactoring, rearchitecting, server and configuration changes) can be calculated by examining the size of your code repository (R), multiplying that by the sum of the features that Marketing (Fm) and your customers (Fc) think you have and then you multiply that by the number of people on your Sales team. Then you divide that by the number of QA (Eq), product (Ep) and developer employees (Ed) on the team.

The equation for bugs is pretty similar but the numerator is your repository size and the number of features you actually have. This is all tongue-in-cheek but either way it seems like these are the only hockey stick graph (exponential growth) the product development team every sees!

Alright in all seriousness, we know that:

1. Bugs are best mitigated by talented QA team members and a development team absolutely devoted to testing. We’ve saved ourselves a lot of customer heartache by ensuring that our code is the best possible quality and we attribute that as the reason our customers stick with us so long, second only to product-market fit.
2. Changes increase as the code and features increase. This part is actually pretty simple math, but never ignore it.

We also track the velocity ratio between our top projects and what we call our “tribute” work. The latter for bugs, small UI tweaks, very minor updates and any out-of-band refactoring. We’re holding at about a 3:1 ratio. This means the next four hires are only going to give us three heads to focus on new projects.

Here is the real key: find your ratios and plan for those as you make hiring decisions.

• Understand how much of your time is spent in each of your types of work
• Understand what the business really needs in each type of work
• Understand how the ratios can change as you:
  • Add more features
  • Add more code
  • Refactor for reusability and modularity

—

* I have to qualify something here. Sometimes teams start to feel some burnout after huge pushes and new features are often the scapegoat. There is a whole area of discussion around helping your team understand why new features are necessary and why bug pushes can be critical for a company’s success. There are also horrible stories about how managers misuse “death marches” and fail to do necessary research and justification for their projects. These big, hard projects can be a harbinger for the death cycle but they can also be just what it takes to succeed.  If you’re in a position to question your company’s approach, I feel very strongly that it’s always on you to get your questions answered and either alleviate your concerns or confirm your suspicion.

I loved Good to Great and although it’s getting dated the principles keep coming up.

Companies that fall into the Doom Loop genuinely want to effect change—but they lack the quiet discipline that produces the Flywheel Effect. Instead, they launch change programs with huge fanfare, hoping to “enlist the troops.” They start down one path, only to change direction. After years of lurching back and forth, these companies discover that they’ve failed to build any sustained momentum. Instead of turning the flywheel, they’ve fallen into a Doom Loop: Disappointing results lead to reaction without understanding, which leads to a new direction—a new leader, a new program—which leads to no momentum, which leads to disappointing results. It’s a steady, downward spiral. Those who have experienced a Doom Loop know how it drains the spirit right out of a company.

Eyes wide open. Success is predicted and planned for.

If a p-value of 0.05 (5%) is too high, and the inventor of that threshold says it shouldn’t be used without consideration–which it is, all the time–then things we think are significant today just aren’t. We don’t always apply this kind of statistical rigor to decision making, but when we do we’re generally satisfied with this threshold. But we shouldn’t be.

Applied to product development teams, methodology matters a lot. But it can matter so much less than the talent of the individuals on a team. But methods and tools are still championed much more loudly than professional development.

In short, we should be looking for more obvious wins, and recognize that when something is close, but still statistically significant, it’s probably still too close to really move the needle.

We spend too much of our lives in ambiguity. So many outcomes, so many possibilities! Even an enumeration of the combinatorics would be a staggering waste of time and so we consent to let things play out. But we don’t completely let go – of course not! We make decisions in the moment, we strive to utilize consistent tactics, even strategies as well as we can. And when we have successes they are the result of our efforts, and yes, some luck. And when we have failures they are bad luck, and yes, maybe poor planning. We can press forward in uncertainty but Sir Arthur Conan Doyle’s Sherlock tells us, “It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”

Certainly, the exploration of risk is rewarding and I don’t mean to take the fun out of it! But making data-driven decisions removes the worst outcomes. And less negative risk means less unpredictability and this is a good thing for business. It is predictability that allows us to make wise investments, to outmaneuver the competition, and it is the predictable inflow of money that keeps the lights on. This is why getting to “know” is so vital. Reorganize your processes to identify and weed out the worst variables first. Answer every question amounting to “why this won’t work.” You need not wait until all questions have been answered, but there is a critical mass that should be addressed before safely riding the long tail of uncertainty. But knowing where you stand on the most key factors of success puts you on the threshold of good decision making. If you decide to stop, you can do so early and with good reason. If you need to pivot, you have the wisdom of understanding just how it should be done. And if you should indeed continue on, do it with confidence that comes from really knowing where you stand. The earlier you know, the less risk you take on and the less time you spend

The decision to move forward in light of the data is much more sound than the decision to move forward despite the risks. Get to “know” then make the call.

If you haven’t already seen these ads from Apple, you need to. If you aren’t doing this in your business, in your job, or in your life, you consider it. Focus on doing what you really want to do.