The Product Complexity Curve

Johny Wudel
5 min readDec 20, 2021

Product teams exist to build great products and deliver features. What happens when you’re delivering features, but the outcomes are getting worse? Churn is increasing, sales are slowing, and customer satisfaction is flat or decreasing. You may be fighting the product complexity curve. Every product has a tipping point, where the additional complexity added by new features actually decreases the value to the user — this is called the product complexity curve.

When building a software product, the product gets better by adding functionality that solves more problems for the user. But every feature has a complexity cost and every product has a theoretical maximum value — a tipping point — as illustrated in this graphic.

New features increase customer value until the complexity tips the balance and actually decreases value

As product and development teams grow, velocity comes under a microscope from executives, boards, and investors. Unfortunately, one of the most visible indicators of success for R&D teams is the speed and volume of feature delivery. So teams respond by building and releasing more features and products. This isn’t inherently a bad thing as long as they are focused on the holistic view of solving customer problems and creating value.

However, each incremental feature adds additional navigation, UI elements, and decisions the user needs to make. It also further complicates the code and architecture of the product increasing the risk of bugs, tech debt, and dependencies. It’s not uncommon that as products get more bloated, changing one thing can break three other things without realizing it. Eventually, this complexity hits a tipping point where each new feature decreases the incremental value for the user.

How do you know when you’ve hit the tipping point of the complexity curve?

Unfortunately, there is no perfect indicator. It would be amazing if we could have a product EKG that would alert a code red when the value curve starts to flatten out. The reality is that we have to use several different data points as proxies and indicators.

Some of these include:

  • Feature usage
  • Product paths and navigation flows
  • Churn and reasons for churn
  • Time to onboard
  • Time to value
  • Net promoter score
  • Support resolution times
  • Customer surveys and interviews

If one or several of these data points are going the wrong direction, it could indicate a product complexity issue. It would be very unlikely to be near or at the tipping point and not have at least one of these data points in the red.

How do you prevent hitting the tipping point?

It starts with awareness. Your entire team should be thinking about the product complexity curve with each new feature they build. Different industries and personas will have different thresholds for complexity. Make sure you understand your customer segments and personas well enough to know this. One thought exercise for new features is, ‘how will the business be different if we build this feature’ vs ‘what happens to the business if we don’t build this feature’. The general rule of thumb is to not build “nice to have” features.

Your team structure and alignment can also help reduce complexity. Self-contained product and development teams are incredible powerful for feature delivery, but they can also often be myopic in the problems they solve. It’s critical to look at the comprehensive product experience to make sure there isn’t unnecessary complexity. This can be done through product leadership roles, alignment meetings, or leverage UI/UX designers to do regular product reviews.

Across the lifecycle of a product, there may be opportunities to redesign the user interface (UI) and create value by simplifying the experience. This type of work could actually change your position on the complexity curve. Facebook and Salesforce are both good examples of this. The original home page for Salesforce was crowded, full of text, and overwhelming. Future releases redesigned the layout, making it much easier to consume and navigate the product. The same is true for Facebook’s profile page comparing the page from 2005 to 2009.

Facebook profile page designs changed to reduce complexity
Salesforce home page designs

As you move up the complexity curve you have a few choices.

  1. Stop building new features
  2. Wall off experiences within your product to hide some of the features. For example, you might have basic features easily accessible and more advanced features accessible through deeper navigation
  3. Change the trajectory of your complexity curve

Option 1 is probably not feasible but options 2 and 3 are both viable options. To change the trajectory of the curve you can combine features (more detail below) or just be ruthless in your product design to make features as intuitive and easy-to-use (and fine) as possible.

Through thoughtful design and hard choices you can change the trajectory of your complexity curve

What do you do if you’ve hit the tipping point?

You may have to face the reality that some features need to be killed off. First, analyze the usage for features and if there are features that add complexity or clutter to the product that are used by less than 5% of your customer base, then you should consider deprecating the feature. This is extremely hard to do because there is usually a small group of customers getting value from even your least used features. You’ll have to decide if reducing complexity for the greater good is more valuable than that small group of users, even if they churn because you kill the feature. If you do kill features be sure to have an internal and external communications plan.

Another approach to combating the complexity curve is consolidating features. Are there features that were created separately, but are attached to the same “jobs to be done” or have a significant amount of overlap? You may be able to combine these features into one experience making them easier to use and reducing complexity.

Respect the curve

The bottom line is that no company is immune from the tipping point of the product complexity curve. There is no perfect way to forecast it or to measure it, but there are a lot of indicators. Be thoughtful about the value to complexity ratio. Staying on top of it and making small, ongoing improvements to ease-of-use and simplicity will be much less costly than waiting for it to topple over.



Johny Wudel

COO of JobNimbus and adjunct professor of product strategy at Brigham Young University.