Tl;dr: “Effort” is a previously hidden measurement in funnels, which in tandem with conversion rate can instruct you on where and how to invest in your product. Without quantifying Effort, it’s easy to misinterpret what’s happening in a given user flow, which can lead a team to miss opportunities or prioritize the wrong product improvements. For every doubling of effort, we’ve observed conversion rate drops by about 16 percentage points.

The easier it is to do something, the more likely we are to do it. This simple claim underpins the biggest successes of the digital era. (See: 1-click, Amazon.) But…

Some fears and truths about data governance and automatic data capture

Data Governance
Data Governance

For some time now, there’s been a misconception that the best approach to maintaining a reliable, accurate, and trustworthy dataset is via manual tracking. While even proponents of manual tracking concede to autocapture’s superiority in ease of use and time to value, they often like to assert that autocapture will produce an ungovernable mess of undifferentiated data. We’d like to set the record straight.

When done right, automatic capture builds in data governance as a core architectural principle, not an activity that’s added on later. This is what we’ve done at Heap. This not only ensures that an autocaptured dataset…

In this ongoing series we document our Product-Led Growth (PLG) wins and losses and share our learnings with the PLG community.

We’ve always believed in the power of our product, but in late 2020 we made a company decision to pursue Product-Led Growth (PLG) in earnest. As we deeply believe in making data-driven decisions — in the power of hypothesis, testing, and iteration — we’ll be sharing our various experiments with the world.

We started by breaking our PLG efforts up into three related KPIs:

  • KPI 1: Increase signup conversion from our marketing site
  • KPI 2: Increase activation rate among…

Some things you should know about using data to build better products

A wise man once said, “When you change the way you look at things, the things you look at change.” There’s no better way to describe how your product team can transform what it does when it starts using product analytics to track user behavior.

At Heap, we’ve worked with teams at every level of digital sophistication. We’ve helped teams that use analytics to drive every decision and teams who have yet to discover data. Along the way, we’ve realized a few things that can help ground your team’s approach to product analytics, no matter what level you’re at.

Truth #1: You don’t just need data — you need the right data


Heap’s Refreshed Company Values

When done well, company values can play an outsized role in employees’ engagement and happiness. A good set of values not only provides a common language and norms around how to operate, but it becomes foundational to everything a company does across the employee lifecycle — attraction, engagement, and retention.

At Heap, we approach our company values like we approach everything else: we hypothesize, collect data, and improve. We know we can’t get all the details right the first time, which is why we lean heavily on feedback from our employees.

And don’t you forget it.

At Heap, we spend lots of time talking about analytics. If you view our pages, you’ll quickly see the many advantages we bestow on teams: you can answer endless questions! Know what users do in your product! Collect behavioral data without relying on engineering! Pinpoint moments of friction in your user flows! And so on. (There are many more.)

What’s the point of these? Well, in the digital insights world, we often frame our goals in terms of “building a better product,” or “optimizing the user experience.” That’s great and all (it really is!), but here’s the thing: if you’re…

The tool is not the action.

Here’s a dirty little secret: Product Analytics is useless.

It’s strange, right? After all, we’re a Product Analytics company. We literally built a tool that delivers product analytics in a new way.

Recently, though, we’ve been looking at the analytics landscape and making some realizations. Primary among these is despite the transformative power analytics has on product development — analytics are like a superpower for product — most of the time analytics ends up doing far less for its users than it could.

Why is this? In our experience, product analytics is useless because of a simple mistake companies (and…


A smarter approach to product. Learn more at

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