Join us 6/23 to hear about "The Insights-Driven Organization" with Forrester

Our $11 Million Series A

We’re very excited to announce our $11M Series A! The fundraising round was led by NEA and includes Menlo Ventures, SVAngel, Initialized Capital, and Pear Ventures.

Analytics software is uniquely leveraged. Most software can optimize existing processes, but analytics (done right) should generate insights that bring to life whole new initiatives. It should change what you do, not just how you do it.

But then why does traditional analytics look more like janitorial work than data science? Let’s say you need to decide whether it’s worth investing more engineers into your app’s new “Invite a Friend” feature. You might ask yourself “does inviting a friend lead to longer-term retention?”

To get the answer, you’ll often have to:

  1. Bother some engineer and spec out a tracking implementation.
  2. Wait for the engineer to get around to instrumenting your app with logging code.
  3. Wait for the updated code to get approved by Apple and pushed live.
  4. Wait a few weeks for data to trickle in.
  5. Bother the data team to run the analysis for you and produce results.

That’s a lot of bottlenecks and wasted time. For many, this process hasn’t changed since the first analytics tools came to market. In fact, today’s most prevalent analytics tools still originate from products designed in 1996! Given the massive technological shifts of the past two decades—the rise of mobile, on-demand compute, ~8000x cheaper storage, large-scale AI—this seems very wrong.

In 2013, we built Heap from the ground up with a different approach in mind: automatically capture all the data. This saves our customers the headache of defining events upfront, maintaining brittle tracking code, and waiting for data to accumulate. Today, in 2016, we’re happy to serve as core analytics infrastructure for thousands of growing businesses across many industries.

But we’re still in the early days of analytics. There’s still much more friction we can remove. As we build out Heap further, we’ll aim to streamline a few layers of the analytics stack:

Data layer. It doesn’t matter how pretty your visualizations look or how sophisticated your predictive models are. Without the right data, you’re powerless. We’ve worked hard to automate data collection, but there’s still cases where our customers need manual tracking code (especially on Android!) or hairy ETL processes to consolidate data. We plan to automate more of these cases.

Information layer. Having data isn’t enough. In order to be useful, data needs to be organized into building blocks for analysis. You care about semantic things like “purchase” and “active usage”, not raw clicks and API calls. A clear taxonomy is key to making analytics accessible to everyone, but it’s often undiscoverable, or maintained in brittle tracking plans. We think this process will evolve.

Knowledge layer. This is exploratory reporting and visualization—by far the most well-understood layer of the analytics stack. Many BI tools exist solely for visualizing data. Even then, we think some fundamental elements are missing here. For instance, we often have people ask us “what questions should I even be asking with analytics?”

**Insights layer.**With gargantuan datasets comes an opportunity to proactively surface “unknown unknowns”. Companies need deep statistics or ML expertise for these types of insights. For many use cases, though, there’s no fundamental reason this should be true. Generating insights is as much a design problem as a technical one, and solving it will require entirely new interfaces for grokking data.

Platform layer. Analytics powers decision-making across many teams: product, marketing, sales, customer success, design, engineering. People on these teams have their own tools and workflows. The more Heap can seamlessly integrate with these 3rd-party tools, the smarter these tools can be, which ultimately lets more people make smarter decisions.

Our goal is to automate as much of this infrastructure as possible. Companies shouldn’t have to reinvent the wheel with messy data science/engineering work. They should be directly getting the insights they need to grow their business.

Though we’ve got a lot of work ahead of us, we’re excited to use this new round of funding to tackle even bigger problems for you and our customers!

If you’re interested in solving this problem with us, let us know. We’re hiring!