I recently had the opportunity to interview Matin Movassate, founder of Heap. Heap is a digital analytics solution on the rise, trying to disrupt the digital analytics solution market.
Heap is an analytics tool for web and iOS that automatically captures every user interaction. This lets our customers retroactively generate advanced analytics, without writing any code.
Please share how Heap was started? What triggered you to develop Heap?
When I was a PM at Facebook, we had access to the best analytics infrastructure in the world. I couldn’t take advantage of it, though.
Why? Because getting the right data took way too much time.
Each time an analytics question came up – or our existing logging broke – I had to:
- bother an engineer to write tracking code;
- wait for their code to go live (which could take up to a month for mobile apps);
- wait for data to trickle in;
- and finally bother a data analyst to collate the data into a report.
This sort of feedback loop made it difficult to actually use data. With Heap, we decided to capture as much data as possible upfront and eliminate this bottleneck.
Digital analysts usually work with the usual suspects in terms of digital analytics solutions (i.e. GA, Sitecatalyst, Webtrends). Why should they look at Heap as an alternative?
A few reasons:
- Retroactive analysis. Since Heap captures everything from day one – clicks, form submissions, pageviews – all analysis can be done after the fact, without shipping code. Every other analytics tool, on the other hand, requires you to define events of interest upfront.
- Integration is as simple as it gets. We have a tool called the Event Visualizer, which lets you define events through a point-and-click interface. Want to know how many people shared a link? Just open the Event Visualizer, click the share link yourself, and you’ll immediately see stats on its usage.
- Powerful reporting. Heap provides a visual report builder that makes answering important business questions easy. You can create visualizations that answer “How does viewing our demo video affect our signup funnel?”, or “Which users signed up 4 weeks ago but haven’t entered their payment info?”
What recommendation can you give web analysts in the Netherlands when it comes to web analytics tool selection? What should they be particularly focused on or give attention to?
When evaluating tools, analysts always underestimate one important feature: data organization.
Data organization is the hardest problem in analytics. If you don’t have the right data at your fingertips, or the underlying data is incomplete/broken, then it doesn’t matter whether your analytics tool is powerful or pretty.
Similarly, if your teammates don’t understand or trust the data, then they’ll come to you for all their analytics questions. At best, you’ll become a bottleneck for your organization. At worst, your teammates will rely less and less on data.
Thus, when evaluating analytics tools, ask yourself two things:
- How easy will it be to setup a new metric or amend an existing one?
- Will my teammates be able to understand this data?
If you look at the future, what are the new challenges you believe digital analysts will be facing?
Creating a data-driven culture for their entire organization.
Demand for analytics is increasing across every single role. Designers want to know which features users actually use. Salespeople want to know which attributes predict a sale. Customer success reps want a list of customers most likely to churn.
In the future, the best digital analysts won’t just be on standby to answer these questions. They’ll proactively build internal tools that let their teammates become more autonomous with data.
What will be the next steps for Heap in the new future?
Our ultimate goal is to remove all the barriers that sit between a question and its answer, one-by-one.
In the near term, this means we need to continue improving our data-out APIs. By virtue of housing lots of data, Heap is slowly turning into the de-facto source of truth for many of our customers. We’re encountering use cases we never anticipated, from large-scale machine learning, to fraud detection, to automated push notification systems. Our APIs need to make it easier for customers to pull their data out and build out these use cases for themselves.
Tip! Also read our interview with Yali Sassoon from Snowplow Analytics in our series about disruptive digital analytics solutions