How Envoy Created a Complete Dataset
Before using Heap our data was messy and trapped in silos. We had no clear view of how customers went through any of our funnels. Our customer acquisition process can be complex. For example, our customer acquisition funnel looks like this:
- A prospective customer finds Envoy through a Google search ad. They check out some marketing pages but don't sign up.
- Later, they’re re-targeted on Facebook and come back to the marketing site. This time they sign up.
- During their free trial, they activate Envoy on an iPad which they install in their company reception area.
- They speak with a salesperson on the phone.
- They decide to purchase and enter their credit card online.
Before Heap, we would track this customer across 5 different systems. To make matters worse, that process is just what it looks like to acquire new customers–as a customer starts using Envoy, increases usage across multiple of offices and teams, renews and expands their contract, and more, it can become far more complicated.
After fully rolling out Heap, the Envoy team for the first time ever had a complete picture of our entire user journey. Heap’s automatic data capture meant we no longer had gaps in our understanding of our users.
Also, Heap’s best-in-class identity management meant that Envoy could seamlessly merge users across devices, merge anonymous identities with identifed users, and more without worrying about a complex sequence of aliasing API calls that many analytics providers require to reconcile identities.
Finally, Heap allowed us to stop relying on multiple vendors for marketing attribution. We finally had a complete, centralized view of which campaigns were associated with outcomes. Heap allows us to get granular on who is doing what, when, and how. For the first time, we’re able to do marketing attribution on our own rather than relying on third party tools.
How Complete Data Changed Envoy
Got Smart About Our User Acquisition Strategy
Heap gave us the complete dataset to look at user acquisition for the first time ever. We realized that our trials to paid customer conversions were a lot lower than we originally thought. It turned out our onboarding flow wasn’t as effective as we wanted in getting users fully activated. We created a new setup guide to take people through those on-boarding steps more effectively. This is a new target for more future optimizations.
Using Heap SQL Gave Us New Levels Of Access
Heap was one analytics tool that worked with so much of what we were already used to dealing with. For us, Heap tracks everything, and pushes that data downstream to our Redshift instance, where we can apply our typical data science efforts in a manner we are used to. This means there was almost no learning curve for my team and I.
Heap Let Us Give Data To Other Groups
We have a rule called the 60/30/10 rule. It means 60% of questions should be answerable by functional groups by themselves, with no assistance. 30% of questions should be answered by functional groups with some assistance. 10% of questions need to be done by a data science team. Heap has allowed us to achieve this goal by democratizing our data.
Heap Let Us Stop Worrying, And Trust The Data
We looked at several other providers, and for most of them it seemed like autotrack was more of an afterthought. Heap allowed autotracked events to heap every part of the product, giving us a complete, retroactive, and rich data set.