ActiveCampaign, a leading customer experience automation platform, helps businesses grow by providing powerful marketing, sales, and email automation tools. Chris Wood, the Director of Demand Generation at ActiveCampaign, oversees strategies to drive revenue growth and enhance customer engagement.
ActiveCampaign was dealing with pretty normal reporting issues. Their existing BI tools were not providing the actionable insights needed for efficient demand generation and revenue growth. Chris and his team were searching for a solution that could offer deeper analysis and better visualization of their data because:
Chris and his team wanted to be able to run campaigns and quickly understand which were having a positive impact on pipeline generation. Beyond this, covering the accounts sales was pursuing and being able to provide air cover for cold outbound was another major initiative Chris needed to support. Finally, being able to work with all their disparate systems and data platforms to provide a unified view was another must. This meant boiling down requirements for a solution to one that could:
This option was quickly dismissed as Chris knew the overhead and maintenance of building an in-house solution wouldn’t yield good results. The time commitment, the specialized knowledge and skills needed and the likelihood of building something worse than a vendor solution just didn’t make any sense.
At ActiveCampaign, Looker was already in use to model first touch attribution. Unfortunately, this wasn’t working as the cycle to build reports and glean insights was pretty broken. First touch wasn’t providing real insight and building out more attribution models would have been a big internal lift. And getting reports up required a cycle of requests that took too long, preventing the team from moving quickly.
Chris had used Bizible in a previous role. In that organization, Bizible was set up with the end goals in mind - aka showing certain elements of marketing were working - instead of what was really driving pipeline.
“My feeling about Bizible was it was like shooting an arrow and drawing a target around it afterwards.”
This option was also dismissed for its inability to provide real insight as a visualization layer reliant upon the data being fed in.
Chris worked with HockeyStack to get up and running within a few weeks. After HockeyStack became the centralized data source for all revenue-facing tools, insights around campaigns, web activity and the user journey quickly began to surface.
Multiple teams were able to get up and running and begin relying on HockeyStack on a daily to monthly basis. Dashboards were built than spanned paid, organic, inbound, outbound and integrated campaigns. This is how ActiveCampaign now uses HockeyStack across teams:
Chris also started to look into his belief that LinkedIn was contributing far more pipeline than was previously understood.
“I knew LinkedIn was working but I couldn’t prove it [before HockeyStack]”
As it turned out, he was right. LinkedIn paid ads were and continue to drive revenue for ActiveCampaign, and now Chris has the data to prove it. He also gleaned more insights around how to structure these kinds of interactions into golden paths (working customer journeys), for example, using Instagram as a second retargeting touch after LinkedIn yielded a much better conversion rate.
And the insight didn’t stop there, on the ActiveCampaign website, raw data was only showing a sliver of truth. One of the “ActiveCampaign vs. competitor” pages appeared to be working based on surface-level analytics. This has previously led ActiveCampaign to spend more time and money on that competitor. But what HockeyStack revealed was that the ActiveCampaign messaging and story wasn’t clear - time on page and scroll depth was a signal that people weren’t getting to the aha moment. HockeyStack showed that another page that had “worse” vanity metrics was actually contributing more to revenue. A clear signal that messaging on the first page needed to be revamped!
At the end of the day, all of this insight really allows Chris and his team to architect golden paths for users to follow based on complete data.
“Now we’re able to be really intentional about building those customer journeys.”
Key features used in HockeyStack:
Chris was able to start making some strategic decisions based on what he was seeing in HockeyStack almost immediately. For example, Chris and his team have:
This ability came from a few different outcomes that HockeyStack enabled, like:
As in the web analytics example, Chris and his team were able to eliminate time spent on what was happening (HockeyStack took care of that) and focus on why it was happening. Because the team was able to see what was influencing revenue (Lift Reports) as well as how LinkedIn Ads impressions were tied to revenue and what combination of activities were resulting in positive conversions, a deeper and more complete understanding of the user journey was formed.
What exactly is HockeyStack? How do we help marketers drive revenue more efficiently?
Jump in to uncover the ins and outs of Marketing Mix Modeling – from its mechanics to its ideal applications – with Drew.
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