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The Who

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.

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The Problems

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:

• knowing something is working and being able to show it can be the difference between making or missing your pipeline goals for the year
• marketers were 3 steps removed from actioning any insights - the process was request → ops team → BI team (then see the report)
• Looker (which Chris used previously) was limited to the data it was fed from other systems and had to be built and maintained like an internal product
• data sprawl across platforms, older data spanning years and a need for several integrations meant there was a need for a highly flexible platform with a well-developed ecosystem

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The Requirements

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:

1. Integrate with multiple systems spanning CRM and paid providers like HubSpot, Salesforce, LinkedIn Ads and Google Adwords
2. Accurately show which accounts were being targeted, which were garnering impressions, and which needed additional focus for coordinated sales outreach
3. Show campaign influence and impact without custom modeling or the need to build and maintain additional internal systems
4. Understand and measure both paid and organic channels and their influence on pipeline and revenue
5. Work across teams and surface insights for:
   1. Paid and Digital Acquisition Marketing
   2. An external agency managing paid channels
   3. Content Marketing
   4. Affiliate and Partner Marketing
   5. Sales

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The Alternatives

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Building a custom solution internally (DIY)

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.

Looker

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.

Bizible

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.

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“My feeling about Bizible was it was like shooting an arrow and drawing a target around it afterwards.”

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This option was also dismissed for its inability to provide real insight as a visualization layer reliant upon the data being fed in.

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The Insights

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:

• daily - paid and digital acquisition - to understand how ads are performing from impression to revenue, as well as how web is influencing the user journey
• daily - paid and digital agency (external) - to report on and manage paid channels for the ActiveCampaign team on a daily basis
• weekly - sales team - to identify accounts that are ready for a first or tenth touch that look likely to convert based on ActiveCampaign’s historical prospect journeys
• weekly - content marketing - to understand and measure the impact of content like blogs and web page visits on the buyer journey and tie it to revenue
• monthly - affiliate and partner team - to understand how partners and channel are driving revenue for the business

Chris also started to look into his belief that LinkedIn was contributing far more pipeline than was previously understood.

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“I knew LinkedIn was working but I couldn’t prove it [before HockeyStack]”

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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.

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“Now we’re able to be really intentional about building those customer journeys.”

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Key features used in HockeyStack:

• lift reports
• dashboarding for multiple teams
• attribution
• account matching

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The Results

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:

• Cut advertising budget by 50% and still hit monthly revenue targets
• Moved from first touch reporting to a journey-based multitouch understanding
• Started tying LinkedIn impressions at the account level to revenue

This ability came from a few different outcomes that HockeyStack enabled, like:

• Understanding campaign impact and influence on pipeline and doubling down on campaigns that are positively influencing revenue
• Architecting the journey based on what interactions and content have positive influence on conversion
• Understanding the why behind prospect behavior

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.

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