See the causal impact of a certain touchpoint or activity without running an experiment. For example, you can measure how likely people who have seen your specific campaign are to become an opportunity vs. those who haven't.
Lift measures the influence of a specific marketing activity on a specified variable like demos, subscribers, sales, or revenue by comparing people who experienced the activity (exposed) vs. those who didn’t (control), then measuring the difference.
Lift uses historical data to create retroactive control groups: holdout groups who haven’t experienced a certain marketing activity. Now you can measure the influence of new channels, campaigns, or touchpoints fast, without a data science team or without running an actual experiment.
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Do LinkedIn ad impressions drive demos, even if they don’t get clicked? What keywords drive qualified pipeline and revenue? What pre and post demo touchpoints do paid buyers engage with most?
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Pair self-attribution with software-attribution. Make better decisions.
We can track more than most, but we can’t track everything. Use self-attribution to find untrackable touch points that matter. We’ll take care of the rest.
We can track more than most, but we can’t track everything. Use self-attribution to find untrackable touch points that matter. We’ll take care of the rest.
Measure contribution, without turning anything off.
Would you lose any sales if you paused branded search campaigns? Do LinkedIn ad impressions drive leads, even if people don’t click? Does a certain channel influence deals more than another? Lift will tell you.
What impact does website live demo views have on demos booked? Does your in-feed education drive leads later? Does a specific product page influence sales more than others? Lift will tell you.
How much influence does your newsletter have on leads? Do viewers of your video series convert higher than non-viewers? If you stopped blogging, would it matter? Lift will tell you.
Does your messaging motivate action? Does your new ad creative drive more meetings than your old? Does your updated homepage influence SQLs more than before? Lift will tell you.
Rowan Tonkin, the CMO of Planful, talks about how HockeyStack helps them overcome visibility challenges and enables all marketing functions to efficiently analyze performance and gain comprehensive insights.
increase in opportunities
increase in ROAS
increase in pipeline
Chris Wood, Director of Demand Generation at ActiveCampaign, shares how HockeyStack empowered their team to overcome BI challenges, optimize LinkedIn performance, and make strategic business decisions that fueled measurable growth.
efficiency, cutting budget 50% and hitting 100% of revenue target
increase in target market coverage
touchpoints recorded and understood in prospect journey
Discover how the content team at Whatfix uses HockeyStack to understand how content influences their pipeline and to showcase content’s impact across opportunities where they previously had no data.
increase in content-influenced opportunities
increase in opportunities
increase in won deals
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How OneFlow used cohort reports, incremental lift, and multi-touch attribution to gain visibility into what channels, campaigns, and creatives influenced purchases so they could optimize ad campaigns for pipeline and revenue, not just platform metrics
Paid ROI in the pipeline
form submission in a quarter
visitor to MQL increase with LinkedIn ads
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I don't often highlight tools I use in SaaS sales, but HockeyStack is an exception.
HockeyStack combines warm intent data and strong buying signals for your target accounts into a simple, user-friendly platform. Sure, you could manually gather raw data from Zoominfo, Hubspot, G2, Google, and LinkedIn, but that’s the old way of doing things.
As part of my morning coffee routine, I log into my dashboards while managing sequences on LinkedIn. These custom dashboards and filters highlight my top target accounts, helping me close deals faster.
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