We’re excited to debut Causal Lift, a new feature that transforms how marketers measure true campaign effectiveness. At last, you can precisely determine which marketing activities truly drive incremental sales.
When every channel claims victory for your purchases, it’s tough to determine what’s really driving results. With privacy regulations tightening and cookie-based tracking becoming less effective, marketers face increasing pressure to prove ROI. Traditional attribution models like Media Mix Modeling (MMM), Last-Click, and Multi-Touch Attribution (MTA) models tell part of the story, often overcrediting touchpoints and failing to provide actionable insights.
While traditional attribution models answer the question, “Did the customer click my ad?” they often fail to address the bigger issue:
“Did this marketing activity cause the sale, or would it have happened without it?
That’s where Causal Lift comes in. This feature helps marketers test, measure, and optimize for incrementality, ensuring that every dollar spent drives real, measurable value.
Why Incrementality Matters More Than Ever
Most marketing measurement techniques focus on correlation, linking customer behavior to marketing activities. For example, if a customer clicks an ad before purchasing, the ad often gets credit for the sale.
But correlation alone doesn’t confirm causation. The customer might have been planning to buy regardless of the ad, making the ad coincidental rather than influential. This misjudgment of impact inflates results, wastes budgets, and leaves marketers unsure of what actually drives revenue.
Where Does Causal Lift Come In?
This is where Causal Lift shines. It is an incrementality testing tool designed to measure the true impact of your marketing campaigns by using scientific experiments and causal analysis by comparing:
- Treated group: Exposed to your marketing.
- Control group: Not exposed.
The difference reveals the true lift generated by your campaigns.
Instead of guessing what worked, Causal Lift isolates the incremental impact by comparing results between audiences who saw your marketing (treatment) and those who didn’t (control). This approach cuts through the noise, such as seasonality or economic factors, and gives you clear answers about what’s driving your ROI.
Here’s how Causal Lift works:
1. Hypothesis Definition
Before diving into experiment design, we start with a kickoff session to define the hypothesis, success metrics, test duration, and acceptable CPA values. By defining these parameters and objectives upfront, we ensure the experiment is tailored to your unique business goals and delivers actionable results.
2. Experiment Design and Setup
We start by analyzing your historical sales data to design a comprehensive experiment. This includes automatically selecting geographic regions for test and control groups, determining the ideal test duration, and providing an accuracy range for the results.
3. Targeting Implementation
We implement geographical targeting for the campaigns you want to test. Some regions are exposed to your marketing (treatment), while others are held back as controls to provide an unbiased comparison.
4. Actionable Insights Dashboard
Results are delivered in an intuitive dashboard where you can uncover incremental conversion value, true CAC, and ROAS for your campaigns. This lets you quickly identify what’s working and what isn’t so you can confidently reallocate your resources.
Why Use Causal Lift?
Geo-based incrementality testing with Causal Lift is grounded in robust data and statistical analysis. Unlike simplistic approaches such as excluding a U.S. state from ad exposure and measuring the difference, we use advanced methodologies to ensure precise, actionable insights.
1. Resilience to Privacy Changes
Causal Lift only uses first-party data to avoid the challenges associated with third-party cookies. It requires no PII (Personally Identifiable Information) or user-level data, relying solely on aggregated sales or session data by location. This ensures compliance with privacy regulations while maintaining the integrity of your marketing experiments.
2. Standardized Methodology Across All Channels
With Causal Lift, you can apply a consistent, scientifically backed approach to measure incrementality across all your marketing channels. This standardization allows for clear comparisons and streamlined decision-making, no matter where your campaigns run.
3. Precision Through Statistical Measures
The methodology is grounded in robust statistical modeling, ensuring every test delivers precise results. Causal Lift calculates confidence intervals (a range within which true result is likely to fall) to accurately measure true marketing ROI and determine whether observed changes are significant or just random fluctuations. By refining these intervals through noise reduction, Causal Lift ensures accurate error estimation, while eliminating impractical measurement ranges, and providing you with the clarity to make confident decisions.
4. Error-Free and Simple Setup
The Polar team automatically handles the entire setup process, from creating test and control groups to configuring experiments. This eliminates the risk of manual errors, giving you reliable insights without added complexity.
Early-Adopter Success
Causal Lift is already helping brands uncover valuable insights into their marketing activities. Here are some real-world results from early adopters:
1. DTC Skincare Brand Tests TOFU Awareness Campaigns
A 3-cell test was conducted to evaluate the effectiveness of top-funnel Meta awareness campaigns in driving new conversions.
- Intervention: Ad spend was divided across three groups. One group received no ads, another group received 4x the current budget, and the remaining group continued with business-as-usual strategies (BAU).
- Results: The test revealed no measurable lift in new conversions. As a result, the brand reallocated a significant portion of its budget toward Google campaigns, which consistently delivered better performance.
2. Supplement Retailer Tests New Channel
A 2-cell test was conducted to measure the effectiveness of YouTube ads in driving new conversions over a 4-week period.
- Intervention: A 4x increase in ad spend was allocated to 20% of the test group (exposed to ads), while the remaining 80% continued with BAU strategies.
- Results: The test achieved a 10% lift in new customer acquisitions, driving the brand to double down on YouTube as a high-performing channel.
3. Gifts and Home Decor Brand Tests Seasonal Campaigns
A Gifts and Home Decor brand conducted a two-cell test during the holiday season to evaluate the incremental impact of top and mid-funnel Google campaigns.
- Intervention: A daily spend of 500€ was allocated to campaigns for 50% of the following markets: France, Germany, Italy, and the Netherlands. The remaining 50% continued with BAU strategies.
- Results: The test revealed measurable improvements in campaign efficiency during this key sales period, enabling the brand to optimize its spend and focus on the most impactful markets and tactics.
Ready to see what’s driving real growth?
Join the growing number of brands already using Causal Lift to gain pin-point accuracy on the impact of their marketing campaigns. Gain clarity on marketing ROI, provide transparency to stakeholders, and optimize resources by focusing your budget on the channels and campaigns that drive the most meaningful lift.
Book a demo today to experience the value firsthand and start making scientifically backed data decisions.
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