By: Melanie Katz, Digital Marketer
Is your marketing strategy based on guesswork, or is it driven by data? Many marketers rely on intuition or simply follow what a competitor is doing, but the real power in digital marketing, especially in paid media, comes from rigorous, strategic experimentation. Without testing, you're leaving potential revenue, better campaign performance, and crucial audience insights on the table.
Think of experimentation as your personal laboratory for marketing. You create a hypothesis, test it under controlled conditions, and use the results to build a stronger, more effective strategy. This shift from guessing to knowing is the key to achieving excellence in your campaigns.
In this article, you will discover the essential steps to experiment your way to better results:
The difference between various testing formats, from the common A/B test to the powerful lift test.
The importance of establishing both primary and secondary KPIs for complete context.
A breakdown of the scientific method applied to marketing tests.
How to analyze surprising test results using supporting metrics.
Key considerations to ensure your tests are valid and informative.
Before you start testing, you need to understand the tools at your disposal. The type of test you choose depends on what you are trying to measure.
A/B Test: This is the most common format. You are testing a single variable—like a headline, a call-to-action button color, or an image—against the original (control) version. It’s perfect for landing pages, emails, and product pages.
Multivariate Test: Use this when you want to test multiple variables simultaneously on the same page (e.g., three different headlines and two different images). This helps you determine which combination of elements drives the best performance.
Lift Testing: This is more common in paid media. It’s designed to measure a campaign’s effect on "softer," top-of-funnel metrics like awareness or consideration. You compare a population that saw your ads against a control group that did not to quantify the 'lift' in brand metrics provided by the campaign.
Holdout Test: This is the inverse of a lift test. You run your full marketing program but intentionally exclude a specific audience or demographic. This helps you determine the opportunity cost of not running your ads to that group, or if the program is creating incremental value.
Once you select your format, you need a clear plan. Ask yourself these four fundamental questions:
What type of test are we running? (A/B, Multivariate, Lift, etc.)
What is the duration of the test? Factor in enough time to gather statistically significant data and allow for campaign optimization and scaling.
What is the metric for success? Define your primary KPI (e.g., Cost Per Click, Click-Through Rate, or most importantly, Conversion Rate).
Why are we running this at all? What is the core hypothesis we are trying to prove or disprove? And crucially, how will we act on this information once the test is complete?
Testing in marketing is essentially following the scientific method:
Observation: You notice something interesting. Example: Product listing images with a green background are outperforming others.
Question: Can we replicate this success? Example: If we add a green background to other product image listings, will it lift sales of those items?
Hypothesis: Your educated guess based on what you know. Example: Listings with the new green background will perform better and increase overall sales.
Experiment: You design and launch the test (e.g., an A/B test comparing the current image vs. the new green background image).
A common mistake is focusing only on the primary Key Performance Indicator (KPI). The true story of your campaign often lies in the supporting metrics—your secondary KPIs.
Imagine you run an A/B test on a product listing where Variant A (the new image) sells a lower gross volume of units than Variant B (the original image). A quick look at the total units sold might make you conclude the test failed.
However, if you check your secondary KPIs, like Conversion Rate and Year-Over-Year Unit Sales, you might find something fascinating:
While Variant B got more total sales, Variant A might have brought in new, different buyers or performed better at specific times, leading to a higher overall sales volume for the product compared to the previous year.
The contribution from both iterations created a "lift phenomenon," meaning both versions were necessary to drive the significant year-over-year increase.
This discovery changes your action plan completely. Instead of discarding Variant A, you learn that multiple product listings are a better strategy than replacing one with the other. Secondary KPIs provide the context needed to make smart, informed decisions.
To ensure your experiments are reliable and your results are actionable, keep these considerations in mind:
Clarify Your Variables: Be absolutely clear about the single variable (A/B test) or multiple variables (multivariate test) you are changing.
Allow for Optimization: Give all variants sufficient time to optimize. If one version has been running for a long time, it may have an unfair advantage over a new variant that hasn't had time to season.
Compare Under Similar Conditions: External factors can skew results. Compare variants under similar seasonality or audience buying patterns.
Look for Indirect and Direct Effects: Don't just look for the immediate effect on sales (direct); look for the downstream impact on customer behavior (indirect).
Balance Data with Audience Knowledge: Quantitative results are essential, but you must balance them with your qualitative understanding of your audience and their shopping patterns.
Document and Learn: Always document the setup, results, and key takeaways from every test. Use these learnings to inform your next hypothesis and future marketing programs.
By adopting this methodical approach to testing, you will move beyond guesswork and achieve excellence in your marketing performance.
Melanie Katz is a digital marketer with 10 years of experience in paid media, campaign optimization, and data-driven strategy.
You can find her on LinkedIn