Every reliable tactic marketers now love, from video content to email marketing and blogging, was once a new experiment that early adopters tested and developed. Creating new marketing strategies is foundational to marketing, helping brands reach new customers and gather data that helps facilitate smarter business decisions.
While experimentation isn‘t new, digital marketing offers brands greater flexibility and potential. Let’s look at experiment types, which metrics to track, and how to design experiments across marketing channels to achieve maximum success.
Table of Contents
Marketing experiments are controlled changes to a marketing message or campaign to improve reach or conversion rates. These tests can be a small, single tweak or a campaign-wide experiment. Successful marketing experiments assess both quantitative data and qualitative factors, and the campaign results directly feed the next iteration of marketing materials.
Experiments are a part of step four in the Loop Marketing cycle: evolve in real-time. Here are quick examples of marketing experiments feeding the loop:
| Experiment Example | How it Feeds the Marketing Loop |
| Change CTA button color on a landing page | Measures immediate impact on click-through rate (CTR); then, iterates the winning version to improve conversion rates |
| Test UGC vs. branded photography in paid ads | Uses engagement and conversion data to evolve ad strategy based on what resonates with audiences |
| A/B test email subject lines | Evaluates open rates, engagement rates, and qualitative replies to refine future messaging |
Before spending any marketing budget on an experiment, make sure it has what it needs to succeed: a solid foundation, clear test factors, predetermined success metrics, and an intentionally selected framework.
Marketing experiments are composed of a few key factors, like a specific hypothesis, subject, and both dependent and independent variables.
Here‘s an example of how this looks: A local coffee shop runs a Facebook advertising campaign targeting people who have liked its page (subjects). The owners hypothesize that offering a 10% off rainy-day promotion (independent variable) will increase Facebook ad conversion rates by 20% (dependent variable), compared to evergreen ads that don’t change with the weather.
Marketing experimentation requires several test factors, like control vs. variant, randomization, and experiment duration.
Measuring the success of a marketing experiment is more nuanced than relying on a single metric. Both primary and secondary metrics must be considered:
Note that the data alone doesn‘t tell a complete story of an experiment’s success (I’ll share more on this below).
Marketing experiments follow three common frameworks: A/B tests, multivariate tests, and holdout tests. Each evaluates different elements of a marketing campaign and shares its own valuable insights.
| What It Does | How It Feeds The Marketing Loop | |
| A/B Tests | Compares one specific change to the control group | Insights are easy to interpret and can be applied immediately to improve future iterations |
| Multivariate Changes | Compares multiple variables simultaneously | Results are more difficult to interpret, but can provide insights that help marketing materials evolve holistically |
| Holdout Tests | Compares viewers exposed to a campaign with those intentionally not exposed to measure incremental impact | Identifies whether marketing exposure drives an outcome that would not have occurred otherwise |
Both A/B testing and multivariate testing are built into marketing software like the HubSpot Marketing Hub. Users can quickly test variations of content and see how they perform:
This type of adaptive testing allows marketers to run multiple experiments simultaneously, facilitating up to five variations at a time:
After understanding the different frameworks, work through the following five steps to launch your experiment.
The first step in designing a marketing experiment is articulating the question (hypothesis) being tested and tying it to a specific success metric.
Below are some sample question formulas and applications. Notice that the questions being asked are all clear and data-driven. This is important because unclear hypotheses increase the risk of interpretation bias and false correlations.
| Question Formulas | Examples |
| Will [changing X] increase [Y] [metric] for [audience/marketing asset]? | Will moving the email opt-in higher increase leads generated by 20% on my most-read blog post? |
| Will [changing X] decrease [Y] [metric] for [audience/marketing asset]? | Will removing steps at checkout decrease abandoned carts by 5% for digital products? |
| Will [changing X] reduce time to [desired action] for [asset]? | Will adding social proof to our email nurture sequence reduce time to purchase for our software demos? |
Where to start? I recommend you experiment with an underperforming page first. Find an ad, landing page, or website page that has low conversion rates and develop a hypothesis for improvement.
After choosing the right question for their experiment, marketers must select a testing framework. Selecting the wrong test type or testing too many variables simultaneously can make results difficult to interpret and act on.
While there are many different types of marketing tests to run, let’s look at three common test types, the variables that they measure, and common examples.
| Test Types | Examples | Variable |
| A/B | Email subject lines, sales page CTAs, button color | One isolated element, such as copy, placement, or color |
| Multivariate | Testing multiple page elements at once, like headings, layout, and images | Multiple elements tested simultaneously to measure interaction effects |
| Holdout | Measuring the real impact of ads, lifecycle emails, or always-on campaigns | Exposure versus no exposure to a campaign or marketing materials |
Where to start? I recommend an A/B test. It’s one of the most effective marketing experiments because it offers instant clarity on a single variable. Use HubSpot’s free A/B testing kit to quickly iterate on experiments.
Marketing experiments need a clear endpoint (stopping rule) that signals when the experiment has gathered enough data (sample) to render the hypothesis proven or disproven. The stopping point should be objective and predefined before an experiment begins.
Some common stopping points for marketing experiments are:
| Potential Stopping Point | What It Determines | Example |
| Traffic/sample size | If enough data was gathered to confidently compare results between the control group and the experiment | Experiment ends after 15,000 viewers have experiential marketing materials |
| Duration | Experiment time frame | Experiment ends after 14 days have passed |
| KPIs met | If the hypothesis was supported by the success metric | The hypothesis of a 5% click-through rate improvement was realized |
| Budget | How much marketing spend should be invested | Experiment ends after $1,000 in ad spend is reached |
| Negative performance | If the variant is causing extreme harm | A social media experiment concludes when it results in a 2% lower engagement rate on the entire account |
| Data quality issue | Whether results can be trusted | Errors or attribution issues are detected |
| External event | If an external force has impacted experiment results | A national emergency dominates news cycle and promotional materials on social media are paused |
Experiment design and execution greatly impact results. Building an experiment with a focus on quality assurance protects marketing effort and spend from chasing inconclusive or biased experimental results.
Consider the following checks and balances during the build, QA, and launch phase of an experiment:
Build:
Quality assurance:
Launch:
I’ll share exact tool recommendations for running marketing experiments below.
Analysis is an essential part of the experimental marketing process. Establishing the success or failure of marketing efforts helps make the data gathered actionable, while also feeding the development of future experiments.
Marketing teams should ask objective, investigative questions to analyze, document, and determine experiment rollout. Here’s a checklist:
Analyze:
Document:
Rollout:
Marketing experiments can be sabotaged by common pitfalls like seasonal effects, skipping qualitative review, selecting the wrong duration, and running multiple experiments at once. Heed these warnings.
While data is important in objectively evaluating a marketing experiment’s success, human review of qualitative factors is essential. Scott Queen, senior product strategist at SegMetrics, advised that marketers must look at marketing experiments from both a quantitative and qualitative perspective.
Using the example of lead generation, Queen shared that “you have to think about it in two ways: the pure number… And then you have to do some analysis of ‘are they the right people?’”
A lead generation campaign that resulted in 1,000 new email signups might look successful, but what if none of those customers live within the shipping range of an ecommerce company? Quantitative alone can‘t determine a marketing experiment’s success.
The duration of marketing experimentation impacts marketing spend and the amount of data gathered. Finding the right duration for a marketing experiment is a balancing act.
How long should brands run a marketing experiment? That depends on the channel.
“Some of your marketing tactics that are reasonably immediate, I would say you look at them weekly,” shared Queen. Other desired outcomes, like growing organic website traffic from an SEO experiment, can take months to gather enough data.
Tests that are executed during atypical periods (holidays, national emergencies, elections) may be skewed due to external influences rather than the experiment itself.
This shift change comes from both viewers and algorithms. For example, as a Pinterest marketer, I know to avoid publishing evergreen content from Thanksgiving to Christmas because seasonal content is so heavily favored by Pinterest’s algorithm. This skew is forced by the algorithm.
During periods of crisis, user attention, or even time spent on social media, can decrease. When possible, avoid running experiments during these periods to reduce the risk of attributing results to factors outside the test.
Running multiple tests at once increases the risk of incorrect attribution. Attribution is already challenging in digital marketing, where many touchpoints (such as influencer mentions or AI-generated overviews) are difficult to capture.
When possible, running experiments sequentially or coordinating parallel tests helps ensure results can be interpreted with confidence. For example, changing a single variable on the homepage and testing these versions parallel to each other:
Consider the following tools to plan and execute your marketing efforts.
HubSpot‘s Marketing Hub is a comprehensive platform that combines data from social media, a business’s website, CRM, search engines, and paid ads into one user-friendly dashboard. Easily filter data by asset titles, type, interaction type, interaction source, and campaigns.
Price: Paid plans start at $10/month
Standout features include:
What we like: HubSpot’s Marketing Hub makes data as actionable as possible, allowing for easy decision-making and understanding across marketing team members. I like that the built-in AI features work with you instead of taking over entire processes, leaving you firmly in control of your own experiments while still leveraging the insights that AI brings.
SegMetrics is a marketing attribution and reporting tool designed to help marketers understand how experiments impact revenue. It connects marketing touchpoints across the funnel to downstream outcomes, making it easier to validate whether experiments are driving qualified leads, customers, and lifetime value.
Price: Starts at $57/month
Key features include:
What we like: The subscription model features. Many reporting tools struggle to measure results for companies promoting recurring subscription purchases. On a demo call with Queen, he showed me SegMetrics’ pre-built tools to help marketers find which experiments extend customer lifetime value (LTV) for subscription-based businesses.
Google Analytics 4 (GA4) measures countless user interactions and events. It provides a famously (or maybe infamously) overwhelming amount of data, but as it relates to marketing experimentation, GA4 helps marketers with funnel analysis, traffic segmentation, and experiment validation across channels.
Price: Free
Some GA4 features that relate to marketing experimentation include:
This GA4 snapshot illustrates how teams can analyze user volume and engagement trends over time to evaluate whether an experiment meaningfully changes on-site behavior.
What we like: GA4 is widely adopted, which makes it a familiar and accessible data source for experimentation. It helps teams validate experiment results by tracking user behavior, traffic sources, and conversions without requiring additional setup.
UTM codes aren’t a software or program, but are an instrumental tool in tracking attribution across platforms and experiments. A UTM (Urchin Tracking Module) code is a small bit of text added to a URL to track the performance of that specific marketing asset.
Price: Free
These codes can contain up to five parameters:
Here’s an example from the HubSpot blog:
UTM codes don’t replace attribution software like HubSpot. Instead, they work together to improve campaign-level attribution and tracking.
You can create a UTM code easily with HubSpot (pictured below, instructions here), as well as Google Analytics Campaign URL Builder.
What we like: It’s not a standalone tool, but UTM parameters are essential to the experimentation process. I like how quick and easy they are to create.
Let’s review some real-world marketing experiments: their hypotheses, variants, and outcomes. Experiments in this section cover different areas of the sales funnel and are drawn from real case studies and companies.
Handled worked with HubSpot to centralize and refine its lead qualification process to improve conversions and sales efficiency at the decision stage of the funnel.
Consider applying this real-life example to your marketing in these two ways.
Teams can experiment with form fields, qualification questions, or gated content to validate whether fewer but more qualified leads drive better downstream outcomes. This helps shift experimentation from vanity metrics to revenue impact.
Another experiment to consider is testing landing pages and ad messaging against real sales objections or FAQs. This validates whether clearer expectation-setting improves conversion quality and reduces friction later in the funnel.
Grene and VWO Services (https://vwo.com/success-stories/grene/) ran an A/B test on Grene’s mini cart (decision stage of the funnel) that reportedly increased cart page visits, conversions, and purchase quantity.
The case study from VWO Services notes that other changes were also made (and goes into detail here), but cites the mini cart redesign as the catalyst.
What we like: In the case study summary, VWO Services noted that they removed certain options from the mini cart’s design to reduce the odds of customers accidentally removing items from their cart. I really like the UX considerations and the ripple effect of simple experiments.
Teams can test removing secondary actions from the cart or checkout flow. This experiment validates whether fewer choices increase completed purchases without hurting average order value.
Another simple test is increasing the prominence of the primary checkout CTA through size, contrast, or placement. This helps confirm whether having a clearer visual hierarchy reduces hesitation at the moment of purchase.
HubSpot ran an A/B test removing top navigation from landing pages to see if this improved conversions at the decision stage of the funnel.
Teams can test simplified landing pages to validate whether fewer choices lead to higher completion rates. This is especially effective when the goal is a single action, like form fills or demo requests.
Another idea is to selectively remove navigation only on decision-stage assets, while keeping it on awareness or educational pages. This helps confirm whether focused experiences perform better once users are ready to convert.
Going and Unbounce ran an A/B test on the homepage CTA to improve conversions at the decision stage of the funnel.
What we like: Ah, the power of focused, smart A/B testing. I think this works because the new language made the value of the premium offering clearer, reducing hesitation from the viewer.
Teams can experiment with CTAs that emphasize access over commitment. This helps validate which language better reduces perceived risk at the decision stage.
Another simple test is matching CTA copy with how the product actually works, like trials or previews. This confirms whether clearer expectation-setting improves conversions by reducing friction and uncertainty.
Rozum Robotics used the social listening tool Awario to strengthen PR and lead generation efforts for Rozum Café.
Teams can replicate this experiment by monitoring brand, competitor, and category keywords to uncover unexpected audiences engaging with related topics. This helps validate whether current targeting assumptions match real-world conversations.
Instead of relying on static media lists, marketers can test social listening to identify journalists, creators, or niche communities already discussing adjacent products or problems. This validates whether real-time signals lead to higher-quality PR and lead to opportunities.
Marketing experiments can target audience members at different points in the customer journey: awareness, consideration, decision, and retention. The 25 experiment ideas below span these four categories to help improve marketing ROI.
Consider using HubSpot’s advanced reporting tools to visually analyze viewers in different lifecycle stages.
Experiments for awareness focus on brand recognition, first contact, and contextualizing the product. Consider these ideas.
Experiments for the consideration phase focus on improving engagement, developing a relationship, and making the product’s value known. Consider these ideas.
Decision-stage experiments test messaging, pricing, customer information intake, and retargeting to achieve higher conversion rates. Consider these experiment ideas.
Retention and expansion experiments analyze customer onboarding, communication, and feedback with the goal of retaining customers for as long as possible. Consider these ideas:
Analyze data easily with HubSpot’s customer journey reporting:
Experiments that aim to improve long-term organic growth, like SEO and social media content, focus on being displayed in search results, meeting user needs, and personalizing experiences with your brand.
The duration of a marketing experiment is determined by the channel and sample size. Experimental paid advertising campaigns can be reviewed weekly, while efforts like organic SEO and organic social media posts may take weeks or months to collect sufficient data.
Testing more than one variable at a time, known as multivariate testing, isn’t recommended for beginners, as the results are often less conclusive than those from tests like A/B testing. However, these tests can be effective for gauging interaction effects.
An inconclusive (or “null”) result is still a win: it proves that the specific change you tested does not significantly influence your audience‘s behavior. In this case, marketers shouldn’t just try again: they should develop a bolder hypothesis.
Marketing experiments should be stopped early if there are errors with attribution or analytics, if they result in an extremely negative outcome, or if external factors (such as national crises, elections, or holidays) interfere with results. Avoid stopping tests just because they look “down” in the first few days, as data often stabilizes over time.
Marketing teams can conduct experiments without statistical software, but data must still be collected reliably for accurate reporting. Good reporting software not only collects data but also makes it actionable. For example, HubSpot has advanced marketing reports inside the marketing analytics suite that provide quick answers, like “which form is generating the most submissions?”
Experimentation is in the DNA of modern marketing. It helps brands uncover more effective marketing messages, promotions, and strategies for converting viewers into customers. Leveraged correctly, a brand’s experiments directly lead to business growth.
With built-in experimentation, personalization, and reporting capabilities, HubSpot makes it easier for teams to turn experiments into insights and insights into growth.
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