How to Plan, Execute & Measure Marketing Experiments as a Growth Strategy [Free Template]

Experimentation is the heart of growth marketing. Running frequent tests allows you to validate ideas, gain new insights, and continuously optimize performance. The most iconic growth stories — from Airbnb‘s famous Craigslist integration to Dropbox‘s referral program — all involved extensive experimentation.

But while one-off experiments can produce interesting learnings, the true power comes from making experimentation a habitual practice. The best growth marketers run dozens or even hundreds of experiments per year as part of a well-oiled experimentation engine.

The keys to building this engine are 1) generating a robust pipeline of experiment ideas, 2) prioritizing tests effectively, 3) planning and executing experiments rigorously, 4) analyzing results and capturing insights, and 5) nurturing a culture of experimentation. Let‘s walk through each step and explore how a marketing experiments calendar can be the scaffolding for your growth engine.

The Compounding Power of Frequent Experiments

Before we dive into the how, let‘s touch on the why. Why make experimentation a core part of your growth marketing motion? Because small optimizations from frequent experiments can compound into massive results.

Consider these statistics:

  • Brands that run 15+ experiments per month are twice as likely to report significant improvements to conversion rates compared to those that run 1-4 monthly experiments. (Econsultancy)

  • Companies that run 7+ tests per month generate 12X the leads and 6X the revenue of those that run 2 or fewer monthly tests. (VentureBeat)

  • For every $92 spent on experiments, top performing teams are able to increase monthly revenue by $1,100. A 12X ROI! (Effective Experiments ROI Report)

Steady experimentation is simply one of the most efficient and impactful levers for driving growth. Even a 1% improvement from a winning test, compounded over time, can have an outsized effect on critical metrics like leads, conversions, or revenue.

Generating Experiment Ideas

Building an experimentation engine starts with ideation. You need a steady stream of ideas for what to test. At HubSpot, we aim to generate 20+ experiment ideas per month to keep our pipeline full.

The key is to engage your full team in brainstorming. Experimentation should be a democratic process. Encourage every marketer to wear a "test and learn" hat and bring ideas to the table, from executives to interns. Diverse perspectives fuel creative thinking.

Host a standing growth marketing brainstorm monthly or quarterly to source new ideas. Try these prompts:

  • What are our current top-performing marketing assets or campaigns? How might we test optimizing them further?
  • What marketing assets or campaigns are underperforming vs. benchmarks? What experiments could improve them?
  • Where are the biggest points of friction or drop-off in our marketing funnel? How might we smooth them?
  • What new channels, content formats, creative approaches, or tactics are we not testing but could have potential?
  • How might we improve the post-purchase customer experience to drive retention, expansion, or advocacy?

Capture every idea, even if it seems a little crazy. Resist the urge to evaluate or criticize in this phase. You‘ll validate the ideas later.

Prioritizing Marketing Experiments

Once you have a robust backlog of ideas, you need a way to prioritize which ones to implement first. Not all experiments are created equal. Some may be quick wins while others are moonshots. Some may require heavy resourcing while others are low effort.

To rank experiments, growth teams commonly use the PIE framework:

  • Potential: If the experiment works, how much impact will it have on your target metric? Score 1-10.
  • Importance: How significant is this experiment to achieving your current marketing goals? Score 1-10.
  • Ease: How simple will this experiment be to implement based on resourcing and dependencies? Score 1-10.

Multiply the three scores together for an overall PIE Score between 1-1000. The higher the PIE Score, the higher priority the experiment.

For example:

Experiment Idea Potential Importance Ease PIE Score
Test new CTA copy on key landing pages 8 9 7 504
Trial chatbot for lead qualification 6 7 3 126
Pilot direct mail campaign for target accounts 7 6 4 168

In this case, the CTA copy test would be the highest priority based on its high potential impact, strategic importance, and relative ease of implementation compared to the other ideas.

Some other factors and data points you could layer onto PIE scoring:

  • Anticipated lift in conversion rate, click-through rate, or other target metric
  • Projected revenue impact if successful
  • Number of experiments run on this asset or tactic previously
  • Time since asset or campaign was last tested
  • Relevant customer or prospect research, like survey data
  • Competitive intelligence or industry benchmark data

Your goal is to queue up and execute on the experiments with the highest probability of moving the needle on your key metrics.

Planning Marketing Experiments Step-by-Step

With a prioritized roster of experiment ideas, it‘s time to turn them into reality. At HubSpot, every experiment we run follows this 7-step process:

1. Define Hypothesis

Articulate the specific prediction you‘re testing and what you expect to learn. Structure it as an if-then statement.

Use this fill-in-the-blank template:
If we [change], then [metric] will [increase/decrease] by [amount].

For example: "If we shorten the form on our demo request landing page, then conversion rate will increase by 15%."

2. Outline Experiment Details

Document the following specifics of your planned test:

  • Metric(s) you‘re tracking and target improvement
  • Specific asset(s) involved
  • Control and treatment variants
  • Number of variants
  • Traffic split between variants
  • Required sample size
  • Test duration
  • Targeting (audience segments, devices, geos, etc)
  • Goals and guardrails

Use an experiment brief template like this to capture key details:

Experiment Name Metric Hypothesis Assets Sample Size Duration Goals
Demo Page Form Test Demo requests If we reduce form fields from 8 to 4, demo request conversion rate will lift 15% Demo page 1,200 visitors/variant 14 days Increase lead volume and form completion rate with shorter form

3. Secure Buy-In

Review your planned experiment with key stakeholders to get their sign-off. Discuss and align on the rationale, approach, and implications.

Depending on the experiment, you may need approvals from managers, designers, web team, sales, legal, or other departments. Build in ample buffer for feedback and approvals so launch timelines don‘t slip.

4. QA and Launch

Build out the experiment in your A/B testing tool or landing page creator. Fully QA the control and treatment variants across devices and browsers. Check for any errors or inconsistencies.

Do a final review with stakeholders, then launch the experiment and let it run for the designated timeframe. Avoid making any tweaks or adjustments while in flight.

5. Monitor Results

Keep an eye on your experiment while it‘s live. Check for major swings or anomalies that could indicate a problem, like a broken page.

Watch for statistical significance. Once you reach 95%+ confidence in your results, you can typically end the experiment early. Most A/B testing tools will automatically calculate this.

Pull benchmark data for your target metric from before the experiment launched so you have a baseline for comparison.

6. Analyze Results

When the experiment ends, dig into the data. Ask questions like:

  • How did each variant perform on target metrics vs. the control?
  • Were the results statistically significant? By how much?
  • What was the percentage change in conversions? Revenue?
  • Did performance vary by device, channel, audience segment, or geography?
  • Were there differences in behavior metrics like time on page or pages per session?
  • How did the results compare to original hypothesis and past tests?

Slice and dice the data to uncover actionable insights. Don‘t just report the topline results. Investigate what drove the outcomes so you can apply the learnings strategically.

7. Determine Next Steps

Armed with your analysis, decide how to implement your findings:

  • If a treatment variant won by a wide margin, consider deploying it to 100% of traffic. But be sure to monitor performance post-launch to watch for regression to the mean.
  • If a treatment performed better but not dramatically, you may want to run a follow-up test to validate the results. Try testing it on different pages or audience segments.
  • If the control won or results were flat, investigate why and brainstorm new experiment ideas. Consider tweaking the design, copy, or offer to see if you can beat the baseline.

Document and socialize your experiment results with the team so everyone has context. Archive all experiments in a central library so anyone can easily access past learnings.

Analyzing Experiment Results Like a Pro

The crux of experimentation is rigorous analysis. You need to go beyond simply declaring a winner or loser. Investigating what exactly moved the needle and why leads to richer insights you can use to optimize future tests and campaigns.

Some metrics and techniques to use when analyzing experiments:

  • Statistical significance and margin of error: Most A/B testing tools automatically calculate this but you can also use a statistical significance calculator. Aim for a minimum 95% significance before calling a winner.

  • Confidence interval: This gauges the reliability of your experiment results. The higher the confidence interval, the more precise the data. Calculators like this one from Optimizely can help.

  • Relative difference: This is the difference in conversion rates between your control and treatment variants. For instance, if your control converted at 2% and treatment at 2.5%, the relative difference is 25% (more on relative difference).

  • Segments: Slice your results by dimensions like traffic source, device type, location, or audience. This can uncover learnings to optimize for specific segments.

  • Micro conversions: Analyze metrics earlier in the funnel, like click-through rate, bounce rate, or pages per session. See how your treatments impacted user behavior, not just the end conversion.

  • Revenue per visitor: This helps project revenue impact by extrapolating conversions and order values to a larger visitor base. Simply multiply your treatment conversion rate by average revenue per conversion.

  • Bayesian inference: This statistical method uses probability to determine the likelihood that your experiment results are reliable and repeatable over a larger sample (Bayesian statistics primer).

Robust analysis and accurate statistics are critical for drawing the right conclusions from experiments. When in doubt, consult an analyst or review the latest digital marketing benchmarks to gut check your data.

Fostering an Experimentation Culture

Weaving experimentation into the fabric of your growth marketing program is as much a cultural endeavor as it is operational.

The most sophisticated growth teams make experimentation a priority from the top down. Managers model the behavior, celebrate innovative tests, and reward employees based on experiments run and learnings gained.

Some tips for building an experimentation culture:

  • Start small: Begin with simple A/B tests to demonstrate early wins and get the flywheel moving. Then scale up to multivariate or multi-channel experiments.

  • Democratize ideation: Encourage every marketer to suggest experiment ideas, regardless of seniority. Set a quota for ideas submitted per person.

  • Standardize processes: Use templates, workflows, and automation to make experimentation turnkey and repeatable. The less friction, the more your team will do it.

  • Celebrate learnings: Showcase experiment results regularly at team meetings. Celebrate both wins and "failed" tests that produced valuable data. Make analysis and insight sharing a ritual.

  • Make it a KPI: Tie experiments run, win rate, or new revenue generated to team and individual goals. What gets measured gets done.

  • Train the team: Host workshops or tap consultants to educate your team on experiment design, statistics, and analysis. The more they understand the mechanics, the better they‘ll get at experimentation.

  • Center on customers: Make sure experiments always tie back to real customer needs and pain points. Use experiments as an opportunity to develop customer empathy and validate solutions.

With the right mindset, processes, and incentives, experimentation will become core to your team‘s DNA.

A Growth Marketer‘s Secret Weapon: The Marketing Experiments Calendar

To build an experimentation engine, you need a command center. Enter the marketing experiments calendar.

We‘ve created a free marketing experiments calendar template for growth marketers to plan, execute, and measure experiments at scale. It‘s the scaffolding for your growth machine.

The template has tabs and sections to help you:

  • Capturing and ranking experiment ideas with PIE scores
  • Mapping out your experiment roadmap on a calendar
  • Outlining key details and metrics for each planned test
  • Recording and analyzing the results of completed experiments
  • Visualizing your experimentation velocity and win rate over time

marketing experiments calendar template

To get the most from the template:

  1. Make a copy and customize it for your team‘s needs and workflows
  2. Have each team member create their own version to plan and track their personal queue of experiments
  3. Review experiment ideas and roadmap during sprint planning to align on priorities
  4. Update experiment details and results religiously so it becomes the central source of truth
  5. Monitor the dashboard charts to watch your experimentation engine pick up steam

Use the template as your launchpad to make experimentation a habit. Commit to running at least 2-4 experiments per month, then optimize and scale from there.

Over time, experiment by experiment, you‘ll be amazed at the compounding effects on your growth metrics. All it takes is a steady drumbeat of "test and learns" to build serious growth momentum.

Conclusion

Frequent, strategic marketing experiments are the not-so-secret weapon behind breakout growth for many of today‘s top brands. By testing new ideas and optimizing based on data, growth marketers can drive outsized improvements in key metrics.

But sporadic experiments are not enough. To see the full impact, you need to build a well-oiled experimentation engine. That means generating a robust backlog of ideas, prioritizing with discipline, executing experiments rigorously, analyzing results deeply, and fostering an experimentation culture.

Using an experiment calendar template like the one provided here is the key to planning, managing, and measuring your experiments at scale. Treat it as your growth command center.

Equipped with these tools and techniques, you‘re ready to launch your own experimentation engine and optimize your way to efficient, scalable growth. Get ready for the breakthroughs and happy testing!