Product marketing is a nuanced, fast-paced, and multi-faceted endeavor. In a competitive environment with no guarantees, marketing experimentation serves as a surefire way to maximize reach, optimize conversions, and improve the user experience.

In this guide, we’ll walk you through what marketing experiments are, why you should run them, how to conduct experiments successfully, and seven experiments to try for yourself!

What is marketing experimentation?

Marketing experimentation is an approach to generating fresh ideas, testing strategies, identifying your mistakes, optimizing campaigns, and making data-informed decisions moving forward.

It can also uncover hidden opportunities for organizations in new markets, segments, or use cases.

Why you should run marketing experiments?

There are many benefits to running a marketing experiment but a few notable ones are:

• Behavioral insights. Marketing experimentation can offer key insights into customer behavior so you have a better understanding of your users’ preferences.
• Sustained growth. Marketing experiments help you achieve sustained growth by testing new ideas and doubling down on the channels that offer the best marketing ROI.
• Data-driven optimization. A marketing experiment can guide future business decisions by highlighting the marketing strategies that produce the best results at a relatively low cost.

Clearly, marketing experiments are crucial to every stage of the growth journey, from gathering insights to driving growth and optimizing results.

How to conduct a successful marketing experiment?

A lot goes into setting up, running, and then analyzing a marketing experiment.

Running marketing experiments generally comes down to these six steps:

1. Deciding the goal
2. Making a hypothesis
3. Choosing the audience
4. Selecting your metrics
5. Running the experiment
6. Analyzing the results

Let’s take a closer look at each step.

Decide on the goal of your marketing experimentation

First and foremost, you’ll need proper goal-setting to ensure clarity on what these experiment ideas are supposed to achieve.

Brainstorm to find the most impactful goals, consider why that goal is valuable, and make sure your goals are SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound).

Make a hypothesis

A hypothesis is just an explanation made using limited data that can serve as a starting point for your experiment.

For instance, you could hypothesize that adding live chat embeds to your website landing pages would increase the flow of potential customers within your sales pipeline.

Choose the audience

Not all experiments are suitable for every user. As such, utilizing user segmentation to run experiments on the most relevant customers will offer the best insights and increase the odds of achieving the desired results.

Decide on the key metrics you will use to track marketing success

A/B testing metrics will only be actionable if you use the right key performance indicators (KPIs) to measure results. These metrics give your experiments a measurable hypothesis to objectively investigate rather than a vague theory to aimlessly pursue.

The metrics you choose will depend on what you’re trying to prove. Metrics could be website traffic, click-through rates (CTR), conversion rates, marketing reach, customer engagement, retention rates, and more.

All that matters is that you select relevant metrics that will be able to prove or disprove your hypothesis.

Run your marketing experimentation

The actual testing process will vary depending on the goal, hypothesis, audience, and metrics.

Not every experiment will be carried out by marketing teams alone. Certain cross-functional projects will also require collaboration from customer success, support, sales, and development teams.

When the marketing team collaborates with other departments, it should ensure that everyone involved is briefed on:

• What the hypothesis is
• Which metrics will be used to track it
• When the experiment will begin/end

Analyze the results

Finally, it’s time to collect data and analyze the experiment’s results. Make sure you have enough data to accurately determine statistical significance. Small sample sizes or shallow data could lead to an independent variable skewing results one way or another.

Using the metrics you’ve chosen and the data you’ve collected, you should be able to conclude whether or not your hypothesis was correct. Proving a hypothesis takes longer when making minor changes like testing button colors versus experiments with a significant impact, like an overhauled checkout flow.

In either case, successful results will guide you on which future experiments to try or future campaigns to deploy. If the experiment had a genuine impact on user acquisition and revenue growth, then you’ll also have more budget to work with on subsequent experiments.

Note: In results, the independent variable is the cause, while the dependent variable is the effect.

7 common marketing experiments to try in SaaS

Now that you know what marketing experiments are, why you should run them, and how to conduct them, it’s time to go through a few popular options.

These marketing experiments are conducted fairly commonly within the SaaS industry and will serve as a good starting point for your experimentation:

Run experiments with different digital marketing campaigns

Experimenting with different ad copy, marketing strategies, and other platforms will help you bring in more customers (or prospects) in a cost-effective manner.

When conducting this type of experiment, it’s best to fail fast (and cheaply) so you can test the next campaign as soon as possible.

For instance, you might test different content formats to see if email marketing has a bigger impact on business growth compared to content marketing campaigns, for instance. The ideal marketing campaign will depend on your company, target audiences, and consumer behavior within your industry.

Experiment with email subject lines to see which one improves the open rate for onboarding emails

It’s no secret that customers don’t always read onboarding emails. As such, you should experiment with your onboarding email subject lines to see which variants yield the highest open rates. Fortunately, software products make A/B testing different subject lines super easy.

Tools like ActiveCampaign make it possible to show one subject line to half of your customers and another version to the other half — so you can quickly experiment to find the messaging that resonates the most with your user base.

A/B test different onboarding flows to improve customer experience

A/B testing lets you experiment with different onboarding flows to identify winners that increase trial-to-paid conversion rates. Be sure to collect research on which flows were completed before a user upgraded from their free account to a paid subscription.

Run multivariate testing to optimize website landing page conversion rates

In addition to A/B testing, a company could also run multivariate testing to optimize conversion rates.

For example, running a multivariate test on your website’s landing page will help you determine which version (or specific changes) leads to the highest percentage of website visitors completing a signup form or compels users to schedule a demo.

When conducting a multivariate test, the variables you should include are:

• Two headline versions
• Three body text versions
• Four call-to-action (CTA) versions

The messaging and buttons used for CTAs are the easiest to test yet provide the largest changes so testing four or more variants is well worth it. In the example above, you would be testing 24 different combinations (2 headlines x 3 content x 4 CTAs) to see which permutation performs best.

By nature of this testing process, the winner of each multivariate test will always be the one with the best combination of variables. This is important because some variables (like a CTA button) may be effective in isolation but lackluster when combined with different headlines or body text.

Bear in mind that multivariate tests require more data than A/B tests to produce statistically significant insights. This is because there are more variables to account for and multiple combinations to compare (versus A/B tests that only compare two versions).

Compare marketing messages in-app to see which resonates with your customer base

While it’s rare for a company to change its brand voice or messaging, experimenting with a different marketing message can offer valuable insights into what resonates with your customers. This could be applied to in-app messaging or communications sent via other channels like email or social media posts.

For instance, you could use two different modals to invite your users to a webinar and then see which modal resulted in the higher number of signups. This will clue you in on which colors, designs, and language your users respond to.

The same is true for other forms of in-app messaging, such as tooltips, banners, and slideouts.

Compare different paths to see which one leads to higher conversion rates

See how users progress through multiple conversion paths and compare the similarities/differences between each path. What touchpoints are prospective customers interacting with in one path versus another, and which factors seem to be the biggest determiner of conversions?

To experiment with conversion paths, simply select a starting point within your product (such as the home dashboard, analytics page, or a specific feature) and see how your users proceed from there. This type of analysis will help you identify problem areas within your paths and patch any funnel leaks.

Userpilot will actually be launching a path-tracking feature soon to help users compare conversion paths faster and extract more detailed insights!

Experiment with automation to see its result on customer experience

Experiment with different automation types to see how they impact the customer’s experience. For instance, you could onboard some users with a chatbot and the rest (your control group) without a chatbot and then see how this affects their engagement, activation, and adoption rates down the line.

You can also use predictive analytics to gauge how your users may respond to a particular automation (but your mileage may vary depending on the amount of data you have to work with). Incorporating new tools and widgets will have outsized — good or bad — effects on the product experience (PX).

The goal of automation experimentation is to find ways to reduce manual effort while maintaining (or improving) the customer’s experience. This type of market research will help you craft a tech stack that’s not only suited to your business needs but that of your customers as well.

Conclusion

As you can see, marketing experiments can help you get your software product in front of more eyes at less cost and with better lead conversion. There are countless variables, from your users’ cognitive biases to the product’s price and even what season it is.

This means that frequent and strategic testing will help you cut through the noise to extract actionable insights that will actually benefit your business. Whether the goal is acquisition, retention, or expansion, a little experimentation can go a long way.

If you’re ready to start running in-app marketing experiments, split-testing messaging and creating effective onboarding flows without writing a single line of code, then it’s time to get your free Userpilot demo today!