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Digital experimentation 101: A Guide to Data-Driven Decision
Learn the essentials of digital experimentation, including A/B and multivariate testing, to optimize your website, app, or marketing campaigns.
Did you know? Just by changing the color of its hyperlinks, Microsoft’s Bing boosted the company’s revenue by $10 million annually.
This is just one example of how digital experimentation can greatly impact an organization's key metrics.
In this guide, we’ll explain why digital experimentation is so important. We’ll arm you with the knowledge you need to maximize the impact of your experimentation efforts with confidence.
We’ll cover:
A definition of digital experimentation
An overview of the main types of digital experiments businesses can run
A simple step-by-step guide to conducting digital experiments
Tips and best practices for digital experimentation
Common hurdles you might find along the way and how to overcome them
Let’s get started.
What is digital experimentation?
Digital experimentation is the process of systematically testing and iterating on digital products, features, or marketing strategies.
This involves making deliberate changes to these elements and then carefully measuring the impact of those changes on user behavior and key performance indicators (KPIs).
The purpose of digital experimentation is to empower businesses to make informed decisions based on real data, rather than relying on intuition or guesswork.
By running controlled experiments, businesses can gain a deeper understanding of their users, identify areas for improvement, and ultimately improve their digital experiences.
Digital experimentation helps companies:
Understand user behavior: By analyzing how users interact with different versions of a website, app, or campaign, businesses can identify pain points, preferences, and areas where users may be dropping off.
Drive conversions: Experimentation can reveal which elements, such as headlines, calls to action, or product recommendations, are most effective at encouraging users to take desired actions, such as purchasing a product or signing up for a service.
Improve performance: By testing different variations of a digital experience, businesses can identify changes that lead to improved metrics, such as increased engagement, higher conversion rates, or greater customer satisfaction.
What types of digital experiments do businesses usually run?
The world of digital experimentation is vast and varied, with a whole toolbox of techniques at our disposal. Let's dive into some of the most common types of experiments that businesses use:
A/B testing
With A/B testing, you pit two variations of a webpage, app feature, or ad against each other. If you’re testing more than two variations, you’ll often find it’s referred to as A/B/n testing. Think of it like a friendly competition where only one champion emerges.
By comparing their performance, you can confidently determine which variation resonates better with your users and drives your desired outcomes. Did changing the color of that "Buy Now" button lead to more clicks? A/B testing gives you the answer.
Multivariate testing
Multivariate testing takes things up a notch by testing multiple variables simultaneously. This allows you to see how different elements interact and influence each other. It's like a recipe experiment, where you tweak multiple ingredients at once to find the perfect blend of flavors.
Perhaps a new headline combined with a different image leads to even higher engagement than either change alone. Multivariate testing helps you uncover those hidden synergies.
Multi-armed bandit testing
Ever wish your experiments could learn and adapt on the fly? That's where multi-armed bandit testing comes in. This dynamic approach automatically adjusts the traffic allocation to different variations based on their real-time performance.
It's like a smart slot machine that learns which arm is most likely to pay out and then focuses on pulling that lever more often. This can be particularly useful when you have multiple variations and want to quickly identify and capitalize on the top performers.
A step-by-step guide to conducting a digital experiment
Here's a roadmap to guide you through the process, with some examples to illustrate the entire process:
Step 1: Define objectives
First things first: What do you want to achieve? Do you want to boost those conversion rates? Get more users to stick around? Or maybe create a smoother, more delightful user experience? Your objectives will serve as your compass throughout the experiment.
Example: Our objective is to increase the click-through rate on our "Sign Up" button by 15%.
Step 2: Formulate hypotheses
Now it's time to come up with some educated guesses about what might work. These hypotheses are based on your observations, user feedback, or past data.
Example: We hypothesize that changing the color of the "Sign Up" button from blue to orange will increase clicks because orange is more attention-grabbing.
Step 3: Design the experiment
Choose the type of experiment that best suits your goals and resources. Will you go with a classic A/B test, a more intricate multivariate test, or perhaps a daring split test? Then, create the variations you want to compare.
Example: We'll create two versions of our landing page: one with a blue "Sign Up" button (the control) and one with an orange button (the variation).
Step 4: Identify metrics
How will you know if your experiment was a success? Choose the key performance indicators (KPIs) that align with your objectives. These could include conversion rates, click-through rates, time on page, or any other metric that matters to your business.
Example: Our primary metric is the click-through rate on the "Sign Up" button. We'll also track the overall conversion rate to see if the change impacts sign-ups.
Step 5: Set up the experiment
Now it’s time to get into the nitty-gritty part of testing. Use a reliable experimentation tool or platform (like Eppo) to implement your experiment. This involves dividing your traffic, assigning users to different variations, and ensuring the data is collected accurately.
Example: We'll use Eppo to randomly assign 50% of our visitors to each version of the landing page and track their behavior.
Step 6: Run the experiment
Launch your experiment and closely monitor its progress. Keep an eye on those KPIs, ensuring that everything is running smoothly and that you're collecting enough data to draw meaningful statistically significant conclusions.
Step 7: Analyze the results
The moment of truth. Once your experiment has run its course, it's time to crunch the numbers. Use statistical analysis to compare the performance of each variation and determine if the results are statistically significant.
Example: After two weeks, we find that the orange button had a 20% higher click-through rate than the blue button, and this difference is statistically significant.
Step 8: Make decisions
Based on your analysis, it's time to make a decision. Do you roll out the winning variation to all users? Do you need further testing? Or perhaps it's time to try a different approach altogether?
Example: Given the positive results, we'll implement the orange "Sign Up" button on our landing page for all users.
Step 9: Iterate, iterate, iterate
Don't rest on your laurels. Digital experimentation is an ongoing process. Use the insights gained from each experiment to fuel new hypotheses and drive further improvements.
Example: We'll continue to experiment with other elements of our landing page, such as the headline and imagery, to see if we can further boost conversions.
Best practices for digital experimentation
Experimentation can be a wild ride. But with a few trusty tips up your sleeve, you can navigate the twists and turns like an expert. Here's a quick cheat sheet for digital experimentation success:
Begin with a crystal-clear vision: Before you dive headfirst into testing, take a moment to define your goals. What are you trying to achieve? When your objectives are well-defined, your experiments will inherently be more focused.
Keep it simple: Don't try to change everything at once. Start with small, focused experiments that isolate the impact of a single variable. This will help you pinpoint exactly what's working (or not working) and avoid getting overwhelmed.
Mirror your real users: Your test audience should be a mini version of your actual user base. This ensures that the results you get are relevant and applicable to the people you're ultimately trying to serve.
Give it time to shine: Rome wasn't built in a day, and neither are statistically significant results. Be patient and let your experiment run long enough to gather enough data to draw meaningful conclusions.
Avoid playing favorites: Randomly assign users to different test groups to avoid any unintentional biases. You want to create a level playing field so that you can compare the results fairly.
Take notes, share the wisdom: Don't let those valuable insights go to waste. Document everything — your experiment setup, hypotheses, results, and learnings. Share this knowledge with your team so everyone can benefit from your discoveries.
Let automation do the heavy lifting: Automation is your friend. Use tools to automate repetitive tasks like experiment setup, monitoring, and analysis. This not only saves you time but also reduces the risk of human error.
Common challenges and how to overcome them
Let's be real, digital experimentation isn't always smooth sailing. It has its share of hurdles, but fear not! With the right strategies, you can overcome these common challenges and steer your experiments toward success.
Not enough traffic
Sometimes it can feel like you're testing in a ghost town. If your website or app doesn't have a ton of traffic, it can be tough to gather enough data for meaningful results.
The fix: Focus your efforts on the high-traffic areas of your site or app. These are the places where even small changes can make a big difference. Alternatively, you can extend the duration of your experiments to accumulate more data over time.
Confounding variables
External factors like seasonality, promotions, or even the weather can sneak into your experiment and mess with the results.
The fix: Control for these variables as much as possible. Run your experiments during periods of relative stability, and consider using techniques like A/B testing to isolate the impact of your changes from other factors.
The significance struggle
Ever run an experiment and found that the results were inconclusive? That’s because it was not statistically significant.
The fix: Make sure you have a large enough sample size to detect meaningful differences. Run your experiments for a sufficient duration to allow those statistically significant results to emerge.
Resource scarcity
Time, money, and manpower — these resources are often limited, making it tough to run all the experiments you'd like.
The fix: Prioritize, prioritize, prioritize. Focus on experiments that are most likely to move the needle on your key goals. As mentioned before, you can leverage tools like Eppo to automate and simplify your experimentation process.
Next steps
You should now have a much clearer picture of why digital experimentation is so important. Actually executing flawless experiments, however…That’s a different story.
Using Eppo turns what was once challenging into a process that’s easy to manage.
Eppo is a comprehensive experimentation and feature management platform that simplifies your entire experimentation strategy.
Its robust suite of features lets your team conduct experiments with remarkable rigor and precision, ensuring that rock-solid data backs every decision.
Here's how Eppo can change your approach to experimentation for the better:
Seamless experimentation: Eppo simplifies the entire experimentation lifecycle, from hypothesis formulation to result analysis, within a single, intuitive platform. This means less time wrestling with disparate tools and more time uncovering valuable insights.
Accelerated insights: With cutting-edge experiment acceleration tools like CUPED++, exclusive to Eppo, you can gather statistically significant results faster. Make decisions with confidence, knowing you have the information you need, when you need it.
Data you can trust: Eppo's native integration with your data warehouse ensures the utmost data integrity. Say goodbye to unreliable data and hello to actionable insights that you can confidently base your decisions on.
In-depth analysis: Eppo's powerful statistical engine empowers you to dive deep into your experiment results. Uncover how different user segments respond to your variations, pinpoint areas for improvement, and identify opportunities for change.
A culture of experimentation: Eppo's user-friendly interface and collaborative features encourage broader team participation. Foster a data-driven culture where everyone, from product managers to engineers, feels empowered to contribute to the process.
Ready to unlock the full potential of digital experimentation with a platform designed for precision and rigor?
Learn the essentials of digital experimentation, including A/B and multivariate testing, to optimize your website, app, or marketing campaigns.
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