Utilizing the Scientific Method for Enhanced Performance Analysis

There’s an ongoing conversation surrounding the optimization of marketing performance and the importance of testing. It’s a completely valid discussion. While technology plays a vital role, the insights gained from non-technical expertise truly drive impactful changes.

My work often revolves around performance testing in collaboration with clients, and I consistently adhere to the principles of the scientific method. Below is an introduction to the scientific method, along with an exploration of one of its foundational components: hypotheses.

The Scientific Method

Adopting the scientific method implements a systematic framework for enhancing performance. It emphasizes the necessity of formulating and examining hypotheses regarding which strategies or approaches might enhance results, including metrics like revenue, conversion rates, or user engagement.

Benefits of the scientific method include:

• Delivers data-driven insights that guide your strategic decisions.
• Mitigates risk by experimenting with changes on a small scale prior to broader implementation.
• Facilitates ongoing improvement by allowing you to discern not only what succeeds, but also why it succeeds.

Such a methodology roots your marketing testing in factual evidence instead of mere speculation.

The scientific method encompasses eight distinct stages:

Creating and refining your hypothesis is a vital element within the scientific method process. Here are some strategies to help you accomplish this effectively.

Understanding Hypotheses

In this scenario, a “hypothesis” refers to an informed guess about what may enhance performance — however, it extends beyond that simple definition.

For example: “I want to explore whether changing the color of our call-to-action buttons from red to green will lead to improved performance.” This declaration lacks a rational explanation for why such a modification may be beneficial.

Case Study: Formulating a Hypothesis

Here’s a practical illustration of how I formulated a hypothesis for a test several years ago.

While visiting a local Barnes & Noble, I stumbled upon a book discussing color psychology in the discount aisle.

The book explained the significance of red in stop signs — indicating that it might subconsciously convey a message to halt. At that time, I had a promotional email featuring red buttons, tied to brand colors of dark purple (near black) and red.

As the product was related to financial advising (providing recommendations on stock purchases to improve investment portfolios), I pondered the implication of red in finance. The term “in the red” signals a negative situation — where expenses exceed income.

This led me to hypothesize that the red buttons might actually hinder engagement.

The question then became: what could I test in comparison to red?

I considered traffic signals, where red denotes stopping and green signifies proceeding. Furthermore, in financial contexts, green symbolizes monetary gain — precisely what our recommendations aimed to deliver.

The resulting hypothesis was: “Transitioning the CTA button color from red to green is likely to boost user responses and revenue based on the reasons provided.”

Notice the distinction? Tests driven by well-reasoned hypotheses stand a higher chance of success.

Finding Inspiration for Hypotheses

Inspiration can stem from both internal and external sources. Here are several suggestions for uncovering ideas for your tests.

Internal Inspiration

Case Study 1: Learning from Failed Tests

Even a failed test can yield valuable lessons for future endeavors. For instance, some time ago, we tested a control email against a version with multiple adjusted components. Our key performance indicator was conversions, requiring recipients to complete a form to achieve conversion.

The control email performed significantly better. However, the alternative version achieved a nearly doubled click-through rate (CTR) on its top CTA button compared to the winning control. Consequently, we endeavored to retest just the elements surrounding the CTA button — including its placement and the exclusivity message.

The hypothesis was: “Given that these modifications appeared to elevate the CTR in the prior test, isolating them from other variables in that test and applying them to the control may improve both CTR and conversions.”

This time, the test was successful.

Case Study 2: Analyzing Campaign Metrics

Much of my client work focuses on multi-effort email campaigns — sending a series of emails to the same audience over a defined period. I frequently draw inspiration from the data we compile, such as…

In this scenario, effort 5, the final push in the series, generated over $18,000 — indicating a possibility of earning an additional $9,000 to $10,000 by introducing effort 6. Thus, the hypothesis was, “Since Effort 5 proved successful, we ought to be able to accrue further revenue by incorporating an effort 6.”

Additionally, we noted that effort 4 yielded revenue of $0.45 per email address, outperforming the $0.32 from effort 3. Typically, earlier efforts see superior performance. Consequently, we tested the hypothesis: “Given that prior efforts generally perform better, we can likely boost revenue by rearranging efforts 3 and 4, placing effort 4’s higher revenue-generating potential first.”

Explore further: The significance of performance marketing

External Inspiration

Your Inbox

Your email inbox can provide a wealth of inspiration for performance tests. For instance, could you draw testing ideas from this Walgreens email?

From this email, I formulated the following hypotheses: 

• “Incorporating first-name personalization at the beginning of the email will capture more readers, leading to increased clicks and subsequently higher revenue.”
• “Positioning the offer behind a ‘scratch-off’ feature will enhance engagement and conversion rates.”
• “Showcasing recipients’ rewards status visually will incentivize them to continue engaging to earn more points, thus boosting revenue.”

Articles, Blog Posts, Webinars, Presentations, and Other Resources

Any online resource holds potential for inspiration; particularly those presenting real-world case studies related to performance tests conducted by others.

Among these resources, Really Good Emails serves as a swipe file, featuring over 15,000 email examples which can be filtered based on various criteria. Perusing swipe files like this is an excellent method to gather inspiration for your hypotheses.

Now is the perfect moment to embark on performance testing or elevate your current testing approach. I trust this overview on crafting hypotheses proves beneficial.