There is always a lot of buzz around marketing performance optimization and testing. Rightly so. Technology of course helps in this regard, but it’s the non-technical expertise that really makes the difference.
I do a lot of performance testing with my clients and I always follow the scientific method. Here’s an introduction to the scientific method, as well as a deep dive into one of its fundamental elements: hypotheses.
Scientific method
Using the scientific method provides a structured approach to performance optimization. Essentially, it involves developing and testing hypotheses about what strategies or tactics will improve results, such as revenue, conversions, or engagement.
Scientific method:
Provides data-driven insights to inform your decisions. Reduces risk by testing changes on a small scale before they are fully implemented. Enables continuous improvement by helping you identify not only what works, but why it works.
This approach allows you to base your marketing testing work on evidence rather than assumptions.
The scientific method has eight steps:
Researching and developing your hypothesis is an essential part of the scientific method. Here are some tips and tricks to achieve this.
What is a hypothesis?
In this context, “hypothesis” is a fancy word for an idea of what might improve performance, but it’s also much more than that.
You could say, “I want to test the color of our call-to-action buttons. Right now they are red, let’s try making them green and see if that improves performance.
This is not a hypothesis. There is no explanation offered as to why this change might be effective.
Case study: Building a hypothesis
Here’s a real-world example of how I built a hypothesis for a test years ago.
I was at my local Barnes & Noble bookstore, reading about the psychology of color in a book I found in the discount section.
He explained why red was the color of stop signs – that subconsciously it could send a signal to stop. I realized I had a promotional email with red buttons, because the brand colors were dark purple (it was almost black) and red.
The product was a financial advice publication (we provided advice on what stocks to buy to improve your financial portfolio), which is when I started thinking about what red meant in the financial world . “In the red” is a bad thing in this case: it means you have more money than you have.
So, I thought, maybe the red buttons were a depressing response.
But what to test against red?
I thought about traffic lights, where red means stop and green means go. Then I thought about the financial world. Green is the color of money, and that’s what we promised stock recommendations would earn them.
The resulting hypothesis was: “Changing the color of CTA buttons from red to green will increase response and revenue for the reasons described above. »
Do you see the difference? Tests based on hypotheses supported by sound reasoning are more likely to yield good results.
Drawing inspiration from hypotheses
Inspiration can come from internal or external sources. Here are some ideas to find inspiration for your tests.
Internal inspiration
Case Study 1: Failed Performance Tests
Even if a test fails, you should look for learnings that you can use in the future. For example, years ago we compared a check email to a version where we had changed a number of different elements. Our KPI was conversions; recipients had to fill out a form to convert.
Control has gained solidly. But while the test version lagged behind in conversions, the click-through rate (CTR) on its top CTA button was almost double that of the control’s top CTA button. As a result, we retested only the elements around the CTA button, including the location and a message that this is an exclusive offer.
The hypothesis here: “These elements appear to have increased CTR in the previous test; Maybe if we isolate them from the other elements of this test and apply them to the control, they will not only increase CTR but also conversions.
This time the test won.
Case Study 2: Campaign Metrics
Much of my client work involves multi-effort email campaigns, in which we send a series of two or more emails to the same list over a period of time. I often get inspiration from data, for example…
In this example, we saw that effort 5, the last effort in the series, still generated over $18,000 — so we could perhaps raise another $9,000 or $10,000 by adding effort 6 Here they hypothesize “Since effort 5 worked well, we should be able to earn additional revenue by adding effort 6.”
We also found that Effort 4 performed well with $0.45 in revenue generated per email address, compared to just $0.32 for Effort 3. Efforts sent earlier in the series tended to yield better results. So we ran a test with the hypothesis “Since earlier efforts tend to yield better results, we should be able to increase our revenue by changing the order of Efforts 3 and 4, since Effort 4 generates revenue.” per email address higher. .”
Dig Deeper: Why we care about performance marketing
External inspiration
Your inbox
Your inbox can be a wealth of performance testing inspiration. Do you have any testing ideas from this email from Walgreens?
Here are the hypotheses I derived from this email:
“Including first name personalization at the top of the email should encourage more people to read it and increase clicks. and generate more income. “Putting the offer behind a ‘scratch’ will increase engagements and conversion rates.” “Visually showing recipients their reward status will motivate them to want to continue earning reward points, which will increase their revenue. »
Articles, blog posts, webinars, presentations and other resources
Any online resource can be a great source of inspiration; It’s even better if it presents case studies of performance tests that others have done.
The last one on this list, Really Good Emails, is a drag-and-drop file. They have screenshots of over 15,000 email messages; you can search on a number of different variables. Browsing through swipe lists like this is a great way to find inspiration for hypotheses.
Now is the time to start performance testing or improve your performance testing game. I hope this introduction to hypothesis development is helpful.
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