Most entrepreneurs wish they had a better grasp of marketing. With better marketing, you can get more visibility for your brand, more paying customers, and you can achieve growth at a faster rate.
But to an outsider with limited experience, marketing is a nebulous, complex, and intimidating field. The people who practice marketing seem like modern-day magicians, capable of seeing amazing results simply by following their intuitions.
In reality, this isn’t the case. The best-performing marketing strategies aren’t the result of magic or intuition. Instead, they’re the ultimate result of hard, scientific experiments, repeated over and over again.
How are marketing experiments so powerful? And what’s the right way to use them to fuel your marketing campaigns?
Why Marketing Experiments Are Important
Let’s start by explaining why marketing experiments are so important. Done right, they can shape your business’s entire marketing future.
- Data and customer information. First, marketing experiments give you access to an abundance of data. Next, you’ll learn more about your target audience and how they respond to different images, different copy, and different platforms. Over time, you’ll grow to understand them better, and you’ll be able to craft better messages for them as a result.
- Incremental improvements. Experiments also allow you to make incremental improvements to your marketing strategy. With each new test, you’ll learn a bit more about your brand, your audience, the platforms available to the public, or even your competition – and with that information, you’ll be able to create better ads and better messages.
- Evidence and cost-benefit analyses. Marketing experiments give you a chance to collect data you can use to analyze and prove the costs and benefits of all your marketing approaches. This could be valuable in justifying your position to an employer, proving results to investors, or just refining your own approach to increase your return on investment (ROI).
There are many ways to approach marketing experiments. Still, one of the most common is the typical “AB test,” in which you make two slightly different versions of a landing page, advertisement, or other pieces of marketing collateral.
These two different versions are labeled “A” and “B,” then tested in a live environment. Once you figure out which version gets more traffic, or more conversions, you can use these lessons to refine your approach – and run more AB tests to gather even more data.
Realistically, you shouldn’t feel limited by the AB test formula. Any test you conduct to learn more about your brand, your audience, and your potential impact can be valuable.
Here’s how to run even better marketing experiments.
Use the Right Tools
For starters, you should use the right tools. Good marketing experiment tools tend to share a handful of traits, such as:
- Accessibility. It should be easy to find and start using this tool. You shouldn’t have to build something new from the ground up.
- Intuitiveness. It should also be intuitive to use the tool effectively. It shouldn’t take weeks of training to learn how to conduct an experiment.
- Robustness. You’ll want a host of features that make it easier and more straightforward to conduct the experiments you want. WYSIWYG editors and drag and drop mechanics, and access to multiple experimental variables are key.
- Transparency and data manipulation. You’ll also want full, unimpeded access to the data you’ve gathered throughout this campaign – and you should be able to manipulate these data as you see fit in the form of interactive charts and graphs.
Luckily, most major marketing and advertising platforms realize the importance of experimentation – and they offer built-in tools to help marketers run better tests. So, for example, if you’re running a PPC ad campaign, you’ll have access to tools that allow you to rotate ads, measure customer interactions, and ultimately learn enough to create better campaigns in the future.
Follow the Scientific Method
Marketing experiments are a kind of small-scale science experiment, so if you want to see better results, you need to follow a loose kind of scientific method.
- Create a hypothesis. Start by defining your current opinion and perspective. How do you think this experiment is going to go? For example, do you believe a red button will convert more people than a green button?
- Run tests. Next, devise an experiment to put your hypothesis to the test. For example, if you’re testing the difference between green and red button colors, consider creating two nearly identical versions of your landing pages. The only difference is button color and sending traffic to both.
- Observe and analyze the data. Then, collect the data and see if your hypothesis is true.
- Form new conclusions. Regardless of whether you prove your hypothesis right or wrong, you’ll likely learn something new in the process.
Nail the Sample Size
The sample size is one of the most important variables to get right in the realm of marketing experimentation. With too few people in your sample, you won’t be able to form meaningful conclusions about the larger population. But, on the other hand, with too many people, you’ll end up wasting time and money unnecessarily.
Keep an Open Mind
You may have ideas for exactly what your brand is and how it should be marketed, and in some ways, this solid consistency is a good thing. But if you want to get the best possible results from your marketing experiments, you need to keep a more open mind. Experiments are great for putting controversial new ideas to the test and challenging long-held assumptions; make the most of them by being willing to try interesting new things.
Compare Apples to Apples
When conducting marketing experiments, you’ll need to compare apples to apples. In other words, you’ll need to make sure your experimental conditions don’t introduce secondary and tertiary variables that could confuse your causal link. For example, let’s say you’re testing a green button against a red button, but your green button is active in the morning, and your red button is active at night. Of course, your red button test wins – but is that because the button was red or because the test was at night?
Control your tests for the following variables, at minimum, and try to isolate a single variable for your test:
- Time. When are you conducting this experiment?
- Audience. Who are the people being tested?
- Sample size. Are you using approximately the same sample size for each test?
- Other variables. Try to filter out other variables as much as possible, and focus your AB experiment on a single differentiating variable.
The closer you’re able to compare your different marketing experiments, the closer you’ll get to a “perfect” strategy.
Filter Out Cognitive Biases
Cognitive biases affect all of us, distorting our perceptions and making it hard to form neutral conclusions. These are just a few ways you can fight against bias:
- Introspect. How are you approaching this problem? Are your feelings steering you in a particular direction?
- Look at multiple data sets. One test may not be enough. Try looking at multiple data sets, especially if you can find some from other experimenters, before forming a conclusion.
- Identify your underlying assumptions. For example, you might have a built-in assumption that one particular marketing strategy will be better than all your others. These can lead you to biased and false conclusions.
- Try to prove yourself wrong. Confirmation bias makes it easy to prove yourself right. Instead, look for evidence to prove yourself wrong. You might be surprised at what you find.
- Work with a team. Intellectual diversity can help you identify the gaps in your own thinking – and new perspectives are always a good thing.
With better marketing experiments in place, your marketing and advertising results will surely increase over time. You’ll have access to a more reliable stream of data, you’ll be freer to experiment with new techniques and approaches, and sooner or later, you’ll stumble upon the perfect formula to convert your target audience.
Image Credit: anthony shkraba; pexels; thank you!