The scientific method has been in use since the 17th century. A tried-and-true process of uncovering the truth without prejudice, it has guided scientists in questioning, testing and proving a hypothesis true or false for more than 400 years. But the scientific method is not only a wildly effective guide for those running experiments on biological, chemical or other science-y subjects, it’s also incredibly helpful to use as a guide when building or creating a marketing plan or method within a marketing plan. Let’s walk through it.
The scientific method is an empirical method of acquiring knowledge that has characterized the development of science since at least the 17th century. -Microsoft Academic
The first step in employing the scientific method is to observe. When in the observation phase, you’re may want to consider thinking about the following:
1) What is the current state of marketing within our business? What seems to be working/what’s not?
2) How are we measuring success? If you just answered the question above, how did you determine what is working or not working? Are you evaluating just based on a gut feeling or are you looking at metrics? What are those metrics and how effective are they when determining success?
3) What are our competitors doing? Does it seem to resonate with the market? What makes us stand out against them?
4) What are our goals? More sales? Increasing referrals? Brand recognition?
After taking in the current state of internal and external factors, formulate a question that you can then try to answer with marketing experiments. These should be relatively narrow in scope, so it’s possible to set up an experiment that can definitively answer the question. For example, asking ‘can we get more leads with more marketing?’ is a very broad question, and therefore going to be pretty tough to answer in one process.
Especially when using this method for the first time, start with a more targeted question, like ‘would setting up Google Ads bring in more leads?’ You can also ask a question like ‘If we send out more email communications each month, will it lead to more website visits?’, or ‘If I directly address the difference between my offering and my competitor’s offering in our digital advertising, will I get more interactions?’
If you start small, with one element of your overall sales and marketing function, you can learn the process, see the results and then apply on a broader level, taking on questions like, ‘If I increased overall marketing spend by 10%, could I increase revenue by 15% over 6 months?
We’ve created a helpful (and free) worksheet to help you through this process. Grab it here.
Alright, it’s time to clearly identify our experiment. Construct a hypothesis by plugging your information into this format:
“If (I do this), then (this will happen).”
Let’s test this out with some of the questions we were asking in the previous ‘questions’ section.
Question: Would setting up Google Ads bring in more leads?
Hypothesis: If I run a Google Ads search campaign for 3 months with an ad budget of $1500, then I will increase incoming leads by 4 per month.
Question: If we send out more email communications each month, will it lead to more website visits?
Hypothesis: If I double the number of email communications I send to our list over the course of 6 months, then I will increase my web visitors by 20% during that timeframe.
Question: If I directly address the difference between my offering and my competitor’s offering in our digital advertising, will I get more inquiries?’
Hypothesis: If I compare (this specific) benefit of our offering directly against xyz customer’s offering in our social media ads over the next 2 months, then I will increase interactions with my ad by double during that timeframe.
Note that we specifically outline a finite amount of time in each hypothesis. Because the intent of this exercise is to find the truth, we have to test it within strongly maintained parameters to have the best chance of gathering data that gives us an objective answer to our question. In other words, the experiment must end so that we can ultimately analyze, learn and iterate.
Here’s where the rubber meets the road. It’s time to develop your ‘experiment’. In our case, this is a marketing campaign or tactical promotional activity, which includes guidelines for success measurement. The goal is to create a set of marketing executables to test your hypothesis. Let’s go back to our examples and outline some marketing experiments that would be designed to test if what happens in real life is the same or different from our hypothesis.
Example 1:
Hypothesis: If I run a Google Ads search campaign for 3 months with an ad budget of $1500, then I will increase incoming leads by 4 per month.
Experiment: Design a Google Ads search campaign with targets within 500 miles of Chicago with a $16 per day budget. The search campaign will be split into 3 adgroups that each have a responsive search ad with varying headlines and descriptions that speak to the different user interests of the people targeted in each adgroup. Leads will be measured by conversions, which are tracked by forms filled on a specific webpage.
Example 2:
Hypothesis: If I double the number of email communications I send to our list over the course of 6 months, then I will increase my web visitors by 20% during that timeframe.
Experiment: Increase outgoing emails from our database in Constant Contact from 2 per month to 4 per month. 2 emails per month will be thought leadership or valuable/entertaining content with a soft reminder of our offering at the end, while the other two will be updates and more direct pitches. Web visitor data will be accessed from Google Analytics.
Example 3:
Hypothesis: If I compare (this specific) benefit of our offering directly against xyz customer’s offering in our social media ads over the next 2 months, then I will increase interactions with my ad by double during that timeframe.
Experiment: (This assumes there is legacy ad data to compare to new data). Design ad creative for Facebook and Instagram ads that directly compare a key differentiator with a competitor and run for 2 weeks with a $20 per day budget for each. Compare interaction rates with data from Facebook Ads Manager to a matching time frame and budget from legacy data.
Note that in all of these cases, we’ve clearly outlined the platform we’re using for our marketing campaign experiments, the budget, the length of time and the method for objectively analyzing results; which, you may have guessed, leads us to the analysis phase.
Now that the experiment is done, we have to test the results against our hypothesis. This requires digging into the marketing metrics we’ve gathered, whether it be web visits, conversions, interactions or other results. Use the Marketing Method Worksheet to plug in your results and easily compare them to your hypothesized results. How did your experiment perform? As expected? Were there surprises? Should you stay the course and keep your new marketing tactics running or should you iterate?
Remember that if you have disappointing results, that’s not a failure; it’s data that you can leverage as you create your next experiment. Marketing is an iterative process, and even when results are disappointing, employing a method like this can reduce losses and make the iterative process more cost efficient.
If your experiment was wildly successful, congratulations! You may want to consider increasing your budget, your cadence or other factors. You may also want to stay the course and start a new experiment with new parameters. For example, if you ran social media ads and increased interactions, you may want to consider experimenting with retargeting, in which you deliver ads targeted to just those who interacted or visited your webpage. If you ran Google Ads and increased visitors to your site, you may want to try email campaigns to new subscribers to your marketing communications with a strong call to action.
If your experiment was a flop, don’t give up, iterate. Consider changing an element of the experiment. If you were running Google Ads, perhaps you need to revisit your keywords or audiences. Perhaps you should narrow the times or days your ads run, or maybe your message is not right. Iterate and try again.
Once you’ve run a few experiments, you’ll gain confidence in the process. Ultimately, marketing should be a well-run machine of experiments, results analysis and iteration.
Happy experimentation, marketers!
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