Data is the buzz word for marketing in the 21st century. Certainly, knowing how to collect and use data has made us better marketers, but applying a science mindset to marketing can make us even better.
A science mindset means moving away from an intuition-based decision-making process to testable and repeatable marketing strategy that can be optimized according to reality and not our perception. Intuition has a part and oftentimes will be proved right, but our biases and past experience may prove unreliable in a field that is as rapidly changing as marketing now is.
1) Match your hypothesis to your marketing goals
The first step to effective call science involves mapping your marketing goals and using those to create an idea of what might happen, a hypothesis. Here’s when intuition plays a significant role — it gives you an idea of what may happen and allows you to project what might happen in the future.
A business objective might be to increase ROI and the marketing objective would be to generate a certain number of calls to your business that in the past has resulted in increased conversions.
From experience, you may know that including a compelling call to action in past advertising has resulted in increased results. The hypothesis would be: Including a click to call button on digital ads would be one way to boost the number of calls generated by the ad campaign, making it easier for customers on mobile devices to phone the business.
2) Create a strategy for testing your data
Once you have your hypothesis, it’s time to devise a strategy to test your idea, but with a science mindset. Here, our hypothesis is that including a click to call button on a digital ad will increase the number of calls.
Trustworthy experiments systematically test new ideas to be sure that the change they made can be reliably tied to the results. A reliable test has two sets, a control group and a test group, to make sure that there is a clear cause and effect, but not a correlation.
A control group, in this case, would have a call to action that asks the customer to phone a number in similar language, but with no click to call button. The test group would include click to call button to test if this increases the number of calls.
3) Make sure you’re testing what you think you are
One of the more challenging aspects to call science is to make sure that you know what you’re testing. In the case of the click to call button, we know that we are testing the impact of the button on the number of calls because we’ve eliminated all other variables (which may not be always possible to do). That means the only difference between the control group and the testing group is the presence of a button.
The two ads should be sent out at the same time and randomized to appear to consumers in all the segments you are targeting to avoid timing bias and selection bias. Be wary of a small number of views. The results may not be accurate if only a few people see the ad. These biases in experiments can impact how trustworthy your results are and produce a meaningless experiment, meaning your idea may or may not be correct, but there’s no way to tell.
4) Choose accurate and reliable tools to measure and collect your data
Measuring and tracking your data with accurate and reliable tools will make the difference between creating future successful campaigns and wondering why your tests show one result and your campaigns another.
Call tracking reliably links the call back to virtually any source and allows marketers to immediate see how their campaigns are faring with unrivaled reporting of all the call details needed to get an accurate picture of your tests. A 99.999% uptime means that all calls are being recorded to give the most accurate results.