Evaluating campaign data is more than counting likes and impressions
The most important thing when working with A/B tests is to be aware of what it is that you want to test. To gain valid insights it is crucial to know the respective evaluation dimensions.
So, if you wanted to measure the success of a digital campaign, what would you do? You might take a look at the likes that your social media posts earned, and evaluate the reach these posts achieved. Next you might compare these posts, and try to assess why some performed better or worse than others. This step is important, and necessary in order to gain insights on what worked and what did not. But many factors affect performance, and we can’t be sure of our assessment until we’ve put it to the test. Only then can we confidently apply our past insights to future campaigns.
This is where hypotheses come into play. They’ll help you build a clear framework in order to analyze your data and test your predictions in the most valuable way for your business. We know that sounds complicated, but stick with us – it’s not rocket science.
Successful testing setups build on well-thought-out hypotheses
Testing different options against each other plays a pivotal role in the evaluation of digital campaigns. We already talked about the most valuable ways to do this in our blog article about A/B testing. The most important is that we structure these tests to effectively test our hypothesis. In order to understand the value of hypotheses in detail, let’s have a look at the different elements of an exemplary testing setup:
- It needs an overall campaign goal or question. Let’s say your campaign goal is to increase website traffic.
- In order to find the best way(s) to actually increase website traffic it is important to think of potential answers to this question. What leads to the most website traffic? Let’s assume you were really interested in the impact of different asset formats on website traffic.
- Now different options can be created, based on hypotheses describing the relationship between the different components. For example, in a possible testing setup these respective hypotheses could be:
1. Working with video snippets in campaign posts leads to an increase in website traffic.
2. Working with images in campaign posts leads to an increase in website traffic.
- In order to make hypotheses testable, the correlation they describe must be translated into measurable values.
Analyzing this data allows us to draw conclusions about the impact of the different options and which one of them helps best in reaching your overall campaign goal. The platforms’ own analytics insights are very helpful when it comes to reviewing our hypotheses. For our example above, it could be very interesting to actually look at the clicks and the cost per click that the different campaign posts generate. Additionally, we would take a look at the website analytics. Being able to identify and to interpret the respective data is necessary to get the most valuable insights for your testing setup and consequently for your digital campaigns.
Continuously test and validate to steer the success of your digital campaigns
At first this might sound complex and highly technical. But based on our experiences, we can say that it is feasible and actually very helpful to work with structured A/B tests in digital campaigns. Every campaign that you create and test provides a wealth of experience that you can build upon as you move forward.
After all, this is where past and future meet. In order to measure the success of future campaigns (or just the next stage of a particular campaign) you need hypotheses taken from the past. They are a core component of the framework that allows you to analyze future-oriented data. Only with this mindset of continuous informed testing can your digital campaigns create actionable insights.