Marketers use advertising to reach their target audiences and inspire them to carry out a specific action. However, to know which advertising strategy will work best for your audience, you need to understand where your audiences are. In this article….
Think beyond traditional digital metrics
Measuring your marketing efforts goes beyond the traditional digital metrics like clicks, CTR and impressions – limiting your measurement to these will in turn reduce your understanding of your campaign performance.
You need to identify the variables which drive performance and target outcomes, these insights can then guide you to plan successful future campaign strategies.
There are four different ways you can measure and analyse your campaign performance:
- Data – Provides an aggregated record when people interact with social media campaigns
- Reporting – Provides data organisation into informational summaries
- Measurement – Provides a standardised approach to your data to better understand your campaign performance
- Insights – Provides guidance to help you improve your future marketing strategies
Unlocking the power of META
To use META effectively, you need to consider your business goals when deciding what metrics to measure. Focus on a key performance indicator (KPI) for your business to assess whether campaigns are reaching your desired outcomes.
META offers four powerful tools to collect information that will help you to assess your campaign results:
- META pixel – Transfers information about actions people take on your website across Meta technologies.
- Conversions API – Allows you to share key web and offline customer actions directly from your servers across Meta platforms. The Conversions API works with the Pixel to help improve the performance and measurement of your campaigns.
- Facebook SDK – Transfers information about the actions people take on your app to Facebook. Please note, if you use the Facebook SDK for iOS, you should update to version 8.0 or above for iOS 14 support.
- Offline conversions – Allows you to match interactions in physical store locations (from point-of-sale systems and CRM tools) to people who saw your social media ad. You can also use Business Manager to integrate offline conversions.
Choosing the right methodology: observational vs experimental
Once you’ve chosen a data collection tool and it’s been set up and integrated, you can then look to test different methodologies that measure ad performance. These tests fall into two categories:
- Observational – Researchers observe and collect data to gather insights, the researcher has no control over the subjects or the variables.
- Experimental – Researchers intentionally apply treatments to the subjects, then they observe the effects of those treatments in a controlled environment.
While both observational and experimental methods involve comparing exposed and unexposed audience groups, it’s important to note that the observation method does not guarantee the groups are equal on all attributes. On the other hand, the experimental method is more likely to ensure groups are equal on all attributes, not just in terms of ad exposure.
You also have less control over observational methods, which can create challenges when using data to obtain insights into causal effects. After an ad campaign is complete, marketers can attempt to measure a causal effect on results with data collected during the campaign. There are a number of reasons for this, such as scroll depth, browsing frequency or simply the target audience selection – which would mean some people might have seen an ad while others haven’t.
Using the experimental method means marketers can share ads with a group of people, while another group can function as a control group. The experiment should randomly assign who sees the ads in the campaign and who doesn’t, so marketers can analyse the effect the ad had on the results of both the exposed and unexposed groups.
How to choose the right approach to campaign measurement
Both true experiments and observational approaches can generate significant discrepancies in results, sometimes results can come close, however, those cases are rare and it’s difficult to predict when it might happen.
However, a collection of randomised control trials shows that measurements are typically less biased and have lower variance than common observational methods for single campaigns. In cases when it’s not practical to run an experiment, this type of model-based attribution is a more reliable method, even when results differ compared to experimental methods.
Ready to harness the power of META? Our savvy social media marketing experts are here to help! They’ll work with you to understand your campaigns and offer expert support and guidance on the right approach that will achieve the best results for your business. Get in touch to find out more!