Understanding the true impact of your campaigns is one of the most crucial aspects of digital marketing. But to reveal the actual influence of your marketing efforts on business outcomes, you need to go beyond traditional metrics, and Causal Impact Analysis will be the formula to your success. In this blog post, I will dive into what Causal Impact Analysis is, why it matters and how it revolutionizes digital marketing strategies.
What is Causal Impact Analysis?
Simple correlation analysis methods only show relations between variables, but cannot determine causal links. This is where Causal Impact Analysis comes in. With this data-driven approach, marketers can determine whether a specific intervention, like the launch of a new SEO website, has caused a change in key performance indicators (KPIs). As the model uses statistical analysis methods, it can reliably determine the significance of these changes. Businesses can the make decisions based not on coincidental patterns, but rather on what strategies truly cause improvements.
Why does Causal Impact Analysis matter?
In the world of digital marketing, outcomes can be influenced by numerous factors – seasonality, market trends and competitor actions, to name a few. Methods used in traditional data analysis often fail to account for these complexities, causing misleading conclusions. With the use of advanced statistical models, Causal Impact Analysis isolates the effect of your marketing efforts from external influences and external fluctuations, providing a more accurate and reliable measure of impact.
Where can we use Causal Impact Analysis?
As you can surely imagine by now, this model can be effectively included in Marketing Analysis toolkits. Some use-cases include the following:
- Recruitment Marketing
- Campaign Analysis
- Website relaunches
- Conversion Rate Optimization
- Process Improvement
- Support dispute resolution with stakeholders and vendors
Use-case: Optimizing Social Media Ads
A few months ago, we supported a client in launching a social media campaign to promote their online products. We then used Causal Impact Analysis to determine if this Ad campaign has had an impact on the Click Flow on the clients website.
In the upper panel, one can clearly see the increase of Click Flow caused by the campaign (solid line) in respect to the Click Flow we would have expected without the campaign (dashed line). After the launch of the campaign, we witnessed an increase in the Click Flow each day with an overall growth of 56%. But was this caused by the new campaign or simply random fluctuations?
This is where Causal Impact Analysis came into play. Using the model, we established that with a probability of 99.9%, the increase in Click Flow was caused by the social media campaign. Revelations like this support our clients in making better decisions with more confidence.
In Conclusion
Leveraging Causal Impact Analysis will transform your digital marketing strategies from guesswork to precision. Our agency will provide you with the insights you need to make data-driven decisions to grow your sales. Contact us today to learn how we will support you in optimizing your marketing efforts and achieving your business goals.
About the Author:
Amelie C. Wittig is Data Scientist in residence at WITTIGONIA. She has a Bachelor’s degree in Biology and develops tools for Business Analytics.
References
R package CausalImpact on GitHub: https://google.github.io/CausalImpact/CausalImpact.html
Brodersen KH, Gallusser F, Koehler J, Remy N, Scott SL. Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 2015, Vol. 9, No. 1, 247-274. http://research.google.com/pubs/pub41854.html