A/B tests are adored by marketers because they show which ideas work and which do not. However, in some cases, it is simply impossible to conduct an A/B test properly due to certain circumstances such as the full-scale launch of a marketing campaign or the inability to split customers into groups. This is when the concept of synthetic control groups appears to be rather helpful.
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Why A/B Testing Isn’t Always Possible
A/B testing can be done through comparison of two groups, where one is exposed to a marketing strategy while the other is not. However, most companies find themselves in a position where such a comparison cannot be done.
For instance, it is common for a nationwide ad campaign to be initiated simultaneously in all areas, with no unaffected group available for comparison. Likewise, there have been instances where campaigns were initiated to the whole customer base simultaneously, thus not allowing the formation of a good control group.
The answer to these questions is what leads marketers to wonder: how can we be sure whether this campaign worked, or whether it was effective?
What Are Synthetic Control Groups?
The synthetic control method addresses precisely such problems. Rather than using a natural control group, this method involves constructing a hypothetical or “synthetic” scenario of what would have been in the absence of the campaign. This involves creating a weighted average of data points from other comparable regions, customer groups, or periods not affected by the campaign.
As an illustration, a marketing campaign launched by a firm in one city will be accompanied by the formation of a synthetic control group from different cities with comparable demographics and previous performance. Such a synthetic control group will work as a substitute for how the city where the marketing was implemented would have performed without the marketing campaign.
How Synthetic Control Groups Work
The process typically involves several key steps:
- Identifying Comparable Units: Choosing regions, segments, or time periods that are very similar to the population exposed to the marketing strategy.
- Weighting and Combining Data: Applying statistical techniques to aggregate the comparable units into one synthetic control that matches the trend of the population before the intervention.
- Comparing Actual vs Synthetic Results: Once the campaign is executed, comparison is made between actual performance and performance of the synthetic control, which will reflect the actual impact of the marketing effort.
- Measuring the Difference: The difference between actual performance and synthetic performance reflects the actual ROI achieved through the campaign.
The technique is highly dependent on accurate historical information and statistical modeling in order to make sure that the artificial group is truly representative of what would happen in the absence of marketing activities.
Why Synthetic Control Groups Matter for Businesses
- Accurate ROI Measurement: Companies can know the actual effect of their marketing activities without using conventional testing facilities.
- Better Budget Decisions: Marketers can learn which campaigns are actually working for them and spend money on them accordingly.
- Flexibility: This approach is useful when conducting big campaigns in which splitting audiences is not feasible.
- Data-Driven Confidence: Instead of assuming and guessing, companies can get reliable information.
Some of the industries that have adopted the use of synthetic controls to test the efficacy of their initiatives include retail, e-commerce, and even public policy research.
Skills Needed to Build Synthetic Control Models
People involved in synthetic control groups require a good understanding of statistics, data modeling, and causal inference. It is important for them to know how to find similar sources of data, how to assign weights, and how to analyze the results effectively.
The combination of these two skill sets makes the modeling process of synthetic control an important subfield of data science and marketing analytics.
Final Thoughts
In circumstances where it is not possible to use A/B testing to calculate marketing ROI, synthetic control groups provide an effective approach to take. In this way, companies can make more informed marketing decisions based on data.
With the increasing number of companies trying to find ways to quantify the real effects of their marketing campaigns, there is no doubt that there will always be a need for such experts. Getting certified as a Generative AI Cybersecurity Expert through a Generative AI Cybersecurity Certification Course can give you an edge by combining analytical skills and new technology skills.
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