Relying on A/B Testing Is Riskier Than You Think: Avoid Turning Your Brand Into Clickbait

With the surge in social media ad spend, brands increasingly rely on A/B testing and click behavior to assess ad effectiveness.

This approach, while useful in the short term, can be misleading and potentially harmful for brands.

For example, Dove’s three second body wash ad faced significant backlash on social media for its perceived racial insensitivity, demonstrating how quick, in the wild creative approaches can miss deeper issues. Without understanding the psychological and emotional responses to the ad, brands risk damaging their reputation and alienating their audience.

Relying solely on A/B testing in the wild—without pre-testing—for marketing optimization is like trying to navigate a maze with only a flashlight—you might see a few paths clearly, but you miss the bigger picture. Just as a flashlight can only illuminate a small portion at a time, A/B testing provides insights into isolated elements of a campaign, often overlooking the broader, more complex interactions that drive long-term brand success.

Validating the PoE Model & Revealing Ad Impact at Scale

  500+ Ads

Implicit testing on over 500 ads to validate the accuracy of methods for future large-scale testing.

Reliability Results:

The year long study with Meta revealed that not only are implicit methods predictively valid, the model is among the most reliable behavioral science measures within advanced marketing research techniques.

Source: Sentient Decision Science was commissioned to study people ages 18+ who reside in the US, Dec. 2017 – Feb. 2018

Why Would You Sacrifice Sales to Test Ad Performance?

Using an A/B test to evaluate the average effect of an intervention can cover up important insights around which customers are likely to be more or less receptive to that campaign (whether the analysis suggests the intervention has a positive, negative, or, as in this example, an insignificant effect), leading marketers to make the wrong decisions around which campaigns to run with which customers. (Ascarza, 2021, Harvard Business Review)

A/B testing typically focuses on small, isolated changes to specific elements of a product or service. This means it may not capture the full impact of more complex changes or interactions between different creative elements. With multiple comparisons being made simultaneously, there is a risk of encountering false positives, where a variation appears to be performing better purely due to chance. Brands also stand a chance at being narrow sighted—deprioritizing long-term needs for short term results. And given this short testing time frame, the results may not be accurate, failing to gain good statistical power. Additionally, because A/B testing is served in a live environment, numerous external factors can influence results. Misinterpretation of these results can lead to erroneous conclusions and ineffective decision-making over time.

Why not make the best creative decisions on the onset?

Validate your approach with behavioral science to save time and money on your next campaign or consumer research project.

While some argue the merits of A/B testing for fine tuning creative, quantitative behavioral science pre-testing offers several advantages for brands.

And pre-testing is not just about selecting an ultimate winner. It’s about improving that winner even further to extend and advance its effectiveness.

(Ritson, 2024, Marketing Week)

While A/B testing can at times help refine your ad by showing which specific changes lead to short-term improvements, it often misses out on the deeper insights needed to predict consumer behavior accurately—which may result in making the wrong campaign decisions. Behavioral science methods, validated by case studies like Meta, reveal the true impact of advertisements on memory, desire, and brand perception—providing a more reliable foundation for making strategic marketing decisions for your brand.