When it comes to optimizing ad placements and understanding visitor behavior, traditional A/B testing has its limitations. While A/B testing can provide some insights, it often falls short in accounting for the complexities of visitor preferences and evolving data patterns. This article delves into the advantages that artificial intelligence (A.I.) offers over A/B testing. By leveraging A.I., publishers can achieve more accurate, dynamic, and insightful data analysis, ultimately increasing the value of their ad inventory. Explore how A.I. reduces data mining, eliminates the need for hard coding, adapts to changes over time, and simplifies complex mathematical processes, providing a robust alternative to conventional A/B testing methods.

A/B testing has several limitations that need to be acknowledged. One of the primary issues is its inability to account for visitor behavior. For instance, if 60% of people prefer ad set A and 40% prefer ad set B, selecting ad set A leaves the preference of the 40% unaddressed. Moreover, metrics like CPM/CTR are averages that obscure the underlying data details and the pricing of impressions.

Another challenge with A/B testing is ensuring that test groups are random. Questions arise about segmenting traffic and assigning users to groups—whether by user, session, or pageview. The complexity extends to the mathematics involved: determining the run duration, understanding the data distribution, calculating the significance level, and controlling random bot traffic are not straightforward tasks.

A/B tests also lack responsiveness to real-time changes. A test run in June may yield significant results, but these may not hold true for December.

In contrast, AI offers numerous advantages over traditional A/B testing. It reduces the need for data mining, eliminates hard coding, adapts to changing data patterns over time, and manages complex mathematics.

For example, traditionally defining a dog involves specifying variables like "animal," "mammal," "barks," and "walks on four legs." However, an outlier, such as a three-legged dog, may not fit these criteria. AI, particularly neural networks, reverse this approach. Instead of following predefined steps, AI learns from the data. Irrelevant descriptors, such as "has red ball in mouth," are quickly dismissed as non-defining features.

AI enhances the value of ad inventory by solving dilemmas regarding ad placements using behavioral data. For instance, comparing the CPM for "top of page" and "sidebar" might yield identical values, but introducing more behavioral variables could reveal differences. AI, like Ezoic’s artificial intelligence powered by TensorFlow, automates and scales this analysis.

TensorFlow is instructed on what features to learn from the data and what model to use—in this case, a deep neural network regressor suitable for predicting numbers like CPMs. The data is then loaded, read, and training is initiated, all within eight lines of code. TensorFlow's neural network can automatically segment data, uncovering additional revenue hidden in the data—up to 12% more ad revenue in this example.

In summary, while summary statistics are often uninformative, AI-driven approaches can unlock valuable insights and optimize ad revenue by delving deeper into the data.