Innovation truly shines when it's able to redefine or even completely overhaul our traditional ways of doing things. Just as data compression technologies have created entirely new industries, from streaming content providers to the requisite hardware, the best technology affords us a new way of looking at the world.

For advertisers, artificial intelligence and its accompanying technologies, modern predictive analytics, in particular, embody that sort of revolution. They are forever altering campaign synthesis and strategy.

In the case of the newest forms of predictive analytics, it's taking the traditional A/B testing, long a stalwart in an advertiser's toolbox, and one-upping it with a far more insightful, precise and reliable tool. In fact, predictive analytics is rendering this old-fashioned, crude technique for campaign optimization completely out-of-date and obsolete.

Predictive Analytics for Modern Times

The new, cutting-edge variety of predictive analytics is a direct result of advancements in computing power and algorithm-based modeling.

While it serves a handful of distinct functions within a marketing context, trend modeling is at the root of each of its current uses. Using consumer data from the past and present, predictive analytics funnels that data into algorithms to create trends that advertisers can use to develop campaign strategies aimed at future consumer behavior.

The ability to predict future market trends has long been a distant but strong desire of advertisers, who are searching to demystify the murky corners of consumer behavior. Predictive analytics can provide a reliable and insightful perspective that is already proving to be indispensable for advertising teams. Suddenly, methodical, resource-heavy A/B testing can be reduced to a much more efficient and foresighted process that's immediate, straightforward and far more perceptive.

Predictive Analytics in Testing

Lying at the root of AI-based predictive analytics is big data. While the existence of the data itself isn't necessarily new, the ability to harness it into well-defined, manageable bundles is a by-product of technological advancements. With machine learning, predictive analytics captures the power of the dataset so it can be used in a number of different testing applications.

Optimize Digital Campaigns

Given the nearly infinite number of asset combinations within any given strategy or campaign, ranging from simple design features to more involved assets like embedded video, the standard A/B testing model is extraordinarily confining due to the sheer vastness of possibilities.

Traditionally, advertisers have had to reduce the number of tested strategies using standard statistical analytics or, on occasion, even a hunch. From there, the testing proceeded according to the laborious procedures of old-fashioned A/B testing to ultimately reach a conclusion.

With machine learning, advertisers are not confined by limited time, money or resources when choosing between a set of variables to find the ideal combination to appeal to a customer base. The intricate multivariate testing that is impossible with standard A/B testing is quickly becoming standard fare for advertisers using AI-based modeling to form strategies.

By using predictive analytics and algorithmic models, advertisers can now test innumerable combinations to search for the most appealing strategy. Perhaps even more importantly, it also allows advertisers to change components in real-time if the data indicates another course would be more appropriate.

Next Best Action

Similarly, predictive analytics is helping advertisers choose the best course of action for a customer base, sometimes even at the individual level, based on data-driven behavioral models derived from past sales data.

While traditional A/B testing has employed a methodical, hit or miss strategy in next best action planning, working in large swaths of demographics to provide insight on particular actions, predictive analytics provide the advertiser a much more powerful option. Using past market data, the algorithms can sort through the extensive data and create models that define the best offers and communication to distribute to customers at precise moments in the sales cycle.

Furthermore, because of the processing power and incredibly insightful data driving the entire process, those offers can even be reduced to individual customers rather than the generalized segments an advertiser was typically relegated to with A/B testing. Machine learning allows advertisers to send personalized messages at specific times that lead to higher conversion rates, an ability traditional A/B testing will always lack.

A Look Down the Road

Given the fact that AI-based predictive analytics is still in its earliest stages, most of its power is likely yet to be discovered or harnessed by advertisers. In the future, the testing abilities of the technology will be able to incorporate even more data to further enhance advertising's ability to precisely deliver targeted messages. Since the machine learning will only improve in its accuracy with continued time and use, infinitely more complicated variables like human emotion and psychology will be further integrated into the predictive models.

For instance, a dynamic variable like the weather is nearly impossible to incorporate into traditional testing simply due to its complexity. However, weather has demonstrable effects on buying patterns due to its physical, emotional and psychological impact on our daily lives. With the power of modern predictive analytics, advertisers will soon be able to integrate real-time weather data into their targeted messages to communicate offers that might be dependent on the weather's effect on a group or even an individual consumer.

Predictive analytics builds upon the rudimentary foundations of traditional testing models. By utilizing the basic premise of those models, only with the astronomically more powerful data and software, advertisers can now test with more efficiency, accuracy, and immediacy than ever before. Such abilities are already making A/B testing and other traditional concepts in advertising extremely outdated and, with a bit more time, likely obsolete as their usefulness quickly dwindles at the hands of machine learning.

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