Marketers love data. Specifically, consumer data trends. They rely on these records and advanced forecasting technologies to predict customer needs and serve personalized campaigns targeting those wants.

At first glance, artificial intelligence (AI) and predictive analytics may seem to be one in the same. Both analytic strategies offer helpful marketing insights, but the extent of their capabilities differs greatly. This blog explores the key differences between AI and predictive analytics, how they’re connected, and why they matter.

What is Artificial Intelligence?

Artificial intelligence is a combination of technologies that analyze records, make assumptions, test, and retest data autonomously. Complex algorithms process massive amounts of data in real-time without predetermined rules, allowing the software to automatically identify and learn from data trends. This machine learning process continuously reevaluates models and data to accurately forecast outcomes at scale and at speeds unattainable by human analysts.

When applied to marketing efforts, AI predicts events like customer-product matches and consumers’ next purchases with high degrees of confidence. Marketers use this generative modeling technique to garner microtargeted insights across large populations and produce highly targeted campaigns while maximizing time and monetary resources.

What is Predictive Analytics?

As a subset of AI, predictive analytics is a statistics-based method that data analysts use to make assumptions and test records in order to predict the likelihood of a given future outcome. Analysts capture historical trends and apply these patterns to current data, then compute a specific value at a future point in time. However, data must be manually retested on a continual basis for up-to-date predictions.

In the marketing realm, predictive analytics takes a more guided approach to data-driven forecasting. This analytic strategy informs decision-making on demographics, targeting, and collateral and predicts campaign effectiveness at a surface-level. Higher time and cost demands restrict insight depth, limiting use cases and campaign personalization.

Key Differences Between AI & Predictive Analytics

The biggest difference between artificial intelligence and predictive analytics is that AI is completely autonomous while predictive analytics relies on human interaction to query data, identify trends, and test assumptions. Due to this, AI possesses a significantly broader scope and more applications than sole predictive analytics. Artificial intelligence also involves continuously expanding multi-variable algorithms compared to the strict forecasting model of predictive analytics.

Mitigating risk and forecasting campaign success has never been easier thanks to AI and predictive analytics. While each strategy takes a slightly different approach to data analysis, both offer advanced insights and 360-degree views of data patterns to help you increase marketing ROI, conversion rates, and customer loyalty.