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
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
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.