Predictive analytics

Predictive analytics puts machine learning to work, comparing and contrasting large volumes of data and helping you identify customers and anticipate conversion opportunities. 

Data Analytics

Anticipating behavior can be troublesome.


The path to predictive intelligence maturity can be filled with organization and technology challenges.
Discovering new audiences.
Discovering new audiences.
Poor data modeling.
Poor data modeling.
Siloed data.
Siloed data.
Predicting customer behavior.
Predicting customer behavior.


Reach new heights with predictive analytics done right.


Predictive intelligence from machine learning and artificial intelligence allows you to transform real-time audience behavior into more rewarding, relevant future experiences.


Smarter audience insights.
Smarter audience insights.
Predictive analytics helps you identify your most valuable audience segments through data clustering and machine learning, driving a deeper understanding of what will happen and enabling you to create and deliver more effective, personalized experiences.  
Propensity models that predict success.
Propensity models that predict success.
Predictive intelligence tools often include propensity models that can predict a customer's likelihood to convert or churn. It can also help you identify and understand anomalies in your data and how to respond to them.
Improved vision from a unified customer profile.
Improved vision from a unified customer profile.
Predictive models are incredibly powerful when based on a unified customer profile, helping to identify the most important insights that lead to more effective optimization of your customer experiences across channels.  
Future engagements turn into conversions.
Future engagements turn into conversions.
With advanced attribution models, mobile app analytics, and real-time data feeds informing your decisions, you're better equipped to anticipate their next move and meet them with the right message, content, and offer at the right time.
Predictive Analytics
Adobe can help.
Adobe Analytics provides the predictive analytics tools you need to improve customer intelligence, identify high-value audiences, anticipate future actions, and make better marketing investments.



Revealing the next steps leads to marketing success.


See how global brands move the needle by anticipating the customer journey and meeting customers where they live.
Ellen Lee, Senior VP of Global Digital, Hyatt


“We want to know what customers respond to. We’re able to take a look and see how customers respond to any given change.”
— Ellen Lee,
Senior VP of Global Digital, Hyatt
Shelley Wise, Director of Brand Marketing, Princess Cruises
“You’re able to target messages. Be more dynamic and efficient. And there’s a great way to get savings, whether it’s through suppression or new audiences or lookalike audiences.”
— Shelley Wise,
Director of Brand Marketing, Princess Cruises


Predictive Analytics FAQ.

How does predictive analytics help identify audiences?
The analytics engine analyzes large variable sets simultaneously to dynamically categorize your visitors and create audiences for further analysis, targeting, and personalization.
Do I need to set rules for predictive analytics to display the dimensions we need to see?
Using Adobe Analytics, you can either use predefined latency tables or create ad hoc attribution models to customize data visualizations.
Can I discover which experiences and content contributed to an event or conversion?
With the right solution you can. The predictive capabilities in Adobe Analytics include a contribution analysis engine that identifies the “differences that make the difference”.  
How is predictive analytics better than our forecasting models?
Rather than relying on aggregate purchase data, predictive analytics give you a propensity score for each customer interaction, which can be used to develop your forecasting models with greater accuracy.
Can Adobe's predictive analytics engine sync with third-party data sources?
Yes. You can get a refined and unified picture of customer interactions across channels by combining experiential data with other customer data sources.
Are data mining and machine learning different?
Yes. Data mining is the process of extracting insights from datasets. Machine learning is the capability of computers to learn without programmer intervention.

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