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Readers meet Artificial Intelligence

In the publishing sector you are always under pressure to produce engaging content.

Audience is a constantly moving target. Gaining deep understanding and extracting fast insights to improve user experience and loyalty is a never-ending race.

Continually new data and analytical procedures appear on the horizon and challenge each data management platform.

Does your DMP look like this?

Two ways to empower your DMP

Traditional Approach


Establish AI project team


Discover use case


Perform feasibility study


Fine-tune project planning


Develop data model


Implement prototype


Make production ready


Advise business users


Generate actionable insights

Blueprint Approach


Search market place and find matching problem description


Select recommended blueprints and load into orchestrator


Orchestrate blueprints to match project needs


Upload blueprints to data fabric and fine-tune before execution


Execute blueprints and generate actionable insights

Use Case: Content Recommendation

Building a recommendation system to help readers find articles that are most interesting to them is a crucial task for every online news service.

Suppose, you run a Content Management system (CMS) that goes beyond linguistic analysis and automatically extracts named entities and turns articles into topic distributions. Let us also assume that the Data Management Platform you operate, supports audience segmentation.

Then, your favorite data infrastructure is advanced already, but still is not able to compute personalized recommendations.

Closing this gap is easy:

Visit the blueprint market place, select audience and content profiler that support your data platforms, pick a recommender that links audience and content profile and arrange these components into a data workflow.

Feed Predictive Works' big data fabric with this blueprint and execute it. Then, you are ready to deliver news articles that really match the interests of your audience.

Your favorite data platforms are not able yet to compute topic distributions or generate segments of similar readers? Visit the blueprint market place.

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