Thought Leadership From Industry Peers

Customer Data Platform (CDP) for Media: Three AI Use Cases

Scott Nugent CCO Ignitho Technologies
CDP for Media Use cases

As media and entertainment becomes ubiquitous in our lives, there are multiple priorities unfolding for media companies and brands. They need to be innovating constantly to understand our preferences and behavior to make sure that their content is geared to reach the right people on the right channels and media.

AI and big data are important drivers of this capability. In this blog we’ll outline three AI use cases in CDP that media companies should be implementing to stay ahead in digital customer engagement.

To deliver the AI and analytics charter, a Customer Data Platform (CDP) becomes extremely important. So, in this blog we will explore the interlinkages between the two.

What is a Customer Data Platform (CDP)?

A Customer Data Platform (CDP) allows you to unify and manage customer data from multiple sources in a central location. Media companies can benefit greatly from the use of a CDP due to the large amount of first, second-, and third-party data that they receive.

For example, clickstream data coming in from their web properties is an important source, and so is the advertising data that is sourced from the networks.

Although it is a common deployment scenario, a CDP does not have to integrate directly with a lot of data sources. Instead, mature enterprises are consolidating their enterprise data into a data lake repository such as Snowflake, and then pulling data from there to do AI modeling, create dashboards, and perform what-if analytics in a platform such as Domo. CDPs can be implemented in different ways because depending on the data maturity of the enterprise, not all data is always in pristine condition in a globally centralized data lake.

Why is AI important in a CDP Platform?

One of the many reasons a CDP is implemented is because it focuses business and technology efforts on a narrow set of AI use cases. This ensures faster time to market.

Today, traditional dashboards that provide a view into the past are not enough to understand the cause-and-effect analysis of the many variables in play. Decision making can be made better. The ability to implement AI has the potential to dramatically improve the overall effectiveness of your business intelligence initiatives because it can predict and prescribe.

The ability to deploy a CDP quickly and deliver results is much enhanced if the CDP platform is capable of data ingestion, AI modeling, dashboarding, what-If analysis, and API integration of insights. Ignitho’s CDP accelerator has these components with the added advantage that it lets you maintain your investments in various technologies such as Snowflake, Domo, Microsoft, GCP, and AWS.

Key Customer Data Platform (CDP) Use Cases for Media

Personalization & Promotion Uplift

In a crowded digital marketplace, it is important to personalize content recommendations and advertising for each user. By unifying data from different sources, such as website interactions, social media, multiple channels, and email statistics, a CDP helps you gain a holistic view of the audience segments and also at an individual level. You can now provide real time and personalized recommendations based on interests, preferences, and behavior.

The insights from AI models in the CDP can be used directly in the customer experience systems to improve performance. Or they can be used for advanced what-if scenario analysis such as gauging the effect of an upcoming promotion being planned.

Subscriber Retention & Content Affinity

This is an important content monetization use case as publishers begin to experiment with introducing new content formats and packages with different prices.

This can be done, for example, by analyzing changes in engagement levels or patterns. By identifying these customers early, we can take targeted actions such as offering personalized discounts or promotions.

Using the API layer of the CDP, the insights from the AI models can be integrated right into the content management and promotion systems, thus improving overall responsiveness.

Price Sensitivity
If we understand customer behaviors and preferences, then we can identify potential churn risks early on. We can take targeted actions proactively and increase engagement.

In addition to the ongoing A/B tests in engagement and adoption, a CDP can help analyze who is likely to churn given a price increase or change. These insights can be valuable, as we can then take proactive action to prevent this scenario from occurring and improve the retention considerably. It is often more difficult to reengage customers than to maintain or enhance their engagement.

Which Supporting Capabilities to Examine?

Implementing a robust Customer Data Platform (CDP) requires a combination of technological, operational, and strategic capabilities.

Capturing Zero Party Data

Zero party data is information that is intentionally shared by customers of their own accord. It is valuable because it can be used to deliver highly personalized experiences and campaigns. So digital capabilities that prompt customers for their input, feedback, and wish-lists are important to implement.

Enterprise Data Fabric

An enterprise data fabric aims to create and provide a unified view of data across an organization’s various applications and data sources. It helps break down data silos. As you look to implement a CDP and/or a data lake, creating a well-designed data fabric is important to maximize the ROI from an enterprise insights program.

Identity Resolution

CDPs rely on resolving and unifying customer identities across different channels and devices. This requires the ability to match and merge customer records based on common identifiers and data attributes. Data quality issues and inadequate analytics can often result from not being able to resolve identities efficiently.

Privacy and Security

A CDP should have robust privacy and security capabilities to protect customer data and ensure compliance with relevant regulations, such as GDPR and CCPA. This includes capabilities such as data encryption, access controls, and audit logging.

Integration and interoperability

A CDP should be able to integrate with other marketing and technology platforms, such as marketing automation tools, CRM systems, and data management platforms, to enable seamless data exchange and campaign orchestration.


An AI-powered CDP (Customer Data Platform) can help media companies monetize the vast amounts of data they have to deliver personalized experiences to customers. In turn this will improve customer retention, loyalty, and revenue growth.

However, to make the most of an AI CDP, we need companies to have a solid data strategy in place that includes data governance, data quality, data integration, and data analytics.

A Customer Data Platform is a strategic initiative to improve audience engagement. A robust data strategy can help ensure that the data used is accurate and relevant so the resulting insights can optimally drive business outcomes. Ignitho’s CDP accelerator for media provides a modular blueprint to make deployments simpler and leverage existing technology investments. It contains pre-built AI models, what-if scenario analysis, and API connectivity that helps integrate AI insights with the right enterprise applications. Contact us to learn more about the CDP accelerator for Media.

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Scott Nugent CCO Ignitho Technologies
Scott has over 25 years of experience in the information and technology business. He started as an analyst at TransUnion. After eight years of production roles, he moved into business development. Post TransUnion, he did sales stints at Experian Marketing Services and Entiera. He later joined Mindtree and was with them for eight years where he started by selling Data and Analytics Services to new clients, eventually selling Mindtree’s entire gamut of services. At Ignitho, as the Chief Commercial Officer (CCO), he heads the North America business and drives partnerships for this market.
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