A Graph-Powered Single Customer View Improves Data Monetization

The Challenge

Perform Media’s customers interact with each of the Perform brands separately, often not realizing that each brand is delivered by the same organization. As a result, Perform Media held multiple accounts and transaction records for the same customers. At scale, this resulted in huge amounts of replicated and incomplete data.

From persona data to purchase history, communication history and subscription preferences, the list of Perform’s customer data went on and on. Adding to the complexity, this data was often collected at multiple touch points along the customer journey and stored in incongruent formats within different data storage platforms.

Perform Media’s key challenge was to identify the same user appearing in different modes across the entire Perform ecosystem. Questions arose, such as: Does a customer sign in to each of his online accounts with the same email address? What if a customers isn’t signed in? How can you tell that a customer browsing Manchester United news on Goal is the same customer who goes to Soccerway expressing an interest in betting?

“The challenge is how to connect the dots between all the customer interactions across our portfolio,” said Florian Diederichsen, Chief Technical Officer at Perform Media.

In the ever-changing world of digital experiences, the success of Perform Media’s business strategy relied on a deeper understanding of each of their customers – not just within each division, but across the entire business.

However, Perform Media wasn’t fully equipped to execute on effective data strategies. They needed a single place where their data could be organized and merged, with relationships mapped and analyzed.

The Solution

Perform Media needed to create a single customer view. They partnered with Marionete to collect data generated from the disparate customer interactions across numerous platforms.

“The data was then manipulated and cleansed,” said Diederichsen, “so that Neo4j graph technology could be applied – the crux of delivering a single customer view.”

The Neo4j graph database enables relationships to be mapped between unstandardized data, in numerous formats; and mapping these interactions creates a consolidated customer 360 view and single profile for each user across the Perform Media ecosystem.

Not only does Neo4j map the data fed into the graph, it’s flexible data model also helps to remove inaccuracies and cleanse the data – the only way to achieve trustworthy insights.

“Also, as time goes on, the technology continues to extract, transform and load [ETL] data from the various sources, adding to the relevant customer profile and exponentially building up each user’s profile,” added Diederichsen.

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