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Overlay second-party data on your first-party data to either validate or enrich your current customer knowledge.
Work with your closest partners to create tailored audience segments to power people-based marketing that delivers greater engagement and reach.
Use second-party data to create a virtual identity graph to target an audience segment without having to transfer data.
An online brand is planning a new advertising campaign targeted at lapsed customers. Using their CRM data on these individuals, they would like to identify the optimum online publisher or group of publishers to use to reach these individuals. There are a number of barriers preventing this project from moving forward:
The brand uploads their lapsed customer data to their own isolated Bunker. The data goes through our AI-powered normalisation and mapping process.
InfoSum’s Unified Data Platform includes the ability for publishers to make their audiences available for analysis. An automated process matches identities using existing identifiers from both parties data.
The brand is able to immediately see the intersection between their first-party data and the various second-party data owned by the publishers. This enables them to instantly determine which publisher their lapsed customers have the greatest affinity with. Additionally, they can identify which data attributes are available from each.
The brand can then request additional permissions from the publishers that hold the greatest intersection that will enable them to firstly enrich customer knowledge with the new data attributes, and then create custom audiences for activation.
This approach overcomes the commercial trust issues, as no individual in either audience is ever identified, and the decentralised nature of the InfoSum platform means data is never shared between either party. Additionally, as no data is being transferred, the consent requirements do not change.
Aggregate level results and differential privacy features prevent any single individual being identified within a dataset.
Federated Architecture and Insights Engine overcomes trust barriers by removing the need to share and centralise data.
AI-powered normalisation and mapping removes the need for a complex and expensive ETL projects.
Each party retains complete control over their data through decentralisation and rich permission controls.