See InfoSum in action!
Fill out the form and a member of the InfoSum team will get in touch to arrange a demo of the world’s first Unified Data Platform.
Fill out the form and a member of the InfoSum team will get in touch to arrange a demo of the world’s first Unified Data Platform.
Safely connect multiple first-party datasets to measure the intersection in customer databases, analyse available data attributes and unlock rich insights.
Access is controlled by rich permission systems. Once these have been granted, analysis is available instantly.
Utilise demographic and behavioural data in the intersection to validate and enrich your current customer knowledge.
Build accurate segments for direct activation, suppression models, and look-alikes.
A US based retailer is launching an advertising campaign and is investigating a new relationship with a multichannel video service provider to serve the ads. The retailer wishes to validate the quality of the data and analyse the intersection in the two customer bases to better understand the synergy in customer profiles. There are a number of barriers preventing this initiative from moving forward:
Each party uploads their data to their own isolated Bunker. The data is normalised and mapped to the global schema.
The video service provider grants the retailer a limited permission to measure the intersection between their two datasets and identify the available data attributes. An automated process matches identities using existing identifiers, removing the need for a universal ID (where a match is not available, an identity graph may be used to create a match, but doesn’t require data to be transferred to the identity graph owner).
The retailer is able to conduct statistical analysis and validate whether the video service provider’s data will help them reach individuals that match their current customer profile.
Where an agreement is reached, the video service provider can grant additional permissions to enable the retailer to use the audience insights to build audience segments for either activation, suppression or look-alike modelling.
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.
