Driving Usability, Adoption, and Customer Value with Strategic Redesign of mParticle's Data Catalog
About mParticle
mParticle is a leading customer data platform (CDP) designed to help businesses unify, manage, and activate their customer data. Its platform enables companies to connect data from multiple sources, including mobile apps, websites, and other digital touchpoints, into a single, secure system. mParticle's features include identity resolution, data enrichment, real-time audience segmentation, and seamless data integration with popular marketing and analytics tools. By ensuring data privacy compliance and providing robust APIs, mParticle empowers businesses to deliver personalized, data-driven experiences at scale while optimizing customer engagement and improving decision-making.
Problem
mParticle merged two acquired companies into its platform, resulting in three coexisting data catalogs from each product. This created multiple sources of truth and feature redundancy. Additionally, the data catalog lacked necessary feature updates, organization, and improvements to address existing gaps.
The data catalog serves as a tool for users to learn about the data model. Poor understanding of the data reduced feature adoption, especially for features like audiences and data plans. The goals for the new data catalog are to:
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Enhance understanding of the data model
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Increase feature adoption
Solution
Redesigned the data catalog to enhance usability and efficiency:
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Integrated the catalog as a reusable library across features, embedding it into workflows for seamless access.
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Improved user experiences with intuitive object selection, customizable categorization, and simplified workflows.
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Enabled global access to the catalog and introduced advanced analytics for deeper data insights.
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Enhanced functionality with generative AI for auto-completing metadata and improved governance for better control.
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Added a reporting feature for quick and accurate tracking of weekly data volumes.
This redesign empowered users with easier navigation, discoverability, better insights, and stronger data management capabilities.
Results
Lowered the barrier to feature adoption by integrating rich data directly into user workflows.
2x
Number of audiences created since release.
23%
Decrease in errors during audience creation.
>65%
Reduction in time to create audience criteria.
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