mParticle Launches New Features to Help Brands Create ‘Data Flywheel’

New Features for Seamless Data Quality Management, and Transformation to Serve as a Foundation for Improved Customer Experience and Better Insights

mParticle, the Customer Data Platform (CDP) of choice for multi-channel consumer brands, announced significant product enhancements to its Data Master product to help increase data trust and quality throughout the pipeline, and across teams. Additionally, mParticle announced the early access to Calculated Attributes, a data transformation feature that allows companies to dynamically create new user-level data based on existing data. With these capabilities, teams can improve personalization efforts and derive better insights, creating compounding effects throughout the entire product and customer lifecycle.

In 2017, Gartner estimated that companies lose $15M on average to data quality problems, and HBR reports that only 3% of organizations’ data meets basic quality standards. With more being created and consumed by more systems, sources, applications, and endpoints; organizations often find themselves dealing with suboptimal data sets that leads to poor analytics, and underperforming marketing strategies. The problem is further amplified in a connected stack, where data quality issues that are left unaddressed destroy value at a compounding rate.

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With mParticle’s latest data quality enforcement capabilities, companies can create a cohesive data strategy and ensure there is truth throughout their pipeline, which benefits consumers of that data throughout the organization.

For Growth Marketers:

• Better data quality for better customer insights.
• Greater scalability across segmentation strategies.
• More value from downstream tools and channels, like analytics, customer engagement, and customer support.
• More accurate metrics such as CAC, LTV, churn, and engagement.

For Product Managers:

• Deliver more accurate insights about product performance.
• Better execution of key personalization strategies.
• Improved governance for consumer privacy.
• Cross product and portfolio.

For Data Science/Engineering:

• Better data integrity and decreased pipeline maintenance costs.
• More complete customer profiles for more diverse consumption.
• Faster operationalization of existing models with full feedback loops.

“In working with some of the most innovative consumer brands, we have seen firsthand the compounding effects of data quality over time. Poor data quality increasingly degrades performance, while high-quality data improves performance over time,” said Michael Katz, CEO and co-founder of mParticle, “Data quality must be managed over time, and in fact, we have never seen a ‘set it and forget it’ strategy successfully executed over any meaningful timespan. If your data quality is not improving, it is degrading.”

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mParticle is also announcing capabilities to assist data teams dynamically create user-level data from existing event and attribute data. With Calculated Attributes, teams can apply various conditions to their existing data in mParticle to dynamically create new user attributes that can be used to enrich audiences, drive increased personalization, enhance analytics, and improve speed to market. Calculated Attributes can be applied to dynamically track lifetime value, calculate aggregate values, map preferences or create propensity scores, user behavior lists, and time-based calculations. The benefits seen by existing customers include:

• Audience Enrichment: Seamlessly create and sync Audiences based on any combination of Calculated Attributes, Events, and User Attributes to sync with various marketing partners, including Facebook, Google, Snap, Sendgrid, Braze, or Airship, among hundreds of others.
• Improved personalization: Deliver insights from Calculated Attributes directly to sites and apps to drive real-time personalized customer experiences in tandem with mParticle’s Profile API.
• Enhanced analytics pipeline: Generate insights from customer data in mParticle for high-value consumer engagement analytics without having to orchestrate a data analytics pipeline with third-party BI tools.
• Improved speed to market: Enrich customer profiles with calculated attributes to operational machine learning models faster.

“Customer Data exists across the entire product lifecycle, but is not managed holistically across all phases. Consequently, data management becomes siloed both organizationally and technically,” said Craig Kelly, Head of Product for mParticle. “These updates will create better collaboration across teams and functions for true cross-channel personalization.”

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