AI and Data Privacy: How to ensure your AI programs are safe, responsible, and effective
AI has the potential to transform marketers' business impact. But if you don't have a compliant data foundation, leveraging ML predictions for personalization can lead to breaches of customer trust. This blog post provides guidance on how you can use data and AI to strengthen customer relationships, and not disrupt them.
How the Wall Street Journal increases the value of their first-party data with AI
Partnering with mParticle Cortex, the Wall Street Journal have been able to generate predictive audiences based on first-party data, increasing advertising performance while also supporting data privacy.
How the New York Post tripled campaign conversions for its premium sports membership
Learn how the New York Post leveraged AI to improve campaign targeting and drive subscriptions.
How to improve ROAS with predictive advertising
As paid media budgets tighten and consumer expectations increase, delivering highly-targeted paid campaigns is critical. This article walks through how predictive advertising can increase paid efficiency, and how you can get started with AI without a team of data scientists.
How to use your customer data to predict customer churn
As the cost of acquiring new customers increases, retaining your existing customer base is becoming more important. This article walks through how you can leverage customer data to predict customer churn risk and prevent churn before it happens.
What’s the difference between predictive vs. prescriptive analytics?
Understand the difference between predictive analytics and prescriptive analytics and get tips on how you can begin using both types of AI insights to personalize the customer experience.
Transform customer data into predictive insights with an AI CDP
You don’t need an army of data scientists to generate predictive models from your customer data, or complicated pipelines to translate this intelligence into business value. mParticle is democratizing AI adoption, and turning what was once a pipe dream into a catalyst for your pipeline.
Data enrichment and machine learning: Maximizing the value of your data insights
Data enrichment and machine learning are two techniques that can enhance the ability of your customer data to drive personalized experiences. While there is some overlap in the end goal of both approaches to enhancing data value, there are significant differences in the time, resources, and overhead they each require.
How to harness the power of a CDP for machine learning: Part 3
Learn how you can activate machine learning insights across analytics and customer engagement platforms in part three of this three-part series.
Meet your marketing goals with customer data
Your marketing campaigns will only be as effective as the customer data that you use to power them, especially if leveraging AI/ML. Check out these tips and tricks to get your customer data in the best shape possible and begin accelerating your marketing strategy.
Generate in-warehouse predictive audiences
Learn how Jayant Subramanian, data science intern, developed a proof-of-concept machine learning pipeline for predicting user behaviors from data pre-processing to model training and beyond using Snowflake and Apache Airflow.
Money20/20: Innovation, AI, and data management
A recap of the need-to-know themes from Money20/20’s annual gathering of FinTech experts.
Improve products with AI and Machine Learning
Traditional digital marketing can’t keep up with machine learning and AI marketing solutions. Learn exactly how these powerful point-and-click tools work and how they can help your team experiment at scale.
Hyperloglog Algorithm: A must-know for data scientists
Hyperloglog (HLL), a powerful streaming algorithm, helps mParticle deliver real-time analytics products. Learn why it's a must-know for data scientists.