Automated Prosper Model Factory portal simplifies predictive modeling using structured zero-party behavioral consumer data

Prosper Insights & Analytics today announced the launch of its automated Prosper Model Factory portal built on Amazon SageMaker, providing organizations with direct access to Prosper’s proprietary behavioral intelligence dataset and an automated modeling environment designed to dramatically simplify and accelerate predictive model development.

This platform combines direct access to a unique behavioral intelligence dataset with an automated modeling and deployment workflow that dramatically simplifies predictive analytics”

— Phil Rist, Executive Vice President at Prosper Insights & Analytics

The new platform combines Prosper’s structured zero-party consumer data with automated machine learning and deployment infrastructure—reducing the time from behavioral data to predictive insight from weeks to hours. Designed for both technical and non-technical users, the Prosper Model Factory portal enables organizations to rapidly create audience targeting models, customer scoring systems, forecasting models, and predictive analytics applications—without requiring extensive data science or engineering resources.

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Democratizing Access to Behavioral Intelligence:
For more than twenty-three years, Prosper has collected one of the largest continuously fielded consumer intelligence datasets in the United States through a nationally representative monthly survey of more than 8,000 consumers. The dataset captures:
• spending intentions and purchase plans
• economic sentiment and financial confidence
• job expectations and workforce outlook
• psychographic indicators such as happiness and impulsivity
• media consumption and influence
• future purchase behavior and demand expectations

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Because the data is collected directly from consumers, it represents a structured zero-party behavioral dataset that measures demand formation and changing consumer expectations before they appear in transactions, employment trends, credit outcomes, earnings results, or government economic releases. The Prosper Model Factory portal now gives organizations direct access to this behavioral intelligence layer through a simplified self-service modeling environment.

From Data to Predictive Models—Without Traditional Complexity:
The platform is designed to simplify and automate the traditionally complex process of predictive modeling. Users can:
• select features and define target variables from a pre-curated dataset
• automatically generate model-ready training datasets
• train and evaluate machine learning models using Amazon SageMaker
• optimize models through automated hyperparameter tuning and ensembling
• deploy models through serverless real-time inference or batch processing
• generate APIs and deployment endpoints automatically

Model artifacts, lift metrics, performance statistics, and documentation are generated automatically, enabling rapid validation and operational deployment. The platform also supports customer file enhancement and scoring workflows, allowing organizations to append behavioral intelligence signals to existing customer, prospect, financial, workforce, and marketing datasets.

Improving Targeting Accuracy, Forecasting, and AI Performance:
As third-party tracking weakens, identity ecosystems fragment, and many AI systems increasingly rely on lagging or inferred behavioral signals, organizations are facing growing challenges around targeting precision, personalization, and predictive model performance. Because Prosper’s platform is built on structured zero-party behavioral data rather than inferred digital exhaust, organizations can create predictive systems based on stated intent, motivations, and future expectations—not solely historical transactions or clickstream activity. Potential applications include:
• audience segmentation and lookalike modeling
• retail media activation and personalization
• customer scoring and churn prediction
• customer lifetime value modeling
• retailer revenue forecasting
• macroeconomic and time-series forecasting
• workforce and hiring projections
• predictive monitoring and early warning systems

This enables organizations to integrate a forward-looking behavioral signal layer into existing AI, analytics, marketing, retail media, financial, and forecasting environments.

Built on Amazon SageMaker Infrastructure:
The platform leverages Amazon SageMaker infrastructure including:
• native XGBoost with automated hyperparameter tuning
• AutoML ensembling
• serverless deployment
• automated API generation
• collaborative model evaluation workflows
• support for imbalanced datasets and batch transformation workflows

Together, these capabilities significantly reduce the operational effort traditionally required to move from data preparation to production-ready predictive models.

Privacy-Forward Behavioral Intelligence:
Because Prosper’s dataset is built from zero-party data collected directly from consumers, the platform provides a privacy-forward foundation for predictive modeling while reducing reliance on third-party tracking and fragmented identity signals. Organizations can build models using consented, self-reported behavioral data while improving transparency, targeting accuracy, and alignment with evolving privacy standards.

“This platform combines direct access to a unique behavioral intelligence dataset with an automated modeling and deployment workflow that dramatically simplifies predictive analytics,” said Phil Rist, Executive Vice President at Prosper Insights & Analytics. “Organizations can now move much more quickly from behavioral data to actionable predictive insights without the traditional complexity associated with model development and deployment.”