MODEL FACTORY DIFFERENCE

The Prosper Model Factory  is a  scalable system for turning its long-running consumer survey databank into predictive models, propensity scores, audience segments, and analytics-ready signals for commercial use.

At a high level, it is built on four core assets:

1 - Prosper’s longitudinal zero-party consumer data

Prosper has more than two decades of monthly U.S. adult consumer survey data, including intent, planned spending, shopping behavior, brand/retailer usage, media habits, financial behavior, attitudes, emotions, and lifestyle signals. This gives the Model Factory a large historical training base.

2 - Forward-looking intent variables

Unlike many datasets built from backward-looking digital behavior, Prosper’s surveys ask consumers what they plan to do, buy, cut back on, switch to, or prioritize. That makes the data especially useful for predictive models tied to future spending, category demand, brand selection, media influence, financial behavior, and market movement.

3 - Automated  model creation

The Model Factory provides a repeatable production process: take Prosper’s survey variables, define target outcomes, train models, score consumers or audiences, validate performance, and package outputs for clients.

4 - Commercial deployment through cloud and data platforms

Prosper models and datasets are deployed through the AWS SageMaker Machine Learning platform.  The Model Factory is not just an internal analytics engine but a productized distribution mechanism  as well.  

Clients can use the models for targeting, personalization, demand forecasting, customer enrichment, competitive intelligence, and investment research.

Bottom Line: The Prosper Model Factory converts Prosper’s proprietary consumer intentions data into predictive models that help companies forecast what consumers are likely to do next.