Impact Optimization Model™

The Impact Optimization Model™ is a comprehensive and practical approach to address the issues of impact, business results, ROI and optimization for programs. 


The Impact Optimization Model™ is a future-forward approach to help professionals create an automated, repeatable process to tell their story of impact, by demonstrating value, while identifying improvement opportunities in their programs.

The model leverages descriptive, predictive and prescriptive data collected from multiple sources, such as evaluations, surveys, verbatim, and operational / business metrics in an automated manner, leveraging the Performitiv measurement technology. A result of deploying the model is the calculation of the Performitiv proprietary Impact Rating, which is a blend of performance improvement methodology combined with causal-modeled learning measurement insight that is scientifically proven to be linked to program impact.


The Impact Optimization Model™ is meant to be a useful tool, not a theoretical methodology. As such, it is implemented by our clients in 4 reasonable steps summarized below.

1. Collect impact data (largely controlled by L&D and linked to learning programs – i.e. Job Impact Rating) using our scientifically-sound impact evaluations.

2. Gather outcome data (influenced by L&D but not directly linked to a learning program – i.e. Compliance Incidents, Sales Growth Rate) using our API, upload or auto-messaging features.

3. Share impact scores on the program, people and results and trend outcome data looking for associations to L&D using our dynamic, insightful report suites.

4. Act on the data to continuously improve using our collaboration and workflow tools.

The Performitiv Impact Measurement Technology automates the Impact Optimization Model™ so that it becomes a repeatable process used on a continual basis to show evidence of impact and identify improvement opportunities.


The Impact Optimization Model™ has numerous benefits that allow your team to modernize its method of measuring impact.

–> Uses scientifically sound impact measures.

–> Based on proven improvement methodology.

–> Data collection is automated, repeatable and a positive experience for respondents.

–> Links impact measures to outcome measures.

–> Identifies pockets of demographics showing value or in need of improvement.

–> A process (not a project) that is practical and scalable to deploy across all programs.

–> Modern technologies, such as APIs and AI, support the model for security, reliability and intelligent insights.

–> Workflow automation creates a collaborative environment to continuously improve performance.

–> Continuously collects a wealth of baseline data for data analysts to embark on deeper impact or statistical studies.

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