simMachines XAI Analytic Workbench
simMachines makes it easy for marketing, risk and fraud analytic teams, not just data scientists, to apply explainable machine learning capabilities quickly and effectively. This happens through an easy to use workbench, explainable outputs at a local prediction level to realize significant gains in performance, insights, relevancy and improved customer experience.
simMachines is the market leader in explainable AI (XAI), combining state-of-the-art precision with actionable insights in an easy-to-use analytic workbench
How simMachines Works
simMachines uses similarity as its machine learning method, combined with state-of-the-art metric learning. Unlike other machine learning methods, similarity is inherently explainable based on nearest neighbor objects that inform and explain a prediction. simMachines technology operates at scale and speed, uniquely enabling dynamic feature weighting for every prediction made.
simMachines software is the only technology that can match or outperform any other analytic / ML method while providing “the Why” factors for every prediction along with the ability to generate dynamic predictive segments with common dynamically weighted features for advanced insights that are immediately actionable.
Features
- Easy to use GUI with advanced features
- Search, classification, clustering and recommendation modeling engines
- Data upload and spec analyzer for assessing data / data quality
- Feature weighting in model builds at local prediction level
- Automated grid search, hyperparameter tuning & optimization
- Model updates, versioning and monitoring
- High scale high speed clustering with automated dynamic feature weighting by segment
- Flexible deployment options
- 3rd party data assets and other data matching support capabilities
- Access to data scientists for support
Benefits
Efficiency
Reduce regression modeling time from weeks to days for models, and months to weeks for segmentation
Cost Reduction
Reduce project costs and timelines by 50 – 80%
Accuracy
Increase modeling precision over regression by 10 – 50%
Insights
Understand predictive behavior drivers (features) in order of importance
Relevance
Leverage uniquely weighted features by segment to further modify ad treatments and marketing messages
Actionability
Leverage predictions for real-time or high speed batch applications
Performance
Increase sales conversion, reduce campaign costs, reduce fraud rates, decrease manual review rates, and reduce speed to insight by 20 – 50%
Compliance
Maintain an audit trail for privacy, compliance management via transparency
Are you investing heavily in digital marketing and want to measure ad effect at a one-to-one level?
simMachines provides an audience centric vs. creative centric one-to-one ad effect measurement application that reveals predictive insights associated with positive vs. negative ad effect in hours and the selection and output of look-a-like audiences in minutes.
Beyond A/B
Features
- Audience centric view of ad effect at individual vs. stratified sample level
- Dynamic feature weighting of most predictive factors associated with conversion behavior
- Positive vs negative audience segments for analysis
- The ability to measure degree of ad effect (e.g. purchase amount, volume, etc.)
- Automated, high speed platform
- Easy to use GUI for executing process end to end in hours
- Intuitive compelling analysis visualizations for analysts and business executives alike
- Restful API’s for seamless workflow integration and output to selected BI tools if desired
- Ability to select and output look-a-like audiences to specific segments in minutes
The XAI Analytic Workbench and Beyond A/B work together to maximize your results.
simMachines’ AB measurement tool automates the way media measurement analysts can use explainable artificial intelligence (XAI) to execute the process of matching test and control groups at an audience record level, facilitate drop outs that are equal between groups post-campaign, and measure positive or negative ad effect at an individual or segment level, clustered by the most predictive features associated with conversion, weighted in order of importance.
