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Innovation
Information Value Extraction
Maximizing the value of a given set of data sources is imperative to building the most effective behavioral predictors. Using state-of-the-art techniques, embodied in code, ensures a consistent and thorough exploration of the data sources. Austin Logistics utilizes the following analytic tools to achieve this:
- Supervised data clustering
- Unsupervised categorical data classification
- Spatial geo-location triangulation
- Velocity aggregation
Data Cleansing and Normalization
Through automation, our modeling team is able to effectively deal with tremendous amounts of information. This allows the modeling analyst to spend more time designing the solution to fit your business problem. Austin Logistics uses the following tools to achieve this:
- Categorical variable performance grouping
- Numeric variable performance grouping
- Segment imputation
Population Segmentation
Because subpopulations often behave differently and have different attributes that are predictive of this behavior, an effective and exhaustive exploration of the potential variations in segment performance is an essential component to maximizing predictive strength. It is not uncommon to have many individual models on these subpopulations contributing to the overall information value of the analytic solution.
Austin Logistics uses automated segmentation discovery to define the optimal scorecard splits for a given population.
Learn about our Analytic Modeling Techniques »
Interested in hearing more?
Contact Austin Logistics for more information.
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