Sales targets are often formed from a simple, unscientific question: what does our number need to be? Whether it's achieving a certain valuation or a bonus, targets are often set by favoring desired outcomes over probabilities. Once top-down goals are set, unit, team, and individual targets are often allocated with similar logic.
Machine learning starts with the basic truth about actual performance among teams. Some perform well while others lag. Our goal in the research is to track the historic performance of private curated cohort groups, recommend traits of the best performers, and decrease the variation of outcomes. Lower variability means better predictability.
Once we determine past performance distributions, we can apply decision trees to see which series of choices have the greatest impact on improving the odds of meeting targets. Some of these choices may pertain to how targets are set. In addition, the relationship of target to past performance may be a reliable predictor. How teams focus, spend time, and organize may also affect odds.
OnCorps will offer complementary research to sales team leaders and operations executives in select sub-industry sectors. These sectors will minimally include the members of the S&P 500, but will also involve equivalent private and public firms. All data will be managed anonymously and securely. No company or group answers will be isolated except to the contributor.
Industry specific research panel participants will see how they compare in target levels, target-to-actual ratios, and odds of meeting targets. OnCorps will organize both calls and meetings to share research findings with participants. Data will be updated quarterly enabling continuous and longitudinal comparisons.
OnCorps offers firms and teams a private research service and forecasting dashboard subscription. This subscription service enables access to private machine learning dashboards, custom questions and monthly or quarterly updates to individuals and teams. Our services can also perform CRM reconciliations of forecasts, data definitions and standards checks, and user engagement analysis.