We deliver highly configurable predictive dashboards and guidance apps in five core solutions. Each area is supported by a high-end team of data and computer scientists. Our services include all analytic, integration, and workflow configurations to make the systems work.
Untested Scoring Methods. Despite investments in CRM and analytic systems, most attempts to score major deals are not tested for predictive accuracy. When scoring systems are untested, leaders and teams may be suppressing win rates by enforcing criteria that don't matter.
Fuzzy Scores. Many scoring systems rely on subjective criteria, making analytic predictions difficult. Under these conditions, teams learn to game the system by scoring favored deals higher.
Access and Latency. Finally, even when scoring systems are well constructed, they are often harbored by a small team and not available to a broader group. When deal teams see scores, they often get the information when it's too late.
Predictive diagnostic systems score deals and assign odds dynamically based on actual performance.
Personalized guidance systems recommend next best actions for deals, recording behaviors and outcomes.
Velocity Index uses elapsed time as a predictor of deal success, reducing subjective success factors.
Once algorithms gain more consistency and accuracy, the system seeks to replicate what’s working across all deals to improve performance.
Missing Time Dimension. Time is perhaps the most critical dimension to forecasting in sales, but often under analyzed. Deals often close in predictable cycle times and decay in odds predictably. Yet few systems use time as a predictive metric.
Odds by Stage. Weightings are fixed by stage with no true-up to actuals. This is a crude a method to project sales and can lead to inflated and volatile projections.
Behavior and Bias. Sales teams are as a group overly optimistic. This pattern can be measured by individual but is often ignored. Loss aversion can also prevent teams from killing deals that have gone stale. This often inflates pipelines and prevents teams from rebuilding their deal flow.
Sales people often hold back reporting bad news, which means sales forecasts are often overly optimistic.
We provide a scenario-based tool that lets sales people see both optimistic and pessimistic scenarios.
Personalized guidance system tracks progress-to-target of each person with personalized deal scenario tools.
OnCorps algorithms learn to optimize deal cycle times, conversion rates, and odds. Decision makers learn the deal scenarios that best meet targets.
A Convenient Excuse. Prospects can use high price as a convenient way of saying why they favor another vendor or offering. Often, the reason is more complex. Sales teams may be relying on lowering price instead of changing an offering to make it more valuable.
Behavioral Patterns. Certain behaviors can suppress margins. For example, some teams may apply a blanket discount, when customers would pay a premium price for select items.
Predictive dashboards identify where pricing can be improved.
Personalized guidance systems nudge changes that optimize margins.
Decision makers learn the pricing scenarios that win at high margin.
OnCorps algorithms learn to optimize supply and demand factors to boost margins.
Cumbersome Interviews. Most risk and internal audit processes rely heavily on meetings and interviews to gather information and assess risk. This is often time consuming and subjective.
Systems and Algorithm Risk. As systems and algorithms learn and adapt dynamically, it is difficult to understand the level of risk this may represent to an organization.
Lack of Predictive Data. Most internal audit groups lack predictive data on internal audit projects time-to-complete.
Predictive diagnostic systems deliver a dashboard to integrate data on changing risks, controls, and behaviors.
Diagnostic systems can map the use of AI and test algorithms for predictive accuracy.
Decision makers learn the areas most vulnerable to risk and actions needed.
OnCorps algorithms learn to predict focus areas that result in the most required actions.
Labor Dependent Process. Most shadow accounting processes remain highly dependent on analysts to perform very detailed and repetitive tasks.
Eye Glaze Syndrome. The process of checking anomalies is laborious and prone to false positives. Under these conditions, it is quite common for true positives to be rubber stamped as false positives.
Lack of Root Cause Data. Most teams don't record actual errors to build statistical algorithms. This makes it difficult to improve on narrowing false positives.
Predictive systems continuously track recent activity to ensure thresholds are adjusted to truly report anomalies.
Predictive diagnostic systems track time, risk, and experience to optimize oversight.
Personalized guidance system guides analysts through exceptions based on materiality and learns from their observations.
OnCorps algorithms learn to optimize time by scoring potential errors by risk. Decision makers learn to spend more time on high risk items and less time on low risk ones.