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 firms aren't improving their deal scoring accuracy. This is because scoring systems are rarely validated for accuracy. Unfortunately, factors that may not matter continue to be used in sales, wasting time and money.
Fuzzy Scores. Many scoring systems rely on subjective criteria, making analytic predictions difficult. Under these conditions, teams learn to game the system by scoring their pet 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. Predictive accuracy is continually tested.
Personalized guidance systems recommend next best actions for deals, recording behaviors and outcomes. Actions are recorded and measured for impact.
Our proprietary 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 in a predictable curve. 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.
The system provides personalized benchmarks to show individuals and leaders how they are progressing, encouraging interaction.
We provide a scenario-based tool that lets sales people see both optimistic and pessimistic scenarios. The tool is designed to encourage more sharing of data.
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.
Users can name, save, and compare Monte Carlo models to improve their understanding of possible outcomes based on different actions.
A Convenient Excuse. Prospects can use high price as a convenient way of saying why they favor another vendor or offering. Often, the real reason is more complex than the stated one. 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.
Captive Pricing Intelligence. Many firms have deep competitive and pricing expertise. Unfortunately, it is often harbored by a small group of experts who can't possibly help everyone.
Pricing intelligence is shared by all users. Winning scenarios can be recommended to sales people in similar competitive situations. Pricing and scoring benchmarks can help sales people adjust their offerings.
Sales people build and compare pricing scenarios, backed by a database of comparable deals and win/loss ratios.
The system applies behavioral science choice architectures to make it easier to gain approval for high margin deals. Users can adjust their pricing until they obtain an auto-approved status, bumping margins in the process.
OnCorps traces all deals and individuals and can follow-up to remind people to take actions or as a post-mortem to continue learning about competitive factors.
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.
Alert Data Explosion. Many firms now have too much risk data available to them through vendor screening systems, online activity alerts, and 3rd party data. Adequately prioritizing this data and assigning alerts to analysts is often not addressed.
Predictive diagnostic systems deliver a dashboard to integrate both 3rd party and internal data sources on changing risks, controls, and behaviors.
Diagnostic systems can map the use of AI and test algorithms for predictive accuracy.
OnCorps scores alerts and learns to identify potential true positives from a field of false positives, then queues the alerts to analysts to follow-through with additional research and resolution.
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.
Our system automatically integrates with custodian and accounting systems, scores each transaction, then prioritizes a queue of alerts to analysts ranked by risk score.
Analysts open anomalies and are clocked by mean-resolve-time. The MRT is correlated to the risk score to ensure analysts are spending the appropriate amount of time by risk level.
The system handles all threshold breaks for trades, expenses, income, and prices. The system also continuously applies AI to check GL trial balances, searching continuously for errors.
The system learns as it guides analysts through exceptions. It learns how long it should take to properly resolve an exception. It also classifies each alert into false and true positives, keeping track of the root causes for each.