Deal Guidance

Why it's needed

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.

What it does

Configure data connection
Deal scoring feature sent to teams
System learns to score deals by odds of winning
System learns behaviors of individuals
Teams and individuals track deals
System nudges users to improve odds
System tracks actions and outcomes

How it helps

1

Predictive diagnostic systems score deals and assign odds dynamically based on actual performance.

2

Personalized guidance systems recommend next best actions for deals, recording behaviors and outcomes.

3

Velocity Index uses elapsed time as a predictor of deal success, reducing subjective success factors.

4

Once algorithms gain more consistency and accuracy, the system seeks to replicate what’s working across all deals to improve performance.

Sales Forecasting

Why it's needed

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.

What it does

Data upload and configuration
Leaders send diagnostics to teams
Leaders build scenarios and see forecasts
System learns behaviors and odds of individuals
Teams and individuals build scenarios
System learns conversion rates by individual
System nudges users with new odds

How it helps

1

Sales people often hold back reporting bad news, which means sales forecasts are often overly optimistic.

2

We provide a scenario-based tool that lets sales people see both optimistic and pessimistic scenarios.

3

Personalized guidance system tracks progress-to-target of each person with personalized deal scenario tools.

4

OnCorps algorithms learn to optimize deal cycle times, conversion rates, and odds. Decision makers learn the deal scenarios that best meet targets.

Pricing Guidance

Why it's needed

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.

What it does

Data upload and configuration
Leaders send diagnostics to teams
Leaders build scenarios and see margins
AI auto approves or escalates
System enables pricing scenario building
System learns to optimize win rate and margins
System nudges users with next best actions

How it helps

1

Predictive dashboards identify where pricing can be improved.

2

Personalized guidance systems nudge changes that optimize margins.

3

Decision makers learn the pricing scenarios that win at high margin.

4

OnCorps algorithms learn to optimize supply and demand factors to boost margins.

Risk + Audit

Why it's needed

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.

What it does

Risk and IA findings data analyzed
Leaders send risk diagnostics to 1st line staff
Leaders model risk mitigation scenarios
System predicts the odds of IA findings
Teams manage risks and projects
Staff invite others to diagnose risks
Traces all profiles and nudges for compliance

How it helps

1

Predictive diagnostic systems deliver a dashboard to integrate data on changing risks, controls, and behaviors.

2

Diagnostic systems can map the use of AI and test algorithms for predictive accuracy.

3

Decision makers learn the areas most vulnerable to risk and actions needed.

4

OnCorps algorithms learn to predict focus areas that result in the most required actions.

AI Shadow Accounting

Why it's needed

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.

What it does

AI-based scoring of transactions by materiality
Leaders send time diagnostics to analysts
Leaders model time management
System scores all transactions for risk/materiality
All decisions guided by system
System guides analysts based on score
Tracks time, urging more time on highest risks

How it helps

1

Predictive systems continuously track recent activity to ensure thresholds are adjusted to truly report anomalies.

2

Predictive diagnostic systems track time, risk, and experience to optimize oversight.

3

Personalized guidance system guides analysts through exceptions based on materiality and learns from their observations.

4

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.