OnCorps | Sales + Pricing Guidance

Sales + Pricing Guidance

Optimize your sales force with the help of machine learning.

Long sales cycles, incomplete CRM data, and opaque pricing practices make large technology and professional services deals notoriously difficult to win. The OnCorps Sales Guidance solution uses machine learning to build and refine advanced predictive models to score deals and assign odds based on algorithms, not arbitrary stage gates. Personalized nudging guides individuals through next best actions and continuously records and measures behaviors for impact. And our algorithms intelligently recommend pricing scenarios that optimize value for both the client and your organization.

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What the solution does

Our insights drive better sales projections and performance.

Intelligent Deal Guidance

Using actual results, our machine learning algorithms continuously learn what characteristics amount to a winning deal and nudge salespeople to take the highest-impact actions.

Accurate Forecasting

Sales forecasts are rarely true indicators of results. Our behavioral algorithms detect biases (like being overly optimistic) and nudge users to reevaluate their forecasts based on historical benchmarks.

Optimized Pricing

Clients derive value from a host of factors beyond solely price. Our algorithms uncover these factors and nudge users to set prices that optimize value and margin for the client and your organization.

How the solution works

We leverage data, machine learning, and nudging to optimize sales processes.

The solution captures data directly from your CRM and uses proprietary surveying methods to enrich the data set for use in our predictive models. Machine learning algorithms analyze real-time and historic data to determine which deals are most likely to win. Our nudge engine recommends actions to improve win odds and tracks actual behaviors of users and their outcomes. The system provides personalized benchmarks to show users how they are progressing and to nudge them to input realistic forecasts. Finally, machine learning algorithms run on pricing data and recommend optimal pricing based on the competitive situation. High-margin deals are auto-approved by the solution, nudging users to follow it’s recommendations.

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