Challenges
C3 AI’s customer is one of the largest professional services companies in the world. With an annual revenue of over $20 billion, it enjoys steady yearly growth. Having so many open opportunities at once makes prioritizing those needing executive attention difficult. Opportunities that are struggling and whose owners need coaching can get lost; promising opportunities can get overlooked. Some opportunities that could close early drag on because they are not flagged in time. These challenges lead to revenue leakage.
With the influx of new healthcare sales opportunities during COVID, the lack of pipeline visibility made it even more challenging to make informed business decisions. Given limited people and time, the company needed to know where to prioritize its effort to sell and execute. To assist with prioritization, the company created a sales forecast at the beginning of each fiscal period to guide its business. Despite this significant effort and financial investment, the average forecast was off by 60%, making it hard to plan the business.
Finally, the lack of pipeline visibility translated into inefficient resource management for this company. The staffing process in professional services companies depends on the expected deal closure time. An accurate deal-level forecast is critical for staffing planning, resource management, and revenue loss prevention.
C3 AI configured C3 AI CRM to help the customer address these challenges.
Approach
Over five months, C3 AI configured the C3 AI CRM application to build machine learning models to forecast sales and score each opportunities’ likelihood of being won.
To generate the AI insights, the C3 AI team began by ingesting, cleansing, and unifying five years of historical data from the company’s CRM system, which amounted to over 50,000 opportunities.
The C3 AI team then augmented the CRM data with almost 200 exogenous data sources, including economic indicators, commodity futures, stock market indices, and industry indicators, to create a unified federated data image. This comprehensive and clean data image allowed us to generate accurate AI insights. It formed a scalable foundation for addressing adjacent AI use cases in the future, such as product forecasting, consumption forecasting, and churn.
Using the historical CRM and exogenous data, the C3 AI team designed 2,000+ machine learning features (individual contributors to the model). C3 AI-trained machine learnings models to generate the following AI predictions:
• Bookings for each month
• Predict which opportunities will be won each month
• Predict which opportunities will ever be won
• Predict when each opportunity will be won
Each opportunity-level prediction comes with an AI evidence package that indicates how to improve the probability of each opportunity, making them complete, comprehensive, and actionable.
Finally, C3 AI configured a rich user interface to enable users to forecast, manage their sales pipeline and accounts, and act against AI insights, ultimately operationalizing a more effective sales motion guided by AI.
Solution Architecture
Proven results in weeks, not years
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