Augmented AI CDR

Boost customer activity with deep learning

Elastic Data Sync

Improve customer engagement for data streaming

Elastic Data Sync

Improve customer engagement for data streaming

Augmented AI CDR

Boost customer activity with deep learning

Deep Learning powered CDR for seamless business integration CDR (customer data recommendations) is a real-time predictive application that leverages real-time machine learning and AI to generate recommendations that drive operational excellence and profitability across sales, marketing, and customer service.

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C3 CRM for improving Forecast Accuracy

Improve forecast accuracy

Apply AI to data from internal and external sources to deliver a more accurate revenue forecast that automatically updates in near real time.
C3 CRM for recommending revenue generation

Recommend actions to Sales

Predict propensity to buy, likelihood of closure, product preferences, and deal risks, and recommend the best actions to sales representatives.
C3 CRM for scoring customers and leads

Score customers and leads

Dynamically predict lead quality, lifetime value, and churn likelihood to reduce cost of acquisition and improve conversion.
Knowing the logic behind the AI

Know the logic behind the AI

View meaningful explanations for all AI-generated scores that augment human experience and give sales representatives the confidence to take action.
Understanding and modifying analytics

Understand and modify analytics

Analyze and monitor performance across algorithms. Edit analytics with a visual interface or with the C3 AI Suite’s integrated Python notebook functionality.
Capitalizing on insights anywhere

Capitalize on insights anywhere

Leverage geolocation and mobile device data to ensure timely and accurate recommendations to representatives in the field.


AI Business Intelligence

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Create data stack based on user agent

CDR simplifies, automates, and accelerates moving and replicating data between on-premises customers and services over the network



Pursue the highest-value sales opportunities using AI-based predictions including closure likelihood, expected revenue, product preferences, and key decision-maker contacts.


Prioritize high-yield sales prospects that match your target profile from CRM’s proprietary prospect-ranking system.


Predict lifetime value of leads as they arrive in the CRM system so that marketers can rapidly assess whether campaigns are meeting quality standards.


Anticipate which customers are likely to churn in the near future and which customer service actions are most likely to prevent attrition.


Categorize and resolve service cases automatically in real time. Enrich CRM views with relevant customer information to accelerate time to resolution.


Focus on adding unique value: selling instead of qualifying leads, marketing instead of collecting data, serving instead of managing crises.

Data Sources CRM integrates enterprise, extraprise, transaction, economic, social, sensor, demographic, geolocation, news, reporting, and financial filing data into a single, federated image in the C3 AI Suite and applies advanced machine learning and AI-driven algorithms to it.

Model-Driven Architecture for CRM

The resulting predictions and recommendations are continuously provided to end users. CRM enables both dramatic improvements to traditional sales, marketing, and customer service tasks, such as revenue forecasting, and net-new capabilities such as predictive opportunity closure likelihood, recommended sales actions, and contact relationship scoring. The underlying analytics can be modified by analysts and data scientists using both visual and programmatic interfaces.

Proven results in weeks, not years

Get insights into’s capabilities, enterprise AI best practices, and highest-value use cases.
Gain insights into the C3 AI Suite's capabilities, its model-driven architecture, and test it against your company's sample data set.
Identify a high-impact business problem and collaborate with the team to rapidly build an AI application that solves it.
Scale and deploy a tested application into production. Incorporate user feedback and optimize algorithms to drive maximum economic value.