Contract renewal prediction

Predict the probability of a supplier or dealer renewing a contract using historical CRM and transaction data, enabling proactive relationship management. Using AI-driven analytics and predictive scoring, teams can identify high-risk contracts, improve dealer retention, and take timely actions with event-driven alerts and real-time dashboards

Core engineering benefits for teams

  • Data-driven insights

    Improve contract renewal decisions with AI scoring and analytics

  • Scalable analytics

    Evaluate multiple contracts across regions efficiently using Catalyst Serverless services.

  • Proactive planning

    Enable teams to intervene before contracts expire to reduce churn.

  • Secure & compliant

    Encrypt and control access to sensitive CRM and supplier data.

Explore related resources

  • Tutorial

    CRM Deal Prediction Tutorial – Step-by-step tutorial on building an ML predictive model using Catalyst QuickML, including data preprocessing, model training, evaluation, and serving real-time predictions

     

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  • Help Document

    Catalyst QuickML Documentation – Official help guide covering dataset ingestion, pipeline creation, model training, evaluation, and serving for predictive analytics use cases like contract renewal prediction.
     

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  • Blog

    Supercharge your machine learning endeavors with Catalyst QuickML – A practical blog explaining QuickML capabilities, model workflow, and predictive analytics applications relevant to forecasting contract renewal likelihood.
     

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  • Ebook

    Learn more about cloud adoption, migration strategies, and modernization techniques.
    Discover how Infrastructure as Code (IaC) can drive automation and efficiency in your organization.

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  • Webinar

    Catalyst 101 Learning Series Webinar – Broad webinar series covering foundational topics such as serverless FaaS, API Gateway, data storage, and ML workflows that underpin predictive analytics services like contract renewal prediction.
     

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  • Cookbook

    Understanding LLMs on Catalyst – explains how to leverage large language models (LLMs) with Catalyst, including prompt design, embedding generation, and intelligent predictions useful for enriching contract renewal prediction workflows with AI insights (e.g., summarization, anomaly interpretation, intelligent alerts).
     

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Key Catalyst components

Catalyst QuickML

Build and deploy ML models to predict supplier or dealer contract renewal probability using historical CRM and transaction data. Generate predictive scoring to proactively identify high-risk contracts.

Catalyst Signals

Trigger event-based alerts when contracts fall below a defined renewal probability threshold, enabling account managers to take timely retention actions.

Catalyst Data Store

Securely store historical contract data, prediction outputs, and risk classifications for audit logging, reporting, and performance analysis

Slate

Deploy real-time dashboards and contract summaries to internal portals or partner platforms with one-click front-end deployment.

Zia Services

Leverage AI-powered insights and anomaly detection to improve renewal prediction accuracy and uncover hidden risk patterns.

Catalyst Stratus

Securely store and manage supplier, dealer, and contract data using scalable object storage. With built-in AES-256 encryption at rest, Stratus ensures sensitive business information remains protected while enabling reliable access for applications, analytics, and reporting workflows.

 
 

Frequently asked questions

Use QuickML’s periodic sync when importing datasets from CRM to automatically refresh data for model retraining.