Harnessing AI for enhanced churn prediction in agriculture – 2024 perspective
Implementation
Manufacturing
USA
Co-Founder at Innsyte
Improved client retention
Problem: using AI to retain clients
AI is also proving to be a powerful tool in churn prevention. Companies like Johnson Controls have leveraged AI to protect over $100 million in annual revenue by identifying at-risk customers and implementing targeted retention strategies.
- Lack of visibility into cross-sell opportunities and segmentation of B2B clients
- Inability to predict churn effectively, leading to consistent revenue losses
- Technical debt that slowed AI adoption and hindered progress
- Fragmented data landscape, with information spread across multiple systems, complicating product management and analytics
Solution: developing churn prediction capabilities
- Salesforce integration: seamlessly integrated with Salesforce CRM, enabling a user-friendly decision-making process.
- Advanced ML models: deployed gradient-based machine learning models for accurate churn prediction.
- Business calibration & pilots: conducted pilot projects and business calibration across seven countries to fine-tune the solution.
- Azure-native ML solution: The AI models, pulling data from SAP, Salesforce, customer call centers, and flat files, marked a major step forward. Initially developed using Jupyter Notebooks and Python, the solution was later enhanced through Azure Databricks and MLOps for improved efficiency and scalability.
Impact: proactive, AI-powered clients experience
- Proactive client retention: by accurately predicting churn, the solution allows early intervention with tailored strategies, reducing client attrition and unlocking new cross-sell opportunities, ultimately driving significant revenue growth.
- Scalable data-driven framework: the project highlights the power of a scalable, well-integrated data ecosystem, capable of being adapted across different regions and business models, setting a new standard for AI-driven client management in the agriculture sector.
Lessons learnt from churn prediction with AI
Check how this project could look like in your company.
In the meeting, we will ask targeted questions about your specific business situation to assess how our solutions align with your objectives.