Industry
Client Overview
"FinSecure," a rapidly growing B2B SaaS company in the USA with a $15M ARR, provides a subscription-based compliance management platform to financial institutions. While their customer base was expanding, they faced a growing churn problem. They could see that customers were leaving, but couldn't predict who would leave or why, making their retention efforts reactive and inefficient.
Client Testimonial
"CIS didn't just build us a model; they delivered a core business function. Their team took the time to understand our user data and business drivers. The churn prediction engine they built is now a fundamental part of our customer success strategy. The 18% reduction in churn has had a direct, multi-million dollar impact on our bottom line. Their professionalism and CMMI Level 5 process were evident at every step." - Samantha Reyes, VP of Product, FinSecure
Problem
FinSecure's customer success team was flying blind. They were losing valuable customers but lacked the tools to identify at-risk accounts proactively. Their existing analytics could only show historical data, not future risk.
Key Challenges
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01
Complex Data : User interaction data was spread across multiple systems (CRM, application logs, support tickets).
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Lack of Expertise : The in-house team were skilled app developers but lacked deep expertise in machine learning and data science.
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Urgent Need : Competitive pressure meant they needed a working solution in months, not years.
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Integration : The solution had to integrate seamlessly with their existing Salesforce CRM to be actionable for the sales team.
Our Solution
CIS was engaged to design, build, and deploy a custom AI-powered churn prediction engine. We deployed our AI/ML Rapid-Prototype Pod.
Data Unification : Our data engineers built a robust ETL pipeline to consolidate and clean customer data from all sources into a single data warehouse.
Feature Engineering : We worked with FinSecure's domain experts to identify key behavioral indicators of churn, such as declining feature usage, support ticket frequency, and changes in user login patterns.
Custom Model Development : Our data scientists trained and tested several machine learning models (including Logistic Regression, Random Forest, and XGBoost), ultimately selecting an XGBoost model for its superior accuracy in predicting churn risk.
Actionable Dashboard : We built a dashboard within Salesforce that assigned a "Churn Risk Score" (0-100) to each customer, along with the top 3 contributing factors, allowing the success team to prioritize their outreach effectively.
Implementation & Execution
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Phase 1 (Weeks 1-2)
Deep-dive discovery workshops and data infrastructure assessment.
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Phase 2 (Weeks 3-6)
Developed the data pipeline and began feature engineering.
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Phase 3 (Weeks 7-10)
Model development, training, and validation. Presented initial model accuracy results.
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Phase 4 (Weeks 11-14)
Built the Salesforce integration and user-facing dashboard.
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Phase 5 (Weeks 15-16)
UAT (User Acceptance Testing) with the customer success team and deployment.
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Phase 6 (Ongoing)
MLOps support to monitor the model for drift and schedule periodic retraining.
Positive Outcome
1. 18% Reduction in Monthly Customer Churn
The proactive outreach enabled by the risk scores led to a significant and measurable decrease in churn within six months.
2. 30% Increase in Customer Success Team Efficiency
The team could now focus their efforts on high-risk accounts instead of blanket outreach.
3. Data-Driven Product Roadmap
The churn factors identified by the model provided valuable, unexpected insights that helped inform FinSecure's product development priorities.
4. High ROI
The project paid for itself within 8 months, based on the retained revenue from customers who would have otherwise churned.
Why Choose Us
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Verifiable Process Maturity
Our structured, phased approach ensured the project stayed on track.
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Zero-Contractor Model
A consistent, dedicated team handled the project from start to finish.
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Enterprise-Grade Security
All data was handled within our ISO 27001 certified environment.
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Business-Outcome Focus
The project was defined by the goal of "reducing churn," not just "building a model."
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Flexible Engagement Pods
The AI/ML Pod was the perfect fit for this specific, outcome-driven project.
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Radical Transparency
FinSecure had full visibility into our Jira board and attended weekly demos.
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De-Risk Your Investment
The project started with a clear proof-of-concept to validate data feasibility.
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Full IP & Data Ownership
FinSecure owns 100% of the model and the code.
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Future-Proof Technology
The MLOps pipeline ensures the solution will continue to deliver value long-term.
Conclusion
By partnering with CIS, FinSecure transformed its customer retention from a reactive guessing game into a proactive, data-driven science. This project showcases our ability to deliver not just sophisticated AI technology, but tangible, high-impact business results.