For one of Canada’s largest fleet tracking companies, growth by acquisition had scattered customer data across systems, and rising churn was going unexplained while revenue quietly walked out the door.
“A churn model only saves a customer if the person who can act sees it in time.”
One of Canada’s leading fleet tracking companies had grown by integrating smaller entities, scattering customer records across multiple databases with different structures. With no unified view, the business could not find the root causes of attrition, putting revenue at risk.
ML arteka blended traditional data analysis with AI. The team harmonized and cleaned the fragmented data, used Supervised Learning with Random Forest and Gradient Boosting to identify the drivers of churn, and applied Neural Networks to decipher the non-linear relationships in customer behavior. Predictive analytics were then integrated into the client’s CRM, delivering real-time, per-customer recommendations and alerts when churn-related markers appeared.
The result: a unified view of the customer, the real drivers of churn identified, and real-time recommendations that moved the client from reactive to proactive retention.
Business Outcomes
Fragmented data became a unified view, and churn insight became daily action inside the CRM.
Harmonized from fragmented, acquired systems.
Key parameters surfaced with machine learning.
Real-time, per-customer recommendations.
The Transformation
From data fragmented by acquisitions to real-time churn prevention.
- 1
UnifyConsolidate and Clean the DataHarmonized records from multiple databases and ran normalization and feature engineering to make the data analysis-ready. - 2
ExplainFind the Churn DriversUsed Supervised Learning, including Random Forest and Gradient Boosting, to identify the parameters most associated with customer departure. - 3
ModelDecipher Complex BehaviorApplied Neural Networks to capture the non-linear relationships in the data, captured in a comprehensive insights report. - 4
ActPut Predictions in the CRMIntegrated predictive analytics into the CRM so each customer carries real-time recommendations and staff are alerted when churn markers appear.
The Business Challenge
Rising Churn, and No Way to See Why
Growth by acquisition fragmented the data behind a churn problem no one could explain.
Rising churn
Customer attrition was climbing and the business could not get ahead of it.
Fragmented data
Integrating smaller entities scattered records across many systems.
Inconsistent structures
Different database structures made consolidation and analysis difficult.
Revenue at risk
With no unified view, the causes of attrition stayed hidden.
Business Outcomes in Detail
What the Numbers Mean
Harmonized from fragmented, acquired systems.
Key parameters surfaced with machine learning.
Real-time, per-customer recommendations.
Technology Snapshot
From Messy Data to Daily Action
Most churn analysis ends in a slide deck. The value is in where the prediction lands: inside the CRM, in front of the person who can still save the customer.
Executive Questions and Answers
The questions leadership tends to ask when evaluating an approach like this.
Data
Our data is fragmented across acquired systems. Is that a blocker?
It is the starting point, not a blocker. The first step consolidates records from multiple databases and cleans them so the churn question can actually be analyzed.
Models
Why several model types rather than one?
Random Forest and Gradient Boosting are strong at identifying which parameters drive churn, while Neural Networks capture the non-linear behavior, so together they explain and predict.
Retention
How does this reduce churn day to day?
Predictions are integrated into the CRM, so when a customer shows churn markers, staff get a real-time recommendation to intervene before the customer leaves.
