This guide shows how improving customer retention with GlorifAI's churn models can lead to real savings for telecom operators. All incremental revenue estimates are based on the GlorifAI team's real-world experience.
The following example models a hypothetical US Tier 1 wireless operator with 10 million targeted postpaid subscribers, operating at a 0.9% monthly churn baseline, consistent with publicly reported Tier 1 operator benchmarks (Verizon, AT&T, T-Mobile). We assume that 20% of at-risk customers contact an AI-assisted channel (voice bots, chat agents, digital self-service). In addition, CLV reflects a blended Premium and Mid-Market postpaid subscriber base.
An initial subset of consumers to pilot enhanced retention strategies.
Churn rates among Tier 1 operators are commonly 0.8% - 1.0%.
$50 monthly ARPU, 40% gross margin and 3.75 years of saved tenure.
Common improvement seen by the GlorifAI team.
The following example applies the same methodology to a hypothetical European Tier 1 wireless operator with 5 million targeted postpaid subscribers, operating at a 0.9% monthly churn baseline, consistent with publicly reported Tier 1 operator benchmarks (Deutsche Telekom, Vodafone, Orange). CLV reflects a blended Premium and Mid-Market postpaid subscriber base. Revenue impact is presented in Euros.
An initial subset of consumers to pilot enhanced retention strategies.
Churn rates among Tier 1 operators are commonly 0.8% - 1.0%.
€20 monthly ARPU, 38% gross margin and 3.5 years of saved tenure.
Common improvement seen by the GlorifAI team.
Define model scope with key stakeholders
Identify, acquire and connect data sources
Perform feature engineering
Evaluate algorithms and tune hyperparameters
Train, test and select the best models
Validate models and simulate business results
Deliver model outputs usually with 3-4 weeks
Demonstrate model performance
Present model insights
Set up data preparation and monitor trends
Set up and execute on-going production processes
Monitor models with seamless refresh
* included with monthly service
Fully personalized interactions with customers
Improved customer satisfaction and conversion rate
* optional add-on
Review value delivery metrics and KPI impacts
Deep-dive model insights session
Business process integration
* included with monthly service
There are many different kinds of churn models - each predicts a unique kind of customer exit behavior, allowing for targeted retention strategies and more saves.
Protect market share by identifying high-risk customers before they leave.
Obtain a crucial advantage by identifying high-risk customers prone to switching to competitors.
Identify customers who benefit from less proactive communication.
Identify customers at risk of leaving due to overages, pricing concerns, overall value perception, or additional fees.
Identify customers at risk of involuntary churn and reduce customer attrition.
Protect market share by identifying and retaining high-risk SMB customers before they leave.
With over 200 models deployed to production, the GlorifAI team is highly experienced in converting your data into ROI. Below are the four steps we commonly take in modeling churn for our clients.
Combine your in-house data into a clear, coherent signal for your AI and ML models. This data should contain advanced features and be structured to productionized training and inference.
Advanced feature engineering tailored for model training and agentic systems.
Building features that capture complex domain and business interactions.
Resolve fragmented identities across customer accounts and systems into a unified, comprehensive view.
Define consistent data sets for training, validation, and inference pipelines.
Build a churn model to predict if an active customer will disconnect a service within a specified time window.
Include data that describes the customers' line-of-service, including usage, billing and omni-channel interactions.
Ensemble methods using XGBoost and Neural Networks often create stable and powerful churn models.
Churn models can be sensitive to aggressive and targeted competitor offerings, especially during the holidays.
Score customers regularly. Monitor and refresh the model as needed to maximize predictive performance.
Combine churn scores and churn reasons into a unified intelligence layer, which allows for user-specific contexts to be sent to human and LLM agents for relevant interactions, improving retention and customer experience.
Determine the semantic meaning for each driving feature, creating a unique profile for each customer.
Condense the customer's profile into text for the AI agent, and inject the text at the beginning of each conversation.
Identify the group of customers that will receive retention offers, and then evaluate the impact of the retention strategy to measure ROI.
Identify customers who are both high-risk for churn and highly likely to accept a retention offer.
Split targeted segment into control and treatment groups, usually a 10/90 or 5/95 split to maximize impact and measure performance.
Evaluate the incremental conversion rate between control and treatment groups, and adjust offer/split if needed.
Assess the difference in churn rate between control and treatment groups, and calculate realized ROI.
Transform fragmented, operational data into high-quality inputs for AI agents and machine learning systems.
Click on the model cards to learn more about our Churn, Offer Taker and CLV model offerings and how they can be used to improve retention and drive business results.
