The first step in developing an accurate customer churn prediction model is determining the relevant features or predictors that influence whether a customer will churn or not. To do this, I would gather as much customer data as possible from the company’s CRM, billing, marketing and support systems. Some of the most common and predictive features used in churn models include:

Demographic features like customer age, gender, location, income level, family status etc. These provide insights into a customer’s lifecycle stage and needs. Older customers or families with children tend to churn less.

Tenure or length of time as a customer. Customers who have been with the company longer are less likely to churn since switching costs increase over time.

Recency, frequency and monetary value of past transactions or interactions. Less engaged customers who purchase or interact infrequently are at higher risk. Total lifetime spend is also indicative of future churn.

Subscription/plan details like contract length, plan or package type, bundled services, price paid etc. More customized or expensive plans see lower churn. Expiring contracts represent a key risk period.

Payment or billing details like payment method, outstanding balances, late/missed payments, disputes etc. Non-autopaying customers or those with payment issues face higher churn risk.

Cancellation or cancellation request details if available. Notes on the reason for cancellation help identify root causes of churn that need addressing.

Support/complaint history like number of support contacts, issues raised, response time/resolution details. Frustrating support experiences increase the likelihood of churn.

Engagement or digital behavior metrics from website, app, email, chat, call etc. Less engaged touchpoints correlate to higher churn risk.

Marketing or promotional exposure history to identify the impact of different campaigns, offers, partnerships. Lack of touchpoints raises churn risk.

External factors like regional economic conditions, competitive intensity, market maturity that indirectly affect customer retention.

Once all relevant data is gathered from these varied sources, it needs cleansing, merging and transformation into a usable format for modeling. Variables indicating high multicollinearity may need feature selection or dimension reduction techniques. The final churn prediction feature set would then be compiled to train machine learning algorithms.

Some of the most widely used algorithms for customer churn prediction include logistic regression, decision trees, random forests, gradient boosted machines, neural networks and support vector machines. Each has its advantages depending on factors like data size, interpretability needs, computing power availability etc.

I would start by building basic logistic regression and decision tree models as baseline approaches to get a sense of variable importance and model performance. More advanced ensemble techniques like random forests and gradient boosted trees usually perform best by leveraging multiple decision trees to correct each other’s errors. Deep neural networks may overfit on smaller datasets and lack interpretability.

After model building, the next step would be evaluating model performance on a holdout validation dataset using metrics like AUC (Area Under the ROC Curve), lift curves, classification rates etc. AUC is widely preferred as it accounts for class imbalance. Precision-recall curves provide insights for different churn risk thresholds.

Hyperparameter tuning through gridsearch or Bayesian optimization further improves model fit by tweaking parameters like number of trees/leaves, learning rate, regularization etc. Techniques like stratified sampling, up/down-sampling or SMOTE also help address class imbalance issues inherent to churn prediction.

The final production-ready model would then be deployed through a web service API or dashboard to generate monthly churn risk scores for all customers. Follow-up targeted campaigns can then focus on high-risk customers to retain them through engagement, discounts or service improvements. Regular re-training on new incoming data also ensures the model keeps adapting to changing customer behaviors over time.

Periodic evaluation against actual future churn outcomes helps gauge model decay and identify new predictive features to include. A continuous closed feedback loop between modeling, campaigns and business operations is thus essential for ongoing churn management using robust, self-learning predictive models. Proper explanation of model outputs also maintains transparency and compliance.

Gathering diverse multi-channel customer data, handling class imbalance issues, leveraging the strengths of different powerful machine learning algorithms, continuous improvement through evaluation and re-training – all work together to develop highly accurate, actionable and sustainable customer churn prediction systems through this comprehensive approach. Please let me know if any part of the process needs further clarification or expansion.