TL;DR
Customer churn prediction is a model that scores each customer’s chance of leaving within a set window, so your team can act before they go. Build it in a fixed order. First agree the churn label, then build features only from data that existed on the prediction date. Start with logistic regression and move to gradient boosting once that baseline works. Judge the model by precision at the number of customers your team can contact, then set the threshold from capacity and the cost of acting. Then push scores into the CRM, match each action to the risk driver and prove the lift with a holdout group.
Key Takeaways
Agree the churn label (entity, event, horizon and exclusions) with the business before anyone picks an algorithm. Train only on data that existed at the prediction cutoff, because leakage is the usual reason a churn model fails in production. Start with logistic regression, move to gradient boosting on tabular data, and use survival models when timing matters. Judge the model by precision at your real outreach capacity and by calibration, never by accuracy alone. Set the threshold from capacity and economics, and remember that high risk is not the same as high response to an offer. Deliver scores where teams work, prove lift with a holdout, and retrain on clear triggers rather than a calendar alone. Watch on YouTube
From AI Pilot to Production | How to Scale AI Successfully
Kanerika explains why AI models stall between a promising pilot and daily use, and what it takes to put them into production. A churn model faces the same gap, since its scores only matter once teams act on them.
Retention Pays, but Only When the Score Reaches Someone
Acquiring a new customer costs anywhere from five to 25 times more than keeping an existing one, according to Harvard Business Review (2014). The article adds a fair warning, since the figure depends on which study you believe and what industry you are in. Still, every version of that range points the same way.
The catch is that a model keeps nobody on its own. It can rank customers well in a notebook and still save no one. The cause is usually a vague label, features that saw the future, or scores that never reached the person who makes the call.
So the hard part of customer churn prediction is the operating design around the algorithm. Get the label, the data cutoff and the hand-off right, and a plain model starts paying for itself. Skip them, and even a clever model becomes a dashboard nobody opens.
What Customer Churn Prediction Does (and What It Does Not)
Customer churn prediction scores each active customer on the probability of a defined churn event within a set horizon, such as the next 60 days. It is one application of AI predictive analytics , and its output is a ranked list your team can work through. Churn analysis looks back and explains who left and why, while prediction looks forward and names who is likely to leave next.
A monthly report on lost customers arrives after the fact, while a churn score arrives in time to act. The same split runs through business intelligence versus predictive analytics , where one side describes while the other forecasts.
Two distinctions shape the model from the start. Voluntary churn (a customer chooses to cancel) needs a different response from involuntary churn (a card fails or a payment bounces). Also, contractual businesses have a clear renewal date, while non-contractual ones, such as most retailers, have to infer churn from silence.
Churn prediction does not measure your churn rate, customer lifetime value or satisfaction scores. Those belong to customer retention analytics , which tracks how well you keep customers overall. The model’s job is narrower and more practical, because it tells a team where to spend limited time this week.
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Explore Predictive Analytics Step 1: Define the Churn Label Before You Build Anything
The label is the design choice that shapes everything else, yet it is often made in a hurry. A data scientist picks “no activity in 30 days” because it is easy to compute. Six weeks later, the customer success team says half the flagged accounts are seasonal buyers who always go quiet in summer.
Pick the Customer Entity: User, Account or Subscription
First, decide what unit you are scoring. In B2B software, a single user leaving is rarely churn, because the account may still renew with other seats. In consumer subscriptions, the subscription itself is often the right unit, since one person can hold several plans.
Choose the entity that matches the decision you want to change. If account managers act on accounts, score accounts. Then define the eligible cohort, for example only customers past their first 30 days, so brand-new sign-ups do not swamp the list.
Set the Event, Prediction Horizon and Lead Time
For a contractual business, the churn event is usually a non-renewal or a cancellation recorded in billing. A non-contractual business needs an inactivity rule instead, such as no purchase in 90 days. That figure is illustrative, so tune it to your own buying cycle. Treat involuntary churn from failed payments as a separate label, because the fix is billing outreach, not a retention call.
The horizon is how far ahead you predict, and the lead time is how much warning the team needs to act. An enterprise renewal may need 90 days of notice to fix adoption problems. A monthly app subscription may only need two weeks, so the horizon follows the speed of the action, not the convenience of the data.
Write a Label Contract With the Business
Put every choice in a short, signed table that both the data team and the business owner agree on. The examples below are illustrative, not recommendations for your company.
Field What to decide SaaS example (illustrative) Retail example (illustrative) Entity and cohort Which unit is scored and who is eligible Paying accounts past day 30 Loyalty members with 2+ orders Churn event and source What counts as leaving and which system records it Non-renewal in billing No purchase in 90 days in the order system Prediction horizon How far ahead the model looks Next 90 days Next 60 days Feature lookback How much history feeds each score Last 6 months of usage Last 12 months of orders Grace period Delay before a lapse counts 14 days after renewal date None Exclusions Who is never labelled churned Mergers, free tiers, test accounts Staff accounts, fraud cases Label maturity When an outcome becomes final 90 days after the snapshot 60 days after the snapshot Owner Who signs off on changes Head of customer success CRM marketing lead
Label maturity deserves a second look. For example, a customer who joined three weeks ago has not had a full horizon to churn. Counting them as “retained” therefore teaches the model that new customers are safe. Leave immature rows out of training until their window closes.
Step 2: Build Point-in-Time Training Data and Features
Training data for a churn model is a series of snapshots. Each row is one customer on one snapshot date. It holds only what you knew on that day, plus the outcome that followed within the horizon. Ten monthly snapshots of 5,000 customers give you 50,000 rows, and each row tells a small story about what happened next.
Join CRM, Billing, Product and Support Data at One Grain
Churn signals are scattered across systems. Usage lives in product logs, renewal dates in billing, complaints in the support desk and relationship notes in the CRM. Reliable data pipelines bring these together at the chosen grain, so every source rolls up to the same customer and the same snapshot date.
Identity is the usual headache here. Billing knows a company by its legal name, the product knows it by a workspace ID, and support knows it by an email domain. So build and test the mapping table before you build a single feature.
Engineer Features That Capture Change, Not Totals
Raw totals mislead because they hide change. A customer with 10,000 logins over three years looks loyal even if their usage fell 60% last month. Trends, ratios and recency carry far more signal than lifetime counts.
Feature family Example Source system Leakage risk Usage trend Active days this month vs the prior three Product analytics Low if windowed to the cutoff Adoption breadth Share of licensed seats in use Product and billing Low Recency and frequency Days since last order, orders per quarter Order system Low Support friction Escalations and reopened tickets in 30 days Support desk High if resolution dates are back-filled Payment friction Failed charges, late invoices Billing Medium, since dunning events can follow the cutoff Tenure and contract Months as a customer, days to renewal CRM and billing Low
Stop Leakage With Point-in-Time Joins
Leakage happens when a feature contains information from after the prediction cutoff. The classic case is a “cancellation reason” field that only exists once a customer has already cancelled. Other leaks are subtler, for instance a CRM status a rep updates weeks later.
The fix is a point-in-time join. For every snapshot, each feature reads only records with a timestamp on or before the cutoff. A feature store can enforce this rule automatically, which is one reason teams adopt one once they run several models.
Feature history ends at the cutoff, and then the action window opens. After that, the outcome window runs until the label matures. A leaky model looks brilliant offline because it peeks across that line.
Check Data Readiness Before Modelling
Before training, run a short readiness check. These five questions catch most problems early.
Do you have at least two full horizon cycles of history with timestamps? Can every source be mapped to the chosen entity without large gaps? Are churn events recorded consistently across that history? Do important fields carry their original timestamps, not overwrite dates? Is there enough churn in the sample to learn from, ideally hundreds of events? When two or more answers are no, fix the data first. Otherwise, teams spend months tuning a model on records they cannot trust, as Kanerika’s guide to AI data quality explains.
Step 3: Choose a Churn Prediction Algorithm
Algorithm choice gets the most attention online and matters least in practice. A well-run logistic regression with fresh inputs and a named owner beats a fancier model fed stale data that nobody acts on. So pick the simplest model that meets the bar, then spend your effort on the steps around it.
Start With Rules and Logistic Regression
Begin with a rules baseline, such as “usage down 50% and renewal within 90 days”. It costs almost nothing and gives the team a number to beat. Then fit a logistic regression, which is fast, easy to explain and often surprisingly hard to outperform on clean, well-designed features.
Move to Gradient Boosting for Tabular Data
Churn data is tabular, with mixed columns and many interactions. Gradient boosting libraries such as XGBoost and LightGBM handle that shape well and usually lift ranking quality over a linear model. Random forests sit in between, because they tolerate noise well but tend to produce less sharp probabilities. For a wider tour of these methods, see Kanerika’s guide to machine learning in predictive analytics .
Use Survival Models When “When” Matters
Classification answers whether a customer will churn within the horizon. Survival analysis answers when, and it handles customers whose outcome is still unknown, which statisticians call censored data. The Python library lifelines supports right, left and interval censoring, so it fits renewal planning where timing drives the action.
Algorithm Best when Strengths Watch-outs Rules baseline You need a benchmark this week Transparent, no training Misses interactions, decays fast Logistic regression First production model, regulated teams Explainable, well calibrated Needs careful feature design Random forest Noisy data, quick wins Handles mixed data, little tuning Probabilities need calibration Gradient boosting (XGBoost, LightGBM) Mature tabular data Strong ranking, handles missing values Overfits small samples, needs time-based tuning Survival (Cox, lifelines) Renewal timing matters Uses censored customers, predicts when Assumptions to check, harder to explain Neural or sequence models Very large event-level data Learns from raw sequences Costly, opaque, rarely worth it early
Handle Class Imbalance Without Breaking the Evaluation
Churners are usually a minority of customers in any prediction window, so the classes are imbalanced. Try class weights first, since they tell the model to care more about the rare class without inventing data. If you do resample, the imbalanced-learn documentation warns that resampling outside cross-validation leaks data and inflates results, so resample only inside the training fold.
Resampling also distorts probabilities, because the model now believes churn is more common than it is. That is why you calibrate afterwards. Scikit-learn’s calibration module offers sigmoid (Platt) and isotonic methods, and either one brings the scores back in line with real churn rates.
Watch on YouTube
Can Data Augmentation Make AI More Accurate?
A Kanerika discussion of when adding or synthesizing training data helps a model and when it misleads. It is useful context before you resample a small churn class.
Step 4: Validate the Model on Future Customers
A churn model will be used on next month’s customers, so test it the same way. Random train-test splits on snapshot data let the model learn from the future and grade itself on the past. The result is an inflated score that collapses on launch day.
Split by Time, Not at Random
Train on older snapshots, then test on newer ones. Scikit-learn’s TimeSeriesSplit does this with forward-chaining folds, where each training set contains all earlier periods. Also keep a gap between training and test periods at least as long as the horizon, so no outcome overlaps. TimeSeriesSplit’s gap parameter does this for you.
Read Ranking, Calibration and Threshold Metrics Separately
Accuracy is the wrong headline, though teams still lead with it. If 10% of customers churn, a model that predicts “nobody leaves” scores 90% accuracy and helps no one. Instead, read three kinds of metrics, each answering a different question.
Question Metric Limitation Does it rank churners above stayers? ROC-AUC Looks good even when the top of the list is weak How good is it on the rare class? PR-AUC (average precision) A random model scores the churn rate, so compare against that How many of the people we call will churn? Precision at k and recall at k Depends on choosing the right k How much better than random targeting? Lift by decile Hides calibration problems Can we trust a 30% score to mean 30%? Calibration curve and Brier score Says nothing about ranking
A Worked Example at Real Outreach Capacity
Take an illustrative book of 1,000 accounts, of which 100 will churn in the next quarter. Your customer success team can reach 100 accounts in that time, so the model’s top 100 is the list that matters. Suppose that top 100 contains 30 eventual churners.
Precision at 100 is then 30%, and recall at 100 is also 30%, since you caught 30 of the 100 churners. Random targeting would find about 10 churners in any 100 accounts, so the model gives you a lift of three times. That sounds modest, yet it triples the useful conversations.
Be careful with what this example proves. It shows better targeting, not saved customers, because nothing has been done yet. The saves only come from the action in Step 6, and only a holdout can show them.
Check Performance by Segment
Overall numbers can hide a model that fails a whole group, so break results down by tenure, plan, company size and region. A model that works for mid-market accounts but misses enterprise ones may still need a separate threshold, or a separate model, for the larger accounts.
Step 5: Set the Scoring Cadence and Threshold
A score is only useful if it is fresh when someone acts on it. So decide how often to score and which customers get flagged, based on how your teams already work.
Daily, Weekly or Renewal-Triggered Scoring
Match the cadence to the action. For example, payment risk changes daily, so score it daily. A customer success queue reviewed every Monday needs weekly scores. Enterprise renewals, by contrast, can be scored 120 days before the renewal date.
Operating condition Scoring approach Caution Renewal outreach Score on a trigger before each renewal date Lead time must cover the fix Weekly CS queue Batch scores before the weekly review Suppress scores older than a week Payment risk Daily scores from billing events Route to billing, not to CSMs
Choose the Threshold From Capacity and Cost, Not 0.5
The default 0.5 cut-off has no business meaning, so start with capacity instead. If the team can handle 100 accounts per quarter, take the top 100 by score, then check whether precision at that depth justifies the effort.
Then add cost. A missed churner costs the customer’s value, while a wasted call costs time or a discount. When missing a churner is expensive and outreach is cheap, lower the threshold, and when the offer itself is costly, raise it.
Separate Churn Risk From the Value of Acting
High risk does not mean high response. Some customers are leaving whatever you do, and others will stay anyway. The useful question is how much a given action changes the chance of keeping each customer.
Expected net value equals the incremental retention probability, times the value at stake, minus the cost of the action. The incremental probability comes from experiments, not from the churn score. The hypothetical table below shows why the riskier account is not always the better call.
Account (hypothetical) Churn risk Incremental save from action Value at stake Action cost Expected net value A 80% +2 points $10,000 $300 -$100 B 45% +8 points $10,000 $300 +$500
Account A is far more likely to leave, but the action barely moves it. Account B is the better use of the same $300, which is why mature teams rank by expected value once they have test results.
Apply Eligibility and Contact Guardrails
Finally, filter the list before it reaches anyone. Remove customers already in an open escalation, those contacted in the last 30 days and those under legal or billing holds. These filters also stop three teams from contacting the same account in one week.
Step 6: Turn Churn Scores Into Retention Actions and Prove Lift
This is the step where churn projects most often stall. The model works, the scores exist, and then they sit in a warehouse table that the people talking to customers never see.
Deliver Scores Where Teams Already Work
Push scores into the CRM, the customer success platform or the billing tool, not into a separate dashboard. Reverse ETL is the usual pattern, because it syncs scored tables from the warehouse back into the operational apps. Each delivered score should carry a small, fixed contract.
Customer ID and the score itself Cutoff timestamp and an expiry date Model version Top three reason codes Eligibility flag and the owning team Reason codes help a CSM open the right conversation. Tools such as SHAP show which features pushed a score up, and Kanerika’s piece on explainable AI covers how these methods work. Keep in mind that a reason code is predictive, not causal, so “low logins” tells you where to look, not what will fix it.
Watch on YouTube
AI Black Box: Can We Trust AI Without Understanding Its Logic?
Kanerika looks at explainability and trust in AI decisions. The same questions apply when a CSM asks why an account got a high churn score.
Match the Action to the Risk Driver
One retention offer for everyone wastes money, so route each flagged customer by the main driver behind the score.
Signal Action Owner Usage decline Enablement session or adoption review Customer success manager Unresolved escalation Service recovery call with a senior contact Support lead Failed payment Card update reminder and dunning sequence Billing team Downgrade or price pushback Plan fit review, with a discount only as a last step Account manager Low engagement in a consumer app Tailored content or offer Lifecycle marketing
For consumer brands, the last row is where AI personalization helps most. For service-heavy businesses, the escalation row matters more, and AI for customer service can shorten the time to resolve those cases.
Prove It Worked With a Holdout Group
Hold back a random slice of flagged customers, for example 10 to 20%, and give them no special treatment. Then compare churn between the treated and holdout groups, counting everyone assigned to treatment even if outreach failed. That difference is your real lift, and it is the only number a CFO should accept.
Once you have enough test data, the next step is uplift modelling, which predicts who will respond rather than who will leave. Open-source libraries such as CausalML support it. Log every action and outcome from day one, though, because uplift models need that history.
Step 7: Monitor, Retrain and Govern the Model
Launch day is only the start of the work. Customer behavior shifts, products change and pricing moves, so a churn model drifts unless someone watches it. Strong machine learning operations practice keeps that watch routine rather than heroic.
Monitor Data, Scores and Outcomes on Different Clocks
Data checks run with every scoring job, catching missing feeds and odd value ranges. Score distributions get a weekly look, because a sudden jump in average risk usually signals a pipeline problem. Outcomes take longest, since you only learn the truth once each horizon closes.
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Explore ML Services There is a subtle trap here. Successful interventions change future labels, because customers you saved now look like customers who were never at risk. So keep a treatment flag on every record, or the next model will learn that risky patterns are safe.
Retrain on Triggers and Keep a Challenger
Retrain when precision at capacity drops below an agreed floor, when input drift crosses a limit, or after a major product or pricing change. Keep a challenger model scoring in the background and promote it only when it beats the champion on fresh data. Tools like MLflow track versions so you can roll back fast, as covered in Kanerika’s MLflow implementation guide .
Suppress scores past their expiry date Alert on failed or late scoring jobs Version every model and keep a rollback path Review outcomes against the holdout each quarter Privacy and Regulation Checks
Most churn scores route a call or an email, which carries low regulatory weight. Automated decisions are different. GDPR Article 22 gives people the right not to face decisions based solely on automated processing that significantly affect them. So automatic price or contract changes deserve a legal review.
The EU AI Act’s Annex III lists credit scoring and life and health insurance pricing as high-risk, and retention scoring is not on that list. A churn model that feeds those decisions, however, inherits their obligations. In California, the CPPA’s rules on automated decision-making technology took effect on January 1, 2026. Before you automate any of these decisions, check with counsel and with your machine learning governance process.
Common Churn Model Mistakes
The same handful of errors shows up across industries. Each one is cheap to avoid once you know it.
Splitting snapshot data at random, so the test set leaks the future. Reporting accuracy as the headline metric on an imbalanced problem. Using a 0.5 threshold that ignores capacity and cost. Treating one departing user as a lost account in a multi-seat business. Shipping scores with no action owner and no action log. Training on a public dataset and expecting the results to transfer. Checklist
Data Analytics Checklist
A practical checklist for data sources, quality, modelling and reporting, so your churn project starts on data you can trust.
Get the Checklist → Churn Prediction by Industry
The build steps stay the same across sectors, but the label and the signals change.
Industry Churn event Strongest signals Typical action SaaS Non-renewal or seat contraction Usage trend, adoption breadth, support friction CSM adoption review Telecom Port-out or contract cancellation Dropped calls, bill shocks, contract end date Plan review or device offer Banking Account closure or balance run-off Falling balances, lost salary deposits, complaints Relationship manager call Insurance Policy lapse or non-renewal Premium rise, claims experience, quote shopping Renewal review before the notice date Retail and e-commerce No purchase in an agreed window Order gaps, basket shrinkage, returns Personalized win-back offer
Banks often pair churn scores with deposit and cross-sell models, a pattern our piece on machine learning in fintech explores. Our guide to predictive analytics in banking places churn among six bank use cases, from credit to treasury. Insurers watch lapse risk next to pricing and claims, and predictive analytics in insurance covers lapse and retention models alongside underwriting and fraud. Marketing teams then turn churn scores into win-back campaigns, and predictive analytics in marketing shows how. Retailers can also read about predictive analytics in retail for demand and loyalty use.
Datasets, Tools and Build-vs-Buy
The IBM Telco sample on GitHub is the dataset most tutorials use. It holds 7,043 customers and 21 columns, and 1,869 of them churned, which is about 26.5%. It is fine for practice, but it is a single snapshot with no timestamps, so it cannot teach point-in-time labels.
AI Assessment
Is Your Data Ready for Predictive AI?
Score your AI and machine learning foundations in a few minutes and see whether to build a churn model in-house or start smaller.
Start Your AI Assessment → A Python build usually starts with scikit-learn for baselines and XGBoost or LightGBM for boosting. Then teams add lifelines for survival, SHAP for reason codes and MLflow for tracking. Enterprises usually run this stack on the data platform they already own, such as a Microsoft Fabric lakehouse , Databricks or Snowflake.
Buying is the other route. Customer success platforms ship a ready-made health or churn score, which is quick to start but hard to adapt to your own label. Building takes longer, yet it fits your definitions and data. Our roundup of predictive analytics tools compares both paths.
How Kanerika Builds Customer Churn Prediction Models
Kanerika’s predictive analytics services list churn prediction as a core use case and run it in three phases.
Advisory: prioritize use cases by business impact and data readiness, match the model architecture to accuracy and latency needs, and set a realistic timeline. Implementation: train models with feature engineering and validation, then deploy them in batch, real-time or embedded setups. Integration guides and API contracts are handed over for you to own. Optimization: retrain on a schedule and on data drift triggers, A/B test model versions, and keep governance audit trails. An engagement with ABX, a flexible packaging manufacturer with eight US facilities, shows how churn signals fit inside a wider forecasting program. Kanerika built an AI forecasting model for sales. It also added an LLM module that sorts customers into churn-prone, inconsistent and consistent buyers, with a clear explanation for each category. The 85% Accurate Sales Forecasting with AI for Manufacturing case study reports 85% accurate sales forecasts and a 50% increase in identifying customer churn risk. Users see those insights in Power BI.
Kanerika owns pipelines, model deployment and monitoring end to end. A well-scoped pilot typically runs 6 to 10 weeks from data assessment to first production deployment. When the work needs deeper engineering, our machine learning services and data analytics services cover the platform and reporting side.
Case Study
85% Accurate Sales Forecasting with AI for Manufacturing
Kanerika built AI sales forecasting for a packaging manufacturer, with an LLM module that classifies churn-prone, inconsistent and consistent buyers, lifting churn-risk identification by 50%.
Read the Case Study → A Churn Model Is Only as Good as the Action It Triggers
Customer churn prediction works when the build follows a clear order. Define the label with the business, build features that respect the cutoff, and start simple before you reach for boosting.
Then validate on future customers, set the threshold from capacity and cost, and push scores to the people who act. A holdout proves the lift, and steady monitoring keeps the model honest as customers change.
Get those steps right, and even a modest model turns into retained revenue you can measure.
Frequently Asked Questions
What is customer churn prediction?
Customer churn prediction is a machine learning model that scores each active customer on the probability of a defined churn event, such as non-renewal, within a set horizon. The output is a ranked list, so retention, success and billing teams know which customers to contact first and can act before the customer leaves.
How do you predict customer churn?
Define the churn label first, including the entity, event, horizon and exclusions. Then build point-in-time features from CRM, billing, product and support data, train a baseline such as logistic regression, validate it on later time periods, set a threshold from team capacity and cost, and deliver the scores into the tools teams use.
Which machine learning algorithm is best for churn prediction?
No single algorithm wins everywhere. Logistic regression is the right first model because it is fast and explainable. Gradient boosting libraries such as XGBoost and LightGBM usually rank customers better on mature tabular data, and survival models suit cases where the timing of churn matters as much as the risk itself.
How much historical data do you need to build a churn model?
Aim for at least two full prediction horizons of timestamped history, plus enough churn events to learn from, ideally several hundred. A 90-day horizon therefore needs six months or more of usable data. More history helps, but only when the churn definition and the source systems stayed consistent over that period.
How accurate should a churn prediction model be?
Accuracy is a poor yardstick because churners are a minority, so a model that predicts nobody leaves can still look accurate. Judge it by precision and recall at your real outreach capacity, lift over random targeting, and calibration. A model that triples the churners in your call list over random selection is already useful.
How often should churn scores be refreshed?
Refresh scores at the speed of the action they drive. Payment risk usually needs daily scores, a customer success queue reviewed each Monday needs weekly scores, and enterprise renewals can be scored on a trigger before each renewal date. Suppress any score older than its expiry so teams never act on stale risk.
How do you choose a churn prediction threshold?
Start from capacity rather than the default 0.5. If the team can contact 100 customers in the horizon, take the top 100 scores and check precision at that depth. Then adjust for cost, lowering the threshold when missing a churner is expensive and raising it when each retention offer costs a lot.
What is the difference between churn prediction and churn analysis?
Churn analysis looks backward and explains who left, when and why, usually through reports on churn rate and its drivers. Churn prediction looks forward and scores each current customer on the chance of leaving within a set window. Analysis informs strategy, while prediction tells a team whom to contact this week.
Does predicting churn reduce churn on its own?
No. A churn score only identifies customers at risk, and nothing changes until a team acts on it. Savings come from matching the right action to each risk driver, then proving the effect with a randomized holdout group. Without action owners and an action log, even a strong model will not move retention.
Can you build a churn model with the IBM Telco dataset?
You can practice with it, since the IBM Telco sample holds 7,043 customers and 21 columns with a churn rate of about 26.5%. It is a single snapshot without timestamps, though, so it cannot teach point-in-time labels or time-based validation, and its results will not transfer to your own customers.
