Customer Churn Prediction Made Simple: 5 Steps to Better Retention

Written by:
Mark Camp | CEO & Founder
Propello Cloud

Key Takeaways

What are the Indicators of Churn?

A Simple 5-Step Framework for Successful Churn Prediction 

Voluntary churn

Where a customer quits because they actively decide they’re unhappy, or have found something better

Involuntary churn

Failed payment or expired card

Revenue churn

And then there’s the sneaky revenue churn — customers who don’t leave, but downgrade their spending

Behavioural red flags

• Decreasing login frequency or product usage
• Increasing gaps in purchases or interactions
• Declining interaction with emails, notifications, or content
• Failure to use key features of your product or service they previously relied on

Transactional warning signs

• Smaller average order values
• Subscription or membership payments that are postponed or missed
• Downgrading to cheaper plans or fewer services
• Shopping carts being abandoned more frequently

Loyalty programme indicators 

If you run a loyalty scheme, look for people who stop earning or redeeming points, drop down tier levels, or earn rewards and never use them.

Support and feedback signals

• More support tickets or complaints filed
• Substantial drops in satisfaction survey scores
• Failure to reply to customer outreach
• Negative feedback on specific features or experiences

Step 1: Collect the Right Data
(What Really Predicts Churn)

The signals that actually count

Begin by studying the customer behaviour that says, “I’m considering leaving.” Frequency of product usage, login patterns, and level of engagement are your early warning system. If a customer who used to log on every day suddenly stops engaging for a whole week, you know it’s not a coincidence.

Support interactions also have a story to tell. Increasing ticket volumes, lower satisfaction scores or customers who discontinue their relationship with your help resources altogether are all warning signs worth monitoring.

Exploit the goldmine of loyalty programme data

If you’re running a loyalty programme, you are sitting on predictive gold. Point velocity — the rate at which customers earn and burn rewards — is one indicator of engagement that, when paired with churn models, can help predict attrition months before it happens.

Keep an eye out for redemption gaps (customers who rack up points but never redeem them) and tier status fluctuation. You see, a customer dropping from gold to silver status isn’t just losing their benefits—they’re losing their emotional loyalty to your brand.

Transaction patterns worth your attention 

Historical customer data clearly reveals departure patterns. Diminishing average order values, longer periods between purchases or customers who stop buying their usual products are all signs of declining interest.

Demographics also count, but not the way you think. Age and location are poor predictors of churn in and of themselves, but engagement levels within different demographic groups definitely are.

The biggest mistake you'll make 

Avoid the “collect everything” trap. Accumulating hundreds of data points leads to noise, not insight. Focus ruthlessly on the signals that correlate with departures — a handful of carefully selected choices beats a random hundred every time.

Step 2: Select Your Prediction Technique
(Keep It Simple)

Start small with logistic regression

Logistic regression is your best friend when starting out with predictive modelling. It's quick, transparent and provides you with clear probabilities for binary outcomes – will this customer stick around or leave? You’ll know exactly which factors cause churn because the model shows you the odds for each variable.

Such an approach shines when you have strong historical data and well-defined churn outcomes. And, “customers with declining usage are 3x more prone to churn” is infinitely easier to explain to your board than some black-box algorithm.

More advanced options for more varied patterns

Decision trees segment customers based on attributes — age, spend, usage — generating visual paths that lead to churn. They’re transparent, they handle various types of mixed data, and your marketing team can understand the logic behind it. The downside? They can also “overfit” to the quirks of your past data.

The random forest option combines multiple trees to increase accuracy, but at the expense of losing some interpretability.

Neural networks excel at capturing complex non-linear patterns in large data. Still, they often turn into complete black boxes requiring heavy computing.

When to use each method:

Reality check on complexity 

Using more complex methods doesn’t always translate into better predictions. A machine learning model that your team can interpret and maintain is better than a nifty algorithm collecting virtual cobwebs. Start simple, demonstrate value, and then progress to complexity only if the results are worth the headaches.

Step 3: Construct and Test the Model

Robust data preparation

Data preparation will eat up about 45% of your project time, and there’s no getting around that. Clean your datasets ruthlessly – fix errors, manage missing values wisely, and standardise units across your systems. Get rid of duplicate data and irrelevant fields that do not contribute to the analysis.

This unglamorous work is everything. You will get messy predictions from a model trained on a messy dataset, no matter how elaborate your algorithm is.

Feature engineering that moves the needle

 Build the predictive features your business will actually use. Calculate rolling averages (recent spend vs. historical averages), trend indication (is the demand trending up or down?), and ratio-driven measures (support tickets per month of tenure).

Feature engineering would factor in indicators like “VIP customer” and “recent complaint”. The big question: will your team be able to react to this information? Knowing that 25-year-olds churn more doesn’t do much if you can’t change their age.

Testing that prevents disasters 

Split your data 80/20 for training and testing. Build your model with the larger chunk, then evaluate performance on unseen data. Don’t just focus on basic accuracy; precision and recall indicate how well you capture true churners versus false positives.

Model evaluation needs to have business metrics: how much revenue lift do you get from targeting your top 10% churn risk segment vs. random outreach?

Red flags that spell trouble 

Pay attention to huge gaps between training and test accuracy (classic overfitting). If your model labels loyal customers who obviously won’t churn or only detects newcomers to your brand, consider reviewing your features.

Step 4: Identify Your At-Risk Customers 

Develop scoring system that your team understands

Create a “churn propensity score” for each customer — basically the percentage likelihood of leaving. Define clear thresholds: flag the top 5-10% as high-risk. Your threshold will be a trade-off between the costs of intervening and the (anticipated) benefits.

Apply scoring systems that make sense. Health scores or even simple deciles do a better job than complex algorithms. When your account manager reads “Customer X: 85% churn risk”, they know what to do next.

Segment for smarter targeting

Segmentation allows you to assign priority to churn data. A highly valuable enterprise client with a 20% churn risk requires urgent action. A low-spend free trial user at 50% risk? Maybe not so much.

Draft up useful customer segments:

• Strategic enterprise (high value, long tenure)

• Growth accounts (the accounts used the most, moderate value)

• Trial users (new, unproven value)

Rank by churn score within each segment. That saves you from wasting resources on customers who are more expensive to retain than to let go.

 Avoid alert fatigue 

Establish triggers that set off alarms if, and only if, real risks are present. Flagging everyone above an arbitrarily low cutoff produces noise that, in the long run, your team will ignore.

Consider alert frequency too. Daily alerts for your VIP tier, weekly summaries for moderate risk, and monthly reviews for low-value segments.

Step 5: Take Action That Actually Retains Customers

Automated workflows that trigger at the right moment

Knowing who is at risk of churn is of no use unless you time interventions perfectly. Proactive retention is effective because it catches customers before they’re already mentally out the door. Implement automated triggers: when your customer’s churn score breaks through a threshold, your system should provide relevant offers in hours instead of days.

Your automation might be something like: "When engagement and usage drop for a silver subscriber → offer an upgrade to gold for free." At the heart of this is personalisation at scale — different customer segments require different strategies.

Reward-based strategies that succeed

Retention strategies which centre around conditional loyalty rewards are proving to be more effective than simple discounts. When people feel sincerely appreciated, rather than chased, they react differently. 

At Propello, our customers typically witness considerable engagement uplifts and retention rates from timely offers and personalised rewards at the point of contract or subscription renewal. 

Provide always-on engagement rather than taking action only when you need crisis intervention. 

Your loyalty programme should provide value continually with completing rewards for completing actions or reaching milestones, and exclusive content for different tiers. 

That consistent positive reinforcement forms the kind of emotional investment that makes customers less inclined to even think about looking for alternatives.

Instead of reactive “sorry to see you go” emails, do some proactive appreciation: “We’ve seen that you’ve been using our platform less — here’s an exclusive loyalty reward for you.” Tier upgrades, greater value rewards and exclusive access all feel like recognition, not desperation.

Calculate and track retention ROI, not just model accuracy

Keep an eye on what really counts: the number of flagged customers who end up renewing when you intervene and how much revenue that’s worth. Construct a basic payoff matrix (if providing support resources to clients costs you £1000 but generates £5000 in revenue, then who’s winning?)

Also keep an eye on engagement lifts from loyalty offers. Reward redemption rates, tier upgrade acceptance, and changes to customer lifetime value let you know if your interventions create enduring loyalty or only postpone the inevitable.

Your 80/20 launch plan

Choose your largest customer segment and concentrate on three primary risk drivers.

Begin with a region, product line, or highest-tier accounts

Ship a working system in weeks, not months — perfect it late

Capture early wins to get stakeholder buy-in for growth

Avoid project killers

Monitor success metrics

Revenue saved as a result of focused interventions

Customer retention cost per saved account

Lifts in engagement from loyalty rewards and interventions

Scale systematically

Adapt successful strategies for different markets gradually

Construct data pipelines to push scores into marketing systems

Develop playbooks for others to replicate your success

Data quality 

You need clean, integrated customer records in CRM, billing, and support systems. Standardise the customer IDs, the currency forms, and the date stamps before you adopt any machine learning model. Even a few months of clean data are better than years of messy records.

Integration headaches 

Building models is not your biggest challenge—connecting prediction to action is. Those churn scores need to automatically flow into email platforms, loyalty systems, and CRM tools to be used for real interventions. Most companies have no idea of the middleware they will require.

Resource allocation challenges 

You need to budget for technical skills and operational costs. Account for cloud computing expenses added on for model training and real-time scoring.

Compliance and privacy concerns

Navigate data protection rules with care. Your churn models will leverage sensitive customer data; you need to have strong security practices and consent to use it. 

Bonus tip: Account for ongoing model maintenance — you need to periodically retrain the model as customer behaviour evolves.