Churn Reduction · AI-powered customer reactivation
Bring your inactive customers back, profitably
Aryng's churn reduction models pair machine learning propensity scoring with predictive profit modeling to find the dormant customers most likely to return and the specific offer that will bring each one back, so your retention budget goes to the customers who will actually pay it back.
Proven with a Fortune 1000 credit card issuer
The problem
Every inactive customer is revenue you already paid to acquire
A 5% lift in customer retention can raise profits by 25% to 95%, according to Bain & Company research.
Acquiring a new customer costs anywhere from five to 25 times more than keeping an existing one.
Share of credit card accounts at US credit unions that were sitting inactive, with an average credit line of $8,700.
The customers who stopped using your product are not lost yet. They are a scored, segmented opportunity waiting for the right offer.
Blanket discounts and one-size-fits-all retention campaigns rarely move dormant customers, because the reasons people go quiet differ from segment to segment. Predictive analytics and machine learning let you see those reasons in the data, estimate what each customer is worth if they come back, and make an offer that pays for itself.
Sources: Frederick Reichheld, Bain & Company, as cited in "The Value of Keeping the Right Customers," Harvard Business Review (2014); Experian, "Inactive Credit Card Accounts" (2021, File One data as of March 2018, ~600,000 credit union card accounts).
What is AI-powered churn reduction?
A model of who will come back, what they are worth and what it will take
Churn reduction uses machine learning to predict which customers are drifting away or have already gone inactive, quantify the expected profit from retaining or reactivating each of them, and match every customer segment with the offer most likely to win them back. Instead of guessing which incentive works, you get a data-driven, AI-ready playbook that ranks customers by expected return.
Where predictive churn models pay off
Reactivate dormant customers
Find the inactive accounts with the highest probability of coming back and focus outreach there.
Diagnose the drivers of inactivity
Explainable models show which behaviors and attributes predict a customer going quiet.
Personalize the offer
Match each customer segment with its next best offer rather than running one campaign for everyone.
Spend retention budget on profit
Rank customers by expected profit, net of cost and loss, so incentives go where they earn a return.
Predict churn before it happens
Apply the same propensity scoring to active customers to flag churn risk while there is still time to intervene.
Measure the lift
Score, target and track activation by decile so every campaign feeds better data into the next model.
How it works
Aryng's Churn Reduction Roadmap
Aryng's SWAT Data Science team runs every engagement on BADIR, the company's proprietary five-step Data-to-Decisions framework, which keeps the machine learning and AI work anchored to a business decision from the first question to the final campaign.
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B
Business question
We use the Business Question framework to pin down the real problem and the decision it should drive, such as reactivating inactive customers through marketing optimization.
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A
Analysis plan
We brainstorm with your product and marketing teams to narrow the hypotheses on activation and profitability, shortlist candidate offers from historical campaigns, and agree an analysis plan covering the goal, hypotheses, methodology and data required.
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D
Data collection
We extract customer and marketing data from your enterprise data warehouse against an agreed data specification, building a clean foundation for every model.
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I
Insights
Regression and classification models segment customers, predict their likelihood to activate, estimate the drivers of profit and identify the optimal offer for each segment.
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R
Recommendations
We score your customer database with the models and recommend exactly who to target, with which offer, along with the expected revenue impact.
Case study · Fortune 1000 credit card company
How Aryng helped a credit card issuer reactivate dormant customers for $20M+ in expected lifetime revenue
About the client
The client is a Fortune 1000 credit card company with a significant share of its portfolio made up of customers who had been inactive for more than 12 months. It had already tried the obvious levers, and neither moved the needle.
The client wanted to reactivate its inactive customer base through marketing optimization, so Aryng applied the BADIR framework and a set of AI and machine learning models to the problem.
The real business question
Which inactive customers can we reactivate, and which offer will make each of them profitable when they come back?
The analysis plan
Stakeholder alignment
We brainstormed with the product and marketing teams to narrow down hypotheses on what drives activation and what drives profitability.
Offer shortlist
Potential marketing offers were narrowed down using the results of the client's historical campaigns.
A plan everyone signed
- Goal: reactivate inactive customers profitably
- Hypotheses on activation and profit
- Methodology and required data
Data collection
We extracted customer and marketing data from the client's central Enterprise Data Warehouse based on the data specification in the analysis plan.
Three layers of models, one decision per customer
We combined regression and classification techniques so that each layer answered one question, and together they produced a profit-maximizing offer for every customer segment.
Who will come back?
Logistic regression
An attrition and activation model identifies the drivers of inactivity and the customers most likely to activate.
What will they be worth?
Linear regression
Separate models predict each profitability driver, revenue, cost and loss, to quantify expected profit on retention.
Which offer wins?
Decision tree
Built with the marketing team, it identifies the optimal offer for each customer segment.
Score and target
Top 3 deciles
The database is scored and the top 30% by expected profit receive their segment's offer.
For each customer segment, the optimal offer came down to one calculation, and the product with the highest expected profit became the recommended offer for that segment.
Layer 1
Probability of activation
How likely is this customer to come back?
Layer 2
Expected profit upon activation
What are they worth if they do, net of cost and loss?
Layer 3
Expected profit
The offer with the highest expected profit is recommended for that segment.
Results
Identifying and rolling out the right offer to the top inactive customers reactivated a meaningful share of them, without resorting to the across-the-board rate cuts and credit line increases that had already failed.
activation rate among the targeted inactive customers
of the dormant base targeted, the top three deciles by expected profit
expected incremental revenue over the reactivated customers' lifetime
Why BADIR made the difference
The client had already tested price and credit levers in isolation. BADIR forced the work to start from a precise business question and a hypothesis-driven analysis plan agreed by every stakeholder, which meant the models were built to answer a decision, not to describe the data.
By pairing a propensity model with a profit model, the team could tell apart customers who would come back and customers who would come back profitably, and only market to the second group.
Free download
Get the complete churn reduction case study
The full PDF walks through each step of the BADIR engagement, from the business question and analysis plan to the attrition, profitability and offer-optimization models behind the $20M+ estimate.
Outcomes
What to expect from Churn Reduction
Know who will come back
A propensity score for every inactive customer, built from your own customer and marketing data.
Know what they are worth
Expected profit per customer, with revenue, cost and loss modeled separately so the math holds up.
The right offer per segment
A recommended offer for each customer segment, chosen by expected profit rather than gut feel.
See the drivers of inactivity
Explainable AI that shows why customers go quiet, so product and marketing know what to fix upstream.
Pair it with LTV
Combine churn scores with the LTV Predictor to protect your highest-value customers first.
Personalize at scale
Extend segment-level offers into a full Next Best Offer program across your customer base.
Frequently asked questions
What is AI-powered churn reduction?
It is the use of machine learning and statistical models to predict which customers are likely to stop using a product, or have already stopped, and to decide which intervention will keep or win back each of them profitably. Aryng's approach pairs propensity models, which estimate the likelihood a customer will activate, with profit models, which estimate what that customer is worth if they do.
Can you reactivate customers who have already gone inactive?
Yes. In the credit card case study, every targeted customer had been inactive for more than 12 months. Scoring the dormant base by expected profit and marketing a segment-specific offer to the top 30% produced a 3.6% activation rate and more than $20M in expected incremental lifetime revenue.
Why not just offer every inactive customer a discount?
The client had already tried lower APRs and higher credit lines across the board, and neither had a considerable impact. Different segments go quiet for different reasons, so a blanket incentive overpays customers who would have returned anyway and underserves the ones who need something else. A predictive model lets you match the offer to the segment.
Which models do you use?
In this engagement, logistic regression predicted the likelihood of activation, linear regression modeled each driver of profitability (revenue, cost and loss), and a decision tree identified the optimal offer per segment. We choose methods that balance predictive accuracy with explainability, so your teams can see and trust why a customer was targeted.
Does this only work for financial services?
The case study comes from credit cards, however, the same approach applies to any business with repeat customers and historical campaign data, including subscriptions, SaaS, e-commerce and telecom.
How does this work with the LTV Predictor?
The LTV Predictor tells you what each customer is likely to be worth, and churn reduction tells you who is at risk and what will keep them, so together they show where retention spend protects the most revenue.
Find out which of your inactive customers you can win back
Talk to Aryng's data science team about building an AI-powered churn reduction model on your own customer data.
About Aryng
Aryng is a data science and AI consulting, training and advising company. Aryng's SWAT data science team solves complex business problems, builds each company's Data DNA through data literacy programs, and delivers rapid ROI using machine learning, deep learning and AI. Clients include Google, Box, HERE, Applied Materials, Abbott Labs and GE.
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