Stop Customer Churn in its Tracks with Predictive Analytics
Noam Brezis
This article on predicting customer churn is part of our Predictive Frameworks series, which explores the most effective use cases for predictive analyticsPredictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future…. More.
No business wants a customer to walk away. That’s especially true when it’s increasingly expensive to acquire them.
But the only constant today, it seems, is change. Industry disruption is the new normal.
Take direct-to-consumer. Countless CPGs are having their flagship products displaced by nimble, data-driven upstarts. The internet has enabled endless choice of products and services. Globalization and multi-device shopping have compounded that trend. Tried-and-true customer retention methods can’t alleviate customer churn brought about by disruption.
In the world of direct-to-consumer alternatives, brand loyalty is waning. The best experience wins.
Churn Isn't an Impossible Problem
Despite sky-high customer acquisitionCustomer acquisition is the effort of garnering new customers for a business. Customers can be attracted through smart marketing strategy for customer acquisition, which may… More costs, many companies can do little but watch when churn happens. Whatever caused the churn probably happened weeks prior. Your daily dashboard will only reveal the hit to revenue. Rarely do companies invest as much in their service and customer satisfaction teams as in sales and marketing.
There can be many reasons a customer churns. Churn can happen in large segments. Factors like seasonalityRefers to patterns that repeat over a specific period of time. These patterns can be observed in various types of data, such as customer transaction… More or increased competition can develop an annoying attrition problem into a critical one.
The seemingly spontaneous nature of customer behavior and churn make this problem perfect for predictive analyticsAnalytics is a business practice that uses descriptive and visualization techniques to gain insight into data; those insights can then be used to guide business… More and churn predictionChurn prediction involves building a predictive model based on past customer data. That model will help identify patterns in customer behavior that correlate with churn,… More.
If something seems random, that likely just means you need a machine to find the pattern.
Predicting Customer Churn with Automated Machine Learning
This blog post assumes the analyst uses a platform to automate parts of the process including: data restructuring and encoding, feature engineeringFeature engineering is the process of manipulating and transforming raw data into forms that are more valuable in a predictive model. Datasets offer many options… More, and ongoing modelIn the context of machine learning, a model is a specific instance or example of an algorithm that has been created based on a particular… More optimizationBroadly speaking, optimization is a process used to either maximize or minimize an output value by selecting the right input values. In data science, this… More for data leakageData leakage occurs when a machine learning model is trained with information about the target/outcome variable that it will not have when used in production…. More and drift prevention. With those capabilities, customer churn detectionChurn detection is the process of identifying customers at risk of churning. A predictive model can look at data about past customers who churned and… More is at its most powerful.
Building churn predictionA prediction is the ultimate goal of a predictive model. In Pecan, a prediction is often tied to a specific customer. After learning from data… More models to identify likely customer churn will constantly reveal your warning signs, automatically engineering new features to inform who will churn in the future and what to do about it.
The spider web of reasons a customer might churn is interwoven with the depth of customer data that can make churn seem random. These factors shape a perfect application of machine learning to mine for patterns and insights. Those insights are within your customer data and your transaction data sets. They’re just waiting to be unearthed and put to work.
Tailoring Predictive Analytics for Customer Churn
You can define churn in the way that matters for your business. That might be a slightly decreased frequency of purchase, or a complete end to a customer’s business. Then, churn prediction models are constructed with historical data to predict that specific kind of churn.
The result? You can identify customers and accounts that will likely churn, with a specific likelihood risk attached.
The likelihood of churn is a useful quantitative data point. You can use this prediction to segment buckets of customers into “unlikely to churn,” “likely to churn,” and “will definitely churn” for different treatments.
You can take action to retain customers that’s the best fit for their churn likelihood, as well as matching your actions to other behavioral patterns identified in the churn analysis.
Every one of your customers should have a churn likelihood score.
Where Business Users Need Control in Predicting Customer Churn
However, when it comes to predicting churn, the process matters as much as the result.
Data people of all kinds—from marketing and sales to data scienceData science combines statistics, computer science, scientific methods, and business knowledge to analyze, model, and predict using data. The data science toolkit can be used… More and analytics—need to affect the model creation process where it counts.
For example, when it comes to churn, the detection vs. precisionIn predictive analytics, precision shows what proportion of a machine learning model’s identifications of an item were actually correct. As an example, imagine a machine… More ratio can have a huge impact on treatment effectiveness, and the resulting revenue or savings. In fact, mathematically, there’s an inverse relationship between increasing detection and precision of a churn model. Consequently, marketing teams can use this ratio to fine tune their campaigns, and determine whether a customer needs a heavy discount to prevent churn—or just a light promotion.
AIArtificial intelligence (AI) refers to the development of computerized systems that can carry out tasks and perform actions that augment or take the place of… More and machine learning are quickly changing the way businesses work with data. To save time and ensure efficacy, automation is key. However, organizations should never forget to include the important input of human control and decision making. That’s what makes automated predictive analytics as a platform so powerful. It combines the best of AI and human inputs for a whole new analytics experience.
Read more in the Predictive Frameworks series:
- Scaling Demand ForecastingDemand forecasting involves trying to determine the likely future need for an item, based on historical data and analytics showing how much of it has… More with AI-Powered Predictive Analytics
- Predicting Success for Cross-Sell & Upsell Offers
- How to Implement High-Converting Cross-Sell and Upsell Strategies with Predictive Analytics
- Why You Need to Predict Customer Lifetime Value
- Is Predicting LTV Really Worth It?
Curious about more opportunities in predictive churn & retention yourself? For more specific stories, read these case studies:
If you want to see how Pecan can identify and prevent customer churn, request a demo and we can help you set up your first predictive model.