Losing customers – churn – is a major headache for Australian SMEs. It’s far more cost-effective to keep an existing customer than to acquire a new one. But what if we could see churn coming, and proactively address it? The good news is, we can. Using the data you likely already collect, we can build a picture of which customers are at risk and intervene before they leave.
Predictive churn modelling isn’t about crystal balls; it’s about spotting patterns. We look at customer behaviour and identify signals that suggest someone is disengaging. Here are a few key areas to focus on:
- Declining Engagement: This is a big one. Are customers using your product or service less frequently? Are they opening fewer emails, logging in less often, or making smaller purchases? A consistent downward trend is a strong indicator.
- Support Interactions: A sudden increase in support requests, particularly complaints, can signal dissatisfaction. Analyse the *type* of support requests too. Are they struggling with a specific feature, or expressing general frustration?
- Changes in Behaviour: Has a customer altered their typical purchasing patterns? For example, a regular monthly subscriber suddenly cancelling auto-renewal, or a frequent buyer making no purchases for an extended period.
- Customer Feedback: Don’t underestimate the power of surveys and reviews. Negative sentiment expressed directly, or a consistently low Net Promoter Score (NPS), are clear warning signs.
The tools to analyse this data are becoming increasingly accessible. Customer Relationship Management (CRM) systems often have built-in reporting features. More sophisticated solutions, like marketing automation platforms, can help us build more detailed predictive models. Even a well-structured spreadsheet can get you started, focusing on the metrics that matter most to your business.
Once we’ve identified at-risk customers, the next step is intervention. This could involve personalised emails offering assistance, exclusive discounts, or simply a phone call to check in. The goal is to re-engage them and address any concerns before they decide to leave. By proactively using data to predict churn, we can significantly improve customer retention and drive sustainable growth into 2026 and beyond.
To get started, audit the data you currently collect. What information do you have about your customers and their interactions with your business? Identifying these data points is the first step towards building a churn prediction system.
