Churn prediction means determining which customers are likely to stop using your service, based on how they are currently using your service. Retention is the cornerstone of growth in the subscription game. You can be sure that if you’re not already using your customer data, your competitors are, and those micro-advantages can be the difference why they choose them instead.
Once you can identify exactly why customers are leaving, you can then take the relevant marketing action to deliver the best possible, evidence-backed, outcome. Improving the LTV of your customers frees up budget to acquire new business, but if current churn rates are not addressed, that budget either isn’t available, or is considerably smaller.
Each customer is different, they’re nuanced, so they subscribe for different reasons and cancel for them too. However, you can place customers into cohorts to uncover patterns and behaviours that are likely to lead to a cancellation. With the wealth of data available (which many businesses do not realise they have) you can predict when this will happen, letting you prioritise cases and personalise them, all driven by AI.
This isn’t to say it’s a fool-proof one-size-fits-all solution. There are still key challenges to prevent churn, however, AI, like any good team member, improves over time, training itself on an ever-growing dataset, ensuring the best possible outcome every time. As AI models can make clear recommendations, the automation helps improve churn rates and frees up your customer service/retention teams to tackle the key challenges:
Some customers have one virtual foot out of the door. Retaining this type of customer, where prediction is less relevant and whose frustrations are pre-existing or spontaneous, requires human intervention to encourage them to pro-actively reconsider your offering. Regardless of whether these customers stay or go, they still generate data, giving your teams access to knowledge and experience of what interventions are likely to succeed.
Proactivity is key. The earlier you can intervene the better the chance of success. AI models can flag the earliest possible moment a customer has entered a churn funnel, which is the steps a customer goes through beginning with the first churn trigger driven by dissatisfaction or frustration with your product or services.
Firstly, you need to analyse key findings surrounding your customer data:
Newsletter open rate?
Have they given feedback?
Which features do they use?
Which features don’t they use?
How regularly do they use your service?
Have they visited the help desk recently?
At Pivigo, our AI-as-a-Service platform looks to understand customer behaviour and placing customers into meaningful segments, then building a predictive model for customer churn based on your data, and identifying the major reasons driving each prediction to ensure actionable outputs.
However, most companies do not have the resource or budget to spontaneously adopt AI into their operations, which is why our AIaaS platform is ideal, as businesses can leverage our experience and talent to benefit from AI and machine learning without the effort, risk and expense of building it themselves.
The last two years have proven complacency is not an option, which is why we provide insights on an ongoing basis and fully integrated to your tech stack. This ensures the model improves over time and adapts to whatever changes the world throws your way. This enables you to always proactively target at risk accounts and reduce churn by up to 85%.
Our AI services are ongoing on a subscription basis for as long as you need us. What would your business look like if 85% of customers ready to leave stay? Imagine the improvements to the LTV of each customer, the benefits and growth from affiliate marketing, and where you could allocate budget elsewhere to support your growth.
Get in touch with us today to find out how you can get started.