Churn critically impacts company success, particularly for startups, likened to a dam's leak losing valuable water, or in this case, customers. Effective churn management, beyond merely recognizing its presence, involves understanding its root causes, which aren't limited to product quality or price.
I'll share an example from a client that shows an unexpected churn cause unrelated to product quality. We explored churn prediction and detection first, using Cohort Analysis to identify engagement trends and potential churn signals among different customer groups.
Cohort Analysis effectively spots churn trends by segmenting customers based on signup dates or behaviors. This technique helps uncover specific patterns, like decreased user engagement, that might signal a higher risk of churn following certain updates or time periods. It is a leading indicator.
The CEO of a fintech company serving SMBs used predictive analytics to track customer churn. Monthly cohort analyses, as detailed in Exhibit 1, revealed retention rates, showing, for instance, that 87% of February's new subscribers were still active and making payments after three months, a fact indicated at a specific point in the data table.
Exhibit 2 highlighted a notable decline in retention rates for January subscribers, dropping from 90% to 75% between the fourth and fifth months, signaling a red flag. This consistent pattern of attrition in the fifth month, as further evidenced in Exhibit 3 for subsequent months, suggests a recurring issue affecting subscriber retention. Identifying this pattern is crucial for understanding and addressing the underlying causes of churn.
Curious about the sudden subscriber drop in the fifth month, the CEO directed the customer success team to investigate: “What was the potential churn trigger?”. The discovery was unexpectedly straightforward: a cumbersome survey was deterring customers. This insight revealed the issue was not with the product but a hiccup in the customer journey. Acknowledging this as a simple fix, the CEO swiftly implemented changes, effectively addressing the problem within days.
The implemented change had a profound effect, notably reducing customer departure rates from May onwards, especially in the critical fifth month. This strategic adjustment led to a 50% decrease in the company's yearly churn rate, yielding a significant and positive impact on revenue. This outcome underscores the effectiveness of addressing specific issues within the customer journey to foster loyalty and enhance financial performance.
Leveraging predictive analytics won't stop churn completely, but it significantly cuts down the part CEOs can control. That's a major win, showcasing the strength of smart, data-led moves to keep more customers on board.
