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Did you know that increasing customer retention rates by just 5% can boost profits by up to 95%?
Despite this compelling data, the focus in many startups remains disproportionately skewed toward customer acquisition rather than retention.
That’s why our goal in this article is to talk about an often overlooked aspect of product growth-churn analytics.
To understand churn analytics, we’re going to look at it from the broad perspective of ‘Why’, ‘What’, and ‘How’.
So let’s get to it.
Before understanding what churn analytics is, it stands to reason that we’d have to understand what churn itself is in the first place.
Churn refers to the percentage of customers who discontinue using your product or service within a specified time frame. The churn rate is usually calculated by dividing the number of customers lost during a period by the number of customers at the start of that period, expressed as a percentage.
The most succinct way to explain why churn analytics is crucial is the following statement:
Acquiring a new customer can cost up to five times more than retaining an existing one.
But why is that so? Take into account the entire funnel for customer acquisition: from awareness and engagement to conversion, each stage incurs costs—marketing campaigns, sales efforts, and onboarding resources.
On the other hand, retaining a customer involves improving upon an already established relationship, which is usually more cost-efficient. Metrics like Customer Acquisition Cost (CAC) and Customer Retention Cost (CRC) make this financial dichotomy abundantly clear.
So it’s pretty clear that you’d want to minimize your churn.
A logical next step is that you’d want to keep an eye on your churn analytics, seeing as how analytics is just a data driven approach to molding user behaviour to your benefit.
Now that we’ve established why churn itself matters, we can understand why churn analytics matters.
Now, what is it exactly?
Churn analytics is just a subset of analytics thats focused on optimizing churn in particular. Of course, there’s a lot more that goes into that.
This is the initial stage where all relevant data is gathered.
This involves monitoring how frequently and in what manner customers use the product. It includes tracking which features are used the most, the duration of usage, and the time of day the product is accessed.
This involves collecting and analyzing the feedback given by customers when they decide to stop using the product. It can provide critical insights into the specific reasons behind their decision.
This involves analyzing the nature and frequency of interactions customers have with your customer service, including the issues raised and the resolution provided.
This step involves preparing the collected data for analysis.
Involves dealing with any gaps in the data. Strategies include imputing missing values, deleting incomplete records, or using algorithms that can handle missing data.
This involves identifying and managing data points that are significantly different from the others. These outliers can distort the analysis and lead to incorrect conclusions.
This involves adjusting the data to make it suitable for analysis. This includes normalizing the data and encoding categorical variables.
This involves examining the processed data to extract meaningful insights
This is a statistical method used to predict the time until a customer churns. It considers both the number of customers that have churned and the time it took for them to do so.
Involves analyzing the behavior of groups of customers over time. It helps identify trends in customer behavior and pinpoint specific time frames where churn is highest.
C: PREDICTIVE MODELING:
This involves using statistical methods and machine learning models to predict future churn based on past data.
This step involves interpreting the analyzed data to generate actionable insights.
Involves determining which features of your product are most closely associated with churn. It helps in focusing product improvement efforts on the areas that will have the most impact.
This involves identifying the segments of your customer base that are most likely to churn. This information is critical for designing targeted interventions.
This involves determining the specific reasons behind churn, such as dissatisfaction with the product, better alternatives available, or external factors.
This involves taking actions based on the generated insights.
This involves designing and implementing marketing campaigns specifically aimed at addressing the identified issues and pain points of at-risk customers.
This involves making necessary changes to your product based on the insights generated. For example, modifying or replacing features that are closely associated with churn.
This involves implementing initiatives designed to improve customer satisfaction and reduce churn, such as personalized onboarding, regular check-ins, and proactive support.
This involves continuously assessing the effectiveness of the implemented actions and making necessary adjustments.
This involves assessing the impact of the actions taken on the churn rate and other related metrics. It is essential to track the churn rate regularly to see if the implemented actions are having the desired effect.
This involves regularly revising and recalibrating the strategies based on the results obtained from monitoring. It is crucial for minimizing churn in the long run.
The 'how' in churn analytics, at least when distinguishing it from analytics in general, largely comes from the kinds of metrics that should be tracked.
The Customer Effort Score gauges the ease with which customers can interact with your product, often following a specific interaction like a support request. A low CES suggests that customers find your product cumbersome, which is a significant churn red flag. But CES doesn't operate in isolation. Other metrics in the same realm include:
Measures how often customers successfully complete specific tasks within your application, giving a clear window into UI/UX effectiveness.
How long does it take for a customer to accomplish a standard task? The longer it takes, the higher the chance of frustration and, ultimately, churn.
While hard metrics offer a numerical view, qualitative feedback gives you the story behind the numbers. Here are some ways to gather qualitative insights:
Extract sentiment from customer service transcripts and tally positive vs. negative comments.
Occasionally, a direct conversation can yield insights you wouldn't get otherwise.
Instead of multiple-choice answers, allow customers to write freely about their experiences.
While "engagement" can be a nebulous term, for churn analytics, it's all about spotting patterns—or lack thereof—that suggest dissatisfaction. Here are some engagement metrics to keep an eye on:
A composite metric that considers how often and how long specific features are used.
While it sounds basic, low or declining login rates can be an early churn warning sign.
Measure how many features or sections of your product the customer uses. Are they only scratching the surface or diving deep?
When choosing metrics, think of them as an ecosystem. No single metric should be your north star; instead, they should all work in concert to provide a rounded view. For instance, a high CES score coupled with negative qualitative feedback could indicate a disconnect between user perception and actual experience.
Moreover, your chosen metrics should be revisited and recalibrated. The importance of each metric may shift as your product matures or as market conditions change.
Like with analytics in general, sourcing, selecting, and then leveraging data to come up with actionable insights can be quite the inefficient process.
Using AI, we solve that at Crunch.
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