A business acquires thousands of customers in its lifetime. However, not every customer is created equal. Some customers provide more revenue and offer fewer costs than others. For businesses to remain profitable, it is critical to identify which customers to invest in and where to cut losses. For instance, an American wholesale manufacturer and distributor of a one-stop resource for bakery ingredients and products wanted to identify consumers who would dissociate from the brand. For this, customers needed to be segmented into various buckets for analysis and identifying the reason behind the higher risk of customer attrition. The challenge, however, was the limited data availability and inconsistency in some variables. Several unknown factors also affected retention, resulting in a less accurate model. Read the entire story here.
Business owners, whether small or large, would like to know which set of customers is more valuable. Estimating the revenue a business generates from a customer for the time they become one is the first step to managing them. It also provides insights on whether to focus on marketing, product development, customer acquisition, or retention efforts. As more businesses adopt subscription-based models, customer attrition rates or churn has become an important factor to consider.
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What is Customer Churn?
Simply put, churn analytics answers the questions, “Are we losing customers? If yes, how?” Churn time is the customer’s life cycle within the brand when they stop being one, i.e if they stop using the product or service you offer. It has become one of the most challenging problems for businesses such as OTT platforms, SaaS, and telecommunication companies all over the world. Nearly all companies experience churn, and all of them want to avoid it.
To borrow a common analogy, customer churn is like a leak in the bucket that you need to fix. This is awful news for product managers. Without proper insights, it becomes difficult to patch the leak. In any customer journey, there are a few obvious pain points where customers lodge complaints, making it easier to resolve them. However, it is difficult to uncover pain points that no one complains about. It is for this reason that churn is often considered a silent killer, especially for SaaS companies. A high churn rate can be expensive for businesses, as it forces them to deal with the stress and difficulty of bringing new customers into the fold. Moreover, churn rates compound over time. This is why it is important to get a proper grasp of customer churn and understand the different reasons behind it.
Why Do Customers Churn?
There could be many reasons why customers churn. Some of them are well in your control, while others not so much. Churn has been categorized into two types to better understand the factors affecting it.
Why is it Important to Analyze Churn?
The concept of churn and retention is far more complex than one may initially assume. Several factors impact the churn rate. For instance, what if a customer doesn’t quit your service but opts for a cheaper subscription plan? As churn is a direct reflection of the value of the product and its features, churn rate impacts other business metrics as well such as customer lifetime value, monthly recurring revenue, customer acquisition cost, and retention rate. Though most businesses attempt to overcome churn by focusing on bringing in new customers, an assured way to ensure customers don’t leave you is to focus on areas where your customers are churning. If customers are leaving because of pricing, you need to focus on pricing strategy, if you are losing customers due to dysfunctional customer support, you need to focus on changing the system.
To determine where the churn is happening, you need to take a good look at your customer data.
Key Performance Indicators: With any kind of analysis, its success lies in keeping track of the right data. Setting up KPI-oriented goals will help get a better look at what caused the churn. Some KPIs to include are customer engagement and usage, support tickets, competitive pricing, etc.
Customer Behavior: A company rarely serves a single category of customers. Since different customers have different needs, they exhibit different behavioral patterns. By separating customers into cohorts based on their history within the brand, you can analyze, anticipate and prevent churn. Mark a few common indicators such as the features of the product or service that are most utilized, customers use what kind of features, the customers at a higher risk of giving up your products, and so on.
Customer Segmentation: Segregating customers into different segments based on different parameters provides you with a better grasp of your customer's behavior. This will help you better engage with them. Observe how customers in different industries use your service and product, and which customers are more susceptible to churn.
Touchpoint Behavior: When customers do not have support to turn to when they are frustrated, they are most likely to churn. Establishing points of contact to resolve your customer queries so that they can get value for their money can help you gain valuable information on how your product or service can be improved. A well-written FAQ page and an easy onboarding process are a few ways to enhance customer experience at significant touchpoints.
Also read: Improve Media and Telecom Growth with Churn Prediction Model
How to Calculate Churn Rate for your Business?
It’s not as easy to calculate your churn rate as it may seem. Churn isn’t always straightforward especially if the data is old. There are several uncontrollable factors that may render past data moot. However, a simple churn rate formula is:
It is difficult to determine the number of customers over a period of time as it includes existing as well as new customers. For this reason, there is no one-size-fits-all solution to this problem. There are four popular ways to calculate the churn rate.
1. Dividing churned customers by the total number of customers on the first day of a given period
2. Dividing churned customers with the average number of customers during the given period
3. A predictive model to anticipate how much churn you will have on each day of a given period
4. The Shopify way of dividing churn by the average number of customers you had during each day of the given period
Though these models have proven useful, there are advanced methods that help companies understand, predict and prevent churn better.
Using AI and ML Techniques to Predict Churn
When the sustenance of the company depends on recurring monthly or annual subscriptions, every customer that leaves can put a dent in the cash inflow. This means that high retention rates are critical for the survival of the company. When it costs five times more to attract new customers than to retain existing ones, it is only logical to focus more on retaining who we have. In order to prevent churn, it is vital to foresee it with accuracy. This is where AI and ML to help companies analyze their customer data and predict churn accurately. A predictive churn model is one of the best tools in your chest when deciding where to focus retention efforts. A well-tuned predictive churn modeling can help weed out both types of churn. ML models remain a popular choice for companies to model their churn analysis systems.
In the scenario cited at the beginning of the article, the client wanted Quantiphi’s help to develop a churn prediction model to identify customers who are likely to churn away. Quantiphi developed a model to segment customers into various buckets through RFM analysis and develop a better understanding of customers who are likely to drop off along the way. This helped the client devise mitigation plans specifically for each segment of customers. The engagement resulted in a data-driven identification of churn factors, enhanced customer lifetime value, and a marked increase in retention rates.
Read more: Deliver Exceptional Customer Experiences With Customer Data Platforms
Nearly every company uses customer churn analysis for a variety of reasons.
There are several churn prediction solutions on the market. The most comfortable way to analyze and predict churn is to use customer data platforms. Though not all off-the-shelf CDP solutions offer in-built churn analysis options. The issue with black box solutions is that there are limitations to usage or they require a specific subscription. Custom solutions, however, are the answer to all the problems. Quantiphi, with its marketing analytics abilities, offers a custom CDP solution with an in-built churn prediction solution. Quantiphi has been at the forefront of helping customers leverage intelligent churn prediction solutions to enable companies to retain their customers. To learn more about harnessing the power of churn analysis and retaining the best of your customers, get in touch with our experts.
