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No business is immune to churn. But having a better understanding of what drives a customer to churn can help post-sales groups, product teams, and C-level executives create more effective strategies and introduce new changes in order to boost retention. That’s why churn analysis needs to be taken seriously today. Let’s dig a bit deeper into what churn analysis is and how it can be done in the most efficient way possible.
Churn analysis refers to the number of customers that leave your product or service. For B2B companies, losing a single customer can have a substantial impact on revenue. Not to mention the fact that sales cycles are longer and more complex.
Churn analysis helps identify high-value customers who are at risk of leaving. By analyzing the behavior and patterns of high-value customers who have churned in the past, businesses can uncover common churn triggers such as product limitations, lack of personalization, or poor onboarding experiences, increasing your CLV, NPS score, and retention rates in the process.
Churn analysis is the cornerstone of any successful business. It unlocks many valuable key indicators of why customers are leaving, enabling teams to develop more targeted retention strategies. By addressing pain points and enhancing customer satisfaction, businesses can build longer-lasting relationships and open the door to new upselling/cross-selling possibilities.
Here are a few ways churn analysis can help win you over more customers.
Reduced Acquisition Costs: Acquiring new customers is a lot more expensive than retaining existing ones. Customer retention is so important that simply raising customer retention rates by a mere 5% can set your profits soaring anywhere between 25% to 95%, in turn reducing your customer acquisition (CAC) costs and improving your return on marketing investments.
Get Smarter and More Accurate Data with Predictive Modeling: Through churn analysis, businesses can build predictive models to forecast potential churn. Predictive models are built by collecting a wealth of customer-related data, including customer interactions, behavior, patterns, feedback, and engagement metrics. It leverages AI and advanced ML to get behind the scenes into why customers are leaving and what strategies you can employ to keep them.
Enhanced Customer Journeys and Experiences: Churn analysis plays a major role in mapping and understanding the entire customer journey, from the initial acquisition phase to the point where a customer churns. Mapping enables you to gain valuable insights into the factors that influence customer decisions, and the touchpoints that impact their experience, potentially leading to churn.
Churn analysis can help you make more informed decisions, efficiently optimize operations, and foster customer-centric strategies.
There are no shortcuts when it comes to performing churn analysis and pinpointing the issues that are plaguing your business offering. You need to collect customer journey information, analyze drops in engagement, and analyze their experiences. Once the data has been accumulated , you can create a CS playbook to cope with good and bad signals, while also measuring your team’s performance and relationship health.
Here are some best practices you should follow to minimize churn:
Customers like to get value fast. Your onboarding and customer journey should be planned accordingly. Create quick wins for customers and empower them to reach their desired milestones faster. Furthermore, you need to get them engaged immediately to prevent partial adoption or in-app frustration. Shorten formalities, encourage engagement, and get notified in real-time if the adoption process is in limbo.
What else can you do?
Customer accounts are very dynamic. As your product gains traction, teams can grow, stakeholders may change, and some customers might transition to alternative solutions.. Every customer success team needs to be at the forefront when it comes to relationship management. Finding the champions and brand advocates is extremely crucial, and so is onboarding new customers that may have just hopped on. Here are a few things to take into consideration:
First, you’ll need to monitor the relevant metrics – churn rate, health score, sentiment score, and customer lifecycle value, for example as a good starting point. You can then define the number of times you are reaching out to churned customers, including some kind of involvement from your marketing team. Try to determine if there are any geographical, industry-based, or demographic-centric trends with your churn.
Every post-sales operation should be driven by data-based insights, unlike traditional strategies that hinged more on NPS surveys and direct feedback, both incomplete techniques with inherited disadvantages. More on this later.
Feel the pulse and track your customers’ sentiments. Client-vendor engagement and communication patterns should also be monitored, along with product or service usage trends. Promptly react to every key signal. For example, if a customer sent a few discouraging emails and service/product usage has decreased – it’s clear that there is a red flag you must address, even if it’s just a 10% drop that seems insignificant.
Align your post-sales team with the main business goal (implement a North Star Metric). Once this is done, your CS team can have more clarity while creating new retention and growth strategies. But internal sync is just the first step.
Your business needs to track your customer’s roadmaps and growth trajectory on an ongoing basis. In response to their changing business goals, your product should adapt while being transparent about any limitations it may have in meeting the new requirements. Even the toughest customers appreciate honesty and transparency. Align the value you are providing with the customer’s expectations.
There is no universal way to calculate the customer churn rate, but whatever route you go will probably help your churn analysis efforts. The formula of choice should not be changed too frequently to make your research more accurate and reliable. The same logic applies to the time period you are taking into account. If you are opting for a monthly calculation, it’s best advised to continue doing so for a while.
Here are some churn rate analysis calculation methods in use today:
You can read here our detailed blog post about Customer Churn Rates.
CLV = (Average Purchase Value) x (Average Purchase Frequency) x (Customer Lifespan)
Churn analysis provides scores of valuable insights into customer behavior and preferences. By leveraging this data, businesses can offer more personalized experiences, product recommendations, and targeted promotions that resonate with customers at that specific point in their journey, ultimately contributing to a higher CLV.
Other ways churn analysis can boost CLV is by identifying customers that show early signs of dissatisfaction. In the same way, skilled car mechanics use data to diagnose engine troubles, churn analysis uses customer data to diagnose the health of your customer relationships. Factors to pay close attention to include:
Retention Rate = (Number of Retained Customers / Number of Customers at the Beginning) x 100
Let’s break down the retention rate formula a bit further.
For example, if you started the quarter with 500 customers, ended with 450, and 400 were retained, the retention rate for that quarter would be:
Retention Rate = (400 / 500) x 100 = 0.8 x 100 = 80%
This means that during that quarter, your business had a retention rate of 80%, indicating that 20% of your customers churned.
Is that a good thing? Not really.
The average monthly churn rate for SaaS companies stands between 3-8% which would mean the average retention rate should be in the 92-97% range.
Churn analysis enables businesses to design retention campaigns targeted at specific customer segments or individuals. These campaigns may include special offers, loyalty rewards, or exclusive access to features, enticing customers to stay with the brand.
Gross Revenue Churn = (MRR lost to subscription cancellations and downgrades in the last 30 days / MRR 30 days ago) x 100
Organizations can take proactive measures to prevent revenue churn and retain valuable customers.
The old saying goes, you don’t get a second chance to make a first impression, but you can win back the confidence and trust of your churned customers. What can you do? Offer special incentives such as exclusive upgrades and dedicated account managers for the larger customers who dropped out. Go above and beyond to deliver memorable customer experiences. In today’s competitive landscape, you have to fight for each customer.
Churn analysis is a crucial aspect that must be addressed with urgency. As acquisition costs continue to soar, minimizing churn becomes even more of a priority. But doing so requires a proactive approach, where churn analysis is complemented by the identification of underlying churn indicators. Detecting at-risk accounts in real-time can help salvage accounts before they are lost.
Staircase AI is helping to fight churn with its next-gen customer intelligence platform that covers multiple bases,enforcing a customer-first approach. Its strong machine-learning model breaks down millions of customer interactions and helps digest siloed data from multiple communication channels to create unbiased human signals, tracking churn indicators that are simply impossible to achieve manually.
Relationship intelligence is another key benefit, where CSMs can stay one step ahead and create a personalized strategy for different accounts. Unfortunately, churn can’t be prevented, but it can be minimized. Staircase AI is here to help you learn how you can reduce churn risks.
In essence, Staircase AI not only enhances customer retention but also helps investigate churned customers to elevate post-sales operations and promote growth.
Predict churn with Staircase AI
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