Most of the startup business owners put a lot of effort into gaining new customers and increasing their sales. But they tend to forget one of the most crucial parameters of monthly recurring revenue or MRR – the ‘customers’.
This behaviour of customer negligence leads to loss of customers over time which is also referred to as customer churn.
As per the authors of ‘Leading on the Edge of Chaos’, a mere five per cent reduction in customer churn can increase your profits from 25-125 per cent.
Further, studies show that as much as 97 per cent of customers leave companies silently. Even the mobile phone industry experiences a churn rate of 21-38 per cent in a year.
This brings us to another term – churn rate. Churn rate is defined as the percentage of customers leaving a company, in a given period.
But, what is the best way to predict customer attrition or loss? Are traditional transactional analytics still relevant for effective customer churn analysis? How to leverage unstructured data for customer churn analysis? And, what is customer churn analysis, to begin with?
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Let us find out answers to all these questions one by one and find out some of the best ways for customer churn analysis.
What is Customer Churn Analysis?
Forbes defines customer churn as an event when customers cancel their subscriptions. It refers to the process of losing customers over a period of time. And the process of analysing customer churn data to measure, monitor and lower the churn rate is called customer churn analysis.
Let us explore the term and its types in detail.
Classification of Customer Churn Analytics:
There are two types of churn analytics:
- Unstructured churn analytics
- Transactional or structured churn analytics
Transactional churn analytics focuses on structured data collected over time to predict, monitor and offer ways to reduce customer churn. Most of the companies opt for this traditional analytical method when it comes to predicting churn.
However, they fail to realise one simple fact – Customers don’t leave a company or a subscription or an app overnight.
The true answer to finding actual reasons for customer churn lies in the unstructured data which also offers 80 per cent of information about customers.
Why don’t businesses leverage unstructured data?
Unstructured data comprises data collected via human-to-human interaction such as emails, chats, messages and communication logs. Creating an organised repository of such haphazardly collected information is a herculean task.
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Though most of the service and customer-centric companies employ automation and smart tools such as an online helpdesk system to collect data, there is a long trail of customer information and interaction left in different places. Event-generated data such as data generated after product failures etc is yet another source of unstructured data.
Now, it is clearly understood that the root of customer churn lies in unstructured data. But still, many retention strategies don’t leverage it. Let us understand why.
Almost all the retention leaders are already using some sort of transactional analytics tools. After investing millions of dollars in the traditional analytics models that can predict churn they believe that they already have a churn analytics tool. So, investing in another novel system seems to be out of the question.
Further, the unstructured data has a lot of redundancy and sorting it to form an organised data repository requires a lot of time and effort.
After having had an overview of customer churn analysis and its types, let us move ahead to find why it is important to analyse churn.
Why does customer churn analysis matter?
Customer churn amounts to a loss of revenue and a higher customer acquisition rate or CAC. Angry customers can spark a chain reaction with their negative feedback which is more than enough to kill your reputation within a matter of days.
Churn works against financial growth and eats up your revenue. Further, acquiring new customers is five to 25 times more difficult than retaining pre-existing customers.
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Let us try to understand the stats with the help of an example.
Remember the time when the world-famous automobile brand Toyota recalled nearly nine million vehicles worldwide because of some mechanical issues? It was one of the most trying times for the brand known for its quality cars. More shocking was the way the PR team handled the situation. The brand reputation was in shambles and the revenues hit a record low. Later it was speculated that a vindictive regulation team might have been the reason behind this massive scandal.
Well, all in all, the brand took a fair enough time to be back on track and the bleak memories of its massive customer churn are still fresh.
An efficient, robust and smart customer churn analysis empowers you to devise a long-term customer strategy. Using the data you can predict whether your current customer support system is worthwhile or not.
Netflix is a recent customer churn analysis example where the brand used AI-based algorithms to analyse data such as demographics, preferences, watch history and ratings, etc to offer personal recommendations. This boosted its revenue and helped it overcome churn.
Other important reasons to invest in customer churn analytics is to deliver better customer experiences and to optimise services and products in a proactive manner.
Hence, businesses around the globe invest millions of dollars in customer churn analysis.
After learning the importance of customer churn, let us move on to find the best ways to do customer churn analysis.
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Leverage unstructured data
Invest in an intuitive, efficient and smart online help desk system to record all the communication with your customers in ONE place. Depending on your setup you can either opt for ML or AI-powered data analytics to find actionable insights into the unstructured data such as messages, communication logs, chats, and emails.
Doing this will offer you a direct idea about the customer happiness index and customer satisfaction. Work on the root cause of problems and proactively communicate the results, updates and novel upgrades to your customers.
Takeaway:
All the answers for customer churn lie in direct vendor-customer communication. Employ this information to improve the churn rate.
Choosing the right automation
Invest in automation that employs innovative analytical techniques rather than traditional transactional analytics. Predictive behaviour modelling, AI and ML-powered analytics, and micro-segmentation of customer data are some of the novel practices in churn analytics.
Takeaway:
Upgrade your traditional predictive analytical system and opt for hybrid automation to gather more relevant data. Use Machine Learning for better customer retention.
Look beyond churn prediction
To stay ahead of attrition, it is important to look beyond churn prediction and simple inferences. It is important to identify the first signs of customer lifetime maturity.
Prediction of customer’s lifetime value, identifying customers with high-risk towards churn and proactively working towards increasing the revenue from existing customers are some of the ways to make your customer churn analysis better.
In addition to this, getting customer feedback on a regular basis can help you realise their potential tendency to churn.
Takeaway:
Don’t limit your scope to predictive churn analytics. Look for more options that can help you identify the customer’s lifetime and value attrition etc.
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Proactive customer retention with targeted goals
Go for proactive customer retention even before a customer reaches a high-risk stage of churn. Sending proactive emails, improving product integration and onboarding processes and employing metrics such as ARR (Annually Recurring Revenue) and MRR (Monthly Recurring Revenue), etc are some ways to fight proactively for customer retention.
Takeaway:
Devise strategies keeping every individual customer in mind. Instead of marketing and targeting a group based on geographical location, employ smart clique-generating principles to form groups with similar preferences, interests, problems, and expectations.
Humanistic automation approach
As per Forbes, opting for a humanistic automation approach for delivering better customer experiences and tackling churn can give a new life to your startup business. Robotic email responses and pre-recorded customer support messages only make your customers feel uncared for. They don’t feel valued if your customer support team doesn’t show a humanistic side during problem resolution.
Doing customer churn analysis with AI-powered algorithms can help you in this regard.
Takeaway:
Your customers are human and expect that distinct feel of being valued by another human while using your products and services. Don’t consider the job done by just buying a customer helpdesk. Try to tailor the customer experience as much as possible.
This completes our discussion on the best practices for customer churn analysis. We hope all our readers find thoughtful takeaways from here to fight churn in a novel and better manner.
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