Today there’s a huge shift in how companies are looking at revenue.
The focus is now rapidly moving towards reducing customer churn instead of acquiring new customers at all costs.
According to Harvard Business Review, it costs 5 to 25x more to acquire new customers than to retain existing ones, which is one reason for this shift in focus for businesses.
Another huge benefit of being retention focused is creating brand loyalty with your customers, not only to retain their ongoing business but so that they will review you positively and spread positive word of mouth
There are tons of ways of improving retention and you probably know quite a few of them already.
In this article, I’ll introduce you to something that’s not mainstream yet but has the potential to revolutionise the way you look at customer retention.
I’m going to walk you through what’s wrong with the current system, and how to make it better with customer sentiment analysis.
I’ll talk about how you need to analyse customer sentiments in your customer service conversations to find out how your customers are feeling - allowing you to identify pain points, solve issues throughout their customer journey and ultimately reduce customer churn and boost retention.
The current way of customer churn analysis
Typical churn analysis today takes a very passive approach to finding reasons why customers are leaving.
Many companies try to find reasons for cancellation through an automated feedback form emailed to churned users, telephonic conversations, or broad-level analysis of manual ticket tags that agents selected during the cancellation process.
Once this data is collected, companies analyse it to find trends and commonalities among the cancelled customers.
But this is only one small piece of a much larger puzzle, so analysing cancellation feedback forms alone can never give you the true insights that you are looking for.
For example, it can tell you that a lot of your new users leave after the 3rd month. Or the most common reason for cancellation tag was 'app experience' and ‘delivery_problem.
These are very broad strokes. Let's look at why this level of insight isn't helping your customer retention
Why is customer churn analysis not helping boost customer retention?
While customer churn analysis is done with the best intentions, the execution using current methods often isn’t optimal.
There are two main problems with the customer churn analysis methods we discussed earlier:
- It happens only after a customer has churned and
- it gives very broad insights, which, more often than not, are not actionable.
Having to explicitly seek feedback from churned users through surveys and follow-up calls carries huge fundamental flaws - which need to be addressed immediately.
Why does customer churn analysis not offer a complete view?
- It’s reactive: Cancellation surveys are done only once customers have already cancelled. At that point, even if companies have strong insights, they can not do much about it. The issues the customer was unhappy with have already lost the business.
Example - Customers who have cancelled because they were facing delayed delivery. Had the company paid attention to it before cancellation, they could have spoken to their delivery partner and fixed it.
- It’s biased: Churned customers only get the opportunity to answer questions that companies ask them. Most often, these questions are coloured by their assumptions about why customers may have churned.
Example - Close-ended questions like "Why did you cancel?" with multiple choice answers like "Not using it enough, Not easy to use, Too expensive" are restrictive and do not let the customers express their true opinion.
- It doesn’t provide the whole picture: A survey will only encapsulate a small snippet of that customer’s experience with a company. It might give a broad understanding of what the trigger was that prompted their cancellation, but not all of the experiences that led up to that point.
Example - Someone may mention 'delivery issues' in their cancellation survey but possibilities are there were more issues that are captured in your customer service database. By only logging delivery problems as the reasons for contact, you can not get rich enough dataset to get granular insights.
- It might not be answered: A lot of people don’t want to take out the time to fill in a survey, especially when they've already cancelled. If companies make it too long and detailed to gain high-quality data, then the completion rate is bound to suffer.
Example - Sending out a 50 questions regarding a customer's journey may give you good data to look at, but its almost sure to have a very low completion rate.
- It doesn’t give granular insights: Related to the previous point, sure, keeping it short may encourage them to spare a few seconds, but then companies would sacrifice much-needed details and context
Example - Asking just a couple of questions like 'Why did you cancel' and 'What should we improve' may give you higher rate of completion, but the data acquired can never give you wholesome insights.
Is it time to shake up customer churn analysis?
The most fundamental step in improving customer retention is to understand the drivers behind what’s causing customer churn.
While post-cancellation data collection can help to a certain extent, it’s far from ideal.
What companies need is to understand the pain points and challenges that customers are facing throughout their journey and fix those issues at the source, before they become a churn risk.
Why you need to analyse customer sentiments to boost customer retention
Conducting customer sentiment analysis is critical to understanding how customers are feeling about a product or service throughout their journey.
The best way to understand customer sentiment is to analyse customer service conversations.
It is the most authentic form of customer feedback because they share it without ever having to ask for it.
Your service tickets, live chats, emails and calls contain loads of valuable information about what’s bothering the customers, what issues are recurring and how they are feeling throughout their journey. You can also deep-dive into particular topics and get to the root of the issue.
All of this fantastic actionable data already exists within your customer service conversations, you just need to harness it.
The benefits of customer sentiment analysis of customer service data:
- It’s timely: Customers raise tickets while they’re facing a problem. This means companies have real-time data rolling in, allowing for spotting problems quickly and fixing them before they become recurring issues or impact other customers
- It’s proactive: As mentioned earlier, understanding customer sentiments through service conversations is real-time so companies can tackle issues proactively without having to elicit their feedback
- It’s unbiased: When customers share their problems with customer service teams, companies get unfiltered, honest feedback on issues. It’s them who’s contacting the companies and not the other way around
- It provides richer data: Since the data is unsolicited and organic, companies always get more specific, granular details relating to individual topics, including how customers were exactly feeling when a particular problem arose
- It has a larger sample size: Companies have the luxury of analysing thousands of data points across customer journeys through customer service conversations. More customers are willing to share problems while they’re facing them to get a quick fix than to provide feedback after cancelling
- It helps in establishing correlations: Companies can relate issues directly to churn and find correlations between customer service contact and cancellations. They can also identify what granular topics go hand in hand with churn - and these might not be the same issues with most volumes
In this article, we touched upon a few flaws in only doing customer churn analysis post-cancellation.
We also spoke about being proactive and analysing customer sentiments to benefit from the wealth of insights that customer service conversations have to offer.
If you’re struggling with a low customer retention rate and have been trying several things, it’s time to pause and think about whether are you proactively finding out the real reasons users are churning and fixing them or are you only focusing on plastering problems as they surface, or worse, not doing anything at all because of lack of insights or direction.
Customer sentiment analysis of customer service conversations is the most holistic way of looking at customer data.
When combined with customer churn analysis, it can give you the kind of insights that can enable you to nip problems in the bud.