3 strategies to reduce your churn rate with semantic analysis
3 strategies to reduce your churn rate with semantic analysis
November 12, 2021
Every business in the world loses customers. According to a study by Harvard Business Review, companies lose an average of 10% of their customers each year. With one-click competitive accessibility, the rise of new players, customer volatility has never been greater. However, acquiring new customers can cost between 5 and 25 times more than retaining existing ones; it is in the interest of calculating and analyzing your attrition rate.
This article will detail how and why to analyze the churn rate and then we will see 3 strategies to reduce it.
1) What is the Churn rate?
Mainly used in the banking and insurance sector, the churn rate (or attrition rate) is an indicator that aims to calculate the total or partial loss of customers for a company. It is the inverse of the retention rate, which represents the ability of a company to retain its customers.
When we are in a customer-centric approach, attrition is, therefore, an indicator to follow closely.
It is possible to distinguish two types of attrition:
- Total attrition, loss of a customer. In this case, very often it leaves the mark to move towards the competition.
- Relative attrition, the customer is redirected to another offer or another product from the same company.
Therefore, it is very important to monitor this indicator very closely in order to be reactive and to draw relevant analyzes.
2) How to calculate the churn rate?
The Churn rate is very easy to calculate. In fact, all you have to do is divide the number of lost customers over a period by the total number of customers over the same period and multiply the result by 100.
For example, if a bank has 15,000 customers at the beginning of January and loses 600 during the month, then its attrition rate in January is: (600/15000) X 100 = 4%
3) How to correctly interpret the attrition rate?
Once the attrition rate has been calculated, the main difficulty encountered is interpretation. Indeed, is a rate of 4% a good, an average, or a bad rate?
Well, it all depends on the industry. For example, the banking sector traditionally has a low attrition rate which will be close to 5% while for telephone operators it will be more around 30%.
You will understand, a high attrition rate in one industry can be low in another, it’s all about context.
Now that you know more about your churn, it is time to investigate the reasons for these departures and take corrective action.
In the rest of this article, we will detail 3 strategies to reduce the attrition rate.
4) What strategies to reduce churn rate?
Following the analysis of several million verbatim, we have identified 3 methodologies proven to effectively reduce attrition. We will take the banking sector as an example.
a) Identify the initial explicit threats and analyze the direct causes
A first method aims to directly target and listen to the customers who threaten you to leave and to locate explanatory elements on these comments. This process is quick because it is quite selective: you only go through attentive listening to customers who tell you that they are going to leave you.
This is why the first step is to look in the client’s words for an explicit threat to leave. A semantic analysis tool can offer you this type of categorization in direct and immediate reading: this allows you to very quickly identify the most critical verbatim and react to retrieve a certain number of customers.
Customer Alerts screens on the CXinsights.io platform
A complementary approach consists in comparing the subjects mentioned in these comments which explain a desire to start with the verbatim of all of your customers.
This method allows the most critical irritants to be detected very precisely.
When comparing the themes mentioned, there can be 2 different scenarios:
o The subjects are totally different. If the explicit comments address themes that are fairly well experienced overall by all of your customers, then these may be isolated cases or the emergence of an irritant that will therefore need to be monitored in the short term.
o On the other hand, if the themes that emerge in the verbatim that threaten to leave you are also generally badly experienced, then this is a critical irritant that must be resolved as a priority to stop the bleeding.
This method, therefore, allows you to detect weaker signals earlier and in a more targeted manner. The causal link between the problem and the customer’s departure is proven and therefore you can mobilize forces to reduce the causes of attrition.
Be careful, however, this approach has two limits:
o If you do not have enough data. It requires a minimum volume of data (at least 10,000 comments) to obtain a minimum volume of initial threats.
o It is not exhaustive. While all of the points identified have an impact on attrition, all the trigger points for attrition are not necessarily identified.
To overcome these limitations, the implementation of a less targeted approach is possible, for example in a second phase or in the event of a methodological blockage.
b) Identify, prioritize and resolve irritants globally
The complementary method is indeed less targeted: it conventionally consists in emotionally analyzing customer comments, in detecting and dealing with the irritating points of the experience.
By improving the experience for all customers, and by solving the main irritants, you are making the — quite logical — bet that some customers will stay longer because they will be more satisfied.
The emotional connection seen above is restored, your customers stay and recommend you.
Irritants are emotional in nature. Detecting the emotion also helps you to understand the limits of acceptability (which generates anger) and the urgent needs for change (expressed by disgust (which is therefore often accompanied by a high risk of attrition), to understand reassurance needs (expressed as fear) and disappointed customer expectations (expressed as sadness).
Finally, you will start from the premise that your survey and feedback methodology is correct: by responding to the problems expressed by your customers who speak up, you should reach the thousands of customers who have not spoken … and generate a beneficial multiplier effect.
Analyzing the most critical comments is often the preferred approach because it is the easiest to achieve and the return on investment on satisfaction is obvious.
This process takes longer than the previous one because it is less selective: you go through attentive listening to all customers and not just those who threaten to leave you. In the absence of focus, you run the risk of dispersing yourself on elements that are a little less priority from a business point of view. In fact, not all irritants necessarily cause the client to leave. Even though a specific point of the experience is often disappointing, it does not necessarily justify leaving you on its own.
So, to go faster, or if your resources are very limited, or you want to focus your actions on what will more directly impact the loyalty of your customers, it may be preferable to focus on the most emotionally critical irritants. On a solution such as CXinsights.io by Q°emotion, you can measure the volume of mentions and the emotional index of each irritant and choose these indicators to prioritize the actions.
In short, solving the most emotionally critical irritants allows you to move quickly and have a higher chance of reaching all of the causes of attrition expressed or not expressed explicitly by your customers.
c) Analyze a posteriori the opinions of customers who have already left and build alerts
The last approach has a predictive vocation. The idea is to identify risks upstream start and take action to minimize your attrition. But how to do it?
Unlike the two previous methods, this time the goal is to analyze the topics raised and the emotions expressed by your customers who have already left.
Unfortunately, not all of them will tell you that they are leaving. On the other hand, you can list your customers who left this year, for example, and analyze the comments they left with you last year.
By looking at the themes mentioned by these former clients, as well as their emotions, you will be able to prioritize the corrective actions to be taken with all of the operational.
For example, in the example above, we can see that out of the 53 customers who left in 2021, all of them have mentioned compensation at some point. This, therefore, means that this subject is particularly sensitive and that it should be treated as a priority.
Once identified, complete profiles can be segmented based on the criteria following:
- emotions and/or
- thematic subjects and/or
- key segmentation criteria (agencies/clients/etc)
- satisfaction or recommendation scores
- keywords
By building attrition personas in this way, we can easily create warning scenarios.
The implementation of automatic alerts in a semantic analysis platform such as CXinsights.io, is then possible: it allows you to send to one or several email addresses the list of alerts:
- by adapting the volume of alerts via the choice of more or less restrictive conditions
- by allocating the message to the right operational level
- by anticipating or saving him time
- by modulating the level of response to be provided to customers
You then go from a reactive to a proactive approach. These predictive alerts will therefore allow you to launch personalized preventive actions to survey customers who are likely to leave your business.
Measuring churn, therefore, provides a better view of customer satisfaction. By coupling this to the measurement of other key indicators such as the NPS ( 5 strategies to improve your NPS), you will be able to determine more precisely what your customers’ needs are and boost your loyalty!
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Originally published at https://www.qemotion.com.
