Customer churn, according to businesswise, is the means that measures clients or consumers not retained by your enterprise. In terms of meaning, interestingly, churn means rambling literally. This anguishing rate is the rate of your customer attrition which is called customer turnover sometimes. As a matter of fact, when the consumer is not used to purchasing the services or products from your company anymore, this is a matter of customer churn.
This business indicator has a great deal of sustainable impact on your income; according to Harvard Business School, if you were able to decrease the customer churn rate up to 5%, then this would increase at least 25% surplus value, rolled up from cutting expenses and enhancing revenue.
In this regard, you should find out why this part of your customer’s club would have ceased to buy the services or merchandises of yours. This should be calculated monthly or even weekly. For instance, if you got 1000 customers and lost 40 last month, then your monthly churn rate is 4%.
Nonetheless, you would never be able to retain 100% of the customers who once bought something from your business, you should be aware of both the importance and implacability of this issue. Being strictly scrupulous to retain all the customers is an unforgiven mistake, simply because you should put 80% of your effort on that 20% of your customers who make 80% of your profits.
The importance of predicting customer churn
Although the discrimination among ordinary and lucrative customers is a new trend in businesses, you should have an ability in predicting the customer churn. Not only has it been participated in the rate of income, it has also shown your foible-whether your customer is disappointed about your employees’ weaknesses or this is a substantial issue in the product.
I listed below some measures through which you could estimate the customer churn:
- Data collection & cleaning;
- Feature selection & engineering;
- Modelling;
- Insights and Actions.
Identifying which customers are likely to leave a service or unsubscribe from a service is the key to doing this. You do this not because you have to put in extra effort to keep them, but because, in essence, to avoid wasting more energy on keeping an already lost customer, and instead, focusing on the rest, maybe a few dyed-in-the-wool customers.
In this regard, the fact that the success rate of selling to a customer you already have is 60-70%, while the success rate of selling to a new customer is 5-20% is not solely pivotal, but this has a great deal more insightful recognition of why this estimation is worth, actually.
In the chart below, in order to give a rule-of-thumb estimation about what is going on in terms of customer churn rate in USA, I summarized some data:
| Characteristic | Customers Churn |
| General retail | 25% |
| Financial/credit | 25.2% |
| Online retail | 22.01% |
| Telecom/wireless | 21.5% |
Poor customer services and some issues in 24/7 support teams lead the businesses away from their desired prospects in terms of minimizing customer churn rate.
As a matter of fact, American Express found 33% of customers will come up with switching companies after merely one poor customer service experience.
The Difficulty of Customer Churn
Like any other prediction, it is never 100% accurate. But the point is, if you diagnosed incorrectly, it loses customers who could have made the dyed-in-the-wool 20% list. So, you should use the means that has the lowest error percentage.
One of the tips that I would like to mention is to use a fraud department. In most companies and businesses, the fraud department runs by selecting a sample of transactions or customers with suspicious behavior, based upon their previous patterns of trading and the like. Judging this way, relying on individuals and even groups is somehow possible. But, when it comes to larger scales, for example organizations like Facebook and Instagram, this is not an easy task.
The impact of customer churn on businesses
Today, companies are under intense pressure to delve deeper to integrate all the information and pieces of data they dug up in order to patch up an insight which leads new differentiators in an upper position from their competitors.
On the flip side, producing a voice-of-customer analysis from open-ended survey questions, call center logs, Emails, online columns, or opinion blogs, a skill that can also be used to increase customer satisfaction, customer acquisition, customer churn, and customer loyalty by reaching out to customers proactively. This is a semi-scene of the impact of customer churn on start-ups and businesses.
So far, Amazon is using these methods to expand its market share in the field of movie and show business products, and thus has been able to provide a population of 85 million of full-fledged customers for itself.
In another example, Winston Lin a strategist in Houston Rockets, put it in an interview:
We have leveraged analytics and machine learning for player evaluation, our core business operation, and we leverage it for our customer churn. We have now opportunities to enrich our predictive models for customer retention with instructed data such as social media, fan engagement, site traffic, and customer satisfaction surveys.
We at Dataigest are ready to provide you with just as accurate these services.
Predicting customer churn through machine learning
The impact of machine learning on our societies, our political system, and the business world has just begun. In fact, the knowledge and the analysis obtained in recent years, which is increasingly used in machine learning, has transformed the market. This, in turn, changes the way you buy, sell, think, vote, hire, play, choose, date, and whatever you do.
Today, at the mercy of machine learning, businesses, in order to predict customer churn, can incorporate a variety of data from social media, streaming content, and Emails to provide real-time insights and commands of claims to the customer touch points, thereby helping companies swiftly resolve some financial affairs of claims and litigations.
In this way, this data is given to the machine to do the work of the Fraud department for you, and tell you which customer behavior is of little concern.
Sentiment analysis is one way here you should have a glance at. Sentiment analysis is the function of oral language processing yet along with text analysis, computational linguistics, and biometrics to systematically identify, elicit, quantify, and interpret affective states and subjective information.
This is a sort of information that provides you with access to more accurate predictions through machine learning. In addition to this, modeling is done to get new customers that called customer acquisition.
