ECommerce is about connecting with customers over electronic channels and conducting business online on the web portal. ECommerce inherently puts layers of the electronic channel between the customer and seller. These layers of technology often deprive the ECommerce business of the insights around why customers choose to buy and want to buy from them continuously. The ECommerce industry is suffering from the inability to identify, control, and manage the nuances of predictable business online. With the increased budgets on customer acquisition, the rate of subscriber churn is drastically rising day by day. The churn rate analysis in ECommerce is getting hampered due to unawareness about simple leading indicators of customer behavior.

How to identify the subscriber churn? 

Customers leave enough clues in the operational systems of the ECommerce businesses so that they can see the subscriber churn coming. It is easy to understand these customer signals when there are less than a dozen customers. However, with hundreds and thousands of customers, the ECommerce businesses need to employ more sophisticated techniques such as AI and machine learning to calculate the subscriber churn. Whether you are using AI and machine learning or not, following are some of the leading indicators for you to analyze the subscriber churn in your ECommerce business quickly.

Transaction History and Usage Pattern

You can always club the customer personas into similar-looking customer segments. The historical buying patterns within each of these customer segments conclusively indicates whether a particular subscriber is likely to churn or not. This data is nothing but a simple record of sales from the past few days or months or years. The typical demographic segments correlate to subscriber behavior. One example of the subscriber behavior is the subscribers who were only interested in trying out your products or services. Another example could be subscribers who are looking for only a specific level of quality of service or products. This behavioral association with each demographic segment of the customers helps predict the subscriber churn pattern.

Customer Complaints

While transaction data is a bit of indirect and mathematical reasoning on subscriber churn analysis, customer complaints are often a direct measure of identifying a churning subscription. What is important to note is that all forms of customer complaints need to be taken into consideration and assess the propensity of subscriber churn for each kind of customer complaint. One can expand the analytics of churning subscribers into understanding the core complaints that cause the business to lose customers. Such insights are actionable and help in retaining more subscribers in the long run.

Customer Queries

Customer queries are a great leading indicator towards predicting whether the subscriber is likely to continue or not. One example of customer queries is service availability in specific areas; another example could be the offers on particular products and so on. These queries are the clues for the ECommerce business to not only predict a member churning but also identify the potential offers that can retain them. Natural language processing as a stream within AI helps examine each customer query, categorize it as the one leading the subscription into churn mode or not.

Seasonality Data

Many times purchases happen as a matter of seasonality.  An example of a seasonal purchase could be the subscribers from a discount campaign.  After the season is over all the subscriptions acquired as a result of seasonality are likely to discontinue. Businesses should look at a few parameters to reduce subscriber churn. These include the seasonal subscribers, their core triggers to subscribe to the service, their key reasons to discontinue and lastly possible reasons for these subscribers to not stop buying. 

As an ECommerce business, any data that you may have is good enough for you to begin analyzing your subscription churn pattern. Subscription churn is like an infestation to the new age businesses due to lack of careful analysis of customer churn behavior. The massive investments that large ECommerce companies have been doing in AI prove the value of AI in ECommerce.  At the same time, the high investment in AI need not draw us back from the effort to make the first move. Datoin is the AI and machine learning platform with data science service for ECommerce startups and SMBs. Begin predicting the subscription churn in your ECommerce business today with Datoin.