How Artificial Intelligence and Machine Learning can improve customer retention
As per HBR, the cost of acquiring a new customer is five to 25 times more expensive than retaining an existing one (Source: https://hbr.org/2014/10/the-value-of-keeping-the-right-customers). Machine Learning can be used to forecast which customers are at risk of churn and hence target them better. There’s no denying machine learning, as it is, already plays a major role in customer experience. And the prospects are even brighter. Here are some ways by which Machine Learning can improve your organisation's customer retention.
Predicting Customer Churn
Forecasts can be used by each company differently depending on its business needs. For example, a company that is spending too much out of its marketing budget trying to keep its existing customers can better use its resources to target only the at-risk customers. Identifying the factors that affect churn can provide valuable insights to businesses. For example, maybe the customers who have churned did business with your company only because of the huge first-time discounts offered and then left as soon as the deals expired. Spending marketing resources on these customers might not be beneficial or impactful.
Analysis of text data from feedback, complaints, remarks, or comments are very useful to identify the causes of dissatisfaction of customers before these dissatisfactions escalate.
Sentiment Analysis is a popular method to gauge not only the overall sentiments of customers but also specific aspects on which customers have said sentiments. For example, in a customer’s review of a restaurant, they might feel very happy about the food but negatively about the service, ambience and prices. Aspect Based sentiment analysis adds this extra layer to the traditional (positive/negative) sentiment analysis.
Information is key. Companies focused on customer experience are using state-of-the-art Machine Learning models to keep their customers informed on the status of their delivery. From the moment an order is created to the exact time, it reaches the doorstep of the customer. This flow of information is helpful in case the initially communicated deadlines can fail to be met.
What hurts more than a delay is the lack of information on the delay. Imagine you have taken a bulk order which needs to be fulfilled in 2 weeks. This deadline that you communicated to your end customer is based on the expected delivery date of raw materials from your wholesale supplier. Now the day on which you are expecting the raw materials to arrive, no delivery happens. Getting impatient, you make a call to the customer service executive, who puts you on hold for a very long time while she checks the status of your delivery, only to retrieve the information which you already knew (but didn’t want to accept) that your delivery is delayed and will not arrive on time. Not only does it hamper your operations and planning but also the relations with your end customers.
Timely communication of the delay could at least save the embarrassment and help you plan your operations better (like planning a backup solution).
Forecasting of Order-Patterns
Stock-outs can be really annoying and inconvenient for any business. Imagine having your child’s favourite toy getting stocked out right before the holidays. Such a pain! Now imagine such stock-outs happening for a small-scale business and that too more than a few times in a year. There is little reason for this small business to still stick with the same wholesale supplier who disappoints him so often.
Stock-outs have enormous costs for customers and eventually, a customer would look for alternate options if this issue is not handled critically. With Time-Series forecasting, it is possible to predict with great precision when will a customer make her next purchase and how much is she expected to buy. With this information, stock-outs can be reduced, plant operations or procurements can be planned more efficiently and eventually we can tackle the problem of customers looking for alternate options because of the unavailability of products.
Analyzing the historical data to identify patterns in what makes churning customers different from retaining customers. Were there any particular products, regions, plants or segments of customers which had more churn? This information can be used to do a Causal Analysis. This is an iterative process and must be done regularly.
- For example, for certain unhappy customers (which we got to know through Feedback data and Complaint data), we analyzed their historical transactional data and found that most of their delayed deliveries were the ones made through the same carrier.
- As a follow-up step, we then checked all the deliveries done through this carrier for all other customers as well (who had made no complaints yet).
- The results were quite dismaying. Out of all the deliveries made via this carrier, over 60% of them ended up getting delayed. This was an eye-opening insight for us to act on. A timely change of carrier saved us from getting complaints from those remaining customers and we could make delivery reliability efficient again for all the affected customers. Although the analysis was done on historical data, it definitely saved us from embarrassment in the future.
Sharcx offers cloud-based analytical services to support B2B supply chain & customer service teams to systematically improve their delivery reliability & customer satisfaction.
Through automated data management, Sharcx allows you to start acting by focusing on crucial information, while identifying critical customers, routes and cases.