According to a study reported by the Harvard Business Review, the cost of acquiring a new customer can be 5 to 25 times higher than the cost of retaining an existing customer.
Frederick Reichheld, creator of the Net Promoter Score, also found that increasing customer retention rates by 5% can improve total revenue by 25% to 95%.
In a particularly competitive context such list of albania consumer email as the one in which companies operate today, loyalty is a particularly relevant aspect and the customer churn rate is one of the most significant risks that can be encountered.
Dropout Rate: What is it and how is it calculated?
Churn rate is an important performance indicator as it measures the percentage of customers who choose to stop using a product or service within a given period of time.
As we can easily imagine, numerous factors can influence the churn rate, some of these are directly attributable to the company itself, for example the cost or quality of the product, rather than the assistance service, others are instead linked to external factors such as, for example, competition.
Generally speaking, even if influenced by external factors, the churn rate is still a KPI to monitor constantly .
It can be calculated by taking into account the number of customers who have chosen to interrupt the company's relationship during a given period, divided by the total number of customers in that same period, multiplying the whole by one hundred.
In a context of monitoring the customer abandonment rate, artificial intelligence and machine learning allow you to carry out detailed analyses on the “health” of your customer base and allow you to implement effective strategies aimed at containing the abandonment rate.
Let's find out how.
Customer Lifetime Value - How to Improve It
AI and Customer Churn: Start with Good Profiling
Insights and customer profiles updated in real time are the first step to fully understand your customer base. Artificial intelligence and machine learning algorithms allow companies not only to process huge amounts of data, but also to adopt a proactive approach focused precisely on preventing churn .
Know before you act. Blendee, thanks to sophisticated advanced profiling
systems and dynamic segmentation, allows you to cluster customers based on their life cycle (CLV) .
It is thus possible not only to recognize the most active customers, but above all to identify lost or at risk customers , that is, those who need to be re-engaged in order to prevent them from abandoning.
In addition to the life cycle and real-time monitoring of a user's purchasing behavior, especially within a brand's digital properties, another modality that can be exploited concerns the RFM matrix .
In Blendee, this can also be used as a profiling criterion in order to identify less involved customers to whom to dedicate special promotions or other engagement strategies.
