Marketers and strategists know it: acquiring new customers is more expensive than retaining existing ones.
According to a study in 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 per cent contributes to improving total revenues by 25% to 95%.
In today’s highly competitive business environment, customer retention is a particularly important issue and customer churn is one of the most significant risks one can run into.
Churn rate: what is it and how is it calculated?
The churn rate, or churn rate, is an important performance indicator as it measures the percentage of customers who choose to discontinue using a product or service in a given period of time.
As we can easily guess, numerous factors can influence the churn rate, some of which are directly attributable to the company itself, e.g. the cost or quality of the product, rather than the support service, while others are linked to external factors such as competition.
Generally speaking, even if influenced by external factors, the churn rate is still a KPI to be constantly monitored.
It can be calculated by taking into account the number of customers who chose to terminate 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 customer churn rates, artificial intelligence and machine learning enable detailed analyses of the ‘health’ of one’s customer base and allow effective strategies to be put in place to curb the churn rate.
Let’s find out how.
I.A. and customer churn rates: it starts with good profiling
Insights and customer profiles updated in real time are the first step to knowing your customer base inside out. Artificial intelligence algorithms and machine learning allow companies not only to process huge amounts of data, but also to take a proactive approach focused precisely on preventing churn rates.
Know before you act.
Blendee’s sophisticated advanced profiling and dynamic segmentation systems allow customers to be clustered on a lifecycle basis (CLV).
It is thus possible not only to recognise the most active customers, but above all to identify lost or at risk customers, those who need to be ring-fenced in order to prevent them from leaving.
In addition to the lifecycle and real-time monitoring of a user’s purchasing behaviour, especially within a brand’s digital properties, another modality that can be exploited concerns the RFM matrix.
In Blendee, the latter can also be used as a profiling criterion in order to detect less engaged customers to whom special promotions or other engagement strategies can be dedicated.
Net Promoter Score and Forms: engines and apps to investigate customer satisfaction
If the data released more or less voluntarily by customers during the navigation or purchase path is not sufficient to detect their degree of satisfaction and thus enable the churn rate to be contained, it is important to design forms and surveys to collect this information.
Blendee’s Form Survey engine makes it possible to create dynamic smart forms that can also be customised according to a user’s browsing context.
A customer who has made a purchase a few days ago in the shop or point of sale can thus be asked not only for a review on the product but also on the service, or a user who has not finalised the purchase may be interested in being asked about the reasons that led him to stop in the purchase process.
All this information can be important to assess possible criticalities and take corrective action before losing the customer for good.
In addition to the Form and Survey engine, Blendee also offers the NPS APP, designed precisely to simplify Net Promoter Score (NPS) analysis.
It allows data to be easily collected and analysed in order to enhance the customer retention strategy and identify possible improvement actions.
Customer abandonment rate: how to intervene with A.I. and personalisation
Evolved profiling and segmentation activities aimed at identifying clusters of users most at risk of abandonment are key to deploying strategies aimed precisely at countering the abandonment rate.
In this context, artificial intelligence algorithms enable really effective personalisation activities of the customer experience. Here are some examples.
A.I personalised recommendation and ‘tailor-made’ offers
Artificial intelligence algorithms and machine learning are particularly effective in offering promotions, discounts, personalised services or products to customers at risk of abandonment. Recommended offers and products can be displayed in real-time or sent via direct marketing tools or ADS campaigns.
Customised loyalty programmes
Another interesting aspect of engaging your customers is the creation of customised loyalty programmes containing initiatives and incentives to keep their satisfaction level with the brand high
Here again, website personalisation and marketing automation activities can be crucial.
Ultra-personalised direct marketing
Another aspect that absolutely must not be underestimated when we talk about the possibility of engaging users concerns the personalisation of communication.
E-mails, SMS, push notifications but also real-time behavioural messages: every content must be designed according to the individual user/customer.
Artificial intelligence and personalisation of the customer experience not only reduce the risk of churn rate, but also help to create a more solid and lasting relationship: a satisfied customer is more likely to remain loyal to the company, make repeat purchases and become a brand ambassador.
