Customer Churn Prevention
Müşterileriniz sizi, sizin yüzünüzden terk ediyor olabilir!
Lots of companies recognize the power of a customer-centric operational model. In good faith, they launch projects to mobilize data and improve customer experience across multiple touchpoints.
Ancak, silo halindeki müşteri deneyimini iyileştirme girişimleri nadiren iyi sonuçlar üretir. Belirli bir departman tüm temel KPI’larını karşılayabilirken, genel ölçümler çok az gelişme belirtisi gösterebilir. Bunun nedeni, müşterilerin işletmelerle etkileşimlerini bütünsel olarak görmeleridir. Faturalama departmanınız sürekli hata yapıyorsa, satış deneyiminizin mükemmel olup olmadığı önemli değildir.
Trying to win back customers after they leave you will cost much more than the effort you put into retaining them. Every customer has different expectations, and it is critical to comprehend what these expectations are based on the clues from data and design an experience accordingly.
After realizing all these, it is necessary to notice that customers are starting to diverge before they churn and to plan artificial intelligence-supported actions to win back them. These actions are effective practices that will differ from customer to customer; but alone is not enough. It is necessary to solve the root causes that cause this divergence to solve the problem fundamentally.
Would you really like to reduce customer churn?
You are late to catch the changing behavior of the customer.
Predict what the customer's next action will be.
Process all your customer data (online and offline) on a single platform.
When the data is ready, you can proceed to the prediction phase.
The fact that your forecasting models are easy to install, flexible and automatically updated increases your analytical success and reduces your operational burden.
What would you do if we told you which customers you will lose next month?