Customer Lifetime Value Prediction
Customer churn and lifetime value (LTV) are the main metrics for the customer retention strategy. The article below describes key use cases which Predicty.AI can handle.
Customer Lifetime Value is the amount of profit or revenue a customer generates over his or her entire lifetime. To estimate user value and planning marketing it’s very important predict customer lifetime value.
CLV is the predicted sum total of all future revenues (or profits) that a particular customer will generate for a business. Using accurate estimates of LTV as the basis for marketing decisions will maximize the company’s revenues (or profits).
One of the major uses of CLV is customer segmentation, which starts with the understanding that not all customers are equally important. So, CLV-based segmentation model allows the company to predict the most profitable group of customers.
In order to predict customer lifetime value we should prepare data for training our models and upload to Predicty.AI.
See how to prepare data for LTV prediction.
How to predict Lifetime Value
Understand key metrics of Customer Satisfaction
Most companies measure customer satisfaction, and while it’s a very important metric, it looks backward at events that have already happened (“How would you rate the customer service you received?”). But what if you could predict in the moment how likely a ticket is to receive a good or bad rating from the customer, allowing your agents to take action to ensure a positive outcome?
That’s the intent behind which applies machine learning and predictive analytics to determine whether customers are at risk of churn, prioritize routing based on customer risk, and guide agents to handle interactions more effectively.
How to predict Customer Satisfaction
Predict Customer Churn
Churn prediction usually chasing three goals : detect the key factors of client attrition, identify the clients most at risk of leaving, and provide targeted insights on which retention actions should be implemented.
Data Insight: data sources can be used on top of the transactional CRM data (profile and purchase history) to improve the performance of the predictive models – service usage, client feedback and customer service requests it all can be revelatory of customer churn.
Data Analytics: advanced data science techniques are often needed to make sense of these different data sources and build smarter models that will take into account seasonality, dynamic user segments, and evolve with the company’s catalog, business processes and client base.
We offer advanced statistical learning to predict customer churn in Telecom segment.
How to predict Customer Churn
See how to prepare data for customer churn prediction.
