Customer lifetime value forecast with AI
Customer lifetime value forecast with AI
We help you to predict the value of your customers and increase the efficiency of your marketing measures.
Make efficient decisions based on CLV forecasts
Which of your customers are really worth their weight in gold? With AI-based customer lifetime value (CLV) forecasts, you can find out and use your resources in a more targeted way than ever before.
Prioritize target groups that are predicted to generate the most value for your company during their time as customers. This increases the efficiency of your marketing measures.
We are a member of the
From the data to the customized product
From the data to the individual product
Do you want to establish customer lifetime value forecasts with AI in your company? This is how it goes on:
Project Kick-Off
In the first workshop, we work together to gain an understanding of your use case and your business. We record your requirements and define project goals.
MVP
In the next step, we develop a minimum viable product (MVP). Through iterative processes, an AI system is created that serves as a solid foundation for future developments.
Further development
Individual extension
In this phase, we adapt to your needs. Our aim is to end up with a product that meets your wishes and requirements.
30 % more marketing ROI
30 % more marketing ROI
by targeting the most valuable customers
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The challenge
Inaccurate predictions of customer lifetime value (CLV) require more precise analyses, resulting in inefficient allocation of resources in marketing and customer service. Necessary consolidation of customer data from different sources.
Solution
Forecasting customer lifetime value (CLV) with an AI-based model. Use of machine learning and advanced data analytics. Use of transaction histories, interaction data, demographic information and external market indicators to gain insights.
Result
The AI-based Customer Lifetime Value forecasting model provides insights into customer behavior. The company uses this to effectively align its marketing strategies. A personalized customer approach increases customer satisfaction and boosts sales.
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25 % less migration
through precise predictions and churn prevention
Telecommunications
- Increased churn rate leads to loss of sales
- Traditional methods are not sufficient
- High costs for new customer acquisition due to customer churn
- Need to integrate and analyze extensive and diverse customer data
Azure, Power BI
- AI-based model for predicting customer churn
- Use of decision trees and neural networks to identify migration trends
- Use and consolidation of data sources such as user behavior, customer service interactions, contract information, demographic data
The introduction of an AI-based churn prediction system effectively reduces the churn rate and significantly increases customer loyalty. Thanks to machine learning, the company is able to make predictions about potential customer churn. On this basis, it takes proactive preventative measures. This leads to higher customer satisfaction and increased customer loyalty. The use of the AI system also reduces operating costs.
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