Strategic Forecasting of customer Lifetime value.pptx

for mca students

1. Strategic Forecasting of customer Lifetime value : Enhancing Business Insights Through Predictive Analytics Name: Daddala sai sri lekha Roll no: 238x1f0038

2. Abstract: Customer Lifetime Value (CLV) is a critical metric for businesses to understand long-term revenue potential from individual customers. This study leverages predictive analytics to enhance CLV forecasting, enabling businesses to make informed strategic decisions. By integrating data-driven techniques, organizations can better allocate resources, optimize marketing strategies, and enhance customer retention.

3. Existing System: The traditional approach to customer lifetime value prediction relies on historical transactional data and rule-based analysis. Companies often use simplistic models such as RFM ( Recency , Frequency, Monetary) analysis, which lacks predictive power and fails to incorporate external factors like market trends and customer behavior . Disadvantages of Existing System: Limited predictive capabilities. Inability to adapt to dynamic market conditions. Lack of personalized insights for customers. High dependency on static historical data. Inefficient resource allocation due to inaccurate forecasts.

4. Proposed System: The proposed system integrates advanced predictive analytics and machine learning models to enhance CLV forecasting accuracy. It utilizes real-time data processing, AI-driven customer segmentation, and dynamic trend analysis to improve forecasting . Advantages of Proposed System: Improved CLV predictions through machine learning algorithms. Real-time analysis of customer behaviors . Enhanced personalization for marketing and retention strategies. Adaptive models that consider external factors such as economic conditions and competitive landscape. Better resource allocation and optimized decision-making.

5. Flow chart for Remote User

6. Flow Chart for Service Provider

7. System architecture

8. Thank you sir