Increase long term revenue using lifetime value projections for customers

Engaging with customers without understanding their full potential can result in a suboptimal strategy. Businesses often make decisions based on a customer’s current revenue potential and cost, which may overlook those who have significant future growth potential.

This approach can neglect customers who could bring substantially higher revenue in the future. Implementing a robust methodology to assess customer lifetime value is essential for establishing long-term engagement and nurturing relationships.

This strategy fosters loyalty among all customers, encouraging them to return to the bank as their needs evolve. This solution also takes cost into the fold so that profitability in long run can be accessed with accuracy.

The solution integrates machine learning and financial modeling.

1. Machine Learning: ML algorithms are employed to map similar customers and track their journeys. This mapping helps predict future paths for the current customer base and establish their potential future needs.
2. Financial Modeling: Financial models simulate P&L and project profitability metrics related to customer engagement. These models provide financial justifications for the costs incurred to secure future customer engagement.

This solution is designed for organizations aiming to utilize their analytics capabilities to boost customer satisfaction and loyalty. It promises long-term revenue benefits by increasing engagement. Organizations with extensive data and a minimum of ten years of stored data for predictive purposes can effectively predict the lifetime value of their customers.

This solution provides long-term revenue benefits by boosting customer engagement. Organizations that have accumulated rich data over at least five years for predictive purposes can effectively predict the lifetime value of their customers.

Revenue: Enhanced long-term revenue through increased customer engagement over a 2-5 year timeframe.

Cost: Reduced attrition and, therefore, lower acquisition costs are anticipated. This solution aims to strengthen customer loyalty over time.

The model incorporates the following characteristics:

1. Category-specific spending data
2. Credit limit utilization
3. Utilization of product value propositions
4. Payment behavior patterns
5. Risk profiling
6. Income estimation
7.Customers holding multiple products

Clustering and network analysis will be utilized to identify customers with similar characteristics and journeys.

Financial modeling will be employed for this solution:

Offer response rates will be combined with profitability projections to rank and order offers in a way that maximizes profitability.

1. On prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1. The final outputs may consist of customer-level lifetime value.
2. Batch files, including Excel files or flat files.
3. Outputs can be delivered via API requests. Send an API request with inputs and receive a response. This can be integrated into an existing application or a separate web application can be developed.