Increase accuracy of profitability predictions through offer optimization engine

Since there are multiple offers for customers, optimizing the right offer becomes crucial. There are two balancing aspects of optimization – The response of customers towards the offer & the profitability of the offer.

At the acquisitions stage, prospects still do not have any data to give us any indication of their performance. So, they are matched with like customers in the existing base and their performance can be used to get a sense of the reward and risk involved with the prospect acquisition.

In such circumstances, it is helpful to simulate multiple scenarios and choose the scenario with the highest profitability. To achieve this, we will need a scientific framework to determine offer choices that increase the overall profitability of the campaign, without sacrificing customer preference.

This is where the optimization framework comes in. This framework simulates the customer behavior for each type of offer and provides a realistic PNL estimate of the profitability.

The solution is a framework designed to maximize both profitability and customer preferences, aiming to achieve an optimal balance between these two critical metrics.

Inputs:
1. Customer Response Rate: Derived from model or segment-level analysis.
2. Customer Profitability: Forecasted over a 2-5 year period from P&L projections from like-matched customers in the existing base.
Output:
The framework provides a rank ordering of offers from the offer basket, along with profitability projections, indicating which offers should be rolled out.

This solution is designed for teams focused on customer engagement:

1. Who have basket of offers for customers to choose from

2. Have 2-5 years of customer level PnL data which can be used for further projection

3. Have run campaigns in the past and have history of response rates for different offers

Increase conversion of offers: Customers are more inclined to respond to offers that feel personalized rather than generic. Detailed knowledge of customer preferences results in the creation of the right product features.

Indirectly impact customer loyalty: Tailored offers and services foster customer loyalty, giving the company a competitive edge over others.

Enhance business planning using data strategy: Utilize data intelligence to make informed decisions for customer acquisition, thereby reducing future losses.

Revenue: Boost short-term revenue with higher conversion rates for offers and increase long-term revenue by attracting customers who align well with the product.

Cost: Lower acquisition costs for campaigns through targeted efforts and reduce expected future losses based on P&L projections.

This solution can be constructed using the following data:

1. Customer-level Profit and Loss (PnL) data
2. Bureau data
3. Historical customer response rate data

This solution will utilize financial modeling:

By integrating offer response rates with profitability projections, we can rank and prioritize offers to maximize 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 PnL simulation results, including profitability projections for each offer.
2. PowerPoint summaries or automated Excel files.
3. Interactive dashboards.