Increase customer revenue proactively through AI-driven offer recommendations engine

Most businesses adopt a reactive approach to customer needs, responding with the best offers only when customers request them or through mass campaigns.

The issue with this approach is that when customers need financing, they often seek offers from competitors and choose the best available quote. This forces companies to either increase costs to counter competitive offers or lose the business to competitors.

With many offers like CLI, Spend Growth, Balance Growth, and Upgrades customers can be engaged at different points in their journey to increase profitability.

Our solution adopts a proactive approach by identifying customer needs in advance, allowing businesses to pitch the best offers ahead of time. This strategy places the offer in the customer’s mind early, increasing the likelihood of customer loyalty and satisfaction.

This solution involves a rule-based/ML model designed to predict the best product for a customer from a basket of products.

Model Inputs:
1. Changes in Category Spending
2. Utilization of Credit Limit
3. Utilization of Product Value Proposition
4. Payment Behavior
5. Lifestage Requirements
6. Behavior of Similar Customers
By considering these characteristics, the model scores the basket of products and selects the best product that meets the customer’s needs.

This solution is designed for business leaders or teams who want to:

1. Already do data-based reactive targeting and want to reach the next level of proactive targeting

2. Focused on customer-centric approach and wants to invest in it

3. Have historical data for at least 5 years of offer targeting and responses

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

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

Enhance business planning using data strategy: Transition business planning from a broad level to a customer segment level, enhancing productivity and providing more precise forecasts of revenue and costs.

Revenue: Boost revenue with higher conversion rates for offers. Customers who respond to offers once tend to respond more frequently. Developing customized, behavior-based offers increases the likelihood of response and long-term revenue.

Cost: Lower costs for campaigns through targeted efforts. Achieving similar or higher conversion rates by focusing on a smaller customer base can result in a more efficient cost structure. The saved costs can be redirected to enhance offers for customers.

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

The following models can be employed:

1. Classical logistic regression models
2. Gradient Boosting
3. Extreme Gradient Boosting (XGBoost)
4. Support Vector Machine (SVM)
5. Random Forest
6. Ensemble models
The best strategy is to test various models and choose the one with the highest performance.

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 a list of products with customer-level rankings.
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.