Increase campaign response and revenue through Machine learning model

At the acquisition stage, organizations only have limited data on customer bureau information, and getting positive responses to offers is crucial for customer acquisition in a competitive market. However, customer needs fluctuate monthly, affecting their response to offers.

As a result, businesses experience low response rates to mass campaigns or segment-based targeting, which fail to consider individual customer behavior. This inefficiency increases marketing costs and acquisition expenses.

Our solution leverages AI/ML predictive models to incorporate changes at the customer level. These features are created from the limited bureau data by extracting various features on ratios of MOM and QOQ changes. These features make the models account for numerous subtle changes in customer behavior.

This approach allows businesses to target customers more accurately and deliver more personalized offers that align with their needs.

The solution entails creating a predictive model that evaluates each customer according to their likelihood of responding to a specific offer.

Advanced predictive models are used to determine each customer’s response probability, considering their unique behavioral characteristics.

Once customers receive a probability score, they can be grouped into different segments to enable targeted actions.

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

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 right product features.

Achieve a competitive advantage through data: When customers receive tailored offers and services, they tend to become loyal, giving the company a competitive edge.

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

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 offers and response data

“1. The final outputs may consist of an offer list with response probabilities at the customer level.
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.”
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 an offer list with response probabilities at the customer level.
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.