Increase marketing campaign revenue by focused targeting through predictive ML models

Responses to marketing campaigns on average vary between 2%-5%. However successful campaigns can generate a 10%-12% response rate. Various factors make campaign response above average, and one of the most important is the personalization of offers.

Since customer needs fluctuate very often, it becomes important to identify them and focus our resources on responsive customers. This helps reduce marketing costs on false positives and yields higher revenue.

To correctly understand the responsiveness of customers, a machine learning model is necessary to capture subtle changes in customer behavior like changes in category spending, changes in overall engagement, and changes in contact patterns.

Certain changes can be captured by changes in averages while others need ratios or measures of deviation from average. Our solution leverages AI/ML predictive models to incorporate changes at the customer level. These models can account for numerous feature changes and are sensitive to subtle shifts in customer behavior.

This approach enables businesses to target customers with greater accuracy and deliver more personalized offers that align with their needs.

The solution involves developing a predictive model that scores each customer based on their likelihood to respond to a specific offer.

Advanced predictive models are employed to calculate the probability of response at the customer level, taking into account the behavioral characteristics of individual customers.

The model takes in thousands of engineered features and detects customers who have previously responded to offers and learns from their behavior, then it uses this learning to predict customers who can respond in the future.

Once customers are assigned a probability score, they can be segmented into various buckets to facilitate targeted actions.

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

Create automated processes for targeting:
These processes help streamline operations process, reduce manual errors, and faster implementation cycle.

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 engagement.

Increase conversion of offers:
Customers are more likely to respond to personalized offers rather than mass offers. Understanding customer preferences in detail leads to the development of the right product features.

Revenue:
Achieve increased short-term revenue through higher conversion rates for offers and long-term revenue by improving customer engagement.

Cost:
Reduce marketing costs for campaigns through focused targeting and lower 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. Transaction data
4. Customer service data

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.a
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.