Business Situation
- A leading telecommunications provider sought to enhance customer engagement and satisfaction. Simultaneously, it expected increased revenue and reduced operational expenses.
- To achieve this, the company wanted to create effective personalized marketing offers and optimize promotional campaigns to improve decision-making capabilities across sales and marketing teams.
SGA Approach
Data Processing:
- Gathered relevant customer data from CRM systems, billing systems, network monitoring tools, and customer service interactions, then cleaned and preprocessed the data to ensure accuracy and consistency
Feature Engineering:
- Created features for ML models based on usage metrics, billing history, network performance, and customer interactions
Model Development:
- Trained predictive models (e.g., logistic regression, random forests, neural networks) to forecast the likelihood of customers responding positively to marketing offers
- Used historical response data as the target variable to tailor offers for each customer segment
Key Takeaways
- Personalized offers increased customer satisfaction and loyalty
- Significant increase in conversion rates as campaigns were targeted at customers who were ‘most likely to convert
- This also resulted in an enhanced allocation for high-response segments and led to the efficient use of marketing budgets
- Optimized marketing budgets by focusing on customers with a higher response likelihood
