Machine Learning Use Cases in Marketing
simMachines supports use cases for marketing spanning customer lifecycle predictions, dynamic predictive customer segmentation, customer experience management, sales forecasting, trending and analysis.
Large international telecommunications provider needed to reduce churn for pre-paid phone cards.
simMachines deployed dynamic predictive segmentation to enable proactive churn prevention.
- simMachines created predictions of who was likely to defect based on past defectors. Every prediction’s Why factors are different.
- We then created clusters of similar predictions. Each cluster can be assigned a corresponding action.
- In the case of this example, the prediction is based on the customer having a family plan but they are divorced with no children in the household.
- The corresponding action for this prediction is: “Offer this customer an individual plan.”
30% reduction in churn amongst contacted customers
Sales Forecasting and Customer Lifetime Value
Specialty retailer needed to calculate the future sales and lifetime value of customers.
Client wanted a technology solution that could learn and adjust over time, as well as accurately predict and classify customers based on limited data.
The solution predicted future purchase volume and net income, as well as classified the customer base into groups based on value and the associated “Why” factors that define them.
Predictions with 95% accuracy compared to ground truth demonstrate that the algorithm can provide an automated calculation of a new or existing customer’s future purchase volume, net profit and LTV.
Machine Learning Fraud Detection
Top 3 financial institution wanted to speed up ability to implement ML fraud detection solutions for e-commerce clients, enable continuous learning, and expose the factors driving the fraud.
ML methods did not continuously learn or expose the Why factors. Increased competition was creating a need to upgrade speed and service value to client.
Ensemble approach using gradient boosting and similarity-based machine learning.
70% increase over current fraud detection performance. Time to implement reduced from 6 weeks to 2 weeks. Continuous learning enabled and the Why factors reported/available behind every prediction.