As COVID-19 has forced society to prioritize telecommuting and embrace digital automation, the telco market has been significantly upended. This new paradigm has opened up opportunities for early adopters, but created massive churn risk for laggards. AI represents new opportunities for data-driven telcos to grow market share, offer new services, operate networks more efficiently, and further reduce cost per megabyte.
AI in Telecommunications
- Energy management for base stations
- RAN Upgrade planning
- Capacity forecasting
- Radio hole coverage
Marketing and Sales
- End-to-end personalization
- Optimization and NBO
- Pricing and customer management
- Customer lifetime value
- Product bundling and new product development
B2B and New Business Models
- Data partnership and monetization
- Advanced advertising
- Credit risk rating
- Micro financing
AI Use Cases in Telecommunications
With rapidly changing market conditions, telcos are faced with many new challenges, such as the massive volume of predictive models needed and the frequency at which they must be updated. Add in the increasing complexity of the production environment, top-performing telcos are turning to automated machine learning to force multiply efficiency without compromising accuracy.
Customer lifecycle management is a key component for any telecom player, which is why intent-based machine learning models for customer acquisition, growth, and churn prevention have been developed by DataRobot. The engine has three common elements: a co-located data layer, an insight layer, and a predictive layer around consumers’ behaviors, engagement and preferences.
Mobile RAN Ops Coverage Monitoring
Currently, incident management is reactive, and troubleshooting is based on customer complaints. Following up takes time, as organizations assess the impact before taking action towards a resolution. DataRobot developed proactive incident management models leveraging a network analytics dashboard for anomaly detection and impact insights.
Next Best Offer
With a large pool of customers and wide range of product offerings, NBO becomes a key decision-maker for organizations to drive value for their marketing campaigns. NBO is important for several reasons.
- Pushes right information to the right customer at the right time using the right channel
- Shifts from product to customer-centric focus
- Increases customer loyalty by offering products that suit their current lifestyle
DataRobot Can Help You With:
Execute AI-based decision-making
DataRobot can significantly increase the number of enterprise AI systems that are deployed inside your telecom business to help you gain a strategic focus on the challenges with the largest returns.
Increase AI Adoption
DataRobot is a native AI solution that provides opportunity for all lines of businesses. Guardrails built within the DataRobot platform ensure you can safely democratize AI across your organization.
Be More Personalized with Customer Engagement
With models that continuously learn from past behavior, DataRobot can easily help you understand your customers’ needs and bring you closer to personalization.
Accelerate Data to Value
The end-to-end automation capabilities of DataRobot help you quickly run experiments for your go-to market strategy. Our built-in applications for AI consumers helps to produce the model results on the fly and develop scenarios for preventive actions.