Decreasing customer effort
The greater a customer’s effort in resolving fixed or mobile network problems, the greater the negative impact on satisfaction and NPS. With Autonomous Customer Care customers benefit by:
- Having issues resolved quickly, with fewer steps and aggravations
- Less personal effort in managing many aspects of their subscriptions
- Pre-emptive resolution of network service issues – before they are even aware there’s a problem
- Pre-empts "silent churners", customers who do not complain but will churn based on a poor service experience.
Decreasing operator effort and costs
Enabled by machine learning and AI, interactive ‘bots’ can support operator CSR’s (augmented care) and will learn to take on direct customer care interactions (autonomous customer care). By seamlessly combining human and machine intelligence, human intervention and OPEX are reduced:
- CSRs work faster and put their skills to better use by focusing exclusively on complex issues or by providing premium technical support
- Decrease time spent on high call volume, low complexity issues
- Increase agent compliance to best practices
- Accurate, speedy issues resolution avoids rolling service trucks or shipping needless replacement equipment
This represents a massive impact to the contact center OPEX and Customer Satisfaction for fixed, mobile and converged CSPs.
Cutting Edge Technology
Digital transformation brings artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) together, allowing digital service providers to significantly automate customer care interactions and reduce customer effort.
- Initial care interactions with IVR enhanced through AI and NLP; better understand the customers intent when making that first call for help
- Machine learning enables development of Dynamic Intelligent Workflows to adapt to unique customer context; and over time, to develop custom paths for resolution
- Nokia Bell Labs algorithms build on workflow history, customer information and network status to select next-best action (NBA) for an agent to use in that specific customer situation
Leading Edge Predictive Analytics
Backed by Nokia Bell Labs innovation, Nokia Autonomous Customer Care provides continuous improvement of the troubleshooting process through analytics and machine learning
- Analysis of data captured from the network, CPE and trouble tickets allows the development of algorithms to better predict service disruptions and to take proactive actions to address issues
- Bell Nokia Labs have developed an algorithm that correlates customer ticket records with network topology and services, allowing real-time detection when outage spikes are starting to happen
- Proactive bots predict service-affecting issues, recommend solutions and execute proactive actions to fix them without any interaction between the customer and traditional support