Traditionally the MNO has applied volume based charging models. Voice services are charged per min. Messaging services charged per message. Data services are charged per bit. The existing trajectory of the MNO will only result in possible market consolidation as seen recently with T-Mobile and Sprint in the US.
The MNO has reached equilibrium in terms of growth in its traditional business and must transform itself into a business where it is operating higher in the value chain. Let’s take look at the use of Artificial Intelligence (AI) in enabling process automation in existing operational processes and creating new digital products and services to put the MNO on two trajectories.
Traditional business for the MNO represents a large share of revenues and eroding margins are forcing operators to grow their data traffic. With increasing smartphone penetration, longer battery life and higher processing capacity, coupled with progress in cloud storage, the MNO’s strategy is to invest in new technologies like 5G, Cloud Computing and Artificial Intelligence (AI), to enable a new set of digital products and services and to operate higher in the value chain. The proposed initiative uses Artificial Intelligence to extract a higher value per bit and implement value based charging models and transform the MNOs business.
Strategies enabled with AI and other technologies
The first trajectory is to use technology to reduce operational expenditures and play a cost leadership role in its traditional markets where access to services is based on volume based charging models.
The second one is to offset the revenue erosion from the first trajectory and use technology to introduce new products and services to create differentiation in the market. In the process of achieving this, the MNO is looking to extract further value from the data over its networks and create new value based charging models. In that case the MNO is playing the role of a digital transformation enabler by providing data to its customers as part of their own digital program.
Technologies used to achieve the proposed initiative
To achieve the proposed initiative, the first point to note is that AI alone is insufficient for the successful implementation of the program. Complementing technologies like
1- 5G to achieve large scale and cost effective connectivity for devices
2- Cloud storage to format, classify and store large volumes of structured and unstructured data.
3- Cloud computing for cost efficient application enablement eliminating network complexity.
4- And BIG Data
Operational Processes that will benefit from AI
The operational processes that are used for OPEX reduction are :-
1- Customer Retention: Machine Learning is used to look for trends to predict the moment when a customer is about to churn and the result is used in a loyalty campaign to incentivize the customer.
2- Customer Care: Machine Learning predicts customer needs and analyzes standard responses to customer care queries. Natural Language Processing is used to analyze natural language data in a traditional customer care service and provides relevant answers/suggestions.
3- Network Quality: Machine Learning is used to analyze network service quality data, user experience data and customer care data to configure the network for optimum performance.
Drones are used as a robot platform to measure parameters like signal strength, handover success and call attempts to tune network parameters in real-time for optimum coverage and radio quality.
In all of the above, it is important to realize that the MNO enjoys a trusted relationship with its customer and if AI is used responsibly, the MNO can be a more attractive brand against the OTTs who are facing challenges with regards to the use of data.