Voice&Data: How Data Analytics is different in telecom as compared to the traditional IT?
Neeraj Vyasa: Telecom network operators deal with massive amounts of data and transactions. It is considerably large even when compared to an internet Company (like Google) or a fintech company (a credit card Company). A lot of this data has time sensitivity to it – for example, there could be certain developments in the network and if you are unable to analyze that within a predefined or short span of time, you are either looking for a fault or a network downtime. With IoT connections world over expected to see a massive increase in the next few years, the scale of data produced, and the complexity will only increase further and, hence, analytics solutions which were built for a lot of these web scale internet companies are really not the ones suitable for telecom operators.
Voice&Data: How can analytics provide a competitive edge to Indian Telecom Operators? How Data Analytics is critical for the next phase of evolution of the Indian telecom industry?
Neeraj Vyasa: Analytics in Telecom has rapidly moved from purely reactive to proactive and even predictive. And what is driving this is the rapid increase in data traffic levels as well as proliferation of IoT devices. The next-generation networks will provide ubiquitous mobile communication not only for people but also for connected things. This will add new challenges, such as the ability for the network to handle a massive number of connected devices at low cost and the need for increased energy performance on both the client and network side.
Analytics driven intelligent automation and self-managing network capabilities will become vital with the rise of IoT and machine-to-machine communications, where in some cases mission critical and live saving decisions will made using Network Data Analytics. With rising customer expectations, demanding services, and complex networks, it is critical that telecom operators understand what customers want and deliver it with quality.
Ericsson Expert Analytics platform can help telecom operators in the following ways:
- Predict, prioritize and resolve customer issues
- Automate actions that increase loyalty, ARPU and NPS
- Tailor an experience for every customer
- Break down data silos to get deeper insights
Voice&Data: Will you please highlight some Global examples and Case Studies?
Neeraj Vyasa: T Mobile: With Ericsson’s Expert Analytics platform, T-Mobile is able to drive customer experience to an entirely new level. Ericsson’s platform allows T-Mobile to capture ingested data from thousands of multi-vendor sources, and every nationwide VoLTE call and data session is processed.
- The platform provided the ability to transform previously invisible issues into actionable insights in real-time which means T-Mobile can proactively identify and diagnose customer-impacting issues in their multi-vendor network, which improves the overall experience of the customers and their ensuring customer stickiness.
- EE, the UK’s largest mobile operator and part of the BT Group, has selected Ericsson Expert Analytics as its next-generation customer experience management (CEM) system
- The solution is aimed at improving EE subscriber satisfaction, net promoter score, propensity to call and first call resolution rate
- The solution will support multiple services, including 2G, 3G, 4G, VoLTE and VoWiFi, and will be supported by Ericsson Managed Services
Voice&Data: What is Ericsson’s approach to Data Analytics?
- Analytics has been a key focus area for Ericsson for quite a few years and with the growing complexity of the network and with the advent of Data Analytics, there is a renewed focus on analytics within the company.
- While there are pieces of analytics built into our product and solutions, we also have a dedicated solution called Ericsson Expert Analytics (EEA). It has been designed to Improve customer experiences and drive new revenue with a real-time, end-to-end telecom analytics solution that offers unique insights and closed-loop actions.
- EEA measures perceived customer experience for each individual customer based on correlated metrics and events from network nodes, probes, devices, OSS/BSS and other sources. EEA allows operators to predict, prioritize and resolve customer impacting events, as well as to retain and upsell customers based on customer experience and customer behaviour profiles.
Gyanendra Mohan Rashali