As engineers, we may know the data, we may master its complexities, but do we believe its potential has ever been truly recognised, let alone realised?

Does CPE data really matter so much? 

The boom in video streaming services, video conferencing and gaming has brought the role fixed-line networks rapidly back to prominence, with higher-than-ever expectations of service quality. Customers naturally judge the quality of their network provider primarily through their day-to-day experience with WiFi-connected devices. It is the performance at the network edge – and beyond into customer premises through WiFi – that has the most direct and regular impact on users’ key experiences of streaming services, phones and tablets, gaming consoles and computers. This performance IS service quality from the customer’s perspective.

Network operators have typically had little visibility and even less control over how this network edge performs, despite its critical influence on perceived service quality. Yet operators abdicate responsibility for this at their peril, as customer perceptions of service quality are unlikely to be influenced by any artificial boundary between their responsibility and the network’s.

Harnessing this data could transform the way network operators think about their networks – from outside, looking in rather than inside, looking out - adopting the customer’s viewpoint rather than the operator’s as their main focus.

The need for engineers with CPE expertise

Of course, data analytics and AI are not new for network operators, in fact the telecoms sector has been among the pioneers of these technologies worldwide. Most have already built large-scale, often cloud-based, data infrastructure and established analytics teams with expertise in AI and ML.

Effort was initially focussed on commercial and financial data, where the models were more intuitive and the data easier for data scientists to understand. However the application of analytics to engineering data has been accelerating more recently, and specialist engineers need to play a far more significant role in these projects to guide problem definition, data selection and intelligent interpretation of results.

In stark contrast, CPE data remains largely unexplored by most organisations. Its status as being ‘outside’ the network, with CPE beyond the control of network engineering, has naturally made it less of a priority historically. CPE has perhaps often seemed to be the ‘poor cousin’ of fixed-line engineering and of network data sources, neglected and under-appreciated.  Yet it holds the key to understanding customer experience, and in the future it could emerge in a central role – the central role – of service quality management.

Specialist CPE engineers will be crucial to this effort, it will be impossible to master the complexity of a heterogenous installed base of devices without their insight. Their expertise will be invaluable in translating a complex web of machine parameters into meaningful understanding of customer experience drivers, and in designing automated detection and correction routines.

This creates an opportunity to forge a new and rewarding partnership between engineering and customer-facing business functions that will elevate the importance of CPE engineering and deliver fundamental business and customer benefits.

Conclusion

It’s a tragedy that the data to truly understand service quality and identify, anticipate and even fix many service quality issues is already available, yet still largely neglected. While CPE engineers understand this data in detail, its true value and even existence often isn’t clear to leaders in customer facing or commercial roles. As a result, the potential value and importance of sophisticated CPE engineering goes unrecognised, and customer service suffers in consequence. As the only people close enough to the data to grasp its potential, it must fall to CPE engineers to initiate the dialogue that will elevate the importance of CPE data analytics to the central role in service quality management.

How do I get started?

The ‘black box’ image of data analytics and AI may seem daunting, but in our experience engineers learn quickly and work exceptionally well with specialist data scientists.

Axiros can help. We pride ourselves, not just on being the leading producer of ACS systems, but also on our commitment to help our clients to get the most from them. Like you, we understand CPE engineering in depth, and our AXTRACT module already makes the data available for connection to your existing data infrastructure. We’re also familiar with the technical complexities of AI, and with the human and organisational challenges of effective innovation. Our analytics advisors can help guide you through the pitfalls and offer valuable insights where you’re less experienced.

Contact us, and we’ll be happy to share experiences in CPE analytics and discuss the possibilities in your organisation.  

And most important, forward this post to a colleague from the commercial side of your business who might really benefit from a true understanding of customer experience. Start an internal conversation and help them to discover what you can see they’re missing.

From this blog series: