Cable and Satellite companies in the US have emerged from a decade of acquisitions, consolidation and shakeout and are beginning to assert themselves as full service providers in the communications and media space. With Comcast just announcing its new suite of cellphone plans this month, and Charter, Altice and Dish ramping up their offerings, the Big Three in wireless – AT&T, Verizon and T-Mobile/Sprint – are looking over their shoulders.
Two big domains occupy these businesses – content and wireless. Fiber and connectivity is table stakes in the cable business, and more homes need to be passed by the network in order to persist growth. But no matter how profitable that business, it remains a commodity game, where consumers change at every opportunity they can. Innovations in software defined networking and network functions virtualization will improve the services range (with attractive options in the enterprise sector in particular), but core connectivity remains a relatively predictable business. The question is how to optimise content for premium pricing, and how to snaffle wireless subscriptions from the main players.
All of these businesses are awash with data. They are using that data to optimize operations, troubleshoot operational and customer problems, and understand as much as they can about their customers’ consumption, buying patterns and preferences. Increasingly, winners are using that data independently of core operations, through data and insights centers of excellence, often dedicated to key business challenges like digital transformation and the network cloud.
The impact of siloed data
Ironically, one of the key questions they can ask is ‘what questions can I ask?’! This is because different departments don’t know what data is silo’d in other parts of the business. It’s often the case that departments guard their data, and partner departments don’t understand the data and can’t access it. But network location patterns (for example) can often be a good predictor of potential upsell – for instance, whether a customer is working from home can be determined from network data. If the network data team is sharing the data, great; but does the marketing team charged with upsell understand the network data? Can they interpret what they’re seeing?
These problems can be solved by breaking down organizational and data silos combined with good data governance and security. The idea of Edge to AI is important, but it’s impact is limited if you aren’t breaking down silos and connecting departments in isolation. Network data from the edge can flow all the way through the business to automating network assurance and fault avoidance, but it can also drive AI applications maximising predictive and prescriptive customer experience! Similarly, consumer content consumption patterns – browsing, partial consumption, sharing, network quality – can indicate propensities that can turbo charge the customer experience. Sometimes that data is retained by the service quality team, while the customer experience team is reliant on billing and care data. These silos cause inefficiencies that rob the business of profit through the lack of insight and missed opportunities.
Today, leading CME organizations worldwide are adopting an enterprise data cloud strategy using the open source-based Cloudera Data Platform to manage the end-to-end data lifecycle. From collecting data from multiple sources, to storing, processing, analyzing, serving and predicting to drive actionable insights and use cases, the platform handles both data in motion and data at rest. With Cloudera Data Platform, data can be ingested from a variety of sources (including both streaming and enterprise data sources), enriched and processed across a hybrid infrastructure. Analytics or ML algorithms can be applied to all data, all while maintaining strict enterprise data security, governance and control across all environments.
There are huge opportunities in the North American cable market to grow the base through smart customer acquisition; grow customer lifetime value through portfolio optimization, content library analytics and enhanced retention; and dramatically improve customer experience through predictive modelling and integrated experience management.The secret? It’s all in the data!
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