In the last decade the telecom industry has experienced a data explosion thanks to the increase in subscription and voice data records, wireless information, geo-location details, social media and data usage by customers and service providers.
The complicated world of telecommunications analytics is nothing new and telcos have been long term pioneers of big data techniques which began with the building of call detail record (CDR) data warehouses. This data was used to better understand customer usage patterns and make data-driven decisions about how to package subscriptions and packages, cross and upsell add on services and options, and reduce customer churn.
Everyone understands that making sense of big data is the key to winning the battle for customers. Data analytics can deliver powerful insights into what customers want to achieve at every touch-point (channel), make it possible for organisations to maintain traction on customers as they migrate between touch-points, and support the generation of content and promotions that are personalized and highly relevant to customers.
But the big data challenge is set to get even bigger. Catalogues with tens of thousands of product variations are becoming the rule rather than the exception as telcos develop and manage ever more sophisticated and complex service bundles that incorporate devices, voice and data plans alongside subscriptions to gaming, live TV, film, music and video content providers.
What’s more, the proliferation of data sources and types means data no longer fits into neat easy-to- consume structures. Today’s omni-channel telcos enterprises need to be able to handle content, physical data points, processes, inventory, search, streaming data, social, text, mobile, web and more. All of which requires real-time data capture and analysis.
Master data management capabilities are now a ‘must have’ if telcos are to leverage their transactional, operational and customer behaviour intelligence to the max. Having data readily accessible so analytics can run in real or near-real time is critical to enabling intelligence-driven merchandising and the delivery of a highly personalised experience that stays relevant as customers move between channels.
Get ready for next generation digital commerce
At the end of the day delivering an enriched customer experience – regardless of channel – is a primary goal and big data, when effectively managed, can power personalisation engines to deliver better and more relevant content that helps move customers along the buying cycle to transact and convert.
Understanding and visualising how customers migrate between touch-points, and what they expect to do (and achieve) at every touch-point makes it possible to give customers ‘what they want, when they want it’. Delivering a unified and enriched customer experience may also include delivering richer and more consistent product information, seamless transaction capabilities across every channel, and options to access the entire product range regardless of channel; for example by providing in-store kiosks that give customers the option of ordering catalogue items currently not available or stocked in store.
Utilising this intelligence, telcos can deliver loyalty offers that reduce the risk of customer churn, stimulate demand – for example, by offering high data consuming subscribers additional value-add services – and minimise the risk of failing to capitalise on opportunities by redirecting customers to the appropriate channel or storefront for their immediate requirements.
Achieving all this depends on next generation digital commerce platforms that make it possible to implement personalisation rules based on an individual’s behaviours and engagement preferences, generating product recommendations and self-care options that are relevant and appropriate to the immediate engagement encounter. All of this will depend on the ability to connect events captured on the network (usage behaviour) with other behaviours, such as topping up a prepaid account or purchasing a new SIM at the individual customer level.
Recognising and responding to these ‘key moments’ is decisive; creating a ‘brand for life’ long tail relationship with subscribers depends on a telcos’ ability to match relevant service bundles and options with consumer profiles at critical break/renew time points. In other words, achieving retention goals will depend on the ability to provide timely and relevant supported selling and appropriate recommendations in real-time.
But next generation digital platforms will need to deliver far more than customer engagement profiling capabilities alone. Telco operators also need to be able to manage billing systems, pricing, subscription-based services, customer self-care and service bundling seamlessly and in concert, and utilise this capability alongside real or near-time data analytics to match available inventory and recommendations precisely to what customers are looking for ‘right now’. That includes the ability to personalise the customer journey between channels and deliver the appropriate customer self-care options that help reduce churn.
One of the most significant challenges facing today’s omni-channel telco retailer is fine-tuning and managing customer service and delivery in terms of the management of stock across centralised locations and individual stores in order to enable a seamless online order and in-store pick-up scenario.
Instantaneous analysis of demand patterns can help optimise inventory and manage the delivery of back end services while keeping the online channel agile enough to maintain the focus on providing rich sales information and tailored propositions – for example, understanding which customers are music fans to deliver time-sensitive subscription promotions.
In the future big data has the potential to revolutionise proposition management, boosting the ability of telcos to literally ‘merchandise in the moment’. Utilising real-time data feeds from across the operational landscape, including inventory at an individual store level, telcos will be able to use data analytics to improve and fine tune stock allocation and inventory distribution. It should also be possible to use this intelligence to instantly undertake rule-based revisioning of regional websites in order to precisely orientate merchandising and customer propositions to available stock (handset or device) lines and colour options.
Making sense of big data
Implementing a multidimensional view that contextualises the customer experience, delivers new customer insights and creates opportunities to deliver a differentiated experience is the ultimate goal of big data. All of which requires the skilful management of data to support real-time data integration and the generation of predictive customer insights.
Operational adeptness is becoming dependent on an enterprise’s ability to harness the data explosion and gain a real-time view of what’s really happening in the business. Navigating the big data challenge will mean effortlessly undertaking real-time analytics on a huge variety of data sources to gain a deep understanding on how individual channels are performing, the customer conversation (across all channels) as well as individual/geographic trends and preferences, and combining this with operational intelligence to deliver real-time commerce capabilities that maximise inventory and makes powerful proposition management possible.