Telecom operators understand that it is no longer enough to offer high-speed data and good quality of service. Their customers regard these as standard services. Operators continue to lose customers to digital players and are being forced to venture into digital services, in which operators have little experience and meagre advantage over any other investors. There is now intense competition from service providers whose business models are founded on a differentiated customer experience and frictionless services.
In response, operators have made customer experience management (CEM) a strategic priority. To succeed, however, CEM needs to be data-driven, with operators going way beyond building accurate customer profiles and digital services. They must establish context aware digital journeys that are appropriate for the individual customer—and build rich data sets, which they can then monetise.
To deliver such a strategy, operators need integrated, consistent, and, most importantly, easily accessible data. With this information, they can offer tailored digital journeys that respond to specific needs. A customer who grew up in the digital era may opt for a digital-only journey, while an older customer may prefer occasional telephone calls. Similarly, corporate customers will want administrative rights to manage their employee accounts, while young professionals are happy to converse with chat-bots.
Too often, however, operators find their efforts are hindered by legacy processes and systems, and a corporate culture that is insufficiently agile and collaborative. Nevertheless, operators do have many things going for them. They have strong capabilities in technology and long-standing relationships with customers, yielding a wealth of data about subscribers and interactions with them. Most importantly, they have an established network of customer channels.
The first step to progress is to secure the foundation. Operators must remove duplication and inconsistency from customer data, and overhaul their information management systems. Operators can only consider carefully expanding their pool of insights derived from their many interactions with customers when they have this consistent data available.
Simply investing in the latest technology to collect customer data and formulate insights and actions will not yield results unless there is a clear purpose. The investment should be focused, based on the eventual commercial uses that the operator has decided to target and the data sets that it needs to expose.
One example of such a commercial use case would be to employ artificial intelligence to produce tailored offers. To realise this objective, the relevant investment could be directed towards social media analytics.
In this way, the operator can gain a better understanding of the individuals or organisations which influence the purchasing decisions of particular buyers, so that marketing activities can be oriented around these influencers. Similarly, using the right technology can tell the operator that one particular customer had been subjected to a poor experience, and so should be offered an electronic redemption voucher to increase the likelihood of retention. Another commercial use case is to identify the different products that operators should be pushing to specific business-to-business clients. They can achieve this by comparing the various services requested by a segment of similar corporate customers through investing in detailed analysis of location data, customer industry types, working capital, and other relevant information.
This targeted, experience-based approach yields results. In some cases, operators have increased digital adoption among corporate customers by over 30%, and have greatly expanded their share of the business-to-business market in a selected industry. Operators should use these commercial use cases to inform their selection of advanced technological tools that collect and store exhaustive data on customer responses and interactions. Such tools provide operators with the necessary information to take specific actions and improve customer relationships. They can subsequently assign relevant performance indicators, based on customer perception, to each employee. Such data insights must encourage cultural change through improved visibility on customer priorities and alignment on a common target to serve customers better.
Collaboration between teams is essential if common customer experience targets are to be achieved. Too often, technology and commercial personnel work in silos. The commercial side is often unaware of how newly developed technological tools could help them, while technical experts may not know how to interpret the resulting data to improve customer experience. Instead, working together from the outset to devise solutions that fuse the commercially useful with the technically feasible is a critical customer experience management success factor.