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The customer's needs must be the starting point for any digital offering – and at the latest since the beginning of the pandemic, this has become not just a “nice to have” but a true “must” for telecommunications providers. Precisely on a saturated market, where various providers’ offerings seem very similar, new customers can be acquired almost exclusively by enticing them away from the competition and customer loyalty is declining rapidly, so it is critical to offer an outstanding customer experience and appropriate content at all times.
Given their experience with other platforms, shops, and services, customers today are used to having control and flexibility with respect to contracts, services booked, and the channels and devices they can use to access services. CSPs still have to play catch-up in this regard. For example, a custom-tailored increase of the data volume for mobile phone customers is not always possible. Other up- and downgrades are frequently also bound to rigid contract dates. In Germany, the latest update of the telecommunications law (Telekommunikationsgesetz, TKG) that grants telco customers more rights emphasizes that this is not user-friendly and especially no longer in keeping with the times.
Of course there are a lot of starting points on different levels to change this situation, increase customer satisfaction, and thus keep existing customers and acquire new ones. Especially promising are those approaches that are based on actual user data and start where customers actually “live.” This is where customer journey analytics come into play. The basis for this:
The above-mentioned tools "data lake" and "data warehouse" enable companies to combine raw data from different sources into a central repository for analysis. Their concepts vary in their requirements and application cases, but they serve the same purpose: to detect patterns and predict probable results.
The user data gained and analyzed can be deployed to display content and offerings for customers that are relevant and interesting for them right now – and only these. Here, personalization can be accomplished on different levels:
The goal: To enable customers to get the information and perform the actions they desire with as little effort as possible. All of this contributes to a pleasant customer experience.
Trends and patterns can be detected and anticipated using data mining, machine learning, and predictive analytics, and measures taken on this basis:
Here too, data lakes and/or data warehouses can serve as a source of information. Only by aggregating and analyzing all the data using relevant questions can one make reliable predictions about the potential or expected behavior of customers. By using these technologies, CSPs can detect entrepreneurial opportunities faster and secure a competitive advantage.
The basis for such a design of the customer experience is ideally a central platform solution, which is able to connect all relevant data sources and systems via interfaces and connectors, while although being powerful enough to work with the required quantities of data.
This can be optimally implemented as part of a composable business model. Not just the technological solution itself is modular and flexible here. In addition, the company works on the basis of agile principles in flexibly combinable, quick-acting units.
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