The journey from CSPs to DSPs: Digital eXperience index
By : Justin van der Lande
Justin van der Lande (Principal Analyst) leads the Analytics and Digital Experience research programmes, which are part of Analysys Mason’s Telecoms Software and Networks research stream. He specialises in business intelligence and analytics tools, the functionality of which cuts across all of the research programmes in this area. In this article, the author explains about things to be considered in the transformation from Communication Service Providers (CSPs) to the Digital Service Providers (DSPs).
The Digital eXperience Index (DXi) for analytics will help communications service providers (CSPs) assess their customers’ current digital experience and plan how they can improve this by leveraging the power of big data to create deeply personalised engagements.
Analysys Mason has conducted multiple surveys and interviews with CSPs worldwide as part of the ongoing research into the progress of their transformations into digital service providers (DSPs). Each CSP has different expectations out of this transformation, but all agree on the pressing need to improve their customers’ digital experience. Improving customers’ digital experience is a massive undertaking that will have an impact on multiple departments and systems.
Big data will play a key role in the successful transformation of CSPs to DSPs. However, many CSPs still do not have the requisite framework or infrastructure to gain insights from customer data. Each department often has its own independent analytics function, thus preventing the CSP from gaining an overall picture.
CSPs must approach the application of analytical techniques from a holistic organisational perspective instead of individual departments designing isolated approach in order to successfully transform into DSPs. Our research for the Digital eXperience Index has identified three key characteristics that will enhance customers’ digital experience from an analytics perspective (Figure 1). Analytical systems will have the greatest impact when customers are able to have highly-personalised engagements.
Figure 1: Top characteristics that will enhance customers’ digital experience from an analytics perspective
CSPs have adopted differing approaches to digital transformation, which has given rise to conflicting views of the ideal process
CSPs are investing considerable time and resources in becoming DSPs and most share a broad consensus on the need to digitise their support infrastructure as part of this process. However, there is no common approach to transforming their underlying network and operational infrastructure.
Each CSP will undergo its own unique DSP transformation which is often dependent on factors such as business priorities, existing support infrastructure and region of operation. The transformation of a CSP into a DSP from the perspective of its customers will mean that they receive a consistent digital experience, comparable to their engagement with other online companies.
CSPs are adopting different approaches to transforming their underlying support systems and there are often conflicting views on the different aspects of the digital transformation journey. A CSP’s ability to extract insights from large data sets and feed these back to improve services is essential to improve customer engagement at a systems level. However, the presence of disparate legacy systems within most CSPs’ support infrastructure severely limits their ability to acquire insights from big data.
The Analysys Mason Digital eXperience Index provides an effective means for CSPs to assess their progress and compare against competitors within the wider market, based on a standard framework.
Robust analytics infrastructure is essential if CSPs are to provide an engaging and fully-digitised experience
Analysis Masons’s Digital eXperience Index research has highlighted that CSPs have different priorities for digitisation (Figure 2). CSPs need to implement a robust supporting framework that will enable them to make informed decisions based on analysis of underlying customer and usage data if they are to benefit most from digitisation from an analytics perspective.
- Churn reduction: CSPs have made extensive use of churn models for over a decade. However, the sophistication of these models has increased dramatically as the number and types of services offered by CSPs and the popularity of OTT services have increased. Future churn models will be more complex and supported by extensive analysis of usage data in near-real time, which demands a stable analytics platform.
- Acquiring new customers: Customer acquisition, especially in developed markets with over 100% saturation, requires CSPs to be aware of clickstream data1 and to understand potential customers’ requirement by tracking this before acquiring them.
- Selling more to current customers: CSPs need to develop contextual awareness to upsell to existing customers – they must know what services customers need and when they need them.
- Cutting costs: Many leading CSPs increasingly rely on analytical models to allocate support teams and network resource to improve utilisation.
Figure 2: Important benefits that CSPs expect from digitisation, October 20152
CSPs must implement automated processes (with use cases driven by business needs, rather than IT functions) if they are to support mass personalisation to improve customer experience.
Traditional operations that require business intelligence or analytics models developed by a central IT department will not scale to meet the requirements of mass personalisation. CSPs must deploy tools that enable business development teams to define broad requirements for different customer journeys, which can then be implemented by automated processes. This will require access to the data in a suitable format, as well as appropriate tools.
The underlying data infrastructure must support real-time data gathering and analysis, and can create actions in near-real time.
Data must be available in real time to provide up-to-date interactions with customers when they are accessing services or systems. The ability to act in near-real time will improve customer experience by enabling CSPs to influence customer journeys as they happen. Changing the routes that customers or prospective customers take will ensure the best possible outcomes.
Personalisation of customer interactions requires complex analysis of large volumes of data – vendors must consider how to support this and if automation is necessary.
Mass personalisation increases the number of potential actions that need to be considered for each customer journey. The necessity of building algorithms based on every customer decision places a high overhead on the teams tasked with creating these algorithms. Machine learning can be used to automate development of these algorithms, based on available historic data and CSPs’ desired outcomes.