How CSPs Can Get the Most from Their Data


Improving customer experience, developing sensible new products, and increasing customer loyalty – these are the important goals for telecommunication providers, and they have one thing in common: Essential prerequisites for achieving them are data and data analysis. Data forms the basis for a better understanding of customers, their behavior, and their current and future needs.

This means that to increase revenue, communications service providers (CSP) do not have to think about big or complicated matters. Instead, they must start with what they already have, for there is enormous potential in the information they already have at their disposal.

The Forrester Consulting Thought Leadership Paper Commissioned By Adobe (February 2021) also names customer relationship management (CRM), customer desires, and E-Commerce transactions as important sources for customer analytics:

What CSPs must do: Collect data centrally, enrich it, and analyze it. The right tools and technologies for this already exist. For example, data mining, machine learning, predictive analytics, and artificial intelligence (AI) enable the detection of trends and patterns in existing information – and the anticipation of these as well.

Or, as the columnist and Internet expert Tim Cole says: “CSPs must use the possibilities of artificial intelligence in order to process some of the existing and anticipated future floods of operating and customer data; to analyze and transform such data into valuable insights that offer better customer experience, improve operation, and increase revenue thanks to new products and services.”

But what specifically do companies need to use data profitably – and how can CSPs best proceed?

Acquiring Information: Collecting & Enriching Data

Important prerequisites for data analysis that telecommunications providers are frequently lacking are clean data management and the networking of all data silos. A study by Omdia and Adobe (Customer Journey Optimization: The Key to Relevant Engagement, February 2020) of 300 media, entertainment, and communication companies indicates that the lack of networked data is often a hurdle for coherent customer journeys.

Normally, data warehouses or data lakes are used for data management; these are suitable for the analysis of large quantities of data. The heterogeneous data – whether it is customer and contract data, orders, support inquiries, or something else – is frequently initially collected in heterogeneous systems and therefore must be aggregated for evaluation. This is a time-consuming step, especially if it has been neglected previously.

Attacking this is worthwhile, however, since insights relevant to business development can be gleaned this way, and predictions and hypotheses developed and checked; in turn, this can result in new insights and improvements to various products and services.

The challenges here:

  • Generally, analysis poses high requirements regarding storage space and computing power. However, with a strategic deployment of cloud infrastructure, these requirements can be managed.
  • Especially with the use of resources outside the European Union (EU), one must comply with requirements for data privacy and security (e.g., EU GDPR, Privacy Shield).

Even more, insights can be gained from this data if it is enriched with demographic data or segmented using specified criteria. For this, telecommunication providers rely on secondary providers such as Acxiom or Schober Direktmarketing.

Customer Journey Analytics: Profiting from User Behavior

Where can information about customer behavior be acquired, if not from the customer journey? Anyone who wants to analyze and learn from customers’ needs and desires cannot escape customer journey analytics. Here, the customer experience is examined across all touchpoints of the customer journey, that is, across various channels, for example.

It is especially effective to evaluate the data from customer journey analytics, CRM, and co. in real time and to use it to react directly to users’ behavior.

The study by Omdia and Adobe also proves this, for, among other things, it reaches the conclusion that the visualization of the customer journey alone is no longer sufficient. Companies must react in real-time and predict customers’ next steps proactively, understand which channels they intend to use, and be prepared for this with relevant content, offerings, and measures.

Artificial Intelligence: Thinking Ahead with Data

Naturally, this also requires the appropriate technological bases. For telco providers, it is therefore recommended that they invest in appropriate systems, namely those that provide them with AI-supported decision-making for the orchestration of customer journeys.

For using AI, it is possible to gain even more insights about users – also from data from the customer journey. For example, customer journey maps can be equipped with machine learning algorithms to detect patterns in customer data from various sources. The goal is to observe and learn which customer interactions and behaviors produce the most valuable customers. For example, how users switch between channels or how they interact with messages. It can be helpful to assign a customer behavior to an appropriate persona in real-time. Combined with AI, this even allows the automation of processes.

Important is that AI is embedded in all applications. Only an integrated ecosystem of AI solutions that is driven by an end-to-end automation backbone that connects a multitude of company systems (e.g., supply chain, IT, finance) with one another will produce the desired results.


In the end, the concern for CSPs is always to give the customers what they are seeking at the right point in time. Used and evaluated correctly, data can even help CSPs recognize needs and, with the correct methods and tools, even gain insights and direction for future events. Real-time data analysis, machine learning, and AI are valuable and necessary tools for telecommunication providers to acquire loyal customers and generate sales.

With the embedding of AI into all functional areas, it, therefore, becomes possible to detect problems and potential faster, identify and implement optimal measures – and frequently, even to do this automatically.

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