Advanced technology like Contextual Decision Intelligence is unlocking the door to a potential 5 percentage point increase in Telecoms EBITDA.

An organization that is not already a math house now, or unable to become one soon, is already a legacy company.

Ram Charan, ‘The Attackers Advantage’

When we think of ‘legacy companies,’ some brands that spring to mind might be Kodak, Betamax, Blockbuster, Woolworths and Toys “R” Us. Once kings of the high street, they now serve as examples of what happens when an organization fails to keep up with digital transformation and become a ‘math house’. Ram Charan’s words suddenly feel loaded. 

 

Is Your Business a ‘Math House’? 

 

Let’s assume that having successfully executed analytics, AI and Machine Learning means your organization is a ‘math house.’ But before you can get the most out of advanced analytics, AI or machine learning, a solid good quality data foundation is required. You also need good data that is operationalized within the business, too.

 

 

 

Underpinning ‘Math House’ Capabilities With Good Data

 

When it comes to industries that are most replete with operational data – like customer, product, and transaction data – telecoms and media are always high on the list, closely followed by banking and retail.

 

The global telecoms and pay-tv sector were worth $1.53 trillion in 2020. The EMEA region accounted for 31%, coming in at $471 billion. According to Oliver Wyman, good data can increase EBITDA by 3-5 percentage points. EBITDA for fixed and wireless business runs at around 7% (globally) – that comes to around $33 billion in EMEA.

 

Taking the mid-point EBITDA uplift number from Oliver Wyman means that 4 extra percentage points in EBITDA are possible from good data. This creates an incremental $19 billions of EBITDA in EMEA.

 

So, if you can underpin your ‘math house’ capabilities with ‘good data’, you could generate an extra 3-5 percentage points of EBITDA.

 

The Journey to Good Data

 

What is Good Data?

Good data is not a destination, but a continuous journey that’s difficult to conquer in absolute terms. Why? Because data is always being created and updated: new customers, fresh transactions, restructures, mergers, acquisitions, mistakes, new products, configuration changes, orders, service requests, incidents, and new infrastructure. The list of factors that continuously reshape your organization’s data fabric is never at a standstill.

 

In the same vein, the quality of that data – its integrity, accuracy, and completeness – is ever-changing. This makes the task of keeping organizational data ‘good’ an impossible one. But managing the inevitable inaccuracies and gaps using advanced analytics can help to overcome these challenges. 

 

Improve the Quality of Your Data with Advanced Technologies

 

Advanced analytics is one way of approaching the inaccuracies and gaps within your data. This can be achieved by leveraging the power of Contextual Decision Intelligence, which is underpinned by three core capabilities.

 

  1. First, entity resolution takes multiple representations of real-life things (think business, an individual, an address, a phone number) which may appear in a customer record, an order, or a transaction and resolves it into a single entity. By bringing together data from disparate internal and external datasets, even where no clear link exists between those sources, you can create a customer 360 view. Further insights can be generated by bringing 3rd party data into play. This might include corporate registry data, watch lists or demographic classification data. 
  2. Second, with network generation, those resolved entities can be joined together in multiple ways. Either by a shared address between individuals or businesses, a common director between businesses or the same bank account. 44% of star-performing analytics adopters analyze graphs and linked data, also combining time series and geospatial data. 
  3. Third, is visualization, which relates to the way in which information is presented. This has a significant impact on how that information is interpreted and the questions it evokes. It could result in more robust hypotheses, and more importantly, could mitigate a business risk. 

Findings from IDC indicate that 38% of leading-edge adopters of analytics and AI integrate a significant amount of external data to maximize business value from their systems.

 

Opportunities to Activate Decision Intelligence

 

While you can have good data and be a math house, you can still miss out on the 3-5 percentage point EBITDA increase if you fail to activate your new decision intelligence within the operational business. 

 

So where are the opportunities to activate and bring in the benefits?

 

Improving the selling/buying process

 

According to Oliver Wyman improving the service and selling/buying processes can deliver around 2% point of EBITDA uplift. 

 

One of the biggest drivers in how people make decisions is influence. People talk, share opinions and post reviews. Influence is huge, especially in B2C and SoHo (Small office-home office), SMB (Small-medium business) segments. The boundary between B2C and B2B is often a blurred one, especially in the SoHo space. In the SoHo/SMB segments it is often the case that businesses share addresses, bank accounts, and sometimes control in the form of directors. These are all influencing factors and levers. This makes for a rich source of contextual decision intelligence and is the prime hunting ground for advanced technologies. 

 

Having a customer 360 degree view provides a holistic view, connecting data across product and service systems to give a clear picture of which products and services they are using today. This sounds like a hygiene factor, but many organizations operate in such a siloed manner that they continue to lack this single view of customers across product lines. Entity resolution allows the connection of this data to create a customer 360 degree view, enriched with external data.

 

Instead of asking “which customers will buy offering with ARPU lift?”, the question becomes “which offering and channel best fits individual customers?”

 

Identifying the likelihood of a conversion

 

This different lens requires a more contextually rich approach to extracting decision intelligence from data. Using context can help to identify where the activation is most likely to result in a conversion. This typically reveals leads based on:

  • Sharing a business address with two existing high-spending, satisfied customers
  • Third-party corporate registry data reveals a director of an existing customer is associated with several other businesses that don’t presently take your service
  • An existing business looks like an outlier in its peer group because it doesn’t have an Internet of Things (IoT) product – an opportunity to prospect and sell the customer IoT.

The Importance of a Single Customer View

 

The telecommunications industry has evolved rapidly over the past three decades, highlighting the importance of a single customer view. Its evolution has been powered by technological innovation (2G to 5G, copper to fibre), competition (price contraction requires digital efficiencies to preserve EBITDA), adoption (processes and systems must scale efficiently), and portfolio breadth (a move from simple communication service provision to digital service provision).

 

One of the by-products of this evolution is a broad variety of enabling technology systems. One of the implications of this is that operational data is held in a variety of stores, often has inconsistent customer identifiers, and uses various formats for names and addresses.

 

Bringing this data together in a data-lake (or data warehouse) is nowadays commonplace and with the integration technologies available, not altogether difficult. However, being able to join it together to form a holistic view of a customer’s portfolio, service experience and value is.

 

Having a clear picture of every customer is fraught with challenges – gaps, mismatches, errors – but by utilizing advanced technologies to enable a connected view it becomes possible to provide single customer views of service through a multitude of different lenses:

  • Engagement – experience across digital, retail, phone engagement channels
  • Consumption – the full product/service consumption picture in one place
  • Service – aggregated service view across product lines and geographies
  • Provisioning – a holistic view of provisioning/MACD through all phases of contract tenure
  • Customer hierarchy insight – clarity of structure and ownership as it changes over time

The list goes on and can extend into continuous credit management and reciprocal business where a customer is also a supplier.

 

59% of organizations see the lack of a single version of the truth being a key challenge in operational decision-making. That’s because being able to generate 3-5 percentage points increase in EBITDA requires more than advanced analytics, AI and ML capabilities – it requires good data, too. 

Is data an obstacle to creating customer value?

Overcome it and become an Intelligent Communications Service Provider. Learn how you can make truly data-driven decisions, empower your data teams and uncover revenue opportunities today.

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