Data Opportunity in Mobile Communications:
New Business Models

Leveraging the value that lies within the data to compete in a digital world

June 12, 2017

After decades of growth, mobile network operators (MNOs) are facing declining financial performance as their services become increasingly commoditized and over-the-top (OTT) players eat into their revenues.

The GSMA believes, there were 3.6 billion unique mobile services subscribers at the end of 2014, and although an additional 1 billion unique subscribers are projected by 2020, this actually indicates a deceleration in the growth of subscriber numbers. This, coupled with regulatory and competitive pressures, is leading to industry revenue growth slowing worldwide. (GSMA study, “The Mobile Economy: 2015”).

Indeed, competition and regulation is now making it difficult for MNOs to earn adequate returns in their core businesses. Early markets are saturated, and drawing customers from the competition grows increasingly expensive.

In short, MNOs must look for additional sources of income.

Fortunately, operators are sitting on a veritable gold mine. With large subscriber bases, the industry generates rich data on customer activity as a byproduct of its daily business. Situated at the heart of their subscribers’ digital universe, telecoms of today have unique access to ever-growing digital and social networking information revealing customer demographics, psychographics, and economic behaviors. Monetizing this underutilized asset could generate billions of dollars in revenue and render operators more competitive.

Data provides telecoms at least four different ways to boost revenue:

  1. Share data with strategic partners;
  2. Sell the data or findings based on it;
  3. Provide the data to entrepreneurs in closely associated incubators;
  4. Use the competitive advantages proffered by the data to expand into other businesses.

By making use of this data, an MNO can accomplish two things at the same time.

Firstly, in order both to reduce expense and grow revenue, an MNO must thoroughly revamp its current business: its core operations and infrastructure; its customer-facing processes; and even the customer experience itself. (See Telecommunications: Revamping Existing Businesses).

Secondly, an MNO must demonstrate to the capital markets that it can diversify and create new innovative business models and partnerships to generate new streams of revenue. This is crucial if the industry is to escape a regulated utility valuation that would make it vulnerable as the digital economy matures and consolidates.

New Business Models

There is a compelling case for exploiting the data sets that are generated in the “exhaust” of telecommunications companies’ daily operations to build new revenue streams.

The most obvious route to monetizing the data is the simple business of scrubbing, anonymizing, and packaging it. Sprint, Telefonica and Verizon, for example, already sell data to platforms that serve the marketing industry.

The problem is that despite keeping a low profile, data sales carries the risk of consumer blowback, as well as regulatory intervention. Whilst these risks may be worth accepting in return for revenue streams estimated at $24 billion per annum – and potentially reaching $79 billion by 2020 – as a general proposition, selling raw material is never as rewarding as transforming it into value-added products.

Rather than merely resorting to rudimentary data sale, MNOs should look to create real value-add by pursuing a more innovative data monetization strategy. It is essential that such a strategy be pursued outside the core MNO business, which depends on a very different skillset and is increasingly constrained by regulation.

The approach should be to create new businesses on an experimental and opportunistic basis, with the intention of either winning big or failing fast.

The following are illustrative of the ways that an MNO could leverage its data resources. In the event, what an individual MNO actually does must take account of regional compliance, privacy, and data protection laws and concerns.

  • Partnering model – The MNO establishes bilateral or multilateral partnerships either with strategic users or providers of customer data. A partnership could take the form of a simple teaming agreement or of a joint venture. The partnership could even lead, in time, to an acquisition of one party by the other. For example:
    • A strategic user could be a lending institution. The MNO data could help match specific lending products to individual customers.
    • A provider of customer data could be an online merchant. The merchant and the MNO could combine data sets for the same customers in order to enrich their understanding of those customers such that both could better hone their interactions with those them.
  • Open source model – The MNO establishes a start-up incubator or innovation lab in order to develop an external ecosystem of talented data scientists and entrepreneurs. Placement in the external program is based on pitches demonstrating the developers can build value-added services for the MNO, and an early-stage equity stake is often the way to participate. The MNO decides what data to provide, but it will usually also offer mentoring, access to internal subject matter experts, work space, and funding as the infant projects meet milestones and deadlines, proving their worth.
  • Ancillary business model – The MNO establishes business units to scrutinize its propriety data and to deliver value-added professional services to outside enterprises. The offered product could be either: insights derived in respect of a specific subgroup of customers, or qualified leads, based upon such derived insights or upon data already accumulated on each customer by the MNO, or both.
  • Vertical expansion model – The MNO enters an adjacent industry vertical, wherein its data assets confer a competitive advantage.

These are merely sketches of types of data businesses that an MNO might choose to build out. Each MNO must look to its market and its regulatory environment, as well as its corporate competencies and culture, before establishing the most appropriate strategy.

Telecoms can use their data both to add new revenue streams and drive down the industry’s costs.

Reducing the industry’s projected CAPEX base of roughly $1.4 trillion by 30% would yield nearly $600 billion. That hypothetical saving is larger than both of the telecom industry’s current “off-core” businesses: mobile advertising is worth $64 billion worldwide though growing rapidly, and television is a $360 billion global industry (GSMA study, “The Mobile Economy: 2015”). While both areas will certainly grow, they are highly contested spaces and for telecom operators to capture even a 10% revenue share would be remarkable.

However, the principal rationale for evolving into a creative data-driven business is to benefit from the huge market valuations enjoyed by the ‘darlings’ of the tech sector—in some cases, irrespective of their ability actually to generate cash. The capital markets will reward telecoms that are seen as data-driven and innovative, and mark down the rest, even if their core business execution is on a par.

Innovate or lose relevance – and with it, the capital to play the long hard game of industry consolidation and transformation.

SteppeChange empowers organizations to innovate through the smart use of data

SteppeChange works as a seamless, multi-disciplined team of experienced engineers, data scientists, and marketing and industry experts that can flexibly and rapidly develop sophisticated analytical capabilities centered on the unique needs of our clients.
We do not have standard consulting templates or off-the-shelf technology solutions. Instead, we are geared to design and implement custom-made solutions specific to the needs of an individual enterprise.
We start every project by building a deep understanding of the client’s business model, competitive position, strategies, organization, and objectives.

We aim to engage our clients actively in a process (that we call “data discovery”) whereby we establish what their data might permit them to do better. The SteppeChange client-centered process requires continuous client engagement in designing and deploying solutions, to ensure detailed alignment with our client’s needs.

We go deep into the IT “plumbing” that supports the client’s operations and customer interactions, both to understand the nature and quality of the data, and to devise the most efficient ways to extract and process it. Understanding the client’s operating environment and systems architecture also allows us to design solutions capable of scale implementation in each production environment.

We do not build data warehouses and, in fact, consider them a constraint on developing effective analytics. Contemporary technology allows data to be extracted from live operating systems at frequent intervals. New tools, “shovels,” allow the construction of analytical solutions from raw data dumps.

This is central to our goal of working quickly and cost efficiently, creating time and space for multiple iterations, with a degree of trial and error in the design of solutions.

In close collaboration with our clients, we design, prototype, and test potential data-science solutions. To do so, we follow – in a thoughtful and flexible way – what is now relatively standard design thinking methodology. This begins with developing customer empathy and the ability to walk in the shoes of those who use the client’s product or services, including internal users.
Next, in collaboration with the client, we conduct structured workshops to identify the full range of potential improvements in processes, decisions, and end-to-end customer interactions and experiences that might be empowered by data analytics. We then forge consensus on which solution or solutions have the highest impact on business performance.

Finally, we move on to building and testing early prototypes. We are keen to build and implement real artifacts that the client can operate on an ongoing basis.

SteppeChange delivers in-market capability end-to-end. Our processes encompass designing data science solutions, embedding models into the production environment, and supporting marketing programs and the customer treatments involved.
When we complete an assignment, our objective is to leave the client in the position to use (and continuously evolve and improve) the solution we have assisted in developing, as a part of the core business process.

To the greatest degree possible, our solutions are modular, allowing clients to select sets of modules that they can configure and customize to create different solutions and meet new needs.


About the Authors:

Ina Goldberg is a Head of Data Science and Kevin Mellyn is a Strategic Advisor in SteppeChange’s New York office.

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