From internal analytics to external Big Data monetization

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Most mobile operators are beginning their Big Data journey by implementing analytics-led programs designed to improve internal business operations and to enhance the customer experience. However, a significant Big Data monetization opportunity lies in the development of new Big Data product offerings that will deliver meaningful value to external clients. We have identified some of the key issues facing operators in transforming their Big Data operations from an internal company focus to a market-facing orientation.

Externalizing Big Data is a change management task

We see six major issues that mobile operators need to address when making the transition from internal analytics to external commercialization of their data assets:

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Issue #1: Leveraging external datasets

The internal analytics effort is intended to leverage the value of network-based transaction data e.g. call records, messaging streams, web logs and subscriber location information. Much of this data is highly-structured and well-ordered as a result of the legacy CRM/BI systems and process infrastructure in place within the operator IT environment.

However, co-mingling this internal information with diverse third party datasets such as demographic profiles, social media graphs, vehicular traffic patterns and retail location footfall is essential in order to produce fully-enriched data products for external clients. This requirement adds great complexity to the task of managing the integrity and veracity of the consolidated data. 

Different performance quality standards are required to render the consolidated data ‘fit for purpose’ on behalf of potential clients, with a consequent time and cost overhead.

Issue #2: Establishing requirements specifications

In-house data analytics and reporting requirements are generally stable and well-understood across the operator organization, with the IT/BI functions playing the central role in meeting the needs of internal stakeholders. The processes for the evaluation and fulfillment of new analytics requests are also institutionalized within a mature operator environment.

In seeking to offer an external Big Data portfolio, however, the operator has to utilize an entirely different set of competencies in order to establish initial client needs e.g. end-user market research, competitor analysis and demand planning. As such the Marketing function within the operator plays a much more prominent role at this stage.

Issue #3: Generating customer demand

Fulfillment of analytics requests within the operator are generally governed via an internal network of direct supplier-buyer relationships, typically with the ‘supplier’ being the IT/BI function and the ‘buyer’ community representing the various business functions e.g. marketing, engineering or finance.

However, an external Big Data service offering has to be packaged and priced such that it may be offered to clients via the operator-owned sales and marketing channels. In addition, any Big Data business partner retained by the operator will likely have access to specialty distribution channels that will extend the market reach of the offering.

Hence the Marketing and Sales functions of the operator have to take the lead in this process.

Issue #4: Managing service delivery quality

The above-referenced internal supplier-buyer relationship is the mechanism by which the service delivery parameters of internal analytics requirements are determined. In essence the negotiation process between IT/BI and the functional areas is a cost-quality trade-off based on the measured or perceived business value.

However, these dynamics shift dramatically when the operator is setting out to offer a Big Data portfolio to external clients. Product and service packaging, fulfillment, billing and post-sales support – all seamlessly integrated for the client – become of paramount importance to the success of the Big Data service delivery platform.

Issue #5: Developing a partner eco-system

Based on the potential scale and scope of an external Big Data portfolio, most operators will require third party expertise at an early stage in the product and service development cycle. These relationships will typically take the form of long term strategic partnerships, rather than ad-hoc business alliances.

The major partner categories are media companies, advertising agencies, marketing data vendors, system integrators and web analytics providers. An integrated partner eco-system is the desired long term outcome for those operators intent on creating and sustaining a successful Big Bata monetization presence.

Issue #6: Securing subscriber and client trust

Making the transition from internal data analytics processes to engaging with external clients in order to monetize customer information requires the successful management of the myriad legal, privacy and policy implications.

The operators have a ‘two-sided’ obligation in this regard: a) to their subscribers who are the source of the raw data and b) to their clients who are being solicited as buyers of this premium data.

The ‘top-of-mind’ questions for these stakeholders at this early stage are:

  • For subscribers: am I confident that the operator will not violate my privacy in the external use of my personal data?
  • For clients: are we able to place strong reliance on the operator-supplied Big Data to inform our decision-making processes?

However, most mature operators have amassed strong reputation capital in the minds of subscribers and clients alike, which in turn positions them well to move with confidence into the external market for Big Data products and services.

This is a result of the trust models that the operators have established by virtue of the quality of their on-going service delivery performance e.g. network security, billing provision and via product innovations such as mobile banking.

Data privacy and security – which in most jurisdictions is governed both by industry-wide standards and by government regulation – is an area where the operators cannot afford any compromises in setting their sights on a successful Big Data monetization play.



The bottom-up approach to commercialize Big Data products

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Many mobile operators have recruited teams of data scientists to harvest their internal data assets in order to improve customer retention, upselling and cross-selling. Once this Big Data team has been formed, it becomes clear that external opportunities exist to sell products and services based on Big Data analytics. This bottom-up approach is a pragmatic and cost-effective path for operators to commercialize Big Data services.

The six realizations of a data analytics team

We regularly converse with Big Data analytics teams in mobile operators around the world. A common theme we have observed is that all teams start with an internal focus, and over time begin to develop an external focus.

Our conversations have led us to identify six distinct phases of a Big Data team’s evolution:

Bottom-up phases to commercialize Big Data products and services

Let’s examine what typically happens in each of these phases:

Phase 1: delivering internal value

The initial scope of operators’ Big Data teams is to develop customer insights that help marketing to stimulate customers to buy additional products and services, and also to improve customer loyalty.

Phase 2: an initial opportunity is identified to trial a Big Data service

Once the Big Data analytics team is established, they begin to develop a broad range of data sets and insights. Some of these analyses have clear value to other organizations, such as retailers, advertisers and FMCG companies.

Before long, a commercially-oriented IT manager or data scientist  sees an opportunity to launch a trial service to deliver Big Data insights externally. Typically this will be in partnership with an external marketing services organization.

Phase 3: the trial turns into a commercial service

Once a trial is underway, a number of requirements quickly become apparent. Data security and privacy issues surface first. Then the need to implement operational processes to ensure data is delivered reliably and consistently.

Once these challenges have been addressed, it becomes clear that the Big Data service needs to be packaged and priced so that it can be sold and distributed through existing sales and marketing channels.

Phase 4: additional product and service opportunities

Once the sales and marketing groups are involved, they quickly spot other opportunities to sell Big Data products and services. This kicks off a market mapping exercise, which leads to the definition of customer segments, the identification of core customer needs, and the development of pricing models and service packages.

Phase 5: the development of cross-functional teams

As various product and service packages are defined, additional service delivery requirements become apparent. Big Data services require new customer support processes, operational delivery processes and billing processes.

It rapidly becomes apparent that cross-functional teams are required to address the various marketing, operations and support issues, in order to reliably launch multiple Big Data services.

The need for third party support also becomes apparent, and partnership discussions begin with ad agencies, market data providers, system integrators and web analytics companies.

Phase 6: creation of a new Line of Business

By the time an organization structure has emerged for the design, launch and support of Big Data services, operators have examined their options in terms of creating a new subsidiary organization or spinning out an entirely new company.

This new Line of Business offers significant potential to operators, both as a source of revenue growth, and also as a strategic move that secures the operator’s central position as a provider of valued and trusted communications services.

External Big Data services will be an industry-wide trend

Leading wireless operators such as Sprint, Verizon, Telefonica and Vodafone have already launched a number of Big Data services, primarily focused on the retail and advertising sectors.

Over the next two to three years we expect to see operators in most markets develop their own Big Data products and services. Within five years, we predict that these will be significant sources of revenue and strategic strength for the mobile industry.