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.