Vivo’s predictive modeling-based marketing program has put up impressive numbers: a 30 percent increase in direct marketing-generated revenue, a 70 percent increase in the opt-in rate for customers who agree to receive ultratargeted marketing messages, and an 80 percent reduction in marketing costs.

Now Vivo and carriers like Globe Telecom in the Philippines are able to drive revenue improvement via dozens of simultaneous, specifically targeted promotions that rely on data-gathering software and Big Data analytics, helping CSPs to better understand and communicate with their customers.

How your CSP can reap the rewards of Big Data

Because the number of CSPs actively immersed in Big Data and analytics is still relatively small, what does the future hold for customer-engagement efforts once more CSPs champion such programs?

Earlier this year the Cisco Visual Networking Index predicted that the amount of mobile-data traffic in 2017 will be 13 times larger than it was in 2012. The amount of incoming data that CSPs are collecting and managing is already staggering, and although initial forays into Big Data have focused on existing structured data, the analytics endeavors of tomorrow will require the integration of unstructured data in order to optimize its relevance and impact.

The forthcoming era of Really Big Data demands that CSPs have a scalable infrastructure in place to handle the increasing demands for data that can lead to meaningful inferences and smart decisions, both of which are essential when it comes to maintaining parity or carving out a competitive advantage in a dynamic marketplace.

A close study of the Big Data and analytics phenomenon reveals a number of best practices that CSPs would be wise to bear in mind as they begin or extend their ventures in this burgeoning field.

• First, they should commit their initial efforts, especially pilot programs, to customer-oriented Big Data initiatives. Because these initiatives will provide the most value to a business in both the near term (by affecting revenues) and the long (by cementing customer relationships), they will also serve to justify the business case for additional investment in, and expansion of, analytics-based programs.

• Second, CSPs need to build a Big Data blueprint that breaks down data silos and encourages an integrated, centralized, enterprise-wide focus. This step-by-step strategic plan creates a common understanding of how the entire company can contribute to and benefit from Big Data’s effect on its business goals. It will also guide investment and staffing options, help prioritize Big Data projects and eliminate turf wars.

• Third, CSPs should start with existing data when making their first forays into Big Data. Taking advantage of internal data while simultaneously scaling data warehousing and analytics capabilities fosters near-term ROI (return on investment) benefits that can fuel future investments, at which point a CSP can justify an orderly transition into more sophisticated analyses—intelligent analytics, prescriptive analytics, autonomics—that are capable of evaluating structured and unstructured data in (or near) real time.

• Finally, as with any business endeavor, Big Data must prove its worth, or else it will be rendered worthless. By identifying the business case for Big Data and accurately measuring and quantifying outcomes from analytics, a CSP’s program will gain executive buy-in throughout the enterprise and establish its own stronghold, just as IT has, within the corporate infrastructure.

One way or another, Big Data and analytics are making a significant impact on the future of every CSP. Based on the findings of IBM’s Big Data @ Work Study and anecdotal evidence, the brightest future belongs to the CSPs that rationally embrace Big Data.