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Although big data has recently taken the mainstream spotlight and become a major initiative in the enterprise, it has always been at play in the wireless space. The challenge now is delivering on the unique monetization opportunity that developing analytics and applications on top of the scale and timeliness of data presents.
Mobile operators are at an important crossroads. New entrants have forced these global brands to rethink how they will effectively compete to ensure long-term viability. For operators, it’s no longer about having the best network or hottest devices; it’s about having the smartest strategy for making the most of their greatest asset – customer data.
Although mobile operators will choose a variety of paths, the monetization of big data will be key to securing a viable future. For some, success will mean the ability to generate insights that improve customer interaction and proactively address customer demands. For others it will mean the creation of new revenue streams, such as selling data to third parties. Regardless of the path chosen, successful monetization rests on one key element – the strength of the analytics at play.
Operators agree that big data analytics should be a strategic priority to drive customer monetization opportunities, but the barriers are often significant. What is required is a major shift in mindset, skillset and technology. Operators must transform their organizations from the top-down in order to become truly data-driven. They must acquire new talent as they shift their focus from aggregating data to gleaning insights from it. And most importantly, they must embrace the latest technologies to be able to act on these insights in an automated fashion.
With increased competition and the heightened risk of over-the-top players infringing on their customer profits, forward-thinking operators are prioritizing their big data analytics strategies and turning to tactics that are proven to accelerate monetization opportunities.
–Identify the strategic need before laying the ground work: Too often, big data is seen as a technology initiative rather than a business one. Heavy emphasis is placed on determining the infrastructure required to support big data, but many times there’s not enough thought as to what’s next. This can result in costly efforts that fall short in return on investment. Operators are making a point to look ahead at what various owners will do with the data, i.e., what problems they may solve and how they can rethink future engagement strategies. Then they are focusing on the most effective plan for extracting, compiling and acting on the data.
–Determine the owner of the problem – and the solution: Misalignment among C-levels and functional teams has been deemed a major barrier for moving big data initiatives beyond the exploration stage. Although the CIO has traditionally “owned” anything in the data realm, we are seeing business owners become more proactive in not only identifying the problems to be solved but also which solutions can best solve them. For example, CMOs within several leading operators are tackling the problem of churn by leading a big data analytics initiative from initiation to execution. This “single-owner” approach ensures alignment between the what, the why and the how.
–Focus on the data that matters: Taking an inside-out approach of analyzing streams of data to then determine the problem you want to solve can leave you trying to boil the ocean. To combat analysis paralysis, operators are identifying specific business problems that when solved can have a substantial impact on the bottom line, i.e. declining recharge volume, data adoption, churn, etc. This focused effort accelerates the process of determining which behavior changes will have the greatest impact on the goal at hand and the specific data which is required for modeling, analyzing and monitoring those behaviors.
–Invest in more sophisticated analytics: Many operators continue to miss the mark on when, where, and how to connect with their customers despite the vast amount of data that they have. To determine what’s relevant for each customer and deliver it in the right context, operators are adopting more sophisticated analytics to understand dynamic customer behavior over time. Flexibility, timeliness and the ability to scale to analyze millions of customers across any number of dimensions are top of mind for operators as they invest in more sophisticated analytics.
–Move toward real-time: Many operators continue to rely on batch processing of events to then determine how and when to engage their customers. Yet to proactively address customer needs, they know they must be able to analyze data in real time. True customer-centric operators are moving toward real-time analytics by confronting the technology challenges that stand in the way of easily and quickly getting the data. It’s not realistic to expect that every data source can be analyzed in real-time but operators are prioritizing the sources that deliver rich behavioral insights, such as usage and transactional records, to drive timelier and higher value customer engagement.
–Pursue plug-and-play for greater ease and speed: While analytics were at play long before the term big data hit the spotlight, many operators remain challenged to advance their capabilities – especially given the explosion of new technologies and techniques specific to mobile. To leverage more advanced analytics, such as behavioral clustering, prescriptive analytics and machine learning, operators are turning to productized solutions which ease the IT pain and expedite speed to market. Operators are also expressing openness to a cloud based approach based on the cost and competitive value of “analytics and action in a box.”
With increased competition and the emergence of OTT players, it has never been more important for operators to leverage their customer data assets to get ahead of the curve from a customer experience and monetization standpoint. Strategically minded operators who are embracing the next wave of big data analytics are transforming their business and customer engagement models. They are becoming more competitive, increasing customer value and profitability.
