How data science could enable digital inclusion for prepaid mobile subs

big data science
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By harnessing data science, mobile operators can start to engage prepaid users and better understand what kinds of services they need.

Over the past decade, more than half of the names on the Fortune 500 list have disappeared, and been replaced by new companies. According to research from Accenture, the principal catalyst of change has been the rise of digital business models.

The companies that have survived and held their spot in the Fortune 500 are those who pre-date the digital revolution. However, they are still under daily threat from all sides by internet giants and fast-moving startups. In fact, the stakes could not be higher for the remaining, established companies to survive. The new watch word for long-standing institutions is digital transformation – that’s if they are to keep their place and compete with the new breed of companies that are the digital natives of the corporate world.

At the center of the decade-old digital revolution is mobile technology. The Fortune 500 stars have put recognizing, adopting and maximizing the power of mobile devices as a top priority. Given the importance of mobile technology in business transformation, global mobile operators ought to have a fighting chance of being in the vanguard of the change. However, they are equally vulnerable. Just like all the other big businesses, operators are struggling to compete with fast-moving internet companies that are exploiting new ways of delivering services and doing business.

Operators are faced with the challenge of maintaining their ground in service provision, not only against the small agile OTT players, but also giants like Facebook and Google. These tech juggernauts are much more adept at making money from the networks that the operators have built than the operators themselves.

Today, mobile operators are challenged to innovate increasingly commoditized communication services. The pressure is on, but the opportunity that comes with offering truly pioneering services is huge. From incorporating personalized financial services to firing up financial inclusion initiatives, operators have yet to fully tap into these fresh approaches that can differentiate their services and ultimately, boost revenue. Even their own global trade body, the GSMA, admits that the majority of its operator members are only at the “relatively early stages” of digital transformation.

Data science = transformation fuel

Despite these gloomy reports, some forward-thinking operators have spotted this potential and are embracing digital transformation fuelled by data science. By turning to data science experts and using advanced machine learning, business analytics, and data-driven growth strategies – especially around mobile financial services – they are already seeing dramatic results. The rise of mobile money [PDF], now available in 93 countries and seeing annual growth of 42%, is an opportunity that operators can’t afford to overlook.

It is evident that understanding and maximizing data has been key to the success of the internet giants. As Google processes some 20 petabytes of data every day, it is clear that without data science to help analyze and process this information, the company would not be what we know it as today.

Mobile operators recognize that they too have a data goldmine. Some early pioneers like Millicom understand that data is a critical asset to the future of the company. These are the companies that are set to be the frontrunners for innovation in their industry. They acknowledge that there are experts they can tap to utilize data and dramatically improve their consumer relationships to find new revenues, grow their businesses and enhance their performance.

Digital inclusion for prepaid subs

One area that is particularly ripe for the use of data science is the vast prepaid mobile market. According to the GSMA [PDF], some 77% of mobile customers are prepaid users, only identifiable by their SIM cards and phone numbers. That’s more than 3 billion unidentified consumers ripe for digital inclusion, based on the application of expert data science techniques. By harnessing the power of data science, mobile operators can understand the nuances of an individual customer’s behavior and not only offer services that are the most relevant to them, but also do it at the precise moment they need it most. This means that mobile operators can start to engage prepaid users earlier and create SIM equity among subscribers, with new services being offered to anonymous customers.

Operators can use data science to better understand these customers, from tracking their smartphone behavior to building their digital identity. They are able to use the data to create ‘identity scores’ for customers, which provides them with actionable insight and allows new services to be offered to otherwise anonymous customers.

A good example of this is expanding access to financial services by creating a financial identity for a prepaid customer that enables them to consume more mobile services. Based on analysis of data and behavior, operators can then unlock access to upstream financial services, driving incremental revenue and dramatically reducing churn, with significant impact on retention and lifetime value.

Recent research from Juvo revealed that using data science in this way can help operators to unlock an additional $70 billion of revenue from the global prepaid market. With these predicted revenues, it’s clear that this is a great opportunity for operators to tap into.

While the GSMA says that most of its operator community are stuck in the early stages of transformation, there are operators that are already beginning to reap benefits as they provide services beyond simple connectivity.

Data science isn’t just for the stars of the Fortune 500. The capabilities and experts are out there for mobile operators to benefit from the appliance of data science to help transform their business – resulting in happier customers, higher incomes, and a brighter future.

Steve Polsky CEO Juvo data scienceWritten by Steve Polsky, CEO of Juvo

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