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Today’s mobile operators are besieged seemingly from every side. Digital disruptors in the form of Over The Top (OTT) players have threatened already tight revenue streams, while direct competitors are battling it out over network and price. Meanwhile, customers are increasingly expecting more specialized and highly personalized services thanks to their on-demand experiences across other industries.
Fortunately, operators are well-poised to dominate in a digital disruption landscape, due in large part to data. Mobile operators have the benefit of access to near-infinite amounts of data on their subscribers; data which, when properly mined, has the power to transform customer experience and significantly impact the bottom line.
Data science is a trending topic for these operators; as it becomes clear, its application holds the key to two distinct concerns for telcos:
- First, how to transition from collection to insight, to application of data, in order to more mindfully engage customers;
- And second, how to tap into the opportunity this data offers even when 77% of the 4.9 billion mobile users around the world are prepaid, and therefore anonymous.
One of the most interesting and beneficial applications of data science for prepaid users is that of assigning financial identity
Juvo recently released a report that dives deeper into the value of using data science to open access to mobile financial services across previously anonymous populations of prepaid mobile users, and found a revenue stream at the ready for operators — to the tune of $70 billion.
So how is this possible?
The key is identity. Just like Internet users with search histories, cookies and IP records, prepaid mobile subscribers leave digital footprints as they interact with their mobile device. Every interaction is a data point and an opportunity to continuously build more robust digital identities.
Think of it this way: when a prepaid user — let’s call her Anna — engages on her phone (be it to talk, text, use social media, video stream or pay a bill), it’s a one-way relationship. Because Anna is prepaid, and she uses an anonymous SIM to operate and transact on her phone, the mobile operator is limited in building a relationship and driving engagement.
To drive and dramatically increase engagement, mobile operators must first harness Anna’s digital footprint in order to establish a financial identity. This is no simple task. It requires experienced data scientists who can mine multiple, massive sources of data such as network data about Anna, as well as data pulled from third-party apps. This data is then combined, cleaned, and analyzed — using machine learning and credit algorithms — to create a financial identity for Anna, progressively and in real time.
When operators partner with data science experts, who are skilled at bringing vast, disparate and previously uncovered data together, they see a dramatic change in the nature of their relationships with these “Annas.” As a result, mobile operators increase, deepen, and personalize their interactions with each prepaid user and, through data science-inspired incentives and modern game mechanics, access more relevant data for establishing financial identity. Thisfinancial identity, in turn, leads to higher levels of personalized engagement, where Anna is able to request financial opportunities and advances that are personalized to her needs. Soon enough, Anna’s financial identity has grown more robust, opening access to a wide breadth of more advanced financial services.
When you consider this opportunity across the 3.7 billion prepaid “Annas” around the world, who each have an annual Average Revenue Per User (ARPU) of $150, access to data science-driven mobile financial services can boost operator revenues by $70 billion. And it’s not just a revenue boost — financial identities rooted in data science lead to a 50% reduction in churn for the operator and a 10% lift in ARPU.
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