Mobile analysis | Pipeline Magazine | AI and analytics

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By: Albrecht von der Recke

The amount of data we produce and consume is exploding at an unprecedented rate in the world. Data growth analysis shows that most of the current global data has been generated in the past two years alone. On average, every human being created at least 1.7MB of data per second in 2020. By 2025, 463 exabytes of data are expected to be generated by the world’s population every day.

The pace will only accelerate. The pervasive adoption of smartphones and the ever-increasing reliance of consumers on mobile devices are the main drivers of growth, with mobile data volumes set to increase tenfold over the next five years.

With mobile users creating billions of touchpoints every day, very valuable – and in many cases actionable – data points flow across all carrier networks. Yet, according to Forbes, less than 0.5% of this data traffic is properly analyzed to extract additional value. One of the main reasons for this is the heavy weight of the traffic involved. A typical network in South America, for example, will have some two billion data events occurring every day. Until now, cost-effectively analyzing these data points to create customer insights has proven to be an insurmountable technical and operational challenge.

In the past, operators mainly marketed their customers along traditional campaign lines using mass, non-personalized SMS offers to purchase top-up or airtime packages, perhaps sent once a month. Typically, the conversion rates for these campaigns are less than one percent. But recent advances in data visualization offer the opportunity to completely transform operators’ understanding of customer behavior and purchasing habits. The sheer volume of data users generate on their mobile devices today, combined with new AI and machine learning-based data analytics capabilities, means operators can now better understand what customers are doing. customers, what are their needs. , and when is the optimal time to contact them. This new capability of microsegmentation and micro-marketing to customers based on real-time needs, whether it’s top-ups, small loans or mobile wallet transactions, can generate conversion rates of up to 10%, which means a more than tenfold increase in marketing. success for operators and a significantly improved user experience for mobile customers.

The big technological step forward in the ability of operators to visualize these billions of daily customer touchpoints has been cloud-based machine learning and AI, which provide the ability to manage, process and analyze data in such a way. much more economical than before. Machine learning and AI provide the processing capability to find highly targeted “needle in a haystack” data at cost levels that make business sense to operators. The key to unlocking the potential of data has always been whether it can be processed at a cost that enables a profit, and AI and ML are finally delivering the elasticity that makes big data operators’ ambitions a reality. . For example, to examine a base of 50 million customers, the cost of analyzing all generated customer datasets and producing actionable results can now be around $ 1,000 to $ 2,000 per day, which, from the operator’s point of view, represents an attractive business model.

What datasets can be analyzed and how can they be monetized to increase operator revenue and better meet customer needs? It all comes down to the granularity and real-time analysis of what the customer is doing at that moment. All networks offer a range of static data points, such as the handset a customer uses, their typical data spend, and other standard CRM information. But the ability to add real-time data about a customer’s interaction with their device helps to detect patterns and create segmentation that can form the basis of much more effective marketing campaigns and create happier and happier customers. more faithful.

Whenever a phone tries to connect to the operator’s network, to check an email, upload a TikTok video, or view sports scores, a data event is generated. Operators can research general trends in these events to come up with timely offers that might be better suited to potential areas of interest, such as football, games, business, etc. For example, when the FIFA World Cup tournament is in progress and data usage may be higher, operators could offer prepaid customers with very low data credit a short-term data pack. which allows the customer to stay connected. With five billion prepaid customers worldwide – and each engaged in an ongoing natural lifecycle of lack of data or airtime – the ability for operators to instantly offer top-ups, product bundles or even extending small loans to ensure continuous connectivity offers tremendous value to both the customer and the service provider.

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