What are Mobile Analytics?
Mobile analytics is the practice of measuring and analyzing data generated by mobile subscribers and their devices. It employs “big data” to capture the details of mobile user experiences and behavior with the primary objectives being service assurance and data monetization. When utilized by mobile network operators, mobile analytics tools can provide detailed, real-time user insights.
What is the Importance of Mobile Analytics Tools?
The explosion of wireless communication and data transfer in recent years has created a challenging environment for mobile network operators through the sheer volume and complexity of traffic and information sources from which to gather useful insights. Effectively deploying big data analytics solutions to capture and harness the potential of this resource is the logical antidote. Mobile analytics tools help provide this big data, and the acumen gained through this approach provides opportunities to enhance service quality and optimize the monetization potential.
- Customer Insight
Converting the mountain of available data into actionable outputs should be the ultimate goal of mobile data analytics. The diverse elements of customer data that can be analyzed and segmented include usage patterns, device and app information and location history. Utilizing this vast array of data intelligently provides a mechanism for personalized product offerings by segment, customer churn prediction and response as well as customer lifetime value determination.
Mobile analytics tools provide the insight to make more informed decisions regarding new service introductions and a means for monitoring service adoption and diffusion. By developing enriched customer profiles, proactive customer service practices can improve overall retention and satisfaction levels.
- Data Monetization
Given the inherent value of big data mobile analytics performed on a massive scale, improved management of user information and services can translate directly to increased efficiency and monetization. Detailed segmentation data makes tailorable product and service offerings more feasible. Customer behavior data can also be packaged in a privacy-compliant format for sale to third parties.
Service providers and content developers are vying for their share of the hyper-competitive telecom marketplace continuously, so mobile network operators must compete aggressively for service revenue within this space. The best mobile analytics tools can be a catalyst for this, since holding the virtual keys to the customer experience vault creates countless opportunities for analytics and data mining that unleash the full monetization potential.
Types of Mobile Analytics Tools
- Location Intelligence
Location intelligence is the process of mining device location data and converting longitudinal and latitudinal coordinates into real time mobile analytics. This is used to identify and analyze the geographical patterns and trends related to devices, subscribers, and venues to produce inferences based on this data.
Slow connection speeds, poor coverage, inadequate service performance, along with deficient customer support have been identified as leading drivers of customer churn. Location intelligence is a mobile analytics tool that can improve these metrics, leading to a more proactive customer retention strategy by enabling improved network and cell tower location strategies, identifying existing areas of high subscriber service volume/low signal strength, targeting specific markets based on location, improving customer service resource allocation based on location and optimizing bandwidth allocation.
Location intelligence can also be monetized more directly in a number of ways. Valuable customer insights based on GEOanalytics such as foot traffic patterns, service usage, and movement and travel history can be used to optimize retail, office and infrastructure planning, among other potential benefits.
Personalized advertising can also be developed based on location data. For example, special offers and discounts can be provided real-time to users for geographically relevant dining and entertainment establishments. With the increasing demand for customer metadata, the opportunities to monetize mobile analytics through privacy-complaint third party sales are seemingly limitless.
- Service Assurance
Service assurance is commonly defined as the application of policies and processes by a network operator to ensure services offered meet a pre-defined quality level. The migration towards increased service velocity as well as increasingly dense and scalable networks with policy-based automation and virtualization necessitates automation and that these services remain reliably on and globally connected at all times.
Mobile network analytics play a vital role in maintaining this level of service by providing end-to-end visibility and automated data mining that enables proactive solutions to service assurance challenges. To provide a differentiated level of service, accurate subscriber-centric, and readily accessible end-to-end workflows must provide an expedient path to issue root cause and resolution while enabling mobile network operators to move close to automated service assurance.
Maintaining uninterrupted service at all levels also requires real-time data analytics for capacity planning and prediction of usage patterns. Understanding how, when and where customers are using their devices can identify pressure points that may be susceptible to failure. Big data captured through mobile analytics tools provides avenues for optimized subscriber-centric service performance analysis. Maintenance schedules can also be optimized and increasingly automated through advanced analysis of historical data trends.
- Performance Analysis
Big data analytics can enhance mobile network and service performance levels by providing more insightful information than traditional data analytics were capable of. Subscriber data scattered over time, device and location can be coalesced into a comprehensive and organized structure.
Big data analytics also provides the distinct advantage of real-time processing, making decisions more relevant and dynamic. These capabilities are becoming indispensable as subscriber proliferation and bandwidth demands continue to push the limits of mobile network infrastructure. Systems that provide mobile analytics to the operator need to display relevant information timely and in an easily understandable format so that rapid responses can maintain the requisite performance levels.
- Deep Packet Analysis
Deep packet analysis is a mobile analytics tool used to review data from captured and stored packets transmitted over an operator’s network. Packet-level analysis can ascertain with precision the programs and services customers are using. Unlike previous forms of packet inspection, today’s deep packet analysis technology scales, enabling in-depth transaction-level awareness of service and usage patterns.
Aggregated data can provide operators with detailed information on application preferences which allows them to adjust bandwidth allocation and evaluate service degradations accordingly. In conjunction with mobile analytics, packet analysis also creates a deeper and more granular level of diagnostic capability.
Mobile Analytics Fundamentals
Mobile network operators oversee the flow of a nearly infinite stream of rapidly growing data on a continuous basis. Mobile analytics and data-driven decisions are the keys to unlocking the potential of this data. Top mobile analytics companies understand that customer behavior can provide insight that drives retention and loyalty while increasing lifetime customer value. By identifying the usage patterns, location, and content preferences of customers, tailored offerings can be combined with optimized levels of service assurance to produce higher levels of customer satisfaction.
- 5G Transition
More innovation is required for mobile analytics to keep pace with the unprecedented exponential growth in traffic, along with the transition to 5G and the advanced level of subscriber services this will engender. In the interconnected, digital world of the future, automated intelligence and decision making will become a necessity rather than a luxury to produce a differentiated level of service. This will require better, smarter and more complex algorithms to organize and characterize the onslaught of data.
