Unlocking the Value of Data in Motion Through Analytics
Written By Kiran Matty, Marketing Manager, and Ola Mabadeje, Marketing Manager
If “Big Data” is crude oil, then Analytics is its refinery. According to a Cisco IBSG report, “if ‘crude” data can be extracted, refined, and piped to where it can impact decisions, its value will soar”. The trends, patterns, and insights that can be gathered from the various sources of Big Data are virtually limitless. However, this blog shall primarily focus on the analytics that can be generated by refining i.e. analyzing the data that’s resident in a mobile network and is largely untapped.
According to Cisco VNI, the number of connected devices will be three times the global population by 2017 and the global IP traffic will also increase threefold in the same time frame. Mobile Networks have not only been primed to sustain this onslaught but have also transformed into a programmable platform that can collect, correlate, and contextualize data rapidly. Network data, Policy, and Analytics interplay in a multitude of ways and form the basis of Data in Motion that’s at the heart of network monetization.
Hidden opportunities exist in the market gaps
As you might be aware, CPM (Cost per Mille) for mobile Ads is lower than that of other advertising media such as online, television, etc. This is because mobile Ads are generally untargeted, which leads to ineffective Ad campaigns, and could be attributed to a large extent to the lack of contextual awareness vis-à-vis location, demographics, browsing history, network conditions, screen size, etc. Although market researchers have perfected the measurements for other advertising media, they haven’t yet cracked the nut for mobile and the mobile metrics remain fuzzy at best which is impeding the flow of advertising dollars to mobile. On the other hand, as much as we love applications like Apple Siri and FaceTime, Angry Birds, etc., and devices like the Apple iPhone, they have turned out to be an operational nightmare in certain cases for the mobile operators around the world because of the data and signaling Tsunami that they can potentially bring about. This is due in part to lack of network analytics that can predict such surges in the network traffic with reasonable accuracy to allow for timely management of the network in terms of network capacity and bandwidth. This would eventually lead to operational efficiency and hence cost savings. Further, think about Internet of Everything and the 50 billion devices that would come online by 2020!
Mobile network operators are well positioned to address the above pain-points. With access to millions of subscribers, they can predict network and consumer behavior with high degree of accuracy quite simply because of the law of large numbers. Unlike many pure-play analytics vendors, network operators have direct access to data from a variety of sources such as CDN (Content Delivery Network), devices, applications, network billing and charging systems, not to mention the various mobile network elements. Some may even have access to subscriber Wi-Fi data. Lastly, many have the cloud infrastructure that’s needed for analyzing data at a bigger scale.
Translating opportunities to $$$
Market Maturity* (Nascent, Growth, Mature)
|Content Analytics||Most visited web categories, popular mobile Apps, popular content, content consumption per Geo, time range etc., bit rate, trend analysis||Content performance measurement to help Ad valuation for content monetization||Growth|
|Operational Analytics||Network Analytics: Traffic view and trends per flow, application and device, drill-down by Geo, time range, and network element, Operator specified KPIs (Key Performance Indicators), etc., RAN congestion heat-map||Network Optimization and Planning||Nascent|
Marketing and Advertising
|Subscriber Analytics, Device Analytics,Application Analytics, location analytics such as Geo-Fencing, Traffic analysis, etc., time series trend analysis, drill-down by Geo, time range, and application||Venue Audience Measurement, Ad Campaign effectiveness and new data related revenue opportunities for an Operator||Growth|
* Based on Cisco internal analysis
Mobile operators around the world see tremendous value in monetizing data through Analytics. Leading operators such as Verizon, Telefónica, and Sprint are on the front end of this trend, with dedicated divisions that are focused on Analytics. Network operators can potentially explore various business models, all of which are based on the basic requirement that the data be Anonymized and Aggregated (in most cases) with customer opt-out mechanism in place:
- Standalone analytics reports – sold to businesses for a fee.
- Analytics as a Service – customized reports based on customer specified KPI (Key Performance indicators) that are charged based on usage.
- API – exposes analytics data to 3rd party developers to create custom dashboards that could be charged per API call or through a revenue sharing agreement.
- User Profiles – Anonymized subscriber information such as location, demographic info, device info, RAN info, video analytics data, etc. to be sold to the Ad Networks for a fee that’s determined by the real-time valuation of the profile in the Ad exchange using Real-time Bidding (RTB)
- Value Added Services (VAS) – based on location, media consumption, and subscriber analytics, etc. provided to the subscriber either free of charge or monetized through monetization models such as Advertising, Freemium, etc.
Operational Analytics can not only guide smart network investments, but also help monetize them. Data Quality (QoS) degradation is one of the causes of subscriber churn. Our analysis indicates that if operational analytics were used to detect QoS degradation so that it could be improved in a timely manner, subscriber churn could be reduced. Furthermore, up to $2.7B (assuming annual average churn rate of 3%, of which 17% is attributed to poor data QoS) could be saved by Tier-1 mobile operators across the world annually. A Cisco sponsored survey shows that if video quality were improved, approximately 40% of users would likely increase their video viewing by 20%. A mobile operator can potentially use video analytics to detect and improve poor video streaming quality which our analysis indicates could lead to incremental cumulative revenue of as high as $1.5billion, CAGR: 34% (2013- 2016) through targeted advertising when combined with subscriber, device, application, and location analytics.
Cisco’s Mobility Unified Reporting and Analytics (MURAL), which is a part of Cisco Quantum Analytics, leverages both Historical (Big Data) and Real-Time analytics to provide an analytics application platform that can be easily integrated into an operator’s network. It is based on Cisco’s ASR 5000-Series Deep Packet Inspection (DPI) capabilities and provides analytics across a wide range of data sources and supports a wide range of applications and use cases.
In Conclusion, Analytics is not only a big monetization opportunity for a mobile operator but also an optimization opportunity that can lead to tremendous cost savings. With the help of a technology partner like Cisco, a mobile operator can capitalize on this opportunity.