Creating Economic Value with Data

The world is facing a data deluge which has been coined as “Big Data”. It consists of massive volumes and variety of structured and unstructured data. A large part of this deluge is caused by increasing presence and creation of unstructured data: Web logs, Social Media data or Machine Data like: Applications logs, Network monitoring devices and Sensor data. At last count somewhere around 2.5 quintillion bytes of data is created every day*(Source: IBM) and the volume of “business” data alone on a worldwide scale is expected to double every 1.2 years (source: eBay Study).

Processing such data’s sheer size and variety within reasonable time frames is beyond the ability of commonly used software tools to capture, manage, and process. While on the technology front rapidly evolved “Big Data technologies” consisting of pure or combinations of Hadoop, Cassandra, MongoDB, In-memory databases etc. alongwith analytics algorithms and programs that mine these massive data stores have solved the volume, variety and velocity challenge to a good degree.  What has not quite been solved is the core applications areas in industries that can utilize this combined Big Data, Analytics and Social Media power to generate sustainable economic value.

Note: For the sake of brevity; Big Data, Analytics & Social Media will mostly be referred to as Big Data in this article.

One of the richest veins of untapped Big Data economic value lies in the telecommunication industry. Spanning fixed line, mobile, broadband and blurring its lines with retailing via m-commerce, e-commerce, app stores while competing with OTT players, the latent potential of striking pure gold in telecoms is unparalleled. With 3.2bn* people and 7bn* connections worldwide using just mobile communications, this industry is generating 7tn* plus SMSs and predicted to generate in 2017 11.2 exabytes* per month just via mobile data usage (* Source: AT Kearney & GSMA- The Mobile Economy 2013). Telecommunication operators, MVNOs and vendors can generate huge “economic value” from this data by mixing the right type of data to address existing challenges, improve existing applications or create revolutionary new paradigms of service and customer experience.

Let us look at some of the existing telecom operator challenges that are shared somewhat by MVNOs or are sought to be addressed by vendors supplying to both. The two main objectives remain the same: Increase revenues & profits. Attached to the revenue are sub-objectives such as maximizing Campaign revenue, Dealer revenue, Advertisement revenue, Partnership revenue plus Churn Management and better Customer Care (both of which keep back subscribers and support current or help boost future revenue). On the profit enhancement side are applications such as Margin Management, Revenue Assurance, Credit & Collections and Fraud Management since improvements in these areas directly improve the bottom-line even if topline remains the same.

The revenue generation application of Big Data has been much talked and written about. Some examples are:

• Campaign & Advertising revenue: With personalization becoming the key factor for the success to click-through rates or campaign and advertising responses, Big Data analytics along-with social can produce stupendous customer response rates, by allowing not only the factoring in of multiple facets of demography, service , device, billing, payment or customer interactions of a subscriber but also enable the mash-up and consequent enrichment of results via utilizing external parameters like weather, economic scenario, seasonal data etc. This can enable telcos to capture a large chunk of the multi-billion dollar advertising potential.

Predictions by analysts say: in terms of format, mobile advertising is growing seven times faster than desktop internet, which is increasing at an average 10% a year and internet advertising spend will increase its share of the global ad market to 24.5% in 2015, while newspapers and magazines will continue to shrink at an average of 3% a year. (Source: www.zenithoptimedia.com)

• Churn Management & Customer Care: Consider the case of a harried subscriber who has made calls to the call-centre for his broadband issues. For better effect, the subscriber also uses mobile and fixed line from the same operator. In such a scenario, without understanding the customer mood and root causes, a typical “churn / retention promo” or “CSAT / NPS” survey call to such a subscriber can be more damaging than containing. It is therefore becoming critical that Customer Care executives are armed with a Customer Sentiment Score and the latest real-time customer info covering a complete 360 degree. However, unlike the days of “BI”, this 360 degree should not be a data dump of usage, subscriptions, billing, payments, loyalty etc. but a carefully articulated snapshot that provides key customer KPIs to help manage and win customer confidence within the 120 odd seconds that a typical CSR gets. Such accurate real-time distilled information from a multitude of factors cannot be achieved without using Big Data analytics enriched further by customer and peer group social media feedback. 

Big Data technologies combined with analytics and social media can also create economic value in some of the existing core operational areas for Telcos-

• Revenue Assurance- Reconciliation across data sources forms the backbone of RA. This means massive amounts of data whether it is Switch to Bill reconciliations for postpaid, IN to switch for prepaid, Network assurance, Interconnect assurance or Roaming assurance. With RA leakages contributing to 1-3% of operator revenues, plugging these billable or chargeable events can instantly boost up profits. Currently, notwithstanding the delays that it takes to apply the business rules needs to make Switch records comparable to Mediated or Billing records or any lag in data availability from the systems themselves; the actual processing of these “reconciliations” takes hours. The problem becomes even more acute when multi-source reconciliations are attempted. Usually the best available from RA vendors is D-1 reconciliation results. Consider what a real time Big Data enabled RA reconciliation could save for the telco in situations like- calls are being allowed on the switch but are not being rated on the IN due to some issue. Or the overall Economic Value of being able to report and resolve the same day root causes in other RA issues rather than waiting 2 days for the final reports to come in, by when other issues could have started as a result of the delay.
• Credit Management –Big Data can enhance Credit Management for telcos. It can enable lightning fast new subscription vetting across multiple Customer Care, Kiosk, Dealer and other portals, enable blacklist database matches and predict subscriber credit exposure with more accuracy based on more variety of events than is currently feasible. Proper Credit Management can save a lot in bad debts for telcos.
• Fraud Management- Fraud Management has massive real time application opportunities. Especially with telcos moving to IP enabled call and data switching, the fraud vulnerability has increased massively as has the volume of data being generated in SIP or IMS enabled networks. Other than enabling traditional rule based fraud detection real time to address Prepaid, SIM Cloning, Roaming, High Usage fraud detection real time, Big Data applications can also help in securing Mobile or E-commerce transactions.

As through the next few years the speed and the hype stays on, it will be the success of Big Data, Analytics and Social Media in solving existing or new business problems at lower costs than current options and at a faster, more efficient mode that will create a shift from firms indulging in separate “Big Data/ Analytics / Social Media projects” to ultimately a standard acceptance as daily practices that provide continued investments via delivered business value.