It's common knowledge that the subscriber growth enjoyed by wireless Communication Service Providers (CSPs) over the last several years is tapering off. The CSPs really need new sources of revenue to deliver the required growth. Over-The-Top (OTT) players on the other hand are moving quickly into the SP ecosystems and reaching out to their subscribers. To top it all, the regulators are encouraging new entrants and driving prices down. In Europe, regulations for low mobile termination rates and new international roaming charges are impacting revenue and profit for CSPs. The growth of mobile data traffic and the customer demands for better and personalized services are forcing CSPs to invest significantly to upgrade their networks and devices. There are a lot more similar challenges that CSPs face; however, the key point is that the CSPs have to focus on new sources of revenue to augment what they generate from their infrastructure, services portfolio and customer base.

One area where the CSPs have a definite edge over the OTT players is the access to real-time subscriber intelligence. CSPs have a lot of information (read it as Big Data) about an individual subscriber's taste, preferences, favorites, location, their consumption behavior of different voice and data services, service experience, payment history, etc. In addition to the current and contextual subscriber information, the CSPs also have access to a massive amount of untapped historical data (e.g., subscriber and service history) that can be aggregated and brought back into the present to build a richer subscriber profile and identity. The information is captured from different sources in the network at different times using different techniques and, in most cases, scattered across different databases and data warehouses. The CSPs can understand a lot more about their customers and deliver a much more personalized experience by investing in deeper real-time analytics capabilities, leveraging all these subscriber data and deriving to actionable customer intelligence-driven business models (e.g., meaningful products and services).

Analytics improves multiple aspects of CSPs' operations. Terabytes of dynamic customer data will continue to grow exponentially as carriers add new services and as IP-based traffic increases. Big Data is an opportunity for CSPs to create the intelligence for operating a network more efficiently, to analyze the success of the services that CSPs are offering, and to create a better personalized experience for their customers. Big Data analytics enables service providers to better segment subscribers to provide more targeted marketing spend and the insight to predict churn, cross-sell and upsell opportunities, the quality of customer experience and the lifetime value of a customer. The product managers get a better understanding of which services are most profitable, the impact of competitive offerings and the effect of cannibalization caused by a new product roll-out. It also gives network operations the ability to predict capacity issues and the impact of a new service launch.

I have listed below a few areas where wireless service providers can leverage Big Data analytics to improve market share and ARPU (average revenue per user). HP has the skills, experience, products and solutions in this area. HP has combined its business analytics solution with deep telecom networking expertise to help CSPs generate actionable insights for their market growth.

• Correlate structured and unstructured data from OSS, BSS and Social Intelligence to act in real-time and overcome the complexity of managing networks, services and subscribers across their technical and user experience dimensions
  • Customer Experience Management: offer the best quality of experience to end-users (network intelligence); Gain a single-pane-of-glass visibility into individual user's experience, services and network; Improve the business processes and functions related to customer experience responsibilities

§  Customer experience management combined with policy management and Real Time Billing can improve ARPU and reduce churn (service intelligence) by identifying:

• Subscribers who need retention activity (e.g., subsidies for new phones, free months of service to compensate for low quality of experience, automated bill adjustments or credits, etc.)
• Quality of network services (voice/data) and root cause of issues to proactively resolve them and proactively fixing of potential issues for high value subscribers
• Integration points between OSS and BSS infrastructures for automation and proactive care
  • Service Personalization: Understand customers to deliver personalized services, digital curation-based content, bundles and offerings in real-time; create a smart unified user profile and analytics with a full panoramic and integrated view of the consumer, network and personal data

§  Campaign management: Improve ARPU and customer loyalty through personalized Campaigns

• Analyze real-time usage to make intra-day marketing program changes, enhanced marketing strategies with real-time analysis
• Develop marketing strategies when customers are approaching the cap, both for mobile (e.g., provide voice minutes to the preferred number, data traffic only for favorite online service) and fixed BB (e.g., Provide data traffic for preferred video on demand provider)
• Offer improvement (and up/cross selling) when users engage call center agents
  • Social Intelligence: understand what customers think, improve customer acquisition/retention and create brand awareness, improve & predict sales. Social Intelligence enables organizations to understand and leverage how people behave and what people do on Social Media channels. Social Intelligence is an enabler for many different scenarios:

§  Acquire, retain, and develop high-value customers

§  Reduce value decay and Improving cross-selling rates

§  Optimize communication costs, Improve customer service, manage Customer service crowdsourcing and "owned communities"

§  Activate influencers and enable advocacy

§  Meaning based advertisement

§  Monetize subscribers' base with advertising, couponing and affiliation-based models.

• Monetize the Customer knowledge and Big Data sssets through advertising and revenue Sharing (e.g., affiliation) business models.
  • The mobile ecosystem is where the service-providers have far more tools and strategies available to engage subscribers and get a slice of the digital advertising market
  • Measure and monitor monetization of campaigns in real time for different business models (from traditional pay per click advertising, enhanced digital Couponing to the end to end digital buyer's journey) through solutions like:

§  Advanced customer profiling (Big Data combining Network, CRM and Social data)

§  Campaign management

§  Advertising marketplace

• Sharing customer data
  • As the most basic first step Service providers could share their data assets with other big analytics and marketing platforms, to help them build and monetize the data and get a share of the revenues in such a case.

Most of the CSPs are aware of these opportunity areas. To implement any of these use cases, it's important to look at the end-to-end processes, integration points, analytics tools, storage and above all, applying the right logic to derive intelligence out of data. It is my view that many CSPs will start investing this year in Big Data Analytics. Those with dominant market share might start with customer experience management whereas others might start with monetizing the customer intelligence. By 2015, Big Data analytics will be one of the critical areas for CSPs to maintain their market-share.

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