The Strategic Analytics Advantage in the Communications Industry · The advantage using advanced analytics is that it can help CSPs address multiple functions across an organization

White Paper The Strategic Analytics Advantage in the Communications Industry

Prepared by Ari Banerjee Senior Analyst, Heavy Reading Sarah Wallace Analyst, Heavy Reading www.heavyreading.com on behalf of

www.alteryx.com August 2012

http://www.heavyreading.com/

http://www.alteryx.com/

HEAVY READING | AUGUST 2012 | WHITE PAPER | STRATEGIC ANALYTICS IN THE COMMUNICATIONS INDUSTRY 2

Executive Summary Communications service providers (CSPs) today are facing more challenges than ever as they try to satisfy the increasing demands of both residential and business customers who want services how, where and when they want them. At the same time, new technologies and services such as smartphones and interactive video put a strain on networks and add to the copious amounts of usage data, which ends up siloed and neglected in back office systems. CSPs can use advanced analytics in their business practice to better organize and use their data, which will help improve the understanding of their customers' needs, improve efficiency of the network and enable them to strategically plan for more targeted marketing campaigns, as well as enhance their overall market entry and planning strategy. The advantage of using strategic analytics in communications is that it can help a CSP with multiple functions across its organization by integrating data from various systems, providing predictive analysis for multi-variant business activity, and facilitating the delivery of actionable, context-specific insight to end users. This leads to many practical use cases of analytics for CSPs. For example, a provider can use strategic analytics for high-level business strategy (using demographics/ firmographics, infrastructure data and geographic boundaries) to determine what new markets they should expand to, as well as how to improve service and offerings within their current market. And, in terms of overall network performance and efficiency, an advanced analytics solution can analyze and pinpoint equip-ment and coverage issues, address operational issues in terms of latency and quality of service (QoS), help with capacity planning, address network usage and asset allocation, enable service control based on customer profiles and provide preemptive service assurance. Strategic analytics also aids CSPs in terms of churn prediction and prevention by providing analysis of current data that enables triggers for preventative churn actions in real time. Hence, an automated action can be taken toward a sub-scriber whose behavior may indicate churn such as consistent complaints or being located in a geographic region that has users with a propensity to churn. The predictive capability of strategic analytics helps contribute toward end-to-end customer satisfaction and ideally extends the lifecycle of the subscriber. Other use cases where strategic analytic capabilities help in overall customer satisfaction are advanced offer management such as loyalty point programs and set-top box analysis for cable/MSOs, which helps determine programming by household. Other approaches to analytics try to address the needs of CSPs but fall short. Traditional mapping, business intelligence and visualization tools along with data warehouse solutions lack advanced algorithms and predictive analytic ability, and may take too long to deploy. Today's market demands a more agile and smarter analytics solution that is quick to implement, cost effective and is able to analyze data at a granular level. Strategic analytics tools will give CSPs an advantage in terms of helping them better understand the individual subscriber and help in network planning, man-agement and overall market strategy. These solutions have the capability to connect siloed data across various lines of business while enabling automation of the data in a predictive fashion. These tools are the most comprehensive and effective, and are recommended to support the rapid decision-making environ-ment in which today's CSP exists.


Today's CSPs Face Complex Challenges Improve the Service Experience for Existing Customers For CSPs to succeed in this hyper-competitive communications marketplace, they need to understand their subscribers and how they use their services, deploy innovative services and business models that maximize revenue, and take ad-vantage of all data available to them to operate most efficiently. Both residential and enterprise subscribers are becoming more demanding, and today's commu-nications customers expect personalized offerings, ubiquitous access, broad choices and a reliable, seamless experience. For example, to better meet the demands of its customers, wireless providers have been updating their networks from 3G to 4G so that service is faster and the devices (such as LTE-enabled smartphones and tablets) run seamlessly across the network without dropped calls or slow download times. Network upgrades will also allow for innovative services to customers such as interactive video services (e.g. Verizon's FiOS TV service) where users can personalize service not just for a TV, but for multiple devices in a household such as tablets and gaming consoles. To maximize the revenue opportunity, CSPs need to personalize more services and provide pricing plans around specific consumer activities and behaviors such as video sharing, online gaming, live content streaming, or newer concepts such as "bring your own device" (BYOD) for enterprise customers.

Compete With Other CSPs for New Customers In addition to retaining current customers, CSPs will also have to acquire new ones. It is to a CSP's advantage to work with a vendor who can help them find and target markets where they can expand their services. Vendors with skill sets such as spatial analysis allow for accurate analysis to determine where networks can be built or upgraded to provide customers with a better service experience. For example, wireless CSPs can use spatial analysis to provide them with guidance about where cell sites should be positioned in the network based on subscriber density, revenue projections, service preference, credit scores and many other variables by combining critical geographical and demographic information with internal business and technical information. Solutions should be able to incorpo-rate call detail records (CDRs), with subscriber and device details combined with location and network data to identify where small cells should be deployed. An advanced analytic solution should also be able to provide a detailed picture of exactly what is happening on an operator's network, and assess the impact of deploying small cells in the network on the customer experience. CSPs also need to use "word of mouth" and strong, effective viral marketing campaigns to reach the influencers within their circles of family and friends. Analytics tools can help identify these influencers, and play a key role in customer acquisition. For example, if a teenage subscriber who texts heavily receives a very appealing rate reduction, this subscriber will more than likely spread the word to those in his or her network. The result for the CSP is not only increased revenue, but a better understanding of patterns of behavior within certain customer segments, and which influencers have the most creditability within a social network. Though the result may be positive for the CSP in all these aspects, this also means that real-time provisioning of the promotion itself must also be in place and be able to meet the demands of the ripple effect.


Solve the Big Data Challenge CSPs have traditionally operated with complex, disparate sets of silos of data, with useful information residing in customer relationship management (CRM), billing, inventory, provisioning and fulfillment, service management systems, network elements, element and network management systems, probes, deep packet inspection devices, application-specific databases and elsewhere. They also have different systems in different generations of network architecture, each holding different types of data, in different formats. In many cases, each of these systems has its own analytics application in order to provide point solutions for particular problems. Integration has been undertaken to push data from one application into another, and analysis of data from multiple systems has been done off-line to identify trends, patterns and behaviors. Unfortu-nately, just extracting the data from individual point solutions – for instance to support decisions about how to respond to a customer inquiry, or how to manage different customers' traffic in an overloaded cell – has historically been challeng-ing, let alone identifying important patterns across and between these sources.


The Need for Analytics in Communications The advantage using advanced analytics is that it can help CSPs address multiple functions across an organization. An effective analytics-driven strategy:

· Involves the creation of an architecture which enables the collection, storage and integration of data sets, from a variety of systems

· Applies advanced analytics techniques to identify patterns of significance across those data sets (perhaps also providing root cause, predictive and outcome analysis, undertaking complex event processing and providing multi-variant business activity monitoring)

· Facilitates the delivery of actionable, context-specific insight to end users An effective analytics solution must be able to access data, analyze it and provide the results of that analysis on demand, so that end users (either people or technology systems) have the insight they need to make better decisions, without delay. Figure 1 illustrates the goals of advanced analytics for CSPs.

In a world where more and more customers interact online, and talk about their experiences and issues online, online brand management has become big business. CSPs that ignore what customers say about them in unstructured envi-ronments risk swift and widespread brand damage. Making sense of structured and unstructured data to understand the mood and transaction pattern of customers is therefore critical, as is social network and sentiment analysis. Strategic analytics can help CSPs take preventive actions so they can avoid churn or customer dissatisfaction by providing targeted promotions or preemptive service assurance. There is need for solutions which can combine customer usage and subscription data with insight into the network, cost, revenue, supply chain, stock control, customer mood and customer preference data to trigger specific actions which helps in enhancing the customer experience.

Figure 1: End Goal of Advanced Analytics for CSPs

Source: Heavy Reading


Given the challenges CSPs face, the ability to draw on that data and gain the required insight in real time is increasingly important, but very difficult to achieve. CSPs' ability to leverage all the information at their disposal will be limited without an overarching system that can enable access and analysis of relevant data to support actionable decision making. Based on a recent Heavy Reading survey of 60 global CSPs, more than 70 percent believe that integrating data sources will positively impact their bottom line. Figure 2 illustrates the key benefits that these CSPs believe they will obtain by integrating data from diverse sources.

From Figure 2, it's clear that the key benefits that CSPs believe they can achieve centers around better SLA management (nearly 70 percent chose it as their most critical perceived benefit), prevention of fraud/revenue leakage, optimization of existing resources, better customer experience and preemptive service assurance. Hence it becomes very clear that data integration helps CSPs make accurate decisions, and if these decisions can be made actionable by influencing CSP business processes, it can provide unprecedented benefits. The result from fluid integration of data sources can help CSPs take a preemptive, results oriented approach to overall experience management for their high value, demanding customers. Personalized offers can also be tailored according to location, with location-specific offers (and even time-sensitive short-term services) made available to encourage migration to new packages, new networks etc., and to reduce pressure on networks in those locations. Integration has been undertaken to push data from one application into another, and analysis of data from multiple systems can identify trends, patterns and behaviors – for instance to support decisions about how to respond to a customer inquiry, or how to manage different customers' traffic in an overloaded cell. Predictive analytics can signifi-cantly impact CSP profitability, especially in the areas of network optimization, revenue/profitability prediction and advanced customer segmentation.

Figure 2: Benefits of Data Integration

Source: Heavy Reading


Applications of Analytics in Communications There are many potential uses for analytics across many departments within wireless, wireline and cable operators. The following are some prime use cases.

Network Performance Analysis 4G networks are intended to be increasingly self-optimizing, with cells automati-cally managing how they interact with one another (e.g. by adjusting their power to minimize interference while maximizing bandwidth and coverage), their power consumption, and how they load-balance traffic and handover traffic between cells. Wireless service providers will be able to do this much more effectively if they can augment network performance data with contextual information which includes subscriber information such as; user experience in specific areas, how that user experience varies according to the different types of services they might use, and typical patterns of user behavior throughout the day. For instance, if analytics can show that reducing the power in one cell in favor of another cell might improve the overall network, but that the experience of a small set of high value customers who typically use demanding services at a set time of the day will be reduced, then a decision can be taken about the best way to sustain QoS without decreasing customer satisfaction.

Capacity Planning Operators are challenged today by tougher economic times and subscriber's preference for bandwidth intensive services for which they are not willing to pay an adequate premium. As a result, operators are faced with a finite asset (network capacity), and the cost of increasing that asset through greater equipment spend can be prohibitive. On today's dynamic IP networks, some network elements will fall under increased strain at unpredictable times and for unpredictable reasons, making the challenge of effective capacity planning even more daunting. In-depth network intelligence can be a key tool in combating this problem. Through a proper understanding of overall network usage and particular usage per application, operators can engage in asset allocation in a more informed fashion. In an environment where operators are often forced to overprovision the network such that it is running at as low as 25-35 percent of peak capacity, more intelligent capacity planning can be provided utilizing advanced analytics to provide an optimal subscriber experience and maintain (or reduce) capex. Operators also have to make judicious capacity planning decisions in terms of the impact of over- or under-provisioning particular assets on the customer experi-ence, and how that might impact customer profitability and customer churn. Moreover, as large-scale operators entertain wholesale offerings for virtual network operators (VNOs) and other alternative service providers, an accurate view of real network capacity, both current and forward-looking, can illuminate the viability of this approach. Rather than guessing at the demand picture for a wholesale service of a certain VNO, the use of granular analytics enables the wholesaler to understand exactly what resources are available.


Set-Top Box Analysis For cable operators, set-top box analysis is a key mechanism by which they determine program viewing preferences by household. Though this practice borders on lines of privacy, there are many benefits for cable service providers to indulge in this type of analysis as they can enable targeted advertising options, set true advertising rates and use that information with demographic information to find similar households for placing ads. By being able to understand viewing patterns and content preferences of households, cable providers can negotiate better rates with their advertisers by promising them more insight about prospec-tive customers. This information can also help cable providers provide more targeted offer bundles and promotions which take into account a household's service viewing preferences and viewing patterns.

Churn Prediction & Prevention Predictive analytics can properly identify those customers who have a higher propensity to churn and possibly take those in their social circle with them. Predictive analytics allows CSPs to shift their business intelligence focus from looking at old data to looking at current data in a predictive and preventative fashion. The key to an advanced analytics solution providing optimal churn mitigation will be its ability to process information about all interactions that impact the customer experience, including network coverage, bandwidth consumption, billing information, support history and device type. Quick responses to customer issues can help keep a subscriber happy throughout his or her customer lifecycle.

Service Control Based on Customer Profile CSPs are already adjusting their networks so that QoS can be tailored according to the types of service being used by a subscriber (e.g., ensuring QoS for a premium gaming service), and limit subscribers' access to content for which they have paid. However, with the effective use of advanced analytics, they can deliver much more. By augmenting service plans with data from identity man-agement systems, behavioral pattern matching, subscriber preferences as well as data about network performance, services can be tailored according to different users on the same subscriber account. For example, providers can give company directors priority service compared to other employees, or manage a parent's business applications in a different way compared to the entertainment applica-tions used by their children. Supported by implementation of advanced analytics, the policies that can be programmed into policy servers for enforcement throughout the network can also be much more sophisticated.

Customer Profitability Analysis & Loyalty Management Dynamic real-time or near-real-time offer management capabilities based on subscriber network usage and traffic-based promotion, loyalty points, event-based promotion and rules-based promotion will be critical for an operator's revenue optimization strategy. Use of analytics can provide CSPs key benefits in business areas of concern, such as marketing campaigns, contract negotiations, churn management, customer loyalty and operational processes.


CSPs need to tailor pricing appropriately to strategic customer segments for both new and renegotiated contracts. Analytics-driven pricing strategies can assess the different combinations of customers, competitors, products and offerings. The resulting modified pricing models can be tested using analytic means to ensure that operations can scale to support anticipated growth. In previous years, CSPs typically focused on retaining customers based only on profitable ARPU. However, the models now include non-financial factors as well. Customer profitability analytics can help to streamline operational processes and reduce associated costs. One obvious key factor is agility because differentiating and building a competitive edge in the communications industry demands quick time-to-market changes. Fast translation of analytics results into operations will make a timely and sizable difference when acquiring new customers and reduc-ing attrition risks with informed interactions. In addition, there is an opportunity to identify which operational processes affect customer value and how to streamline costs to improve profitability.

Preemptive Service Assurance In today's hyper-competitive communications market, quality of experience (QoE) will be the next key differentiator for CSPs. Preemptive service assurance will play a vital role in this context and will depend on real-time correlation and analytics so that latency-sensitive services (such as those based on video content) can be fixed before any fall in QoS impacts subscriber experience. Analytics systems can offer root cause or predictive analysis taking into account a range of factors that may not be visible to the network management system. For example, it may identify a correlation of poor performance for subscribers all served by a single server; it may be that sufficient bandwidth is being delivered but that the fault lies in the original encoding; it may be that turning up QoS in one area has uninten-tionally impacted on quality of another type of service. Advanced analytics solutions can help to diagnose suitable fix strategies based on experiences extrapolated from other networks. Disparate software solutions that exist today – such as service fulfillment, service assurance, performance management and SQM – need to be evolved to provide a unified service experience model, focused on optimization of subscribers' experiences in a real-time and seamless manner to prevent problems before they arise (or at least before subscribers notice them). An advanced analytics solution can provide the bridge enabling the cross platform analysis.


Recommendations for CSPs With many uses for business intelligence and advanced analytics in the communi-cations industry, Heavy Reading has witnessed many different types of vendors trying to address this segment:

· Mapping (GIS) tools provide spatial, demographic and geographic data, but lack the advanced algorithms to conduct complex modeling and help CSPs conduct "what if"-type predictive analysis. Traditional mapping tools lack the analytical mechanisms to give CSPs any significant insight.

· Visualization tools are great for reporting purposes and they make the data look organized and cool, especially on mobile devices. However, these solutions also lack the analytical capability to handle the big data requirements of a CSP.

· Traditional BI and analytics tools are complex and require long IT or con-sulting engagements to customize them to CSP business needs. Also, cer-tain solutions in this category are solely focused on reporting capabilities, and lack the advanced analytical firepower that is critical for operators.

· Data warehouse solutions effectively handle the big data requirements of CSPs. In conjunction with their storage solutions, major data warehouse players also bundle analytics applications to perform predictive analysis on this deluge of collected data. Unfortunately, typical data warehouse solutions take months to deploy and customize according to CSP business requirements, and are also the most expensive.

· Strategic analytics tools can be placed in the hands of the users who are closest to the data because they are easy to configure and deploy, cost-effective, and easy to integrate with different data sources to provide a richer context to the entire analytical process. The key difference be-tween these tools and those of the previous types is that they include powerful analytic capabilities, yet are easy enough to be used by existing employees, not expensive consultants or statistical data scientists.

Vendor Profile – Alteryx One vendor that is doing some exciting work in this space is Alteryx. Its solution, Alteryx Strategic Analytics, closely mirrors the analytics solution that operators need today. Based in Irvine, Calif., Alteryx combines strategic analytics, spatial processing and predictive capabilities with household, business and industry data to give users everything they need to make confident, informed decisions. Alteryx offers three components to give users of all skill levels and needs a powerful strategic analytic software experience:

· Designers Desktop: Used by data artisans to manage and analyze data, then create analytic applications.

· Analytic Applications: Simple and easy-to-use applications used by busi-ness decision makers to solve specific problems with embedded analytics, reporting and visualization.

· Cloud Services: The ability to publish to private or public cloud environ-ments, allowing the critical sharing of analytic IP to business users, both in-ternal and external, to the enterprise


Alteryx serves a number of industry verticals, including retail, restaurant, real estate and consumer products, but also has significant traction in the communications industry. In fact, Alteryx Strategic Analytics has been deployed by eight of the ten largest wireless service providers in the U.S., as well as many other CSPs. Alteryx recently released a dedicated version of its analytics engine optimized for CSPs' unique needs. Alteryx Strategic Analytics: Communications Edition allows a CSP to bring together everything it needs for its most critical decisions, from internal business and technical data from its BSS, OSS and CRM systems, to integrated third-party demographic, firmographic and communications industry-specific data, in a common analytics environment. Highly visual, drag-and-drop tools allow for the definition and building of business process workflows that support critical network and customer decisions. Integrated communications industry-specific data and analytic application templates provide immediate answers to common industry problems, and can be easily customized for unique business priorities. Alteryx contains massive data processing capabilities and allows a CSP to bring together data from a virtually unlimited array of data sources:

· Technical Data (from OSS): Combine data from third-party management systems (e.g. TIRKS and NMA), with internally-developed inventory systems and other data sources, including RF propagation plans, drive test results and call detail records.

· Customer Data (from BSS): Extract customer information, including service history, billing records and customer support details.

· Prospect Data (from CRM): Include sales and marketing data from CRM applications to assess the proximity of lucrative prospects to their network.

· Infrastructure Data (integrated within the product): Leverage critical spa-tial information about each market, including central office locations, tower locations, LATA/rate center boundaries, NPA-NXX switch transla-tions, Public Safety Answering Points (PSAP) and retail store locations.

· Household/Business Data (integrated within the product): Match house-hold and business segmentation data for the U.S. to internal data and get projections on network spending, bandwidth consumption and behavior/ psychographic insight without the cost of additional services.

Figure 3: Alteryx Strategic Analytics Desktop-to-Cloud Solution


Summary CSPs possess a tremendous amount of information, but unfortunately most is trapped in disparate BSS and OSS infrastructures, disconnected from each other and from the bigger, strategic picture. Advanced analytics tools allow operators to free data from these silos and put it to work to provide a granular-level under-standing of the individual subscriber, manage the network, and analyze and plan for future network and market expansion. Strategic analytics stands out in its ability to allow users who are closest to their data to quickly pull, overlay and analyze any combination of internal business and technical data, plus external industry content, and give it spatial relevance so decisions can be made based on as many variables as possible. Strategic analytics can provide wireless, wireline and cable/MSO operators with the market insight, location intelligence and industry context to quantify and manage strategic growth opportunities, prioritize network expansion by revenue potential, decipher competitions weaknesses (and be prepared to combat their threats), accurately identify prospects for new services (as well as at-risk, high-value customers), and detect and preempt potential network and service issues before they impact customer experience. In today's highly competitive communications market, we believe that CSPs must remain agile and focused on ways they can improve their network and the experience of their top customers in order to differentiate their services from those of their competitors. Strategic analytics performed on the big data assets that a CSP possesses is, and will be, a critical means to achieve that goal.

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The Strategic Analytics Advantage in the Communications Industry · The advantage using advanced analytics is that it can help CSPs address multiple functions across an organization