Northstream White Paper

Transcription

1 Analytics beyond the hype: CSPs achieve tangible benefits How CSPs leverage analytics internally to improve operational efficiency, financial performance and customer experience Northstream White Paper September 2013 About this Paper This Northstream white paper examines the opportunities for communications service providers (CSPs) to improve all areas of their business through the applications of data analytics. With ongoing expansion of data sources, data types and data volumes, CSPs are facing the opportunities and challenges of big data. Big data is a buzz term used loosely to describe everything from the world s largest data sets to the call records of a modestly sized CSP. There is hype surrounding big data, but it is also important to acknowledge that CSPs are achieving quantifiable improvements through analysis of subscriber, network and third party data. Data analytics is used to optimize operational efficiency, customer experience and financial performance. In this paper, we explain how data analytics works in the context of the CSP and why some of the new applications are so critical to performance. We illustrate the structure of data analytics solutions and provide numerous application examples within a framework spanning the telecom business. Finally, we explore through case studies some implementations of data analytics that have already improved the performance of a diverse set of CSPs. Northstream would like to thank the data analytics vendors Comptel, Guavus and Salamanca Solutions International, which provided the information for the case studies in this white paper. Highlights Ø Ø Ø Ø Ø Ø CSPs are adopting more sophisticated data analytics solutions, which are real- time, granular to individual customers and combine data from multiple sources. Analytics moves beyond reporting and into predictive models that anticipate future performance and prescribe (automated) corrective action. Selling data to third parties can be an attractive revenue stream. However, it is the internal uses of data that offer CSPs the most sizable benefits through impacting differentiation, churn, costs, planning and ARPUs etc. The Extraction- Processing- Application framework is a model that can be used to describe a data analytics system. Extraction refers to the process of gathering data from sources, processing transforms the data into usable information that is subsequently applied for reporting and optimization of business areas. Analytics use cases are numerous and varied, but can be structured using 1) operational efficiency, 2) subscriber lifecycle and 3) financial performance as a three dimensional framework. Analytics case studies illustrate real- life improvements achieved by CSPs, such as a multi- fold increase in the revenue upside of campaigning through churn prevention analytics; or reducing customer care interaction costs by analysis of customer care drivers. CSPs achieve the best results when using analytics to accomplish specific business goals. CSPs find it useful to develop a strategic plan for data analytics that has a long- term vision, but at the same time build the implementations incrementally, beginning with individual use cases. 1

2 1. Big data in the context of the telecom industry 1.1. What is big data for CSPs? The operations of communications service providers (CSPs) have always generated large amounts of data. The data collected from the network has provided information on its performance. Every call made, or SMS sent, by a customer has produced data about the identity and location of both the initiator and recipient of the communication, about the duration of the call or the nature of the SMS; and about the functionality of the supporting equipment. With the digitization of services and the proliferation of data services, that information has expanded to include web sites visited, content of downloads such as apps and video, mobile payments and more. CSPs are also starting to discover that there may be value in other data sets such as billing records, sales channels and other customer interactions. The concept of big data lacks a universal definition. In a broad sense, big data is used to represent the rapidly expanding availability of data, from a diverse set of sources, which can be used as input to business decisions. In this context, big data is not limited to the applications of the largest and most complex data sets but also applies to small CSPs extending their application of data analysis tools to new sources and to new business areas. Big data is a relative measure for each organization, with big implying more volume, more sources and higher value. Big data is an important asset for CSPs. Yet, its true value is not extracted until the application of data analytics, which transforms data into insights and inputs to decision making. Traditional business intelligence tools have been failing to cope with the volume, variety and velocity of big data. Operational efficiency Sales Analytics Subscriber lifecycle Financial performance quantifiable improvements through analysis of subscriber, network and third party data. Data analytics is used to optimize operational efficiency, customer experience throughout the subscriber lifecycle and financial performance The evolution of data analytics CSPs have long been applying different levels of analytics to support planning; especially after the digitization of telecom networks. However, CSPs have been inhibited by the availability of appropriate analytic tools, computing power and affordable storage. Due to these constraints, the data analytics used by CSPs has been until recently focused on descriptive models and historical analysis of past events, general trends and group behaviour. This type of analytics is usually performed in isolation, within separate departments. As a result, the information from this analysis is backwards looking and lacks a holistic view of the CSP s complex, ever- changing environment. Though past applications have been limited to reporting on the state of the network, customers or finances, new possibilities have emerged. Rather than only describing performance, optimization and efficiency applications are used to improve performance. Predictive applications go further and anticipate future performance. The final and most advanced step for these tools is to automatically correct the predicted future performance inefficiencies. Although many CSPs have so far largely been limited to reporting analytics, the most advanced CSPs have some predictive analytics or data mining in place. CSPs are now starting to adopt solutions that allow analytics in real- time, granular to individual events, network elements or subscribers. In addition, they are starting to combine data and insights from multiple sources within the organization (network, marketing department, customer care etc.). The real- time, or near real- time, aspect of telecom data analytics solutions is becoming an increasingly crucial requirement. Receiving and visualizing information through real- time dashboards helps detect critical events that affect customer experience and take real- time corrective action. For example, address problems with dropped data sessions. It can also be used for contextual marketing campaigns when a customer is in a specific location where a certain offer is relevant. Network Figure 1: Data analytics applications Another key aspect of the evolution of data analytics is moving from understanding general trends and group behaviour to understanding customers as individuals. Being able to make a personalized offering increases the success rate of up- sell and cross- sell campaigns and improves customer experience. There is hype surrounding big data, but it is also important to acknowledge that CSPs are achieving Data analytics solutions are increasingly able to integrate multiple types of data coming from multiple sources in the organization or even from sources external to the CSP. 2

3 Breaking down data silos is the only way to achieve a complete and holistic view of the CSPs world External uses of big data An increasing number of CSPs have also begun to view internal/customer data as a revenue stream when suitably packaged and sold to third parties. Data unique to CSPs, such as content consumption and communication patterns, is of value to advertisers as well as retailers and other businesses. The external selling of data is a revenue stream that has potential but also challenges. Privacy and regulatory questions need to be carefully navigated. Additionally, only the largest Tier 1 CSPs have scale of subscriber base to provide a clear path to monetization. The greatest challenge may be that companies such as Google and Facebook, which have built their business models around customer data and advertising networks, are better at extracting information across broader audiences and geographies than any CSP. Although global net digital ad revenue (including online and mobile) was estimated at $104B in 2012 and is growing quickly, that is equivalent to only 7% of the $1.5T global telecoms revenue. Almost half of the net digital ad revenue is captured by eleven companies, among which Google is the leader with 31% of the total. CSPs would find it hard to outcompete these big established rivals and at best could capture a small fraction of the total digital ad revenue. For CSPs, a 1% increase in their revenue, or the corresponding smaller reduction in expenses, accomplished through the performance improvements possible with internal uses of data analytics, would have a similar impact as becoming a market leader in digital advertising. It would also be more easily and consistently accomplished than what is realistically achievable by monetizing customer data. Although we acknowledge that the external uses of telecom- generated data is a topic that rightfully receives increasing attention; in this report we will focus on CSPs internal uses of data. Northstream finds that it is the internal uses that offer CSPs the biggest and most impactful benefits. 2. Why CSPs need data analytics solutions Today it is a widely shared view that telecom services are becoming commoditized. Many markets are reaching saturation, which, along with more expensive user devices, is making customer acquisition more expensive. Continuing network investment requirements, driven by data usage growth and LTE, keep CAPEX requirements high. At the same time, in most markets ARPUs are often stagnant or declining (in Europe the average revenue per user declined by 6.6% YOY to US$ 27 in ). Revenue pressure comes from competition within the industry, and also from over- the- top (OTT) players, such as Skype, Facebook and Google, which are driving increased data revenues but are eroding revenues from traditional core services like voice and SMS. This trend is expected to continue as total global voice revenues are forecast 4 to decline at a CAGR of 2.5% in the period With this set of pressures on the industry, new methods are required to maintain margins. Incremental improvements to all business areas will replace reliance on strong industry growth. While data analytics is not the only tool that will be used to address the challenges of the telecom industry; analytics can be used to help alleviate them all. To address the high level list of challenges listed above, Opportunities for differentiation increase as CSPs can make improvements to service quality, customer experience and product design. Global telecoms revenue 2012 $1,500B Global net digital ad revenue 2012 $104B Customer churn can be reduced by using predictive analytics to better target churning subscribers with retention offers. Customer acquisition can be made more cost- effective when improved accuracy allows for selective marketing. The global net digital advertising market (including online and mobile) was estimated to have revenue of $104B in 2012, equivalent to 7% of the $1.5T in telecom and related services revenue in Figure 2: Comparison of Global telecoms revenue 1 and Global net digital ad revenue 2 for Analysis Mason, Global telecoms market: trends and forecasts Networks can be operated more efficiently to derive the maximum value from existing assets, and can be planned more cost effectively by better matching capacity supply with demand. 2 emarketer, June Bank of America Merrill Lynch Global Wireless Matrix 1Q13 4 Ovum, Global Mobile Market Outlook:

4 ARPUs can be increased by individually tailoring offers to ensure each subscriber gets as much value as possible from the service. Services can be improved to strengthen quality leadership in the competition with OTTs. 3. Example use cases and case studies illustrating how CSPs apply analytics Because of the availability of data throughout telecom operators, data analytics can have an impact on all facets of the business. There are so many and such varied use cases that a framework is required to present them. Northstream believes that data analytics solutions can be mapped using (1) operational efficiency, (2) subscriber lifecycle, and (3) financial performance as a three dimensional framework. Presented in no particular order, the dimensions are described as follows: (1) Each operational area of the CSP organization (networks, customer care, sales and marketing, regulation/governance, and executive management) can benefit from data analytics to improve the efficiency of its operations and the quality of its outputs. Some applications rely only on data internal to those operational areas, more advanced applications rely on opening silos to share data across the organization. (2) Data analytics is used to improve customer experience throughout the entire subscriber lifecycle. Better segmentation, and targeting, allow marketing resources to be optimized to attract new customers. Once the customer is acquired, the service offering can be customized and provisioning tools automated through real- time and close loop functionality. Service delivery is enhanced through network and usage analytics that optimize network performance. Predictive analytics enables better- targeted upsell and cross sell offers. Customer retention is increased by more accurately identifying potential churners and approaching them with suitable offers. (3) The financial performance of a CSP, reflected in both the income statement and the balance sheet, is improved with the help of analytics. CSPs can optimize existing sources of revenue, or identify new sources, and in parallel achieve savings both in the serving of subscribers and in the overhead of the organization. In addition, CAPEX can be made more efficient by identifying the most critical investments and by lowering the overall bill. Finally, by analyzing the factors underlying higher- level KPIs, trends can be better understood and forecasted. Tables 1, 2 and 3 illustrate each dimension and category through examples. Each example can be listed under one category in each of the three dimensions, but is placed under only one of the categories arbitrarily. Examples marked in red are explored further in the case studies that follow. 4

5 Operational efficiency Use cases Network Capacity management Performance management Customer care Customer problem case analysis Priority customers service Customer sentiment Products, Sales and Marketing Customer profiling/segmentation Top-up optimization Product analysis Regulation/Governance Contract/SLA enforcement Roaming analytics Regulatory reporting Management Continuous business optimization Predictive planning Internal staffing Subscriber lifecycle Use cases Attraction Customer insight and targeting Sales and channel analysis Acquisition Value segment prediction New customer analysis Service Delivery Contextual offers Service quality improvement High value service upsell Billing Fraud detection Bad debt forecasting Retention Churn prediction Churn prevention Competitor destination prediction Financial performance Use cases Revenue Channel optimization Product portfolio optimization Pricing optimization Variable cost Acquisition cost optimization Retention cost optimization Fixed cost Customer care cost reduction Marketing analysis/optimization CAPEX Infrastructure planning Traffic optimization Accounting/Forecasting Wholesale reconciliation Revenue leakage Customer lifetime value Description identify and prevent network congestion based on service usage analytics monitor and ensure consistent service quality regardless of location, device etc. analyze customer problems, speed of resolution etc. to improve customer care identify priority customers and ensure their customer service satisfaction detect customer sentiment through social media analysis 360 customer insight based on demographics, product, digital usage, billing etc. create promotions, tiered pricing etc. based on individual subscriber behavior analyze product performance, margins, cannibalization, price changes etc. track network performance to ensure vendors compliance with contracts analyze national and international roaming patterns and usage monitor QoS to ensure compliance with spectrum license requirements optimize business processes based on identifying organizational bottlenecks etc. plan allocation of resources for future needs analyze, predict and plan internal staffing needs Description create target profiles based on analytics of product usage, customer behavior identify the most suitable channels and sales strategy for each product predict the future value segment of a new customer based on initial data analyze new customers to assess success of marketing campaigns tailor offers based on context such as customer s location configure network to optimize service quality through performance data target subscribers most likely to acquire additional service detect sources of fraud such as cloned SIMs, device theft, top-up vouchers misuse forecast bad debt based on analysis of subscriber payment history identify the most likely churners based on predictive analytics tailor personalized offer to potential churners predict which service provider customers are churning to Description predict the best channels for each product and optimize distributor margins analyze product portfolio to identify unserved customer segments etc. predict customer price sensitivity for complex plans (roaming, voice and data etc.) predict customers most likely to respond positively to new offers focus resources on at-risk high value customers and identify best retention offer reduce care calls, tickets and truck rolls through identifying problem commonalities improve efficiency and execution of campaigns plan infrastructure investments based on network and data usage analysis route traffic to efficiently load networks identify sources of discrepancy and reconcile interconnect charges identify revenue leakage due to system misconfiguration or failed components predict customer lifetime value through behavioral and service usage analysis Tables 1, 2, 3: Examples of analytics use cases 5

6 The benefits of data analytics can best be illustrated by real- life examples. Northstream has therefore reviewed a number of case studies from three vendors (Comptel, Guavus and Salamanca Solutions International) that depict actual implementations. Each of the case studies presents the background and context, the implemented solution and the results achieved as well as how these use cases fit into the three dimensional framework described earlier. The case studies address different business challenges, different areas (ranging from products and marketing to customer care to finance) and different markets across five continents. Yet, the common denominator is that they have all led to concrete and measurable improvements. These improvements have affected the operational, financial and customer experience side of the CSPs business. Top-up optimization analytics increased the campaign net revenue in prepaid segment by 63% Background and market context! An African CSP is the leading operator in its country and has managed, through a successful strategy focused on low cost handsets and underserviced areas, to increase its prepaid customer base! However, the above strategy, together with competitive pricing from other players, has decreased prepaid ARPU and pushed down on margins! The CSP faces the challenge of increasing stickiness among prepaid segment and top-up revenues Top-up optimization solution! The top-up optimization solution identifies the customers likely to respond positively and tailors a personalized offer with a top-up reward (e.g. Top-up $10 now, get $3 extra)! The CSP deployed the top-up optimization solution. For the analysis they used data sources such as CDRs, credit balance etc. in order to select customers to target and identify a personalized offer Results The results were compared between a series of monthly topup stimulation campaigns executed by the CSP without using any analytics and a series of campaigns using the top-up optimization analytics. The target group for both campaigns was 40%. The resulting impact was put in the context of the CSP s overall business performance. By extracting and analyzing raw data (CDRs, CRM customer profile, top-up server data, service usage etc.), the top-up optimization solution provided a 63% increase in campaign net revenue. The solution can be implemented in near realtime with 'closed loop' features, i.e. selecting the right action for continued campaigning. The data analytics vendor was Comptel. Increase in campaign net revenue from analytics solution Increase in operator s total prepaid Revenue Use case mapping Operational efficiency Products, Sales and Marketing Subscriber lifecycle Service Delivery Old Campaign Campaign using analytics 63% 0.6% 1.0% Financial performance Revenue A targeted upsell campaign using subscriber analytics led to a 5-fold increase in Mobile TV uptake and usage Background and market context! A South-East Asian CSP observed a slow uptake of mobile TV service after its launch! The CSP s marketing department had the objective to understand mobile TV usage, accelerate its adoption among subscribers and increase the overall usage for the current viewers Mobile TV Upsell Solution! The CSP conducted a SMS/MMS marketing campaign promoting a premier league football mobile TV channel! The campaign used analytics to target subscribers based on demographics, device type (subscribers with the devices that were best suited for mobile TV) and content history (content interest, past viewing habits etc.) Results Supported by analytics, the CSP was able to conduct a successful marketing campaign that raised awareness for the football channel and, by targeting the most likely viewers, increased adoption of the service. The subscribers who received messages showed an initial fivefold increase in uptake of the service (which stabilized at twofold after a month) compared to subscribers who were not targeted in the campaign. The campaign tracked a control group and included untargeted segments in order to benchmark performance and learn best practices. Among the subscribers who were targeted by the campaign and saw the promoted football match, 60% returned for viewing of next match. The overall viewing time per subscriber increased by 16%, creating deeper service loyalty. The data analytics vendor was Guavus. Increase in uptake for mobile TV channel Increase in avg. viewing time (1 month) Effectiveness of targeting segments Use case mapping Operational efficiency Products, Sales and Marketing Campaign benefits Immediate 5x for targeted subs, stabilizing at 2x 16% 2-4x more uptake than off segment Subscriber lifecycle Service Delivery Financial performance Revenue 6

7 Churn prevention analytics increased the campaign revenue gain in prepaid segment by 259% Background and market context! An East European CSP is the country s second largest operator by revenue and subscriber base! ARPU has been relatively stable the past few years but as the market has matured and mobile penetration has increased, the new subscriber growth rate has dropped! The CSP faces the challenge of retaining existing customers, while attracting new ones from a limited pool Churn prevention solution! The churn prevention solution is an extension beyond prediction as it not only identifies potential churners likely to respond positively but also tailors a personalized offer! It allows CSPs to increase the success rate of retention campaigns as the better personalized offers are more likely to be accepted by potential churners and a series of campaigns using the vendor s churn prevention analytics. The target group for both campaigns was 12% of the prepaid customers. The resulting impact was put in the context of the CSP s overall business performance. By extracting and analyzing raw data (CDRs, CRM customer profile, service usage etc.), the churn prevention solution provided a 259% increase in campaign revenue gain. The solution can be implemented in near real-time with 'closed loop' features, i.e. selecting right action for continued campaigning. The data analytics vendor was Comptel. Use case mapping Campaign benefits Increase in retained prepaid customers from analytics solution Increase in campaign revenue gain from vendor s analytics solution 3.6 times 259% Results The results were compared between a series of monthly campaigns executed by the CSP without using any analytics Operational efficiency Products, Sales and Marketing Subscriber lifecycle Retention Financial performance Revenue Analysis of customer care drivers is estimated to reduce interaction costs by $11m and the churn rate by 0.1% Background and market context! A North American CSP had a lack of timely, in-depth insight into the drivers behind customer care interactions! The CSP was interested in improving their understanding of the drivers of customer care costs, but were having a hard time overcoming the difficulty of correlating data from numerous, disparate sources! The CSP needed the information to be available quickly to CSP employees from a variety of groups Customer Care Solution! The application collects and analyzes data from numerous disparate sources and provides actionable insights! The solution identifies which attributes are common or outside of the norm regarding calls, tickets and truck rolls by using advanced analytics techniques! Examples include device interoperability issues and unexpected impacts from scheduled maintenance Results Estimates of processing requirements are more than 1m data records daily, coming from more than 12 different systems, in near real time. A decrease in care events resulted from a reduction in mean time to understand issues and more accurate, targeted call deflections and the decrease in churn would come with better customer experience. Initial estimates put expected future savings to the CSP at about $11 million in calls, tickets, truck rolls and operational man hours. Additionally, an estimated 0.1% reduction in churn will be achieved; churn today costs the CSP about $816 million. The data analytics vendor was Guavus. Use case mapping Campaign benefits Decreased call center, trouble ticket, and truck role costs Decrease in churn rate through more effective customer care $11m over lifetime 0.1% Operational efficiency Subscriber lifecycle Financial performance Customer Care Retention Fixed Costs 7

8 Wholesale reconciliation analytics helped CSP reduce discrepancy in interconnect charges by decreasing mismatch in incoming minutes reported from 15% to less than 1% Background and market context! A Latin American CSP suspected a local interconnect partner of fraud based on large and systematic differences in usage reporting. The CSP did not have the expertise to reconcile the differing sets of records to identify the correct wholesale cost and identify the cause of the discrepancies Wholesale reconciliation solution! The wholesale reconciliation solution was used to analyze the CDRs of both CSPs. The system collected large quantities of CDRs and the records were filtered down to those of interconnected calls during the periods in question. The records were then transformed to the same format for direct comparability and matched based on a variety of call meta data fitting within certain tolerances! The application was able to resolve the CDRs of the two CSPs and guide network engineers towards the common point of failure in the interconnect records keeping Results Based on the analysis performed, it was found that incorrect core network configuration was the reason for the records discrepancy. While revenue was lost, it was not a case of fraud. Within two months, the CSP was able to reduce the mismatch for the incoming minutes reported by 93% and the difference for outgoing minutes by 80%. The application provided information that aided in the root cause analysis of the records discrepancy and let to its correction. The data analytics vendor was Salamanca Solutions International. Use case mapping Operational efficiency Network Analytics benefits Reduce the mismatch for the incoming minutes reported Reduce the mismatch for outgoing minutes reported Subscriber lifecycle Service Delivery from an average of 15% to less than 1% from an average of 5% to less than 1% Financial performance Forecasting/ accounting 4. How data analytics systems are structured There are as many possible implementations of a data analytics system as there are possible applications. No single technology/algorithm can necessarily solve all problems. CSPs need different models, algorithms, etc. to address the different business objectives. Therefore, one of the key values of working with experienced vendors is the accumulated knowledge of combining multiple models. Even with the great variety of possible implementations, there is still a functional model that can be applied to any data analytics system. One of many possible models is Extraction Processing Application (depicted in Figure 3). The specifics of each of these steps are determined both by the application requirements and by the existing data systems at the CSP. The implementation of each of these steps can have large variations in terms of scope and complexity. The boundaries between the steps are subjective in many cases. They are meant to be an abstract guide to overall functionality rather than provide a strict definition. Extraction Each system has its own requirements for data inputs. Currently, common applications can utilize a single, existing source (such as call data records (CDRs) for customer support), but more complex applications may need inputs from disparate areas of the CSP. Regardless of what data is required, each implementation is different due to the varied structure and distribution of sources at each CSP. The list of data sources is continuously expanding as CSPs discover novel applications. Potential sources can be categorized based on key areas such as the network (network elements and probes that provide information on the functioning of the network), the billing and financial databases (from which business and customer data is extracted) and many others. A newer class of sources is those of 3rd party data sets; this is the most open- ended category, but early examples include geo data, demographics, social media and more. A fundamental distinction of types of data sources is stored versus real- time (flow) data. Stored data exists in databases, which can be queried either on demand or at regular intervals determined by the stability of the data. The alternative is a data stream, which is automatically generated by network elements or high frequency event- based reporting. Real- time data has traditionally been focused around network alarms, but new applications are extending the use cases. Extracting data in real- time requires significantly more system capacity and in many cases pre- processing to make the data streams more manageable. With the increasing variety of data sources and types, a growing requirement is the use of open standards for interconnectivity, standardized interfaces and data structures. While a wide range of tools are needed to address the range of challenges some examples include etom (interface structures), Apache, Hadoop and Linux (open source software). This approach helps to avoid vendor lock- in and will also ensure that difficult to replace legacy systems will remain compatible as data systems 8

9 develop. CSPs should be also able to configure, through a simple interface, new analytical logic from existing data sources. By not going to the vendor every time a new idea comes up, more ideas can be tried and a better variety of tools can be developed. Processing The function of processing, also called mediation, is to transform the varied data sources into usable information. The process of transformation is different for each set of data sources and application requirements, but follows a standard progression: Validation ensures that the data is intact and complete. Normalization restructures the data so that it can be handled in an efficient way, simplifying the following steps. Logical rules are defined which build information from the data. Correlation of multiple data sources, matching events, subscribers, or assets, provides a complete view and identifies relationships between the information. The processing requirements of a system are largely determined by the types, and volumes, of data that are handled. Static data can be processed according to a schedule; and therefore can be done very efficiently. Real- time data inputs, especially when used for real- time outputs, require full capacity to be available at all times. This requirement is generally met by distributing processing closer to the source, or provisioning the capacity for fewer sources. Continuing advancements in data processing (e.g. MapReduce with parallel computing) have only recently made it possible for CSPs to cost- effectively work with large, complex data sets. Application The applicability of data analytics goes through the business processes of a CSP. All functions, decisions and plans can be impacted; the key is identifying challenges for which analytics can have the largest impact. Classes of applications include: Reporting and visibility provide an increased knowledge of a CSP s performance, thus enabling better- informed decision making. This is the focus of most CSP efforts to date. Optimization and efficiency applications can identify non- trivial solutions to operational and planning problems. Predictive analysis uses causal relationships and underlying trends to more accurately plan and forecast. Closed Loop systems automate the process of reacting to data analysis results and allow for real- time responses to changes in the operating environment. One functional area that has been left out of our description is data storage. The need for storage can be driven by caching requirements, to maintain the capability of historical benchmarking, or regulatory requirements. This functional area has been left out because it is a technical requirement to be determined for each individual implementation, rather than a driver of the expansion of data analytics possibilities. 5. When and how a CSP should deploy data analytics 5.1. Data analytics as a tool to achieve specific business goals CSPs should use big data and data analytics in order to achieve specific business goals rather than as a broad strategy for discovering useful insights. Some of these business goals may have a clear business case and a measurable result (e.g. the impact on revenue by reducing churn by 1%) while others have more intangible and hard to measure results (e.g. more effective management decision- making through improved business awareness). In any case, the objectives to be achieved need to be clear and specific, with a quantifiable result to the extent possible. The insights provided by the data analytics need to be timely, relevant and actionable. Existing organizational structure (systems, processes, people) can be a barrier to innovation and adopting new analytical tools. In order to make analytics a useful tool in making decisions and achieving specific business goals, a key challenge is to integrate the results of data analytics into the organization. This means that line managers need to have the necessary skills and training to understand the output of data analytics and how to best integrate that in decision making by combining it with soft data, based on their own business experience and intuition. 9

10 Example applications from case studies:! Top-up optimization! High-value service upsell! Churn prevention! Customer care cost reduction! Wholesale reconciliation Application Operational efficiency Subscriber lifecycle Financial performance Sales Network Rules / Logic Correlation Extraction Processing Network! Nodes! Routers! Servers! Probes! Validation! OSS/BSS! Inventory! Fulfilment! Assurance! Billing! OSS/ BSS 3 rd Party! Social Media! Geo Data! Financial! Consumption! Normalization Customer! CRM! AAA! HLR/HSS! Devices! Figure 3: Structure of data analytics systems 10

11 During Northstream s engagements relating to data analytics deployments, we have seen that the solutions offer not only quantifiable benefits but also have very short payback periods, (as fast as a few months). This is not to say that all applications and deployments will be inexpensive or as efficient, but there are certainly those that are low hanging fruit. The best results are obtained when the CSP is focused on solving a specific business goal in the most efficient way Having a high level strategy/architecture is important but CSPs should implement piece by piece In order to develop their data analytics strategy, Northstream suggests that CSPs conduct an inventory of how they are currently making use of analytics tools throughout the organization. Attention should be paid to what data sources are available, what systems are used and their level of compatibility, and who is driving and owning the tools. While lessons about best practices, and inefficiencies in deployment will be gathered in the inventory, this will also guide the formation of an overall strategy. Legacy systems, especially those built by vendors working with proprietary specifications, can be a challenge to integrate. Some will remain outside the strategic roadmap; others will need to be replaced in order to integrate with other systems. The approach to data analytics should comprise two parallel tracks. On one track, a strategic plan to move all data analytics solutions towards a long term vision and to ensure that all solutions are implemented within the best practices framework of the CSP. On the other track, new solutions or additional functionalities to existing ones should be deployed individually, after completing an assessment of the specific opportunity. In other words, rather than focusing on what applications can be implemented, consider which problems in the organization, from an operational, subscriber lifecycle and financial perspective, can or need to be solved. Each CSP needs to develop their own strategy for how they will leverage data analytics. For guidance of individual application deployments, the strategy should set the standards to ensure implementations are compatible. A process should be defined to enable data sources from one area of the organization to be accessed by any other. In addition, a centralized control point should be established to keep records of each application and facilitate cooperation between groups. A more long- term component of strategy is to develop and progress a roadmap towards greater integration of individual applications and systems. Maintaining a strategic vision will help steer all efforts towards eventual convergence. Northstream recommends that CSPs avoid starting with large- scale, costly and time- consuming deployments but rather build the functionalities incrementally, like a jigsaw puzzle built one piece at a time. There are a number of reasons to support this approach: Develop competence within the organization each deployment incrementally improves the understanding of requirements and establishes best practices for subsequent implementations. Avoid scope creep because of the unlimited possibilities of data analytics, requirements can stream in from all departments and system integrators will gladly include them in the bill. As with any new initiative, mistakes can be made but smaller deployments allow time to learn from mistakes, and more importantly, to avoid overly critical impacts when things do go wrong. Allow for test pilots a limited customer base / network area can be targeted first in order to assess the model s effectiveness and fine- tune analysis before wider implementation Each CSP should develop its own processes for managing big data and analytics As applications span the whole organization, CTO, CIO or CMO (or other) departments may be selected to lead the data analytics project. Each CSP organizes their business functions and responsibility centers differently, so there is no established best practice here. However, it is important to have a clearly appointed champion/project owner at the strategic level. This role doesn t need to manage the design, implementation or operation of individual solutions, but should be empowered to ensure each deployment fits with the CSP s strategy for convergence and best practices. This champion should also be responsible for maintaining the processes for sourcing and integrating data from different departments in the organization. They should also be defining the process for sourcing systems and selecting vendors. Different vendors have different strengths. For example, some vendors excel in data extraction and are particularly relevant for CSPs that do not have well- established data collection processes in place, while others have particular strength in the mediation process and predictive or advanced analytics. Therefore, it is important to choose the vendor with the appropriate capabilities for each project. That said, as systems will converge over time, it is essential that any selected vendor is capable of working in open standards and non- proprietary interfaces. The details of the standardization should come from the CSPs strategy, which remains consistent across all solutions. Northstream finds that, overall CSPs are still in the beginning stage of adopting more sophisticated analytics solutions and replacing old methods and departmentalized use cases. Therefore, we see a strong growth opportunity in the near future for the internal applications of new analytics systems. The CSPs that lead this process can translate analytics into a competitive advantage. As illustrated by the case studies presented earlier, there is strong evidence that the results achieved are measurable and lead to improved customer experience, operational efficiency and financial performance. 11

12 About Northstream Founded in 1998, Northstream is an experienced management consulting firm providing strategic business and technology advice to the global telecom and media industries. We help our clients through independent and objective analyses, advice, problem solving and support that are tailor- made to our client s situation. Our work is based on a well- balanced combination of innovation, industry best practices and in- house methodologies. Northstream typically works with: Business strategy development and planning Strategic sourcing of systems and services Technology & product strategy evaluation Operational review, optimization and support Investment analysis and due diligences Clients across the world include mobile operators, network and device suppliers, application providers, investment banks, regulators and industry fora. Contact us to learn more about how we can work together to ensure your success in the mobile voice and broadband business. Strategy and Sourcing About data analytics vendors who contributed to the case study research Since 1986, Comptel has helped more than 290 service providers across 86 countries meet over one billion subscribers communications and infotainment needs. Comptel s solutions are built on an Event Analysis Action strategic framework that leverages the company s strengths in collecting and analysing Big Data and turning intelligence into opportunities in real time. Comptel s service fulfillment, mediation, charging and policy control, and predictive social analytics products with implementation and professional services enable service providers to automate customer interactions and other business decisions, to create revenue, reduce costs and lessen churn. Comptel has a global team of over 600 professionals, and net sales were EUR 82.4 million in Guavus is a big data analytics company ushering in a new class of business analytic applications that allow companies to put all their data to work to uncover new insights and make better informed and more timely decisions. The company offers a suite of decisioning applications for network, marketing, monetization and care, that are embedded with powerful data science that turns the non-stop processing of all your data into streaming insights to get a bigger, more informed picture of your entire business. Guavus brings business professionals fine-grained, precise insights continually correlating and instantly analyzing an unlimited amount of dynamic and static data. Guavus leapfrogs traditional BI and big data solutions with the industry s only compute-first approach that takes computing power to the data source, eliminating the constraints of the past. The world s most data-intensive companies trust Guavus to help them take strategic advantage of their data assets to grow revenue, improve operating efficiencies and delight customers. The telecom industry is more competitive than ever before. To be profitable, you need to be different. We will help you make that difference with our experience supporting your business transformation objectives. Salamanca Solutions International is very different from other OSS/BSS software companies. Our team was spun off from Trilogy International Partners, the successful, multi-national operator group. We know, from more than 10 years of direct experience in telecommunications that to succeed as a service provider, you must be as efficient as possible. To make that possible we have concentrated on tightly integrated architecture that reduces the time needed for every operation. We know that your network is unique. However, our team has experience in implementation with a wide variety of systems, including Comverse, Nokia, Nortel, Alcatel and Huawei. 12