Four Methods to Monetize Service Assurance Monitoring Data

Transcription

1 whitepaper Four Methods to Monetize Service Assurance Monitoring Data Using Service Assurance Analytics in Voice and Data Network Monitoring to Increase Revenue and Reduce Cost

2 Introduction In general, service assurance monitoring is tactical in nature. Carriers must continuously monitor the network to ensure it is performing as designed. When a failure occurs, the operations group needs to quickly isolate and resolve the issue. Return on Investment (ROI) for service assurance is typically measured in terms of improvements in operational efficiency and the amount of revenue saved from faster detection, isolation and repair time. In addition to this traditional use, why not take it one step further? Why not use this wealth of information to increase revenue and brand value, reduce customer churn, or better understand service usage and the end-user experience? Instead of simply reacting to issues, why not proactively prevent bottlenecks, spot trends and measure the quality of service before it impacts the customer? How do you turn mountains of monitoring data into the precise information necessary for improving decision support? The answer is Service Assurance Analytics (SAA). This white paper presents an overview of SAA and how to use this multi-dimensional approach to analysis to strategic power decision making across many departments. In this way, you can further monetize your service assurance monitoring data and increase ROI. What is Service Assurance Analytics? Service Assurance Analytics includes the technologies, applications and practices that turn monitoring data into strategic information for decision support in Operations as well as Engineering, Marketing and Sales. The practice and technology, also commonly known as Business Intelligence (BI), includes Extract, Transform and Load (ETL), On Line Analytical Processing (OLAP) and flexible analysis tools to identify, extract and analyze network quality data for strategic decision support. SAA uses these techniques to identify: What is the service impact What customers are effected The extent and location of the problem Why the issue is occurring If there is a trend What will happen next (i.e. predict). In other words, SAA allows you to answer important questions such as: 1. What was the average voice quality last month for the largest revenue customers? 2. Did call completion rates decline, increase, or stay the same in the southwest region? For any type of analytics to be meaningful, the underlying data must be accurate (i.e. garbage in=garbage out). SAA is no different. It requires accurate quality measurements and Key Performance Indicators (KPIs) for services and the underlying network. Necessary VoIP KPIs include: Answered Call Volume Unanswered Call Volume Call Length Answer Seize Ratio (ASR) Post Dial Delay (PPD) Protocol Errors

3 Voice Quality/Mean Opinion Score (MOS) Lost, duplicate and dropped packets SAA must provide the ability to view these KPIs from a network, service, or customer centric perspective. For maximum benefit, it should also provide a 360 degree perspective (e.g. to view end user quality for enterprise account management or quality metrics to peering partners). In addition, the KPIs must be available across key dimensions for network, service and customer analysis. These key dimensions include: Customers Interconnected carriers Services Network elements Time Adding these dimensions to existing real-time service assurance monitoring data and then layering on powerful yet flexible analytical tools yields a pervasive SAA solution. What are the Challenges? Operators must consider the following challenges when implementing an SAA solution: Obtaining accurate measurements and KPIs Turning terabytes of data into valuable, explicable information Selecting the right technology and the right scope of tools Managing the cost Inaccurate monitoring data or missing measurements for KPIs can lead operators down wrong paths and waste time when isolating problems. Moreover, missing or incorrect measurements may leave operators thinking a service is high quality and fully operational when, in reality, end-users are experiencing poor voice quality. The lack of alarms does not mean there are no problems. Shifting from alarm driven network assurance to proactive customer service assurance requires a multidimensional view of the KPIs. Service assurance monitoring is extremely valuable with the right diagnostic tools to sort through all of the data. The ability to store and manually search the data for fault patterns is the first step. SAA takes this process a step further and turns the monitoring data into valuable information to support decision making across the organization. Choosing the right SSA technology is critical to achieving operations cost reduction. Getting it wrong means operators will spend more time on database administration, writing SQL scripts and loading/crunching data rather than focusing on the actual analytics. Older technology that does not use OLAP cubes and requires SQL based reporting tools can unfortunately lead to a reporting treadmill. Finally, operators must consider cost. Solutions using a generic Enterprise data warehouse have very high CAPEX and OPEX price tags. Moreover, once implemented, the operator can be right back on the reporting treadmill by selecting the wrong reporting tools. SAA requires a highly focused data mart designed specifically for service assurance.

4 Four Methods to Monetize Service Assurance Monitoring Data Increase Revenue and Brand Value Every service provider could use another competitive differentiator. As we have seen in many high-profile, national advertising campaigns, service quality can definitely be a valuable one. However, in order to turn service quality into a differentiator, you must have measureable metrics and the ability to interpret/display them. The value of service quality metrics is directly related to their verifiable accuracy. However, obtaining truly accurate metrics is not simple, particularly for complex call flows that involve multiple calling parties, network segments, protocols, and/or transcoding schemes. With the right service assurance monitoring solution, a service provider can confidently measure these valuable, yet highly complex services. But measurement is not enough to effectively use service quality to increase revenue and brand value. To accomplish that task, you need powerful analytics to find the key nuggets of information that can be used as competitive differentiators. For example, a service provider who can prove continued excellence in service quality with a weekly / monthly / quarterly / yearly track record has a better foundation for increasing revenue. Specific measurable metrics by interconnect carrier may be used to highlight high ASR/call completion rates, high MOS scores, or low packet latencies. Whatever the metric, the ability to show (graphically for maximum visual impact) how service quality is maintained and improved will result in a competitive differentiator. Additionally, a service provider could use SAA and the resulting information as important content on a customer portal. Providing access to service quality information can be a direct benefit to end customers to help them optimize and troubleshoot their own network infrastructure. While it might seem like this benefit could be a value-add to generate revenue, offering this as a pro-bono capability may be an even better way to enhance brand value as well as increase customer satisfaction. Churn Reduction It s a well established fact that acquiring a new customer is far more expensive than retaining an existing one. The first challenge with any effective retention strategy is to understand why your customers leave. The two main reasons usually found for customer defection are service quality not meeting expectations or a competitor offering the same service at a lower cost. SAA is an invaluable tool for identifying customers with the highest likelihood of switching carriers due to poor service quality. Four key metrics can be linked to VoIP service quality, namely: 1. Short duration calls 2. Excessive Post Dial Delay (PDD) 3. Setup failures 4. Voice Quality (% Jitter, % Packet Loss, % Latency) or MOS With SAA predictive modeling, a rule-based engine looking for customers with the worst quality in two or more of these metrics, as illustrated by the Venn diagram s yellow intersection, identifies customers with imminent churn risk due to service quality. Churn risk also increases significantly for customers that call customer care and do not get their issue resolved. Our experience has found that: 1. Churn propensity increases in relation to metric frequency; for example, customers who experience any one of the key metrics are more likely to churn and people who have higher call volumes will experience the metrics more frequently. 2. Churn propensity increases in relation to metric diversity; for example, customers who experience multiple metrics are more likely to churn.

5 3. Churn propensity increases in relation to calls to customer care; for example, customers who report issues to customer care are more likely to churn if the issue does not get resolved. However, not every customer who experiences poor voice quality due to any of the key metrics will report the issues to customer care. The customers identified by SAA as high risk for churn can be proactively contacted by customer care with reassurance that their problems are being fixed. At the same time, SAA will alert the operations staff to the service issue and the network elements that are likely causing it. Service Level Agreement Verification When interconnecting networks, a service provider has likely conducted a series of interoperability tests. For every new customer, they had to verify service and for many large enterprise customers, agree to Service Level Agreements. With a service quality monitoring solution it was possible to set baselines for SLA metrics and very quickly determine if there were any service turn-up problems. So what happens next? Using SAA, a service provider can routinely verify KPIs to answer two essential financial questions: 1. Are obligations to customers being met (not incurring penalties)? 2. Are interconnected partners meeting their obligations (ensuring revenue or identifying potential areas of cost)? Analyzing KPIs and creating customized reports gives an operator the best chance of spotting patterns and providing decision-critical information needed on a daily basis to support SLA commitments regarding network performance and their customer s experience. Furthermore, having confidence in the accuracy of SLA verification means a service provider can avoid wasting time playing the blame game when it comes to potential network problems. For example, having clear, definitive and shareable information on network and service behavior enables network peers to collaborate in problem resolution, making them true partners instead of simply an interconnection point or worse, an enemy. Predicting Future Customer Behavior The introduction of new services is a constant in today s business climate. Some are wildly successful and others languish. While multiple factors must be considered, it is important to analyze customer behavior and their quality of experience. More specifically, using SAA can help answer: Did service quality affect service introduction and market success? Is the new service providing a positive customer experience? Is service uptake projectable in the next market segment? It should even be possible to predict, ahead of market introduction, at what point service success/growth will begin to affect service quality and demand subsequent investment in network/service infrastructure. Examples of using SAA to predict future customer behavior include: Measure service take rates, for example traffic to a set of Application Servers Assess service success against expectations, tracking usage and traffic patterns Determine if any service quality issues exist; for example, excessive abnormal service terminations or network congestion conditions Track if any recommended changes (pricing reduction, better marketing campaigns, or network configuration) can be associated with better service uptake

6 Conclusion Today s Service Assurance Analytics solutions not only provide operations with the data it needs to ensure high service quality, but also offer key insight into the company as a whole. By investing in an SAA solution that tracks the right information and provides powerful tools to sort through it, a service provider can easily turn mountains of monitoring data into decision-making intelligence. Here, we ve shown a few examples of using SAA to increase revenue and brand value, reduce customer churn and improve service usage and the end user experience. Once in your hands, you will find endless ways to monetize your SAA solution. Acronyms BI Business Intelligence ETL Extract Transform and Load KPI Key Performance Indicator OLAP Online Analytical Processing QoS Quality of Service RA Revenue Assurance ROI Return on Investment SAA Service Assurance Analytics For a complete list of offices worldwide, or to find an authorized distributor in your area, please visit Empirix. All rights reserved. All descriptions, specifications and prices are intended for general information only and are subject to change without notice. Some mentioned features are optional. All names, products, services, trademarks are used for identification purposes only and are the property of their respective organizations. xs:wp:fmtmsamd:0510