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Abstract

  1. Top of page
  2. Abstract
  3. The Customer Experience
  4. Customer Centric Management
  5. Actionable Insights
  6. Addressing the Inhibitors to the Usage of Big Data
  7. Conclusion
  8. References
  9. Biographical Information

In an environment where communications service providers (CSPs) increasingly have the same service offers and devices, offering a superior customer experience is a priority to compete. Solutions that have the ability to highlight what really matters in driving customer satisfaction and deliver actionable insights from their wide-reaching customer, network, and service data are key differentiators for CSPs. This paper explores ways of integrating big data insights with automated and assisted processes related to key customer touchpoints to ultimately improve the customer experience. We show how innovation from Alcatel-Lucent and Bell Labs helps CSPs improve their business performance, using unique methodology designed to select the right key quality indicators, build accurate key business objective “formula,” predict customer behavior, and ultimately understand which factors are influencing the most. This can be used for example to improve the Net Promoter Score (NPS). The net result is a happier customer and a higher customer value. © 2014 Alcatel-Lucent.


The Customer Experience

  1. Top of page
  2. Abstract
  3. The Customer Experience
  4. Customer Centric Management
  5. Actionable Insights
  6. Addressing the Inhibitors to the Usage of Big Data
  7. Conclusion
  8. References
  9. Biographical Information

Communications service providers (CSPs) are facing fierce competition as they strive to win consumer and enterprise business with their fixed and mobile services. Intense economic pressures, escalating consumer demands, and increasingly complex technologies are raising the stakes, forcing service providers to work harder than ever to attract customers and keep them happy.

CSPs can keep customers on board and spur long-term success by putting more focus on improving the overall customer experience. The previous areas of competitive differentiation such as faster bandwidth, unique services, and device innovation have largely disappeared with the advent of fast broadband, multi-play services, third generation/fourth generation (3G/4G) smartphones and over-the-top applications. Customer experience remains one key differentiator, when you consider that network reliability, coverage, care, provisioning, and billing all have an impact on the customer's perception of their service provider.

Measuring Customer Experience

In order to improve customer experience, service providers must first be able to effectively measure and model customer experience. This means that service providers must also understand what matters most to their customers. Finally they need to make the necessary targeted investments and actions to optimize customer experience.

Although they hold vast amounts of data about their network and subscribers, CSPs are not effectively managing it today. Challenges exist in the fact that customer data is “owned” by separate departments, which exist as disengaged silos in the traditional telecom organizations [18]. Each organization has a partial view of its own customer touchpoints, but lacks a structure in which to piece together the entire end-to-end customer view across all of the customers' touchpoints with the CSP. Furthermore, the sheer vastness of customer-centric data that exists across the organization creates technical challenges in getting to the bottom line: what is driving customer experience? A market segment has emerged for customer experience management (CEM) analytics tools which promise to help solve these technical challenges and also to overcome organizational barriers [10, 11, 14].

Panel 1. Abbreviations, Acronyms, and Terms
3G—Third generation
4G—Fourth generation
ACSI—American Customer Satisfaction Institute
ARPU—Average revenue per user
BSS—Business support system
CAPEX—Capital expenditure
CDN—Call processing data node
CEM—Customer experience management
CES—Customer effort score
CLV—Customer lifetime value
CSP—Communications service provider
DSL—Digital subscriber line
IT—Information technology
KBO—Key business objective
KPI—Key performance indicator
KQI—Key quality indicator
NPS—Net Promoter Score
OLAP—Online analytical processing
OLTP—Online transactional processing
OPEX—Operational expenditures
OSS—Operations support system
QoE—Quality of experience
RoI—Return on investment
SLA—Service level agreement
SNA—Social network analysis

A good example of a specific measure of customer experience that is becoming widely used by CSPs today is the Net Promoter Score (NPS*) [16]. NPS is a customer advocacy metric used to rate how likely customers are to recommend the product or service. NPS is measured directly and subject to perception biases and environmental factors. A strong motivation and business case exists for leveraging available customer-centric event data from across the different customer touchpoints in order to create an objective measure of customer experience.

Big Data to the Rescue

Today CSPs manage their businesses using operations support systems/business support systems (OSS/BSS) reliant on traditional database, data warehouse, and business intelligence tool sets. These technologies are typically applied to the data in each organizational silo, and are configured to create reports and dashboards aimed at solving the business problems of the individual organizations. As the traditional tools are not scalable and cost effective for very large data sets, very often customer-centric data is left unanalyzed. Data from multiple organizations in the CSP are not correlated. New technologies designed to handle data on a massive scale have emerged with the buzzword label “big data” technology.

Measuring CSP customer experience holistically is fundamentally a “big data” problem, and to be effective, CEM analytics tools must incorporate big data technology. Gartner defines big data as having three attributes: high volume, high velocity, and high variety [5, 9]. High volume means that there is a growing quantity of data. This is manifested in the exponential growth of broadband data traffic, which generates multiple billions of customer event records per day in large CSPs. High velocity indicates an acceleration in the speed of data, such as the need to detect customer issues in real time to avoid service issues. High variety refers to the increase in the types of data, which for the CSP brings new challenges for managing data related to application usage, web browsing, and social media.

Big data results in data sets that are so large and complex that it becomes difficult for traditional databases and business intelligence software to process. A number of new technologies have emerged which work to solve the challenge of big data. A big data solution for CEM needs to incorporate these technologies.

To solve the high volume challenge, new data management technologies such as Apache Hadoop* [3], NoSQL databases such as Apache Cassandra* [2], and next-generation column-oriented data warehouses have been introduced. Hadoop and NoSQL technologies like Cassandra achieve scalability by adding more and more server clusters (horizontal scalability) which process the large data sets in parallel. Column-oriented data warehouses achieve scalability by organizing the data by columns in the relational database, rather than rows, which is much more efficient for aggregation calculations over many rows but with a limited set of columns. This allows parallel access to data across many hard drives rather than sequential access across a single drive.

The high velocity challenge needs to be addressed in several ways. First, as customer-centric data is generated, it needs to be rapidly stored. The new data management technologies outlined above each have their own mechanisms to accomplish massive fast storage, leveraging their parallel architectures. Customer-centric data must also be processed rapidly from the data store. Apache Cassandra provides real time data access, and is very effective in processing data on a per-customer basis. Another technology known as streaming analytics (also known as event stream processing), is used to process customer-centric data “on the fly” without the need for long-term storage. Streaming analytics technology is used to detect preset conditions and to trigger actions in real time. For example, streaming analytics can be used to detect when a mobile subscriber has experienced three dropped calls in the past hour—and can issue a trigger to a person or an automated system to take action on behalf of that subscriber.

The high variety challenge can be solved by combining the same technologies in a solution architecture that leverages the technology strengths to make sense of the data. Apache Hadoop is useful for storing and processing vast quantities of structured, semi-structured, and unstructured data. Domain-specific advanced analytics algorithms can also help to derive useful insights and correlation of the high variety data. For example text and speech sentiment analysis of unstructured call center transaction logs provides insights into the customer's care experience.

What Really Matters to the CSP

Service providers are in business to maximize profit and value to their shareholders. In the end it is the money that really matters. However, in order to grow and maintain a successful profitable business, CSPs need to attract and retain customers that are willing to pay for the products and services they receive. As markets for communications services have become hyper-competitive, CSPs have been faced with the choice of either competing on price or putting more and more focus on customer experience as a way to differentiate and build profitability.

Service providers can enhance profitability by improving the lifetime value of customers. This can be done with direct actions (such as price increases), or can be achieved indirectly by managing key business objectives (KBOs) that have an impact on cost and revenue. For example, if a service provider focuses on improving network quality, this can lead to decreased customer retention costs and increased usage revenue. However this is offset by the costs required for network capital expenditures (CAPEX) and operational expenditures (OPEX) in order to achieve the higher level of network quality. It is a complex business problem and these complexities lead to risks; how does the CSP ensure the CAPEX is spent optimally? Is there a level of quality that achieves the best profitability without over-engineering?

Service providers have begun to focus on understanding and optimizing their customers' experience as this has been shown to be a better way to drive up profitability [17]. By focusing on KBOs that are related to the end customer experience, CSPs are able to direct their investments towards improving that experience. By taking a holistic approach and considering the entire customer experience, rather than just the network experience or the care experience, CSPs can prioritize their spending to tackle the biggest pain points facing their customers. This has benefits in creating satisfied customers which on average spend more and cost less, with higher customer lifetime value (CLV).

Customer Centric Management

  1. Top of page
  2. Abstract
  3. The Customer Experience
  4. Customer Centric Management
  5. Actionable Insights
  6. Addressing the Inhibitors to the Usage of Big Data
  7. Conclusion
  8. References
  9. Biographical Information

Ensuring that a CSP's approach to improving customer experience is appropriate and structured first requires an understanding of the primary elements that constitute customer experience. There are three areas for a CSP to consider in delivering the customer experience:

  • The customers and what motivates them,

  • The customer perception of products and services and their relative importance, and

  • The market context within which a CSP finds itself.

The work of Frederick Herzberg [6], Noriaki Kano [8] and the American Customer Satisfaction Institute (ACSI) [12] provide some appropriate frameworks for understanding these components. Herzberg's research, while focused on a worker's motivation to do his job, revealed a fundamental structure for individual motivation where factors affecting motivation were either creators of dissatisfaction when perceived as inadequate but not necessarily motivating beyond being perceived as acceptable (hygiene factors), or factors whose presence generated motivation in the individual (motivating factors).

Noriako Kano's study and model shows a similar structure for the relationship between customers and products or services where the absence of essential or “must-have” characteristics create dissatisfaction and optional or “nice-to-have” characteristics drive satisfaction.

The ACSI's consistent measurement over time provides a consistent and methodologically sound proxy for customer experience both within and across market segments within defined geographies and markets.

The implications of these foundations are that customer experience measurement could be categorized and focused on three key areas:

  • Driving behavior to eliminate a perceived deficiency in hygiene factors by focusing on reducing customer effort—whether in the form of solving customer problems on first contact (first call resolution), ensuring customer effort scores (CES) are low for key touchpoints, or by being proactive with customers to reduce any need at all for the customer to expend effort,

  • Removing potential sources of customer dissatisfaction and increasing trust through continuous improvement—for CSPs this means ensuring networks, services, and operations are meeting their customers functional needs by working flawlessly, and

  • Identifying the motivating factors for customers, leveraging that knowledge to create moments of memory or delight and, thus, to generate advocacy which can be as simple as a positive retail experience and small random acts of kindness as a matter of policy in the care cycle, or as complex as a seamless multichannel purchase experience.

The quest to create sustainable competitive advantage using customer experience can be summarized as shown in Figure 1.

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Figure 1. Creating sustainable competitive advantage.

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While the means and modes of interactions between people and organizations have changed, the people and cultures they are part of evolve much more slowly. The understanding of what drives human choices as well as their relationships with products and services are the essential foundations for looking at customer experience in a meaningful way. Today customers interact with CSPs in a multitude of ways. These sequences of interactions, or customer journeys, together form the customer lifecycle: starting with learning about the provider's brand, products, and services; deciding to make a purchase; using products or services; paying for them, and at times even having to deal with customer support. The form of this customer lifecycle is often itself a concatenation of different cycles between stages of the overall lifecycle.

For example, the traditional decision making process for an intended new purchase is influenced by factors that alter the decision journey [4]. Change in the consumer decision journey from the traditional funnel model (where the consumer considers a number of brands, evaluates them, and makes a decision to buy followed by focus on the use of a product and service) to a more iterative approach where the consider/evaluate/buy loop is sometimes replaced by the loyalty loop (enjoy/advocate/bond). This occurs when loyalty to the brand is no longer rational. Therefore, when the consumers' bond with a brand is strong enough, they go directly into repurchase without cycling through the earlier decision-journey stages.

Structuring the approach to improving the customer experience requires building a bridge between the lifecycle as seen by the customer and its reflection in the CSP. It means creating an end-to-end framework that allows an aggregate as well as a detailed view of the customer experience at a given point in time at multiple levels of granularity with different frames of reference according to the observer's span of control. This is a non-trivial task as we will show below.

Customer Lifecycle

The customer lifecycle includes a comprehensive set of phases shown in Figure 2 that start with awareness (user's perception of the brand), continue with interact (forming a value perception of products and services offered and their prices) and are followed by agree/get (making the decision to acquire a product or service). The next phase in the customer lifecycle is consume which covers the “in-service” experience, followed by support which covers the customer care and payment experience where the simplicity and accessibility of the payment interaction is very desirable. The last two phases are the reward phase where the CSP offers loyalty programs, promotions, and special offers and leave which represents the end of the customer lifecycle.

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Figure 2. Customer lifecycle.

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Any given customer is less likely to go through the different phases in sequential order than to follow their own particular journey. For example a customer sees an ad (awareness), goes online and researches the CSP offers (interact) after which he leaves the portal and goes to a shop where he continues the research (again interact) and decides to purchase the product or service (agree). The customer then requests delivery to the home (get). The customer journey generates “events” along the way, induced by the customer interaction with the provider.

CSP Lifecycle

The CSP lifecycle is a reflection of the customer lifecycle and represents the CSP journey with each and every customer. Alcatel-Lucent brings to these constructs a unique contribution on providing structured linkage between the customer lifecycle, the CSP lifecycle, and the primary business tool that CSPs use to deliver their services.

The CSP lifecycle follows, as illustrated in Figure 3, the customer lifecycle. It starts with acquire, continues with market/sell, followed by fulfill order and manage customer. The next phase is service assurance followed by charge/bill, retain/optimize, and in the end, retire. In reality, an example of the CSP journey is not a complete list of CSP lifecycle phases but rather a fragmented set of interactions. The customer journey we looked at in the previous section is reflected in a CSP journey where the CSP sees the interaction interrupted on the portal (market/sell) and after a while, sees a person in the shop. The lack of integration between systems doesn't allow for a single view of the customer. CSPs would benefit from a customer-centric data model together with a higher level system integration. This would allow the service provider journey to generate “events” in network BSS/OSS, capture process, and network usage activity and accompany the customer through the lifecycle as required.

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Figure 3. Communications service provider (CSP) lifecycle.

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Bridging the Two Lifecycles: How We Measure the Quality of Experience

The CSP needs to be able to measure and quantify customer experience across all the phases of the customer journey and, if expectations are being met, areas which require improvement and areas in which things are going well. Alcatel-Lucent proposes a model that defines a holistic view of the quality of experience (QoE) by identifying the key drivers of customer satisfaction across the entire journey. A large component of the QoE is related to the “in-service” experience (or the consume phase) which is connected to the quality of the services and devices used. Often the CSPs focus only on network quality, which is only one part of the quality puzzle, and do not consider other attributes such as the quality of the service used, the performance of the device, or its physical design, feature set, and ease of operation. The next significant part of the holistic QoE is care which covers the support experience. Care is linked to the categories of problems that affect the customer experience and the cost associated with these key drivers. Perception is the next QoE component and it maps the awareness, interact, and reward phases of the customer lifecycle to the holistic QoE. This includes the brand image—the perception of the value the CSP offers, the loyalty programs, and promotions. The remaining customer journey phases of agree/get and pay are part of the ease component of QoE. The key drivers of customer satisfaction related to ease are the billing experience and to a lesser degree the activation experience, since there is much more interaction with customers in the pay touchpoint.

The CSP requires an aggregate view of the QoE as well as the view from an individual customer. Depending on the customer's profile, key drivers to their customer satisfaction could be different. For example a teenager in a family of cell phone users may be sensitive to the quality of the mobile messaging service (e.g., text transmission failures and late text message notifications) while a parent is sensitive to the quality of the mobile voice service (e.g., dropped calls and audio issues) as well as the ease of using the billing system.

The aggregate view allows the CSP to monitor status across all customers as well as by customer segment. For example, the CSP might want to identify high value customers to ensure that they enjoy a very high QoE. The CSP would also want to monitor trends in churn propensity and proactively target retention campaigns towards this customer segment.

Drilling Down to What Really Matters to the Customer

The holistic QoE view described in the previous section provides a construct that covers all phases of the customer journey with the flexibility of aligning the weighting of the QoE drivers to the CSP's unique profile and target and the importance of categories of problems that affect the customer experience. The QoE at the top level should be aligned to the CSP's key business objectives (KBOs). These KBOs are in turn calculated from the key quality indicators (KQIs) and the lower level key performance indicators (KPIs) that drive the KBOs. The holistic QoE approach illustrates how big data is used to improve customer experience and loyalty. It allows the CSP to identify what really matters to the end customer. It provides a comprehensive framework that allows the CSP to select the right KPIs, KQIs, and KBOs based on the key drivers to customer satisfaction for an individual customer, a particular customer segment, or across all customers. Figure 4 shows the holistic QoE in a hierarchy of indicators represented by a pyramid. The four sides of the pyramid are the key QoE components: quality, care, perception and ease. A CSP doesn't need hundreds of KPIs, only the right KPIs and KQIs to measure and improve customer satisfaction. Further, an initial selection of metrics is not valid indefinitely. As we explore in the next section, in a changing environment it is necessary to refine the key drivers behind the QoE using KBO calibration and feedback loops.

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Figure 4. Customer quality of experience.

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Actionable Insights

  1. Top of page
  2. Abstract
  3. The Customer Experience
  4. Customer Centric Management
  5. Actionable Insights
  6. Addressing the Inhibitors to the Usage of Big Data
  7. Conclusion
  8. References
  9. Biographical Information

“Never measure unless you want to record, never record unless you want to analyze, and never analyze unless you want to take action.” This is a conventional wisdom that stands the test of time. Presently, although the data that is available comes in more vast amounts, turning it into real information still requires a touch of art above science.

In today's digital networks, the volume of event data is immense, but what kind of insights are CSPs looking for in this data? At large, there are two classes of analytics: descriptive and predictive.

Descriptive analytics encompass all information that describes the status or history of the system or process under investigation. Such descriptive analytics can provide the investigator with insights they are required to act upon. Under descriptive analytics we find root cause analysis and diagnostics. Diagnostics may involve both the passive reading and interpretation of data, as well as actively triggering particular actions on the system under test and reading out the results. Root cause analysis is a more elaborate process of iterative digging into data, and correlating various insights such as to determine the one or multiple fundamental causes of an event. Descriptive analytics, although starting from deterministic measurements, often result in root cause identification subject to statistic boundaries. For instance, monitoring line state information on a Digital Subscriber Line (DSL) over a period of time may reveal that the root cause of increased bit error rates can be traced to loop unbalances or bridged taps or missing splitters. Figure 5 shows a system where root cause analysis is determined subject to Bayesian probability models[7, 15]. Many more such probability models exist.

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Figure 5. Line quality diagnosis using Bayesian probability models.

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Predictive analytics take analytics a step further. Under predictive analytics, data is used to seek to derive a future state of the system under test, hence to allow for anticipative action. An example here would be determination of customers' propensity to churn, by correlating behavior over a period of time with network event data such as usage records and fault indicators.

While analytics may come in various forms, as stated, the end goal is to influence the system under test to correct anomalies, or to further optimize its function, changing its behavior. In this paper we concentrate on those actions that ultimately influence the customer experience.

Different types of analytics may be leveraged for use cases such as reactive and proactive care. In reactive care, the CSP intervenes and seeks to correct an anomaly at the time the end user has signaled the degradation or failure of a service. At that point, the elapsed time between the identification of the issue and the actual resolution is key. Although this includes a long chain of sequential steps, the root cause analysis in many cases determines the resolution time. In call centers, time is costly, and long wait times may further deteriorate the end-user experience. This creates a need for real time or near-real time analysis mechanisms.

As for proactive care, the CSP seeks to intervene in the system to anticipate issues and/or to improve on the overall customer experience. Here too, one may seek to influence the behavior of an end user (e.g., offering free communication time), the end-user terminal (pushing firmware updates proactively to correct known deficiencies), the network (e.g., changing power levels for mobile terminal communication), or CSP processes (e.g., eliminating a redundant step in a customer workflow). In the next sections we examine these target systems in further detail.

Earlier we established that the portion of QoE related to the “in-service” experience is connected to the quality of the services and devices used. The end terminal is by far the most visible personalization of the service in the hands of the end user. End user terminals are always a combination of hardware, software, and customer-specific settings, and are typically prone to changes made by the user, either intentionally or unintentionally. Given that the CSP doesn't have physical access to the device, having the capability to remotely perform telemetry and system changes is key to influence the behavior of the end terminals, hence the high importance of remote device management and remote control in today's digital services industry.

The second significant driver for the quality component of the QoE is the quality of the service, determined by the network infrastructure, inclusive of communication and application logic. The network infrastructure must serve the needs of many customers at the same time, and hence the challenge exists to find a configuration that optimizes the service offering to many end users. Even more important is ensuring that addressing one customer's issue doesn't result in deteriorated service for other customers. As such, policy control and its impact on optimizing customer experience is emerging as a key capability. This is again where big data analytic capabilities can “come to the rescue” by influencing network policies in such a way as to optimize the overall customer experience and economies for the CSP.

Complementary to the network infrastructure, business processes play an important role in the overall perceived customer experience. Business processes that interact with end users can largely be identified as either self-service or assisted-service oriented. Both are implemented by business process logic, which are sometimes called workflows, by which either the end user or a customer service representative is guided through a number of steps interacting with the system.

Business processes have more of a hygiene function in that process anomalies, or missed opportunities for automation have detrimental effects on the customer experience. By measuring interaction times, and logging events while those processes are being executed, today's business process systems can reveal anomalies like steps that are too complex (exemplified by humans pushing the back button), processes that routinely break down before completion, or processes that are restarted too often.

Identifying the actions associated with end users, their terminals, and the CSP's physical assets and processes is not a unique and one-time activity. Advanced CSPs are leveraging big data capabilities and analytic tools to create a continuous feedback operation whereby action, measurement, analysis, and updated action are repeated on a very frequent basis. Advanced CSPs deploy systems that change workflows on a daily basis, leveraging the learning experience, even creating target testing groups in real time to watch and learn differences about how end-users are interacting with the systems. Modern customer experience management solutions should then also allow those modifications to occur in an agile way in very frequent steps, rather than being limited by information technology (IT) release cycles.

Where to Focus?

Another key aspect of transforming data into insights, and insights into actions, is selecting the customer experience use cases that yield the highest financial return for the service provider—those that make immediate business sense or are considered business critical—and starting with the most simple to articulate with the most demonstrated value.

According to a Gartner study [13], an average CSP could potentially generate $10 per subscriber per year in additional margin when using analytics to turn customer data into insights and subsequently into actions. Six use cases account for 55 percent of the total revenue and cost savings:

  • Churn propensity scoring. Uses customer, network, usage, and support data to identify key churn drivers and predict the likelihood to churn. Combining churn propensity with the customer lifetime value allows prioritizing corrective actions on churn drivers. This is further described in the next section.

  • Improved self-service. Provide customers with the ability to diagnose or even control their services from a self-care application. Improved self-service includes proactive alerting, enhanced root cause analysis, decision support/recommendation through multiple channels, and consistency with assisted care.

  • Customer segmentation. Segment customers based on usage patterns, value, customer experience, demographics, or other criteria to improve marketing, customer understanding, and targeting. Used in acquisition, cross-sell/upsell and customer retention campaigns.

  • Best next offer. Propose personalized offers based on usage, billing, and other customer data analysis.

  • Pre-pay recharge. Real time offers to encourage prepaid customers to recharge their account, depending on their balance.

  • Tracking dissatisfied customers. Identify dissatisfied customers based on explicit feedback (customer contact), indirect feedback (forum, social media) or inferred feedback (usage trends, predicted QoE) and proactively fix issues or propose attractive offers at the next customer contact.

Other high-value use cases include:

  • Tracking video experience. Monitor customer experience when using multi-screen video, identify root cause, and optimize video delivery across the network from the source to the end-user device through dynamic line management, admission control, prioritization, efficient caching, and network/call processing data node (CDN) selection.

  • Predict network outages. Predict network outages by analyzing customer complaints and network data, and dynamically adjusting network policies to prevent failure.

  • Track devices with high data usage. Identify devices with high data usage to enable efficient quality of service planning and best fair-usage policy, or optimized tariff plans.

  • Tracking data experience. Monitor mobile data customer experience at the application layer rather than the network layer and proactively fix quality or configuration issues, or propose devices and tariff plans best adapted to customer usage patterns.

  • Improved assisted care. Use analytics to improve the efficiency and value of assisted customer care workflows. Use outage prediction to avoid or deflect calls. Use customer troubleshooting data to avoid chronic calls. Monitor customer experience to proactively fix issues and avoid calls. Provide consistent customer care across multiple channels.

  • Tracking of enterprise customers. Monitor and report service level agreements (SLAs) for enterprise customers, identify changes in behavior/usage patterns for improved marketing, cross-sell/upsell, and retention.

Churn and Customer Lifetime Value

Bell Labs worked with a range of partners to build an analytics foundation based on developing analytic patterns for QoE, propensity to churn, and lifetime value. This innovative work combines social network analysis (SNA) and other social factors with more traditional metrics and applies machine-learning methods to compute the propensity to churn for individual users. Bell Labs algorithms serve as the foundation for proprietary predictive analytics models which help CSPs use customer data to identify the best opportunities to maximize revenue.

Use cases based on churn propensity scoring are at the top of Gartner's list for potentially generating additional margin when using analytics to turn customer data into insights and actions. Churn prediction algorithms foretell the likelihood of a given customer to leave a CSP within a given period. Understanding which customers are at risk of churn helps the service provider to better target customer retention campaigns, resulting in a higher success rate to retain existing customers. By leveraging data from network and non-network data sources, analytics provides insights that promote a better understanding of how service usage and behavioral patterns influence churn. This results in actionable intelligence on key drivers of churn, supporting new retention offers that target the root causes of customer dissatisfaction.

Customer lifetime value (CLV) prediction algorithms foretell the profitability of a given customer over their service lifetime and enable CSPs to identify valuable customer segments to be used in acquisition, cross-sell/upsell, and customer retention campaigns. CLV algorithms help CSPs use customer data to identify the best opportunities to maximize revenue. By making incremental investments to improve CLV, a CSP can improve overall company business performance and corporate valuation. The concept of CLV can help improve customer segmentation. By building a customer segmentation model around clusters of CLV, a marketing team can focus on retaining and growing high-CLV segments, rather than focusing on subscriber groups that generate high average revenue per user (ARPU) but may offer lower profitability or loyalty. Up-selling and cross-selling campaigns are designed to move subscribers to higher-value CLV segments by increasing the spending of loyal customers. Loyalty campaigns are designed to move subscribers to higher-value CLV segments by increasing their service lifetime. CLV allows network operations departments to prioritize the resolution of network performance issues based on their impact to high-value customers. Over time, a CLV-based segmentation approach can help increase the proportion of customers in higher-value segments.

Addressing the Inhibitors to the Usage of Big Data

  1. Top of page
  2. Abstract
  3. The Customer Experience
  4. Customer Centric Management
  5. Actionable Insights
  6. Addressing the Inhibitors to the Usage of Big Data
  7. Conclusion
  8. References
  9. Biographical Information

In a recent study [1], the top three barriers to implementing customer experience management were difficulty in securing cross-organizational co-operation, poor understanding of the benefits of improving CEM, and poor data quality.

To pave the way for a successful CEM strategy to manage customer experience, service providers must empower an executive who is charged with creating an enterprise-wide CEM strategy. This executive must control or influence the budget for CEM tools. With this top-down leadership, the CSP is able to optimize its investment in big data to benefit the entire customer experience. Silo projects and barriers to data sharing are minimized. Service providers have begun to make this change and are appointing customer experience officers and others in similar roles; however in many large organizations the silos are deep rooted, and require a strong management focus to overcome. In some countries, market regulations also create silos by separating service provider operations into separate businesses such as wholesale, retail, or infrastructure. A successful CEM strategy also needs to consider these constraints.

Service providers have begun to develop data management strategies that look to consolidate customer data across multiple organizations or across the entire enterprise. Software tools for data quality assurance, data integration, and master data management are used to create a single trusted view of customer-centric data [18]. The immediate benefit of this approach is that it maintains “one version of the truth,” so that separate transactional systems and applications are working with a consistent view of each customer's data. Many benefits stem from a sound telco data management strategy. Dirty data can be avoided. Once a common repository of customer data is available, it can be leveraged by business analytics to serve the needs of multiple stakeholder organizations.

Data management and consolidation of telco data has often been approached using enhanced subscriber data management products for online transactional processing (OLTP), and traditional data warehousing solutions for online analytical processing (OLAP). However, these standard approaches do not scale to manage the vast scale of customer-centric telco data, and CSPs have begun to adopt big data technology as a cornerstone in their data management strategies.

It is often helpful for the CSP to bring an external consultant on board to serve as a trusted advisor to guide in planning the right strategies for data management, CEM, and business analytics. The consultant assesses the current state of the CSP versus its peers in both local markets and across the globe, and identifies key gaps in achieving the priority business goals.

Consultants should be engaged during the entire lifecycle of a CEM transformation project. In the planning phases, the consultant helps with analysis and benchmarking prior to building the strategy and business case. In the implementation phase, consultants help to define the architecture. Following deployment, consultants can help by ensuring that each organization is achieving the maximum business value from the newly available tools.

Technical consultants can help a CSP to design the optimum data management and analytics architecture which incorporates big data technology. Business consultants that are focused on customer experience can aid the CSP in understanding where to make priority investments that have the highest return on investment (RoI). Often it is difficult for a CSP to understand the business case behind improvements in customer experience, which can become a barrier for CEM transformation projects. The business consultant helps to estimate cost savings from reduced customer churn or more efficient customer care, and revenue increases from increased personalization and more accurate targeted marketing campaigns.

Conclusion

  1. Top of page
  2. Abstract
  3. The Customer Experience
  4. Customer Centric Management
  5. Actionable Insights
  6. Addressing the Inhibitors to the Usage of Big Data
  7. Conclusion
  8. References
  9. Biographical Information

The paper has described how innovation from Alcatel-Lucent and Bell Labs helps CSPs improve their business performance using a unique methodology designed to select the right key quality indicators, build accurate key business objective “formula,” predict customer behavior, and ultimately understand which factors are influencing the most. Alcatel-Lucent provides structured linkage between the customer lifecycle, the CSP lifecycle, and the primary business tool that CSPs use to deliver their services.

As service providers advance in their implementation of CEM strategies, the direct result will be a more personalized experience for telecommunications customers. With actionable insights and a 360 degree view of each customer's behavior, issues, and desires, the CSP provides a better experience by enabling a positive perception of their company coupled with quality, customer care, and ease of doing business that satisfies or delights them. Further research in this area must focus on enabling that precise personalization that maximizes customer satisfaction together with customer profitability.

Acknowledgements

The authors would like to acknowledge the following members of the Customer Experience Solutions Department for their contribution to this work: David Stevenson, Hilary Mine, Ann Marie Vega, Rhodo Odysseos, and Geeta Chauhan.

(Manuscript approved October 2013)

*Trademarks

  1. 1

    Apache, Apache Cassandra, and Apache Hadoop are registered trademarks of The Apache Software Foundation.

  2. 2

    Net Promoter Score is a trademark and NPS is a registered trademark of Satmetrix Systems, Inc., Bain & Company, Inc., and Fred Reichheld.

References

  1. Top of page
  2. Abstract
  3. The Customer Experience
  4. Customer Centric Management
  5. Actionable Insights
  6. Addressing the Inhibitors to the Usage of Big Data
  7. Conclusion
  8. References
  9. Biographical Information
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Biographical Information

  1. Top of page
  2. Abstract
  3. The Customer Experience
  4. Customer Centric Management
  5. Actionable Insights
  6. Addressing the Inhibitors to the Usage of Big Data
  7. Conclusion
  8. References
  9. Biographical Information
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JEFFREY SPIESS is director, product management in Alcatel-Lucent's Networks and Platforms group in Plano, Texas. He is currently managing the Motive Analytics product line for customer experience analytics and care analytics. Since joining Alcatel-Lucent in 1997, he has held numerous positions managing carrier applications, including software development, systems engineering, product and solution management, product and field marketing, strategy, solutions architecture and consulting. He holds three patents in telecommunications technology. Prior to joining Alcatel-Lucent, Mr. Spiess was employed with Texas Instruments where he designed hardware and software systems for defense and telecom applications. He holds a bachelor of science degree in electrical engineering from the University of Cincinnati, Ohio, and a master of science degree in computer science engineering from the University of Texas at Arlington.

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YVES T'JOENS is the vice president and head of Product Development, Customer Experience Solutions in the Alcatel-Lucent Platforms group in Antwerp, Belgium. Prior to that, he served as chief technology officer for Alcatel-Lucent's Access Network Division and as manager of the company's Digital Home Care business. He is the author of multiple papers, holds multiple patents, and has actively contributed to the standardization of access networking.

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RALUCA DRAGNEA is director, product management in the Alcatel-Lucent Platforms group in Ottawa, Ontario, Canada. She is currently is responsible for a suite of content management products, an integral part of customer experience management solutions. She has extensive experience in senior technical and management roles within the communications industry. In her previous role she was responsible for strategy and incubating new solutions with a focus on customer experience analytics, optimization, security and cloud computing. Prior to that she was the founder and general manager of the Web Services Alcatel-Lucent Venture which was successfully acquired by the Enterprise Product Group in 2008. She has served as a Bell Labs research project leader, and has also held director positions in Alcatel-Lucent R&D groups responsible for bringing to market leading products in Internet Protocol (IP), Multiprotocol Label Switching (MPLS), and Asynchronous Transfer Mode (ATM). Previously she was the principal architect and she founded groups responsible for developing and introducing to market several new and innovative Newbridge/Alcatel-Lucent products such as 5620 Common Management Information Protocol (CMIP) Network Management System, 5520 Element Management System and 5660 Network Design System. Ms. Dragnea has a master's degree in electrical engineering from the University Politehnica of Bucharest, Romania. She has authored or co-authored 14 papers and holds two patents.

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PETER SPENCER is vice president, Customer Experience Strategy at Alcatel-Lucent. In this role he leads engagements devoted to helping Alcatel-Lucent's customers improve their own customers' experience. He has worked in the communications and information technology (IT) field for more than 22 years, during which time he has held executive roles with Alcatel-Lucent and Ogilvy & Mather. He has a broad understanding of economics and technologies across fixed, mobile, and all service types (voice, video and data). Mr. Spencer's experience includes customer experience management and measurement; market research and operational marketing; brand strategy and communications management; sales and channel management, including partner program design and implementation.

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LAURENT PHILIPPART is director of the Motive Analytics program in Alcatel-Lucent's Networks and Platforms Group. This program encompasses the Alcatel-Lucent Data Management Platform and associated Analytics Templates for fixed, mobile, and cable service providers. Mr. Philippart has over 20 years experience in OSS service assurance and he holds a telecommunications engineering degree from the École Nationale Supérieure des Télécommunications de Bretagne in France and a master of science in satellite communications and spacecraft technology from the University College London in the United Kingdom. From 2006 to 2012 he led the TeleManagement Forum Service Level Agreement Management project and served as editor of the Forum's IPTV Application Note. He has been involved in a number of large projects, including consulting and implementation for performance and service quality management systems for service providers worldwide and has specialized in voice and audio/video quality assessment for IP-based services.