Transcript
Telco CEM Strategy from Big Data Solutions A White Paper
Written by Andres Hernandez
[email protected] www.optimiquest.com © OptimiQuest. All rights reserved. August 2013
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Executive summary Due to the high competition, regulation, and economical global situation during last years operators have experienced high percentages of churn of their subscriber base , when millions of customers change from one provider to another. Adding to the operators challenge are the saturation of voice services, with voice subscriptions reaching 100% penetration, and increased competition from alternative voice and messaging communication platforms via over-the-top (OTT) players, such as Whatsapp, Skype, Facebook and Google. Clearly, this is having a significant impact on mobile service providers’ profitability and cost structures, and has been the focus of many telecom executives. The telecom business is, traditionally and fundamentally, a technology focused service industry. In service business, where technology has a dominant role, company differentiation can be difficult. Commonly, technology based services are easy to copy, and this often leads to competing with price, when the industry arrives into a maturity state, where all the service providers are essentially the same from the customers point of view. Investments in network technology are, therefore, only meaningful if the operator has the ability to transfer those investments into improving the service experience of their customers. The companies’ ability to manage the customer experience is, then, a potential success factor of tomorrow. This is one way for companies to differentiate themselves in the market. Here the approach can be to focus the company’s efforts on how their offering addresses the customer needs, make their services convenient, and provide more meaning to the customer. This can mean a fundamental change in the companies’ business mindset and working culture. The ownership of the customer experience, however, belongs to all functions of the organization, not only the customer service, but sales, marketing, networking, social media, systems, processes and other people in the company need to be taken on board. Within this transformation Big Data have climbed to the top of the corporate agenda, with ample reason, and together, they promise to transform the way many companies do business and deliver performance. For operators big data analytics requirements is composed by different data silos affecting the customer experience during all the subscriber journey, and touch points from where carriers are trying to define their Customer Experience Management (CEM) Strategy. What are the questions operators are trying to answer to define their CEM Strategy?
How can operators be organized to deliver an integrated and consistent customer experience analysis? Which are the internal and external data sources and touch points to design the entire Subscriber Journey? Which is the Big Data architecture need to overcome the CEM requirements and define the Customer Profile? And finally, which is the ROI of integrating Big Data /CEM Solutions?
Throughout this paper we are going to try to answer these questions.
© OptimiQuest. All rights reserved. August 2013
3 1. Big Data In the face of shrinking revenues and declining operating margins from their core communications services, mobile operators are retooling their business models in pursuit of new sources of top and bottom line growth. Mobile carriers have access to unparalleled amounts of customer and network data, and yet they are only just beginning to unlock the value of this information. Historically, operators have assumed the roles of enablers for information flows, with surprisingly little visibility into the context and content of their captive data assets. Utilized intelligently and creatively, this data holds the key to an intimate understanding of customer needs and preferences, and in a broader sense, the successful evolution of the role of operators at the center of the mobile communications eco-system.
for their own subscriber base, but also for third party organizations seeking a better understanding of their mobile users. Structured data is the accumulation of bulk transaction and profile records amassed by operators on a daily basis:
Itemized calling and messaging records – CDRs/ xDRs Electronic data records – Web logs, searches Geo-positioning data records – Location coordinates, time, duration Billing profile records – Gender, age, address, spend (sec.), consumption (MB), etc.
Figure 1 –Data network structure
Inferred Data
What is Big Data for the operators?
Social and Third Party data
Mobile operators are the ‘natural’ Big Data companies, given they have a unique view into the behaviour and preferences of millions of customers by virtue of the network data that they process on a daily basis. Any activity which touches the wireless infrastructure – voice calls, data transmissions and app downloads – creates a digital footprint which can be analyzed and synthesized into valuable insights.
Unstructured Data End User content
Structured Data Transactions records
Unstructured data reflects the detailed interactions between subscribers within the network represented by the exchange of textual, numeric and graphical content: And with the average smartphone now being active on the network for twenty hours per day, it is clear that mobile devices are becoming deeply embedded into the lives of the majority of users. The diagram in Figure.1 outlines the diverse nature of the available network data. The decreasing cost of data storage coupled with the availability of high performance computing applications now enables operators to create information-based offerings – not only © OptimiQuest. All rights reserved. August 2013
Over-The-Top (OTT) Services Social media posts Web browsing Media downloads and streams E-books and newsreader content App usage and interactions
3 Inferred data comprises the patterns of behavior de-
This adds a powerful dimension to the market seg-
rived from observed social media activity and point-to-
mentation process, as well as informing the customer
point movements within the network service area
experience and campaign design programs that flow
from it. By knowing more about their subscriber base,
Social graph and influence graph – relationships, personal interests, attitudes, sentiments
Location-based activity and context – retail footfall, travel dynamics, social preferences
operators can devise precise marketing messages that reach the right target customers, at the right time and most importantly in the right context.
Third party services and applications.
With this wealth of data becoming accessible to operators, their attention is turning to the use of advanced data analytics in order to drive internal initiatives – such as customer loyalty programs and service personalization – as well as monetizing the latent value of this information with the development of new offerings for B2B markets. Over time subscriber data with this degree of specificity and richness has the potential to become a new asset class – with a corresponding balance sheet valuation – for network operator
The fusion of subscriber, structured and unstructured network data, together with social media and third party data will allow operators to perform their ‘business-as-usual’ processes more efficiently and effectively – defining new market segments, interacting dynamically with both customers and channel partners and proactively addressing their specific demands as individual stakeholders. Figure 2 –Enhanced Big Data for Business as Usual
How are the operators exploiting this opportunity? The mobile operators with ‘smart’ capabilities such as dynamic network policy management, high performance computing and in-memory analytics are able to utilize these data assets in order to manage the customer experience in an increasingly intimate manner. The “who, what, when and how” dimensions of marketing campaign design and management take on new levels of specificity and precision with the so-called ‘720-degree’ view of customers uniquely available to operators:
The competitive environment in which any incumbent mobile operator is participating today is radically different to the market place in which they first launched their services. Invariably they are confronting a new
An internal, 360-degree view of their customers i.e. what are their product and service needs? how and when and are they using the services? what stimulates greater engagement levels?
What are the new revenue sources from Big Data?
breed of competitors resulting from convergence, where traditional device and service lines blur and companies diversify outside of their original markets. Intense competition and commoditization of communications services are driving increased network traffic
An external, 360-degree view of how they utilize the available services in their daily lives i.e. where do they live, work and play? who are they interacting with? how and why are their services preferences shifting?
© OptimiQuest. All rights reserved. August 2013
and usage, but currently it is the new wave OTT services, and messaging providers that are winning the battle for consumer mindshare and hence taking increasing revenue share.
3 Figure 3 –Enabling Big Data
In order to assert their position in this newly-emerging eco-system, mobile operators are pursuing new lines of business that have the potential both to monetize the value of their big data assets and to entrench the operators as a pivotal player in these markets: Customer insights – aggregated and anonymised mobile subscriber data has extreme value for a wide array of commercial and public services organizations: retail-
The demand for these hard-wired services will grow rapidly as e-commerce, mobile healthcare and machine-to-machine (M2M) applications become ubiquitous. The incumbent network operators therefore have the opportunity to create a ‘two-sided’ business model, whereby they leverage the latent worth of their unique assets – namely their captive subscriber data and their accumulated reputation capital.
ers (store location planning, campaign design), local government administrations (urban planning), adver-
Firstly, they create more intimate relationships
tising companies (outdoor media mapping), sports and
with their existing ‘downstream’ mobile subscrib-
entertainment venues (audience profiling).
ers, through a relentless focus on customer experience management and service personalization
Data hosting – by virtue of their service provider legacy, strict industry-wide regulatory framework and data protection obligations, the mobile operators have developed very strong trust relationships with consumers and businesses alike. This opens up the prospect of operators utilizing their data assets in order to host security and authentication-based services on behalf of financial institutions, government agencies and e-commerce companies. Identity management, access control and user authentication are all part of the service portfolios being developed by the leading US and European operators. Third party applications – rather than viewing companies which provide digital services over the network as competitors, increasingly operators are seeing them as business partners. In principle, any service that requires a set of enabling utilities such as customer billing, problem resolution, security and authentication services can be interfaced directly with the host systems and databases in the mobile operator environment. © OptimiQuest. All rights reserved. August 2013
Secondly, they form ‘upstream’ strategic partnerships with both private and public sector organizations, underpinned by a new product and service portfolio founded on the principles of privacy, trust and security
Executed well, this two-sided approach is virtually impossible for any communications service provider without a self-provisioned network to emulate successfully. The pathway for the network operators to assume the central position in the rapidly-evolving mobile services eco-system is wide open. Next point resumes how Big Data is applied, structured and organized to overcome the Customer Experience Management (CEM) for carriers.
2 2. Customer Experience Management Customer Experience is defined as the sum of all experiences a customer has with a company, over the duration of their relationship. From awareness, discovery, attraction, interaction, purchase, use, through to cultivation and advocacy. Customer Experience Management (CEM) is defined as a holistic approach taken to monitor, measure and improve all aspects of customer interactions between the operator and the customer. A recent Ericsson end-user study (Figure.4) shows that almost 40% of subscriber churn can be attributed to perceived poor levels of experience from service providers. In extreme cases, such as in the Indonesian, Malaysian and Sri Lankan markets, nine out of the top ten reasons for churn are related to poor levels of customer experience. But customer experience are composed of different user data (touch points) to where an individual customer might be in the overall relationship lifecycle Carries must find the internal and external touch points to build and analyze the entire subscriber journey. As previously explained the data set from these touch points are composed by structured, unstructured and inferred data which must collected and stored in a unique data warehouse.
Figure 4 -Customer View: Main Reasons for Telecom Services Churn. Source: Customer Experience Consumer Business Insights, Ericsson
Some studies [2] recommends three fundamental steps to capitalize on customer experience (Figure 5): 1. Identifying—what is important in your relationship with the customer: touch points, and respective data sources. 2. Monitoring— those interactions and define a clear transformation journey to improve. 3. Enhancing— the experience: from Data to Actions.
Figure 5 -Key questions mobile service providers need to answer to their Customer Experience Strategy
1. Identify
2. Monitor
3. Transform
“What are the most important elements affecting the my customer experience”
“How can I measure those interactions through the customer life cycle”
“How can I implement improvements programs to deliver financial beneftis”
How to determine the key areas where I need to give a differentiated experience to my customers?
What capabilities do I need to develop to measure those key interactions?
How to create improvement programs accross different organization units?
How to set thresholds and alarms?
How to link CE improvement plans to tangible financial performance benefits?
How to develop quality indicators to measure those key interactions? How to determine the impact of those interactions in my business performance?
© OptimiQuest. All rights reserved. August 2013
What type of data correlation model does my company need? What is the best solution architecture to cope with my business needs?
How to establish processes that empower teams to fix CE issues in a timely and effective manner? What are the organizational considerations for this change?
2 To IDENTIFY, MONITOR and ENHANCE first need is to re-organize the lack of leadership carriers have on their CEM strategy. Subscribers are not only concerned about network service performance; they also want a better experience across their entire service lifecycle. Experiences such as coverage and service reliability, billing accuracy, ease of recharging/reloading of prepaid credit and flexible service bundling have become increasingly important to subscribers as illustrated in Figure.4 Clearly, more work is needed in order to reach the latter objectives and maximize the user satisfaction, operators must together with the lack of leadership to overcome one of the biggest problems: harnessing their various legacy systems to provide a complete picture of their customers and, therefore, enable joined-up customer service to match customer expectations while assuring the quality of services delivered. Following the fundamental steps recommended to capitalize on customer experience we define the steps to define CEM strategy:
IDENTIFYING which are the key areas and internal/external data sources need. ARCHITECTURE: what is the best architecture solution to cope CEM strategy TRANSFORM: what are the organizational changes for this change.
2.1. CEM Strategy: Identify To define a CEM strategy based on Big Data depends initially to find and categorize the correct data sources. These data sources as explained previously can be internal, and external unstructured, structured and inferred data to where an individual customer might be in the overall relationship lifecycle— Subscriber Journey. To find the correct data and extract the insights we must correct first the lack of alignment across the departments and channels responsible to maximize user satisfaction across the subscriber journey, and assure the quality of the services delivered (Figure.6). For an operator this can initially include. Marketing, Customer Care and Network Departments. Marketing Business Intelligence departments provides to Marketing with solutions, dashboards and reports designed from data gathered from CRM and Business Support Systems (BSS), such as billing, call usage, consumption, etc; with the objective to optimize acquire new users, and maintain actual subscribers. Other relevant online platforms as Web, Social Analytics, and Campaign Management solutions are also an internal data sources providing valuable information about the market and users. Customer Care Care platforms are feed with subscriber information from CRM and BSS systems, as demographics, tariffs, consumption, etc.
Figure 6 -Telecom Executive View: Key Obstacles to Implementing Customer Experience Initiatives
© OptimiQuest. All rights reserved. August 2013
2 Customer care are also provided with remote access to user network data to analyze the network/services technical problems. From the user complaint and previous interactions, logs are generated and saved. Network Data Network departments use core and access network data to deploy, optimize and monitor the network. As we will se later, network data is a key data source on the CEM strategy
Understanding this cause and effect relation between certain events and customer experience, aids the operator to detect and solve possible user experience issues before they have an impact on their customers, then the need to IDENTIFY, GET, SAVE and CATEGORIZE the different user data sources is a must; that includes internal and external data sources (user touch points) to define the Subscriber Journey and Analytics from the same set of data for the individual departments and transversally for all the company .
External Data (Inferred Data) External data source that can considerable increase carrier business intelligence from the particular data information, or by mixing the external data with operator internal data. An example of these data sources are: audience, social media, mobile applications, Point of Sales (POS), etc.
2.2. CEM Strategy: Architecture
As we see carrier departments use large number of tools and applications to monitor and analyze particular user touch points, but these tools are not connected to share information to allow carriers to evaluate the entire Subscriber Journey in one shoot. The problem is that customer behavior is no linear, and although predictive analytics has evolved significantly, many propensity models are still largely developed by reverse engineering outcomes like purchase and churn events to identify leading indicators. Figure 7. IDENTIFIES the internal and external data sources needed to provide visibility of the entire subscriber journey, to study the market trends and user usage and interest.
Telecom experts vision over 50 billion connected by 2020 and the greater portion of that connectivity will be provided by wireless networks, while many devices will remain utilizing the fixed broadband as well.
Designing and deploying a customer-centric information architecture involves understanding the entity type, creating the entire relationship structure, linking events to the structure, and then populating the entire structure with relevant data over time.
This amount of connected devices will mean a lot of data to store, categorize and analyze to work internally to avoid operator business to evolve in the direction where they are more and more providing access to customers, and the actual content is purchased somewhere else as an Over-The-Top (OTT) service providers. Here is where Big Data technologies are considered essential to cope with this amount of data.
Figure 7 –Subscriber Journey: Internal and External Data Sources
©
Optimi-
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A technology-enabled strategy for gaining richer, deeper insights into customers, partners, and the business, and ultimately gaining competitive advantage. Working with data sets whose size and variety is beyond the ability of typical database software to capture, store, manage and analyze. Progressing a steady stream if real-time data in order to make time-sensitive decisions faster than ever before.
A recent development in ETL (Extract-Transform and Load) software is the implementation of parallel data processing. This has enabled a number of methods to improve overall performance of ETL processes when dealing with large volumes of data. In addition to the ETL and Big Data storage systems, vendors and third party companies have added analytics capability by developing or conducting a series of acquisitions in the market. In order to build CEM strategy from Big Data architecture main elements are :
Data Selection and gathering: Here we might be gathering data from internal and external data sources (Figure.7) providing valuable information for Subscriber Analytics.
Data Cleaning-Transformation-Integration and Storage: Here we must design a database into which our data is to be stored. We must select a database management system that best fits our data (variety, volume, velocity). The data gathered must be cleaned for such things as corrupt, missing, or inaccurate entries, or transformed by data dictionaries. Then, data from different sources needs to be integrated with each other into a cohesive dataset.
Data Correlation and Analytics: the main goal here is to consolidate/combine our variables into more easily digestible chunks if at all possible. In this step we could use such techniques as principle component analysis or other transforms making the data easier for us to handle and extract value and insights to create the Subscriber Profile for individuals and defined segments, Ex: Services, Devices, Apps, etc. Visualization: this step has the challenge to convert big data variables and analytics to actionable insights presenting them through interactive user interfaces.
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Figure 8 –CEM Architecture RICH INTERACTIVE USER INTERFACE MARKETING
FROM DATA TO INSIGTHS
Big Data Analytics can be defined as
NETWORKING
CARE
DATA CORRELATION & ANALYTICS (Subcriber Profile and Segments)
DATA STORE (Big Data Warehouse)
DATA INTEGRATION (ETL, Cleanse, Aggregate, Summarize, Audit, Metadata)
EXTERNAL DATA SOURCE (Panel, Apps, Others)
INTERNAL DATA SOURCE (OSS,
BSS, Probes, DPI, CRM, etc)
Figure.8 shows in detail the CEM architecture and how is organized to extract information from different data sources to provide the carrier with the ability to design the Subscriber Profile to provide local and global analytics, and actionable insights to Marketing, Care and Network departments. As mentioned previously the identification of data sources is important , as today’s consumers use a variety of mobile devices (e.g., smart phones, tablets) on a daily basis. This activity generates traffic on several systems in telecom networks, leaving a “breadcrumb trail” of information at each network component. Gathering data from many sources, all of which will probably be in different data formats: structured and unstructured data. At this point is highly important to transform the unstructured network data before store it together with the user structured data (CRM, Billing, etc) to maximize the profit of our analytics. In order to achieve these data transformation carriers must carefully benchmark the appropriate Deep Packet Inspection (DPI) solution and Data Dictionaries. Deep-Packet-Inspection (DPI): DPI uses any part of the packet for detection to identify convergent network conditions, manage and evaluate business rules in real-time, enforce policy actions locally and remotely, and - including the user generated information in the packet payload, combining application-level awareness of per-subscriber Internet usage, and quality of services indicators. As well as data storage providers, DPI providers offers real-time analytics solutions together with the hardware. Here carriers must carefully benchmark the DPI
2 functionalities regarding traffic management, network security, control center, user management and analytics, but focusing on CEM Analytics the most important indicators to benchmark on DPI providers are:
User Usage: indictors to help carries to build individual subscriber profile around usage: Services, Devices, Apps, Interest (URLs). This information is useful for Marketing departments to analyze and optimize traditional voice, data and messaging services and tariffs, understanding particular segments and market trends (OTT usage). QoS-QoE: key performance and quality indicators (KPI-KQI) to help carriers to measure the Quality of Service (QoS) delivered by their networks and services, and the Quality of Experience (QoE) perceived by the user.
Here we would like to differentiate the traditional network key performance indicators affecting the voice and data quality of service (QoS), from the algorithms needed to measure the Quality of Experience (QoE). Each service has different requirements in terms of packet delay tolerance, acceptable packet loss rates, and required minimum bit rates, and industry has developed different algorithms to measure the delivered quality affecting streaming data services as video, music, and voice. An example for VoIP is the MOS indicator measured by PESQ and POLQA algorithms, which thanks to latency, jitter, packet loss, transcoding, and echo data gather by the DPI can directly calculate the user satisfaction using this type of service (Figure.9).
Data Dictionaries: Cleansing and transformation process are useful here, because they remove redundant, obsolete or trivial content, and data transformation allowing the mapping of the data from its given format into the format expected by the appropriate application. Database users and application developers can benefit from an authoritative data dictionary document that catalogs the organization, contents, and conventions of one or more databases. Industry provides different data dictionaries: open source or license, that carriers can deploy in their CEM architecture to transform, and categorize the user network data: Services, Devices, Apps, and Interest (URLs), as shown in Figure.10. Figure 10 –Data Dictionary for Service Categorization
For mobile devices classification carriers can use internal repositories, if available, or external repositories as the Wireless Universal Resource File (WURFL), a Device Description Repository (DDR), i.e. a software component that maps HTTP Request headers including device non-structured information to the profile of the device client (Desktop, Mobile Device, Tablet, etc.) that issued the request. Here below an example of the Samsung Galaxy UAProf received on the HTTP Request.
Figure 9 –Mean-Opinion-Score (MOS) Quality of Speech
Score
Excellent
5
Good
4
Fair
3
Poor
2
Bad
1
Similar solutions exist for video which today’s represent the big percentage of the data consumed. In resume, it is highly important to select, update or replace the DPI provider having on mind data requirements for analytics together with their traditional functionalities. © OptimiQuest. All rights reserved. August 2013
Mozilla/5.0 (Linux; U; Android 2.1; xx-xx; GT-I9000 Build/ECLAIR) AppleWebKit/525.10+ (KHTML, like Gecko) Version/3.0.4 Mobile Safari/523.12.2
This unstructured data is correctly translated by the Device Repository sending to the storage system the structured data as: Device Manufacturer: Samsung. Device Model: Samsung GT-I9000 (Galaxy S) O.S: Android v2.1 These repositories/dictionaries also includes other device information as technologies, content type, browser information, display size, etc. Services and Devices dictionaries are normally already in use on the carriers analytics, regarding to URLs and Apps carriers can develop their own dictionaries, find open source dictionaries, or licensing them from third party providers.
3 2.3. CEM Strategy: Transform
tation of dashboards and reports for global analytics, and specific dashboards for individual departments.
Defining and executing a business transformation program across the organization to address key areas of improvement is a critical step. In the case of customer experience, a transformation program takes an even more important role. Customer experience improvement program must be driven across the whole organization and involves multiple different stakeholders, processes, systems and domains.
One of the key barriers for service providers to improve customer experience is related tot he lack of governance and competence of managing this complexity.
Figure.11 shows how CEM department works transversally to convert the company from network-centric to user-centric; solving the existing analytics fragmentation which can be in some cases too expensive to integrate and maintain, not only in CAPEX, also in OPEX.
From OptimiQuest we propose to create the CEM organization from the top management level, and transversally to all company:
Chief CEM Officer (CCO): Coordinate, define and validate with marketing, technology, customer care departments, and management teams the global analytics requirements and budgets for a CEM Strategy implementation. In charge of global team composed by the Analytics Director, and Data Director.
CEM Analytics Director: Lead implementation of the analytics tools for Market and Subscriber Analytics based on the internal and external data sources. Responsibilities will include translating business, care and technical requirements to lead the design and and imple-
CEM Data Director: Lead definition, integration and validation of CEM architecture: data sources, ETL systems, and data warehouse. Interface with OSS/BSS, CRM, Network, and other engineering / systems departments to find, propose, and validate data models from the analytics requirements.
Some departments as network department must still define and benchmark their own solutions for network deployment, capacity management, and performance monitoring, but is important to educate engineering departments, with the rest of the company, to take into account CEM Strategy when defining and benchmarking OSS, Probes, DPIs, and other systems which main initial scope is focus on network and services inventory, management, and monitoring, as some of the data delivered from them could be an important peace of the internal data for CEM Analytics. Some carriers have different OSS/BSS systems for the fixed and mobile networks, multiplying the effort and cost, and reducing the data integrity to build the 360º
Fig 11: CEM Transformartion
CEM
Marketing
Network
Customer Care
CEM Dashboard Subscriber and Market Analtyics
NETWORK MARKETING
CEM Analytics
Marketing Dashboard
Network Dashboard
Care Dashbaord CARE
Inernal Data CEM Data
External Data
© OptimiQuest. All rights reserved. August 2013
2 3. Subscriber Profiling One of the main reasons to develop a CEM strategy is to allow carriers to build what we previously called ‘720-degree’ view of customers by integrating the internal and external 360º view. This profile is a valuable information to customer care point—user level—,and other departments as Marketing, and Networking—aggregated level— as we have to take into account privacy and policy aims stating clearly with the methods of processing personal data related to the user. Figure.12 shows the Subscriber Profile & Analytics: Data Service Analysis— categorizing data usage by service type: Web Browsing, Streaming, Messaging, VoIP, etc. Device Analysis—categorizing device data by type (smartphone, tablet, dongle, others), operating system, brand and model including some technical and functional device specifications. Apps Analysis— identifying the data generated by the applications, its media provider name and categorization the apps media type: social, travel, music, etc. Interest Analysis— categorizes user URLS usage by OTT provider and its respective Media Category and Subcategories: Social Media, Music, News, Games, etc.
This categorization allows to analyze Tariffs by Service type, Device, Apps, User interest, consumption and other variables as localization, demographics, etc. Finally adding performance and quality indicators to the user categorization allows carrier to complete the internal 360º customer view. To complete the 720º view of the customer carriers must identify the external data sources that are able to add value and visibility to the global market and customer analytics. Some examples of external sources are: Digital Panels—panels composed by a determined number of user which are willing to share their usage data: web, apps, payments, etc. Applications—mobile applications collecting information from the users, network, and third party platforms/services. Applications are a valuable data source as they can add information to end-to-end subscriber journey which data collected directly on the device. Social Networks— analyzing unstructured social media data add information from the social network where users openly share information as interest and opinion. CEM data team is responsible to find and integrate these data sources, categorize, store and extract insights to convert in actionable actions to optimize traditional business and find new monetization opportunities.
Figure 12 –Subscribing Profiling and Analytics Care Analysis
Data Service Analysis
by Tariff by Performance and Quality by other issues: purchasing, billing, etc. (logs categorization)
Service Categorization: Web, Streaming, Messaging, etc.
Device Analysis Performance and Quality Analysis
by Service Type by Access Type by Device by Media Provider by Apps by Location
Device Categorization: Type, Brand, Model, O.S, Specifications.
Apps Analysis
Apss Categorization: Provider, Media Category and Subcategories.
Tariff Analysis
by Service Type. by Device by Apps by Interest by Consumption Others: localization, demographics, etc.
© OptimiQuest. All rights reserved. August 2013
Interest Analysis
URL Categorization: OTT, Media Provider, Media Category and Subcategories.
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4. Resume Mobile operators are the ‘natural’ Big Data companies, given they have a unique view into the behaviour and preferences of millions of customers by virtue of the network data that they process on a daily basis. Defining CEM strategy is something carriers must evaluate form the data to the insights, and from the insights to actions with the objetive to maximize their traditional business, optimize budgets, and find new monetization opportunities. As some use cases example the subscriber profile generated can feed other internal platforms as:
Content platforms— to optimize the content delivering based on user interest.
Advertising platforms—to optimize the internal (CRM) and external adverstising (web and mobile ads), minimizing the bugets and maximizng the conversion rates.
About the Author Andrés Hernández has more than 10 years experience in the IT sector working within Mobile Network Operators, Device Manufacturers, and Big Data Software Providers. With MSc in Mobile Communications at Tampere University of Technolgy –TUT (Finland), and Master on Internet Business at ISDI (Spain) he is an entrepreneurial person, seeking out solutions while exploring new opportunities through the combination of experience, creativity, analysis, and persistence. Main interest as Founder of OptimiQuest is to provide consulting services, analysis, innovation, and generate uses cases for Big Data (CEM for Telcos), and Online/Offline Added Value Services to optimize actual business, or find new monetization opportunities. Partner at Making Business Ideas, Investment Fund company for digital business created by # startups and # MIBers (MIB ex-students). es.linkedin.com/in/ahernandez @andresdez
Client areas—adding information in the client areas to help them to evaluate better its consumption, and offering proactively the offers that better covers their needs.
References
Care Platforms— mapping to the care point the subscriber usage for voice and data, and automatically showing the best offer to allow them to negotiate the best option to reduce churn, and upsell.
[2]
These are some of the consideration to take into account when internally analyzing the ROI to integrate Big Data /CEM solutions. CEM must be considered as the “Second Analytics Eye” for those carriers only with “One Analytics Eye” based on CRM and Billing information to optimize tariffs to compete with other carriers.
© OptimiQuest. All rights reserved. August 2013
[1]
[3]
Is big data monetization the Jewel in the Crown for telecom operators?. August 2013. Redwing. Capitalizing on customer experience. A comprehensive, three-step approach to monetizing network assets. September 2012. Ericsson. Customer Experience Survey. Q1 2012. Eurocomms.
