using big data to build value for nica dynamic insights launch smart steps with market research...

Whitepaper

Using Big Data to build value for operators May 2013

Justin van der Lande

.

Sponsored by

Using Big Data to build value for Operators | i

© Analysys Mason Limited 2013 Contents

Contents

1 Executive summary 2

2 Recommendations for Service providers 2

3 Introduction 3 Defining Big Data 3 3.1

4 Market drivers 4 There are three significant opportunities for CSPs that derive direct income from Big Data 5 4.1

5 The readiness of CSP to support new business services with Big Data 6

6 Examples of new uses cases for Big Data 11 China Mobile Communications Corporation captures search terms to deliver near-real-time 6.1

contextual offers 11 TomTom use of mobile subscriber data from Vodafone and other operators for detailed road traffic 6.2

information 11 Telefónica Dynamic Insights launch Smart Steps with market research company GFK 12 6.3 Verizon wireless has launched Precision Market Insights (Precision), to provide marketers with 6.4

access to mirco segments of the market 12 SingTel purchased Amobee a mobile advertising company in 2012 as part of its Digital L!fe line of 6.5

business 12 SFR use location based information to track spectators’ journeys to plan transport requirements 13 6.6 UK Weve consortium provides coverage for services on top three mobile networks 13 6.7

7 Summary 13

About the authors 15

List of figures Figure 1: Big Data IT infrastructure requirements ............................................................................................. 4 Figure 2: Telecoms service revenue by service type, worldwide, 2011–2016 ................................................. 5 Figure 3: CSPs need to decide where in the Big Data value chain they are to operate .................................... 7 Figure 4: Rating of potential contribution area for Big Data analytics in next 2 years for CSPs ..................... 8 Figure 5: Rating of potential contribution area for Big Data analytics in next 3-5 years for CSPs ................... 9 Figure 6: Readiness of CSPs to leverage Big Data for marketing campaigns ................................................. 10 Figure 7: Barriers for Big Data use in context aware marketing ..................................................................... 10

Using Big Data to build value for Operators | 2

© Analysys Mason Limited 2013 Recommendations for Service providers

1 Executive summary

Big Data, although poorly defined, has created considerable interest amongst both vendors and communication service providers (CSPs) and Big Data is now on the agenda of most CSPs at board level. However, tight fiscal conditions, new propositions and privacy issues are causing some delays in decision making. This does not detract from the increasing realisation by CSPs that the data that they is hold is valuable and should be used to optimise current internal processes or should be offered as a service for external consumption. The market for Big Data analytics (BDA) tools is immature with little information on what the best uses are for vendors to focus on building their solutions to address. There are many different ideas on what uses are most valuable, leading to a highly fractured market for vendors which is less attractive for them to develop applications for. This has resulted in few out-of-the-box applications on the market, which makes implementation more costly and complex, and in turn makes CSPs less likely to implement solutions. Business decisions on prioritizing which projects or “use cases” need addressing must be guided by CSPs to provide the impetus for vendors to develop targeted solutions and in the medium term this will provide CSPs with better, more developed applications to deploy. Where close partnerships have been forged between vendors and service providers it has resulted in the creation of stronger solutions, such as the development of appliances for specific use cases. This paper presents the results of an operator survey, which highlights the use of Big Data to enable context-aware marketing campaigns and to help improve customer care as two areas of immediate interest. Operators are generally in the early stages with such projects. On a slightly longer timescale, there is strong interest in using the same toolsets to add value to services offered by third parties, through the selling of customer data. The use of Big Data analytics to build new data services to be sold to external partners is immature, but holds the potential for some significant revenue streams. In the short term revenues are likely to be restricted by privacy and regulatory issues that are reducing the ability of CSPs to offer these new services, whereas over the top (OTT) players are less impacted by the same regulations. A change in attitude by the CSPs is necessary to build up a subscriber base which has opted-in to services and applications that would then enable CSPs to use their data more widely. CSPs also need to continue to lobby regulators to give them more open regulations to enable them to more readily compete with over the top services.

2 Recommendations for Service providers

Service providers should consider BDA at an enterprise level and develop a long term strategy to ensure that any specialised point solutions, if adopted, will be able to provide and use its data across the organisation. This does not necessarily mean the adoption of a single analytics or data solution for all uses, but where specialised solutions are adopted that they can be utilised within a larger corporate infrastructure. A BDA infrastructure must be capable of supporting all use cases, which includes real-time capability for in-process transactions. Different uses will determine different data, processing and analytics requirements, from


© Analysys Mason Limited 2013 Introduction

high-value real-time capabilities to being able to access a huge volume of data off-line. The optimal infrastructures are likely to include a hybrid of technologies to provide performance needed at the right cost. The adoption of open source technology can provide cost benefits over more established technologies. Consideration should however be given to the operational costs and associated management technologies needed to ensure the total cost of ownership is kept low. Service providers should consider if this is best achieved with in-house resources; using a systems integrator; or by adopting technology supported by a vendor solution. Service providers should consider how to make data available from different data sources across the enterprise. The ability to ingest and integrate various data sources through well-defined extraction loading and transformation adapters and tools will ensure better quality data that is delivered reliability even in near real-time scenarios, as well as reducing the implementation effort. Service providers need access to and use of a rich set of customer data. The current restrictions around privacy make this difficult in many markets without the user’s consent. Service providers need to adopt strategies that build-up their opt-in data sets through the updating of their terms and conditions and by developing and using their own set of apps.

3 Introduction

This whitepaper analyses the impact of Big Data on communications service providers and uses primary research through an industry survey to understand how CSPs are currently using Big Data and what their plans are in the future. The report looks as some of the market drivers that makes Big Data and associated analytics tools important for CSPs. The report also focuses on the use of Big Data to create new services based on selling data outside of the CSP. It is however recognized that in order to build the skills and capabilities for these types of services, Big Data will be first used on internal facing functions. The report provides some examples of how and where CSPs have begun offering data services. This report helps CSPs consider the following questions in regards to Big Data and externalisation of data:

• What types of services can Big Data be used for? • What timescales are being considered by CSPs in the industry? • What might the challenges be in offering these services?

Defining Big Data 3.1

Big data is poorly defined but is generally accepted to have three key characteristics:

• Volume: The large volume of available data is a key characteristic of Big Data. • Variety: Data comes from various different sources (CDRs, data sessions, social networks, internal

reports, transaction based systems) and in different formats (alphanumeric, XML, audio, etc.). • Velocity: The velocity is understood as the frequency at which data is generated. The high

frequency of data capture brings new opportunities in terms of real time management and reporting.


© Analysys Mason Limited 2013 Market drivers

This combination of Big Data characteristics is driving substantial changes within IT requirements, with the greater use of particularly unstructured or semi-structured data changing storage and modelling requirements. Big Data also makes use of tracking transient data, sometimes referred to as data in motion, which has a much higher value if it is analysed and acted on quickly. The cost to do this in the past was prohibitive, making business cases poor and the value of the data low, but as computation prices fall the business cases that utilise this type of data become much more viable. This requirement for near-real-time analytics to be performed on large data sets, combined with a need to act on the results for huge volumes of data, is moving BDA to need tight integration into a business process management engine as an integral part of the system. In the past most processes were done off-line and used manual interventions, but new uses need to act faster, at a lower cost and on larger number of insights, driving the need for automation. Figure 1 shows the move to process driven and in-line analytics where processes are automated from insights derived from analytics tools and systems. Figure 1: Big Data IT infrastructure requirements [Source: Analysys Mason, 2013]

4 Market drivers

The use of BDA has grown over time within CSPs and should be viewed as an evolution of technology and businesses requirements. Technology is driving increasingly sophisticated subscriber devices that are using communication networks in new ways and becoming part of the fabric of every telecoms market, both developed and emerging. The types of services that are now available are increasing and the content and


© Analysys Mason Limited 2013 Market drivers

applications that use them changing in variety rapidly. Technology changes also provide the ability to monitor, store and analyse data cheaper than ever before, allowing for much greater detail on every aspect of a consumer, device, network or service to potentially be used to optimise every process that is impacted by it.

Business requirements are driving CSPs into needing to better utilise their resources, as the once powerful growth of telecoms services are in now in decline in some mature markets. This has generated headlines: for example the Dutch mobile market declined by 4.5% in 2012 (reported in the Dutch daily news), and Spain lost 5% of its mobile subscribers in 2012 (reported in Forbes). These financial pressures have shifted an agenda based heavily on growth to one based on extracting greater value from current assets. BDA is widely seen as one of the tools that can be applied to current processes in order to do this. In addition BDA can be used as a revenue generation tool to provide potential new streams of income outside traditional telecoms services.

Figure 2: Telecoms service revenue by service type, worldwide, 2011–2016 [Source: Analysys Mason, 2013]

2 For further details, see Analysys Mason’s Worldwide telecoms market forecast 2012–2016. Available at

www.analysysmason.com/Worldwide-telecoms-forecast

This modest revenue growth is set against increasing expectations for cheaper data that is growing in volume. Analysys Mason estimates the data growth at about 40% each year within the mobile market. The ability of a CSP to provide more services at lower margins is driving alternative revenue streams into the planning of many CSPs.

There are three significant opportunities for CSPs that derive direct 4.1income from Big Data

Mobile advertising: Advertising to the mobile device based on static and behavioural information as well as using predictive profiling to provide highly targeted contextual marketing. Location data is significant, with usage data such as browsed web sites and applications in use also very significant, especially when combined with static data such as gender, age and home address. CSPs are also able to deliver promotions in near real time through SMS/MMS or if they have advertising real-estate on the mobile device through their own mobile


© Analysys Mason Limited 2013 The readiness of CSP to support new business services with Big Data

applications. The total advertising market is a significant opportunity as outlined in the Ofcom international communications report 2012 extract below.

“Global advertising expenditure grew by 3.8% in 2011 to £298bn, the highest total spend since 2007. While expenditure on internet advertising grew at a compound annual rate of 16.0% between 2007 and 2011, to £48bn, the compound annual growth rate of newspaper advertising was -6.9% falling to £60bn, while for magazines it was -6.8%, falling to £28bn.”

This USD 500 billion market is significant enough to impact the much larger USD 1.8 trillion global telecoms market, if CSPs are able to prove significant advantages over other consumer data and delivery mechanisms.

Market research: Highly detailed market data provides information for retail, government and infrastructure planning. In the past this was achieved from market surveys and modelling techniques, while now it can now be achieved with behavioural information for very large samples, enabling much greater accuracy. This information can trend over small time increments and is highly detailed providing some unique data into the market. Users of the data are many and varied, but include:

• planners for transport that are looking at details of traffic or people movements, • stadiums or events planners who need to understand transport requirements or confirmation of numbers

of people, • retail outlets that want to plan for the optimum location for their new stores or shopping malls, • bill board advertisers that want to optimise locations and content

…as well as a host of other location based requirements. The market is also significant on a global scale with an estimated USD 45 billion value.

M2M monitoring: These services are highly specific to each industry sector and include applications such as m-health monitoring, device monitoring and security as well as the example given later in the report for traffic data in a satellite navigation device. This market is the least defined but may have great potential for the selling of data by CSPs.

5 The readiness of CSP to support new business services with Big Data

The readiness of CSP of to utilise Big Data and analytics was assessed based on primary research carried out by Analysys Mason. It involved questioning of over thirty five CSPs from across the globe. Each CSP was surveyed on a number of questions and the results collated.

Big Data and analytics are still at an embryonic stage in terms of revenues generated from the delivery of data services to external partners. There are five options that CSPs are able to take to participate in the market, each with a growing level of operational change required to accomplish it. These different options are outlined in figure 3 below.

There are various points at which CSPs are able to participate in the selling of data to third-parties. Where the data is made anonymous there are fewer restrictions on its uses and this is generally non-real-time, helping to



reduce the IT requirements in its delivery. As the data becomes more personalised and the requirements move toward providing the data in nearer real time the IT systems require greater investments. CSPs will typically not actually release data to third-parties but provide the data as a service, which gives them stricter control over who and how often their customer base is contracted. This is a similar approach to fulfilment houses in other industries such as radio, TV or printed magazines.

Figure 3: CSPs need to decide where in the Big Data value chain they are to operate [Source: Analysys Mason, 2013]

From our work with CSPs it was also clearly understood that there is a number of organisational challenges in being able to offer new data services. It was also clearly understood that many planned to use Big Data analytics for their internal processes (eg marketing and customer care) before offering this as a service externally. In the shorter term it is in fact far more likely to see a better return on any Big Data investment. This was very much reflected in the survey questions below.

CSP keeps business as usual, allowing third parties to extract data directly from IT/network systems

CSP captures and aggregates the data from its own systems into a Big Data mart, providing anonymised data to third parties

CSP captures, aggregates and segments data and provides data to third parties

CSP uses segmentation algorithms and packages the data for different types of clients as a service for mobile advertising

Operator offers turnkey customizable solutions based on client needs, providing access to customer data to third parties only as a service



Figure 4: Rating of potential contribution area for Big Data analytics in next 2 years for CSPs [Source: Analysys Mason, 2013]

Within the next 2 years, Big Data analytics was deemed to be more likely to be used for promoting new offers for a CSP’s own services, improving customer care and creating better customer experience, than offering new advanced data services. The one ranked highest was use cases associated with customer care. Advanced new business services offering capabilities to third-parties also got the largest number of very low ratings, but what was significant was that over 31% ranked it has having high potential in the next two years and over 7% ranked it as very high. This divergence of options was probably driven through service providers’ different views on the privacy issues associated with using customer data.

With other much more established processes such as customer care, the ability to use Big Data to help decision making is a relatively modest goal, when compared to the creating a new set of data services. The survey suggests that is the case with nearly 70% stating that using Big Data for customer care is high or very high value.

Unsurprisingly there was a strong expectation that Big Data analytics would continue to be used to build new revenues through better cross selling and up-selling of current services. This is primarily driven through greater customer segmentation driven by deeper customer information and context awareness that is delivered in nearer real-time to grow new revenues or retain current customers. The survey then asked CSPs what they considered likely in the longer term, beyond 2 years. The results of these questions are outlined below.

0% 20% 40% 60% 80% 100%

Additional revenue from up-selling/cross-selling your services via

context aware marketing

Opening new business areas by providing context aware marketing

capabilities to third parties

Improving customer care though predicting customer issues

Verifying/ensuring that the promised customer experience is being

delivered

Other

How would you rate the potential value contributed by Big Data analytics

solutions towards the following in the near term (0-2 years)?

very low

Low

Don't know or N/A

High

Very High



Figure 5: Rating of potential contribution area for Big Data analytics in next 3-5 years for CSPs [Source: Analysys Mason, 2013]

The results, though still supporting customer care and upselling of a CSP’s own services, now also ranked “the opening new business areas by providing context aware marketing to third-parties” more highly. The expected potential for this area increased from under 40% (near term) to over 75% in the longer term.

This would indicate that many CSPs are seriously considering, or have plans underway, to implement Big Data projects for the externalisation of data. Understanding the current situation of systems offering these services provides a strong indicator of the organisation’s ability to offer services externally.

The survey asked CSPs what the current state of readiness was for internal systems offering Big Data driven marketing and camapign services. About 40% responded that they were already operational or were in the process of being built, and a further 29% stated that they were at the palnning stage.

0% 10% 20% 30% 40% 50% 60% 70% 80%

Additional revenue from up-selling/cross-selling your services via context

aware marketing

Opening new business areas by providing context aware marketing

capabilities to third parties

Improving customer care though predicting customer issues

Verifying/ensuring that the promised customer experience is being

delivered

Other

How would you rate the potential value contributed by Big Data analytics

solutions towards the following in the long term (3-5 years):

Very low

Low

Don't know or N/A

High

Very High



Figure 6: Readiness of CSPs to leverage Big Data for marketing campaigns [Source: Analysys Mason, 2013]

Addressing why services or systems were not yet in place was considered within the survey. The most significant reason why context sensitive marketing was not being implemented were based on privacy issues preventing the use of customer data. Nearly 70% of the respondents stated that this was a contributing factor. This was also reflected by many who cited regulatory issues as being a significant factor.

Figure 7: Barriers for Big Data use in context aware marketing [Source: Analysys Mason, 2013]

Privacy and regulatory factors are not fully in the control of CSPs, but it is possible to execute strategies and services that are able to resolve some of the barriers outlined in figure 7. The most significant of these is the use of subscriber data that is delivered through opt-ins. These applications’ terms and conditions can allow for data gathered to be used by third-parties. Other methods used are to update the terms of services that CSP have with their subscribers for their current services to allow the use of data.

0% 5% 10% 15% 20% 25% 30% 35% 40%

Not started/no interest

In process/building such capability

Fully operational

At planning stage

How would you rate the readiness of your organization to leverage Big Data for context aware marketing campaigns to up-sell/cross-sell your own

services?

0% 10% 20% 30% 40% 50% 60% 70% 80%

Lack of funding/business case

Necessary technology not deployed internally

Necessary technology not available in the market

Privacy issues preventing the use of customer data

Don't know/not sure

other

What are (or were) the barriers to using Big Data analytics solutions for context aware marketing

in your business? (tick as many as apply)


© Analysys Mason Limited 2013 Examples of new uses cases for Big Data

CSPs are starting to act more like over-the-top vendors and will need to continue to do so to become competitive within the market, for example tracking Google search terms, or what applications a subscriber is currently using, to provide context aware push marketing messages.

6 Examples of new uses cases for Big Data

China Mobile Communications Corporation captures search terms to 6.1deliver near-real-time contextual offers

In order to encourage more subscribers to experience the “Cool Car Reports” service, available from the China Mobile digital services portal, China Mobile configured the tracking word ’Car’ in their real-time context-aware marketing system (which combines stream computing with Big Data analytics). Customers who belong to the Mobile News preferred customer group receive a push link to the “Cool Car Reports” service at the moment of searching for ‘Car’ on Google, Baidu, Bing and other search engines.

With this real-time precise promotion, the click-through rate for the “Cool Car Reports” campaign increased from 1.5% to 4.28%. Further context can be added through opt-in applications as well as further search terms to trigger appropriate and timely offers.

TomTom use of mobile subscriber data from Vodafone and other 6.2operators for detailed road traffic information

The need for road users to understand up-to-date traffic situations has always been apparent. This data has been available for years through standard media; however it is really needed on-demand in the driven vehicle. This real-time data then enables drivers to make alternative routes to avoid traffic jams. RDS-TMC is a widely used technology but has a number of drawbacks that can result in poor information for drivers. To improve the traffic data TomTom Traffic subscription service uses a wireless data connection (GPRS, EDGE, UMTS or CDMA) between the TomTom device and mobile phone via Bluetooth. The mobile data network is used to provide the best available traffic information. The use of a mobile data connection means that drivers have consistent and reliable access to traffic information, as they are not reliant on the radio network that the RDS-TMC services use.

There is still a downside as the data fed over the mobile data network is not always accurate and up-to-date and does not cover the whole road network. There is also the cost of running up data charges on the mobile phone. TomTom has developed a further service: High Definition (HD) Traffic. HD uses anonymous data on the direction and speed of mobile phones traveling in cars. This data was initially made available through a partnership with Vodafone.

This location based service actively tracks mobile phones as they move from location to location, including their direction and speed. TomTom accesses this anonymous data from millions of Vodafone customers, giving an accurate view of the traffic situation throughout the road network. This data is compared and merged with information from traffic authorities, road operators, and commercial third parties. Vodafone are a strong supplier of this type of data but TomTom also uses other providers.


© Analysys Mason Limited 2013 Examples of new uses cases for Big Data

Telefónica Dynamic Insights launch Smart Steps with market research 6.3company GFK

Telefónica Dynamic Insights, part of Telefonica Digital, launched ‘Smart Steps’ in late 2012, which helps retailers, councils, and public safety bodies to understand footfall through the use of anonymised and aggregated mobile network data calculated from subscribers’ mobile phone records when visiting an area or location.

Smart Steps is a joint initiative with GFK, a global market research company, and content is sold and distributed by both companies. Smart Steps uses anonymised and aggregated mobile network data to provide trends about footfall, based on location and time. This data is supplemented with other data such as gender and age, which have been added already, but also further information can be added to the service as required (and as legally allowed). The data provides fine grained information on places visited by the hour, day or month. Consumers of the data can use Smart Steps to understand footfall across their estate, tailor product promotions in existing stores, determine the best locations and formats for new stores, and measure the impact of marketing activity on footfall. Smart Steps is launching in the UK and other countries in the Telefónica group will follow.

Verizon wireless has launched Precision Market Insights (Precision), to 6.4provide marketers with access to mirco segments of the market

Verizon is using data collected from subscribers that have opted in to its Verizon Selects programme to provide access to micro-segments of the market that are made available in the Precision range of data services. Verizon Selects enables location, web browsing habits and mobile application usage data, as well as other information including customer demographic and interest data, to create specific insights.

Verizon has two broad service types:

• Firstly one that uses aggregated anonymous data for location, browser and mobile application usage habits to provide segmentation information for use by marketing companies looking to address groups of subscribers. Individuals are not targeted directly but selected on demographics (age ranges, gender, etc.) and interests (such as “pet lovers” or “tennis enthusiasts”) for instance. This service enables venues to provide better targeting of advertisers on billboards for example.

• Secondly a programme to make ads more targeted for each subscriber, so when a subscriber sees an ad on a websites or in an app, the ad is especially relevant. The program uses certain consumer information such as demographics and postal address to determine the fit within an audience an advertiser is trying to reach. This program does not use location or browsing information. Fulfilment of these messages can be in various ways such as email, text, postal mail or online or mobile advertising.

SingTel purchased Amobee a mobile advertising company in 2012 as part 6.5of its Digital L!fe line of business

SingTel have rolled out a number of new mobile advertising offers that are available to third-party partners based on their Amobee technology, purchased last year. Amobee is a software house that will continue to develop solutions for the whole industry not just SingTel.

Targeted advertising is based on a number of mobile advertising opportunities for SingTel’s partners, which includes context aware advertising based on:


© Analysys Mason Limited 2013 Summary

• Adverts embedded within consumer mobile applications (Mo4U, iLoveDeals, Go!Shopping, Property Buddy and Price Pal)

• Targeted SMS/MMS adverting • Location based advertising • Vouchers

SFR use location based information to track spectators’ journeys to plan 6.6transport requirements

SFR are using technology that allows them to track and document the locations of their customers from a single starting point. There are many potential uses for this type of data: for example, analysing the spectators at a football match and tracking their return journeys to understand what transportation is required to support a specific event. From analysing this geographical pattern, the Paris public transportation company was able to develop specific transport requirements around Paris for match days at the Stade de Frances, thus avoiding city centre traffic congestion.

UK Weve consortium provides coverage for services on top three mobile 6.7networks

Weve was formed in 2013 as a joint venture for mobile marketing and mobile wallet services between EE, O2 and Vodafone in the UK.

Weve provides secure consolidated and standardised access to a set of services in the three top networks for: mobile marketing and advertising, payments and transactions, loyalty programmes and coupons.

The first adverts to run were for Morrisons supermarket and Nike, which were able to contact 15 million mobile users via Weve, using location-based, text and video messages on all three UK networks. O2 customers account for the bulk of the 15 million users as a result of the network’s long-running O2 More mobile ad platform, which has previously worked with Pizza Hut, among other brands.

7 Summary

Big Data, although poorly defined, has created considerable interest amongst both vendors and Communication Service Providers (CSPs) and Big Data is now on the agenda of most CSPs at board level. However, tight fiscal conditions coupled with the newness of technology are causing some delays in decision making. This does not detract from the increasing realisation by CSPs that the data that they hold is valuable and should be used to optimise current internal processes or should be offered as a service for external consumption.

The market for Big Data analytics (BDA) tools is immature with little information on what the best uses are for CSPs to give vendors guidance on, giving them the focus needed to build their solutions. There are many different ideas on what uses are most valuable, leading to a highly fractured market for vendors to address, and a less attractive one for them to develop applications for. This has resulted in few out-of-the-box applications on the market. This in turn continues to make implementation more costly and complex, and to make CSPs less likely to deploy solutions. Where close partnerships have been forged between vendors and service providers this cycle can be broken, such as the creation of appliances for specific use cases.


© Analysys Mason Limited 2013 Summary

Business decisions on prioritizing which projects or “use cases” need addressing must be guided by CSPs to provide the impetus for vendors to develop targeted solutions and in the medium term this will provide CSPs with better, more developed applications to deploy.

The use of Big Data analytics to build new data services to third-parties is immature, but holds the potential for some significant revenue streams, though not in the short term. Privacy and regulatory issues are reducing the ability of CSPs to offer these new services, whereas over the top (OTT) players are less impacted by the same regulations. CSPs will need to build up a subscriber base that has opted-in to services and applications to enable CSPs to use their data. CSPs also need to continue to lobby regulators to given them similar laws that govern OTT companies.

Nevertheless, CSPs can (and are) gaining strong benefits from using Big Data analytics on their current services today, through providing greater segmentation and more targeted real-time offers based on customer context.


© Analysys Mason Limited 2013 About the authors

About the authors

Justin van der Lande Justin is a senior analyst for Analysys Mason with 20 years’ experience in OSS and BSS systems. Justin heads the Revenue Management and Analytics software research programmes for Analysys Mason. The Revenue Management programme covers software for mediation, business optimisation, settlements, interconnect and billing systems and tracks over a hundred vendor solutions in the market. The Analytics programme covers strategic uses of analytics within the telecoms markets and follows key vendors. Justin holds a BSc in Computer and Management Sciences from the University of Wales.

About Analysys Mason

Knowing what’s going on is one thing. Understanding how to take advantage of events is quite another. Our ability to understand the complex workings of telecoms, media and technology (TMT) industries and draw practical conclusions, based on the specialist knowledge of our people, is what sets Analysys Mason apart. We deliver our key services via two channels: consulting and research.

• Consulting: Our focus is exclusively on TMT. We support multi-billion dollar investments, advise clients on regulatory matters, provide spectrum valuation and auction support, and advise on operational performance, business planning and strategy. For more information, please visit www.analysysmason.com/consulting

• Research: We analyse, track and forecast the different services accessed by consumers and enterprises, as well as the software, infrastructure and technology delivering those services. Research clients benefit from regular and timely intelligence in addition to direct access to our team of expert analysts. Our dedicated Custom Research team undertakes specialised and bespoke projects for clients. For more information, please visit www.analysysmason.com/research

About AsiaInfo

AsiaInfo is a leading provider of high-quality software solutions and IT services to the telecommunications industry. Headquartered in Beijing, we employ more than 11,000 professionals worldwide. In China we have more than 50% market share in Billing, CRM and Business Intelligence through our longstanding partnerships with China Mobile, China Unicom and China Telecom.

AsiaInfo provides a full suite of business and operations support systems (BSS/OSS) and associated professional services. Our core Veris™ product suite includes billing and customer care systems that serve nearly a billion subscribers globally, plus business intelligence, network management and security solutions.

Of particular relevance to this report, Veris C3 is a Big Data Analytics solution which gives operators detailed knowledge about each individual customer’s context in real-time, and the capability to trigger relevant and personalized marketing offers and policy changes as appropriate to the customer’s needs.

For more information about AsiaInfo, please visit www.asiainfo.com.

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