Monetizing Customer Insights

– Are Telcos Ready and Motivated?

Version 1.0 | October 2015 | Katie Matthews & Andy Tiller

© AsiaInfo 2015 2

Contents

1 Executive Summary ......................................................................................................................... 3

2 Background ..................................................................................................................................... 5

3 Survey & Interviews ........................................................................................................................ 6

3.1 Question A: Priority given to real-time contextual marketing .................................................. 6

3.2 Question B: Perception versus the large internet players ......................................................... 7

3.3 Question C: Personalized marketing capabilities....................................................................... 8

3.4 Question D: Existing technical capabilities ................................................................................ 8

3.5 Question E: Use of data sources ................................................................................................ 9

3.6 Question F: Barriers ................................................................................................................. 10

3.6.1 Regulatory restrictions ...................................................................................................... 11

3.6.2 Internal access to data ...................................................................................................... 11

3.6.3 Technology and cost ......................................................................................................... 12

3.6.4 Customer loyalty and trust ............................................................................................... 12

3.6.5 Additional commentary .................................................................................................... 13

3.7 Question G: Vision ................................................................................................................... 13

4 The Business Challenge ................................................................................................................. 14

5 Repositioning for the future ......................................................................................................... 17

5.1 The ‘Readiness and Vision Score’ ............................................................................................. 17

5.2 Where do operators want to be? ............................................................................................ 17

6 Conclusion ..................................................................................................................................... 18

© AsiaInfo 2015 3

1 Executive Summary

Telecom operators recognize that change is essential if they want to compete in the digital economy,

and not get pushed into a corner by Large Internet Players (LIPs) who have built their business

models around sophisticated data analytics. One potential approach for operators to regain the

initiative is to use their own data analytics to generate and monetize customer insights in similar

ways to the LIPs. But do operators want to take this path, and are they ready to do it?

AsiaInfo commissioned Analysys Mason to conduct a global operator survey in order to ascertain

how operators ranked their own performance against LIPs in terms of their capability for generating

and monetizing customer insights. Operators were asked to complete a quantitative survey, which

identified both the organization’s vision for creating new business opportunities based on customer

insights, as well as their actual readiness to compete (see Figure 1).

Figure 1: Categorization of telecom operators by Readiness and Vision

The majority of operators indicated that they recognize the need to monetize their customer insights.

Some saw the key benefits in terms of enhancing their existing business. Others saw merit in using

customer insights not only to sell more of their own products and services but also to add value to

third-party partners, and thereby create new data-centric business models.

62% of operator respondents believe they are more than two years behind the internet players

However, although the customer insights which operators are able to generate can potentially be even

more sophisticated than the insights which many internet players have access to, operators are

typically not monetizing those insights to the same degree as the internet players today. The majority

of operators surveyed said that it could take several years for them to reach the same level of

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READINESS SCORE

MEA APAC Europe Americas

TransformersWorriers

Head DownersUnbelievers

© AsiaInfo 2015 4

sophistication in exploiting customer data as major online providers such as Google, Amazon,

Facebook and Apple (GAFA).

For these LIPs, the ability to exploit customer data is central to their entire business model whereas

for operators customer data is gathered as a by-product of their core business. Nevertheless,

operators do not appear to lack motivation in this area. The survey probed the factors which are

currently preventing operators from exploiting their customer insights, and lack of interest was not

one of them.

Regulation is the biggest barrier preventing operators from monetizing customer insights

In some cases, access to the relevant data or the technology to process it was a problem, but the

biggest single barrier globally is clearly perceived to be regulatory constraints. This is especially the

case in Europe where almost 60 per cent of operators cited regulation as the most significant barrier.

In contrast, operators in the Americas considered technical constraints to be the most important.

Perhaps surprisingly, the survey results suggest that operators are not held back by fear of negative

customer reaction to the use of personal data. Fear of losing customer loyalty and trust through

inappropriate use of data ranked low compared to other constraints.

Based on the survey responses, each operator was scored for their ‘Readiness’ to generate customer

insights and their ‘Vision’ for monetizing them1 (see Figure 1). By mapping operators in this way it

became clear that Vision and Readiness do not necessarily correlate. Indeed, in terms of their

Readiness and Vision to adopt new customer-centric business models, operators fell into one of four

categories:

‘Transformers’ have the vision to transform their business models and have the tools

necessary to do so

‘Head Downers’ are ‘Ready’ to compete but are focusing their capabilities on maximizing their

existing business, rather than applying them to new business models; for these operators, a

lack of ‘Vision’ does not imply lack of motivation

‘Worriers’ are keen to adopt new business models based on monetizing customer insights but

are not actually ‘Ready’ to do so

‘Unbelievers’ are not investing in the technology to generate customer insights and don’t see

the need.

Overall, the survey suggests that operator attitudes and capabilities for monetizing customer insights

vary widely. Some differences can be attributed to regional factors, such as the strength of regulation

1 The Readiness score was based on the data analytics and marketing automation technology in use today (survey

questions C, D & E), as well as the respondent’s perception of the level of advancement compared to the LIPs (survey

question B). The Vision score was based on the level of priority attached to personalized, contextual marketing (survey

question A) as well as attitudes to market demand and customer perception (from survey question F) and motivation to

create new business models based on customer insights (survey question G).

© AsiaInfo 2015 5

in the European market, but Figure 1 shows that a mix of Transformers, Head Downers, Worriers and

Unbelievers can be found in all markets.

2 Background

Large Internet Players such as Google, Facebook, Amazon and Apple dominate our lives. We take it

for granted that they know a lot about us. We happily part with our data in exchange for highly

personalized and innovative products and services – and, most of the time, most of us accept that we

get value from this relationship. Meanwhile, for these companies, our data is what keeps the show

on the road in the form of valuable aggregated customer insights that drive product development and

generate significant third party advertising revenues.

Mobile operators increasingly operate in the same digital ecosystem as these LIPs but, until now, have

overseen very different business models. The sophisticated subscriber data they have accumulated

has largely been an incidental by-product of the once lucrative communication services they supplied,

not the core of their business model. So, perhaps understandably, customer data has not been

exploited by operators in such a comprehensive way as the LIPs.

Now, with revenues from traditional voice and messaging services in decline, operators are looking to

plug the revenue gap and repurpose their business models for the new digital economy. One

approach is to take advantage of the superior subscriber data that they have access to, and leverage

insights from advanced analytics both to improve their own marketing effectiveness and to develop

new sources of revenue. Subscribers today do almost everything on their mobile devices, so

operators potentially have better insight into their customers’ profiles and behaviors than many

internet companies. The opportunity exists for operators to develop new business models based on

the value held in their customer data, and challenge the status quo in which LIPs are all-powerful in

this space.

Our research looked at how the current approach of operators compares to that of the LIPs,

particularly in regard to the use of contextual, personalized marketing and data-driven segmentation

for creating and sustaining business models that rely on customer insights.

Based on the Analysys Mason research, our hypothesis is that operators will increasingly be forced to

adopt a similar, or indeed better, approach, capabilities and tools for their own marketing in terms of

generating and utilizing customer insights. This challenge will intensify as margins are squeezed and

the competitive pressures that come from operating within the crowded digital ecosystem continue to

build.

© AsiaInfo 2015 6

3 Survey & Interviews

For this study Analysys Mason surveyed 50 telecom operators worldwide. A breakdown by region

and job role of respondents is shown in Table1. The survey was confidential, so operator names are

not provided in this report. In addition to the survey, Analysys Mason conducted detailed interviews

with a number of operators from Europe, the Middle East and Africa in order to validate the survey

results, as well as one comparative interview with a large internet player. Again, the interviews were

conducted in confidence, but some insights are provided in this report via anonymized quotes.

Region Responses Job Role Responses

Europe 14 General Management 15

Middle East & Africa (MEA)* 14 IT & Project Management 6

Americas** 14 Product Management 9

APAC 8 Sales & Marketing 15

Total 50 Strategy 5

* Middle East 9 | Africa 5 Total 50

** N. America 11 | LATAM 3

Table1: Breakdown of survey responses by region and job role

3.1 Question A: Priority given to real-time contextual marketing

Figure 2: What level of importance is placed on personalized, contextual marketing today in your

organization that uses real-time data sources2?

2 Examples of real-time data sources include account balances, apps in use on a smartphone, content being viewed,

current location, etc.

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Low / None High / Very High

© AsiaInfo 2015 7

Two-thirds of the operators surveyed rated the level of importance currently placed on personalized,

contextual marketing within their organizations as being ‘High’ or ‘Very High’ (see Figure 2).

Operators in the Asia Pacific region appear to place an even higher importance on personalized,

contextual marketing than operators in other regions, with 100% of APAC respondents rating their

interest as High or Very High.

More than half of operators who responded with High or Very High importance have already deployed

key technologies that will enable them to generate personalized customer insights (eg Big Data

Analytics and Complex Event Processing, see question D below). However, most are not yet using

real-time data sources (question E) which are needed to provide contextual awareness.

3.2 Question B: Perception versus the large internet players

Figure 3: How many years do you consider it will take for your organization to reach the same level of

marketing sophistication as key on-line providers like Google, Amazon and Facebook?

Nearly all of the operators surveyed believe that their organizations are behind LIPs in terms of

sophisticated marketing (i.e. personalized, contextual marketing that uses real-time data sources).

While 32% thought that it would take 1-2 years to close the gap on LIPs, more than 60% considered

themselves to be at least two years away from leveling the playing field compared to their internet

counterparts (see Figure 3). Only 6% considered themselves on a par with the LIPs in terms of

marketing sophistication.

“Operators have the potential to do what we do. Although there are some legal and technical

challenges, the largest constraint is the current mindset. They don’t have the right mindset that

data is the most important thing they have.” Large Internet Player

Operators in the Asia Pacific region appear to be slightly more optimistic about the gap between their

own capabilities and the LIPs (see Figure 3), which is perhaps a reflection of the high importance they

attached to this area in the responses to question A above.

38%

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Less than 2 years More than 2 years

© AsiaInfo 2015 8

3.3 Question C: Personalized marketing capabilities

Figure 4: What is your smallest campaign size?

Operators are used to targeting their marketing campaigns at very large numbers of subscribers.

Only 26% of the surveyed operators currently have the ability to target a segment size of 1,000

subscribers or fewer, and only 8% of telcos can target a personalized campaign message to an

individual subscriber. In practice this capability is important for contextually aware campaigns, which

are triggered for an individual subscriber based on the occurrence of a specific context. Without this

capability, the operator may be able to target a campaign at the right customer, but not necessarily at

the right moment.

“Campaigns normally run to the low millions of subscribers. Within these campaigns there is

potential to provide some sub-segmentation based on largely static data – such as the services that

are currently subscribed. More specific segments tend to be manually created – that for example

will target Value Added Services. These may come down to tens of thousands in their scope.”

Tier 1 European Operator

3.4 Question D: Existing technical capabilities

While almost all of the operators surveyed are currently using some form of centralized campaign

management system, far fewer have deployed key technologies to enable personalized, contextual

marketing such as a Big Data Analytics and Complex Event Processing (CEP). Less than a quarter of

the operators surveyed are using both of these technologies.

“We use centralized campaign management, as well as analytics tools of different types. These are

not all used within marketing but by IT services.” Tier 1 European Operator

1 (personalized)8%

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1,000-10,00024%

10,000-100,00034%

Over 100,00016%

© AsiaInfo 2015 9

Figure 5: What marketing and data processing technology does your organization have? (Breakdown

according to responses to question A above)

As might be expected, operators expressing High or Very High interest (see question A above) have

typically done more to introduce advanced technology, but there is still a discernible gap between their

interest and their capabilities, with only 70% using Big Data Analytics and less than half using CEP.

3.5 Question E: Use of data sources

Figure 6: What type of data is being used in your marketing campaigns today?

(Breakdown according to responses to question A above)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

“Big data” analytics to support streaming data feeds, such as DPI.

“Complex event processing” to deliver highly personalised, and contextually accurate …

Centralised campaign management using multiplemarketing channels

Don’t know or unsure of the technology we use

High interest Low interest

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Profile datafrom CRM

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Network dataprocessed inreal-time (eggeo-fencing,

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Low interest High interest

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Figure 6 shows the importance attached to various data sources which can be used to generate

personalized, contextual information about end customers. The chart shows the importance of each

data source compared to the average score; therefore sources with a positive score are given the most

emphasis, and those with a negative score are not used so much.

Static customer profile data, along with previous service usage history, are the most important data

sources used by operators today, whereas less emphasis is given to network data and social media

sources. Real-time data is given even less importance, even though it is essential for personalized,

contextual offers – for example, offering a day-pass for YouTube to a subscriber who has a low balance

(real-time BSS data) and is currently watching videos on YouTube (real-time network data).

“For digital services that require online data understanding and application information, we are of

course not as advanced as digital service providers. However, as we increasingly offer these

services, we will use more real-time data. This is likely to take at least two years to get to the same

level as digital providers are at.” Tier 1 Middle East Operator

Surprisingly, operators expressing a stronger interest in personalized, contextual marketing, (according

to their response to question A above) appear to focus even more strongly on static profile data to

guide their marketing campaigns, and place less importance on real-time data sources than those

operators with Low interest (see Figure 6). This may reflect the fact that a High level of interest does

not always translate into actual readiness to generate and monetize customer insights (see Figure 1).

3.6 Question F: Barriers

Figure 7: What are the biggest constraints in being able to use data today to build business models based on

customer insights?

The survey asked operators about the barriers to using customer data to build new business models

based on customer insights (see Figure 7).

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3.6.1 Regulatory restrictions

The main barrier by far was perceived to be regulatory restrictions over the use of customer data.

This was emphatically the case for operators who indicated that pursuing a customer-centric business

model via personalized and real-time marketing was of ‘High’ or ‘Very High’ importance (question A).

The global breakdown (Figure 7) shows that regulation is a particular concern in Europe, where almost

60% of operators cited regulation as the most significant barrier. Operators in APAC also consider

regulation to be their most significant barrier, but technical constraints are a more important factor in

the Americas and Middle East.

“There is a huge amount of regulation around the use of customer data, and it is this complex mix of

regulations that operators are struggling to navigate. During the interviews, multinational operators

pointed to the difficulty of exporting as well as processing data overseas – for example, Germany’s data

laws prevent this practice.” Atul Arora, Analyst Telecoms Software at Analysys Mason

European operators have long complained of an uneven playing field between themselves and the LIPs.

They face more stringent regulatory obligations than internet players for the provision of similar

services. Many have argued that this is a competitive distortion that generates extra costs and

constraints for mobile operators and stifles investment and innovation. For example, Orange in

France has publicly stated their belief that operators are unfairly penalized in their attempts to use

personal data, particularly in regard to the use of location information3. There are strong political

and legal limitations on European operators’ ability to monetize statistics from customer data, even

when anonymized.

Fortunately, there are signs that the tide is changing in Europe, with the European Commission

planning to probe big internet companies as part of its Digital Single Market strategy 4 . The

Commission has announced an ambitious overhaul of the bloc's regulation of the telecoms sector,

scheduled for next year, which will take into account the increased competition from services such as

Facebook-owned WhatsApp and Skype, a decision that will be cheered by the telecoms industry.

Outside Europe, Analysys Mason found other examples of regional regulation hindering operators’

ambitions. For instance, one Tier 1 operator in the Middle East cited as a barrier to developing new

services the fact that they are not allowed to track URLs visited by their subscribers while browsing

from their mobile devices.

3.6.2 Internal access to data

Data is often held in silos across an operator’s business. The inability to access this data was cited as

a significant barrier, especially by operators in the Americas (see Figure 7). This issue was frequently

brought up in the interviews which Analysys Mason conducted with operators.

3 http://www.orange.com/en/content/download/24112/526672/version/4/file/CS93_ALLOUET_et_al.pdf

4 ‘European Commission starts work on review of telecoms rules’ http://www.telecompaper.com/news/european-

commission-starts-work-on-review-of-telecom-rules--1094326

© AsiaInfo 2015 12

“IT staff still own much of the data, and network data is not readily shared with marketing.”

Tier 1 Middle East Operator

“[The organization] came from multiple acquisitions and mergers. This has left data in different data

systems and sources making it hard to get complete 360 customer views. This is now being addressed

but lack of investment over the years has made this too expensive to do until recently.”

Tier 1 European Operator

“We are constrained at the moment because data segmentation requests are done by the IT

department, and not within Marketing.”

Tier 1 European Operator

3.6.3 Technology and cost

Access to the technology which enables real-time, contextual marketing, as well as the cost of storing

data, were only slightly above-average concerns.

This seems at odds with the operators’ responses to question D which suggest that investment in the

necessary technology is clearly still needed. One possible explanation is that the barriers considered

in question F come in a sequence: if regulation is preventing operators from starting work, then lack

of technology to carry out the work will not yet be perceived as a significant issue.

Some operators indicated during interviews that a combination of internal access to data, as well as a

lack of the necessary technology is preventing them from monetizing their customer insights.

“We want to use more real-time data and make better use of social media data but we need better

tools to enable marketing staff to build or research new segments in a more timely manner.”

Tier 1 European Operator

“We have been investigating real-time campaigns for some time and are in ongoing discussions with

vendors. Digital providers are probably more advanced, in terms of their use of online data –

probably a year or two ahead of us. However, we are probably more advanced when it comes to

offline - many of our campaigns uses pre-paid billing information and balances.”

Tier 1 European Operator

3.6.4 Customer loyalty and trust

Interestingly, the survey results suggest that ‘fear of losing customer trust and loyalty’ is a relatively

minor concern for operators across all regions. This is perhaps surprising given the criticism

operators routinely take when they misuse or fail to protect their customers’ personal data. Indeed,

McKinsey cites this public perception issue as the most challenging hurdle preventing operators from

realizing the opportunities provided by big data and advanced analytics – an opportunity which they

value at $300 billion5.

5 Big data in telecoms: How to capture value from customer information, McKinsey, March 2014

© AsiaInfo 2015 13

“Far more consumers hold operators accountable for breaches of privacy than they do app

developers, national regulators, governments, or handset manufacturers.” McKinsey & Company5

It’s possible that the explanation again lies with the sequential nature of the barriers: if regulation or

lack of access to data are preventing an operator from starting out on the journey, it’s unlikely that

customers’ concerns over data privacy will be the primary consideration just yet. It’s also possible

that some operators view this constraint as more within their direct control, and have confidence in

their organization’s ability to use insights sensibly in a way which is not upsetting to their customers.

3.6.5 Additional commentary

While regulatory constraints surrounding the use of customer data are currently perceived to be the

biggest barrier, the mechanics of accessing the data from various internal departments, as well as the

technical ability to process it, are potentially significant roadblocks.

Many operators are keen to pursue new business models based on customer insights, but relatively

few have the technology in place now to support a personalized, real-time approach. For example,

while operators are confident in using static/historical data from their CRM and Billing systems, few

are currently exploiting insights from their network data and social media sources, especially real-time

information about usage, location and behavior which are critical to understanding the customer’s

context.

“Our survey has laid bare the chasm that exists between operators’ new business ambitions and their

readiness for them. Operators want to innovate and make the most of the abundant customer data

they have. However, in technical terms, they have a way to go.” Justin van der Lande, Principal

Analyst at Analysys Mason

3.7 Question G: Vision

When questioned about their vision for future use of personal data and advanced analytics, operators

acknowledged the need to use personal and contextual information both to enhance their existing

business model, as well as to support new business models based on customer insights (see Figure 8).

“Our use of Real-Time data will increase, and we hope to make better use of network data to target

customers for new service types.” Tier 1 European Operator

Supporting real-time contextual awareness was identified as a top priority for many operators

(particularly for those who identified themselves as being more than two years behind the LIPs).

Coming in a close second was enabling personalization of offers to existing customers. A shift

towards new business models driven by customer insights was the third most popular response.

© AsiaInfo 2015 14

Figure 8: What future changes are important to your organization?

The survey showed that the majority of operators recognize the market demand for new customer-

centric business models, derived from the data that they already store and have access to. But, as

we can see from the varying levels of Readiness and Vision, local regulatory environment, historical

business model, current business strategy and other factors are influencing where each operator is on

this journey.

4 The Business Challenge

The Analysys Mason study showed that the majority of telcos claim to attach a high importance to

personalized, contextualized marketing using real-time data sources. However, the survey also

highlighted where the gaps exist for operators vs. LIPs in terms of their current marketing capabilities.

LIPs, such as Google and Facebook, have built multibillion-dollar businesses around monetizing

customer data. They have established sophisticated data-driven business models that are focused

on collecting data on every aspect of a user’s behavior across numerous domains, and using this insight

to, among other things, drive third-party advertising revenues6.

6 This approach has seen Facebook and Google significantly increase revenues – 1500% and 180%, respectively - between

2009 and 2014, while many operators have seen their revenues decline over that same period.

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© AsiaInfo 2015 15

For example, Facebook has massive capabilities to collect, store and analyze data, which enables them

to sell highly targeted advertising. Their platform doesn’t only rely on the information that users

knowingly display on their profiles, either. Facebook goes beyond simply analyzing and 'mining' the

profile data which users have shared and the posts they have written. When a user creates an

account, Facebook inserts a 'tracking cookie' into their browser that allows them to track each website

the user is visiting7. This means when a user is logged into Facebook, and is browsing the web

separately from their Facebook activities, Facebook knows what sites they are visiting. For

advertisers working with Facebook this is a goldmine. This level of insight allows them to target

customers and influence their purchasing habits based on intelligently understanding the content they

are engaging with.

“Without data we would not exist. We ship no goods, we do not manufacture anything; we just

provide an electronic digital service. The use of the data is at the heart of the whole of our business.

We need to provide recommendations, as well as prompts, through email and other e-media to

encourage usage, but it has to be personalized in order to be effective.” Large Internet Player

Internet players have put significant investment into facilitating the exploitation of their customer

insights. Although they differ in their approach, as well as in the tools they use to support their

respective strategies, there are some commonalities to their use of data and analytics to design and

deliver highly personalized, context-sensitive marketing campaigns – in real-time. More

fundamentally, by establishing business models that continually exploit customer insights, they have

been able to stay ahead of the curve with products and services that are popular with the user-base.

Consider the strategy that Google has pursued in expanding its access to the user data it depends on

to support its targeted ad platform. Google has consolidated its success by zeroing in on functions,

such as email (Gmail) and document management (Google Docs) that would have previously

commanded a premium from users. Google offers these services at no cost to the user, capturing

the value by monetizing their data through third-party advertising instead. Google has the capacity

to capture the value latent in users' emails and documents by offering advertisers targeted access to

these users via its AdSense and AdWords platforms, which is where it makes the bulk of its revenues8.

Google has recently pushed its consumers to use a single identity across all Google services, including

Search, Gmail and YouTube. Integrating all these digital activities gives Google a more complete

picture of a user’s preferences. This in turn enables Google to further differentiate its targeted

advertising proposition.

Online retailers such as Amazon and eBay also use personal data and algorithmic marketing to gain

significant advantage9. For example, online auction site eBay uses sophisticated customer insights

and personalized marketing to sell products to consumers. With enhanced data-driven

7 ‘Facebook Tracking is under scrutiny’, USA Today http://usatoday30.usatoday.com/tech/news/story/2011-11-

15/facebook-privacy-tracking-data/51225112/1?csp=34money

8 ‘How does Google make money?’ http://www.minterest.org/how-does-google-make-money/

9 Need for speed: Algorithmic marketing and customer data overload:

http://www.mckinsey.com/client_service/marketing_and_sales/latest_thinking/need_for_speed_algorithmic_marketing_a

nd_customer_data_overload

© AsiaInfo 2015 16

marketing capabilities, eBay focused primarily on making its email marketing personalized and

relevant. For example, Daily Deals are set up for individual users, with new deals based on search

history being sent every day. Reminders on ‘hot items’ are sent to users, updating them on items

they’re watching and the available inventory.

Trending Items show users the keywords that are being searched most often, so if a customer has a

high propensity in the category, they’ll get an email about that trending topic. First-time buyers

also receive an email encouraging them to get involved in selling items on the site as well as buying

(eBay has found that sellers are their most engaged customers). The high level of relevancy and

contextual offering that eBay has mastered means that customers are likely to find what they are

looking for - plus more. Using technological capabilities and individualized insights in this way is

key to their success.

Operators can be forgiven for not pursuing a similar path, however – despite the fact they have, in

principle at least, access to even more sophisticated customer insights. Until now they have been

successfully focused on providing communications products and services. But the huge market shift,

which has seen basic communication services – calling, texting and data – rapidly mature, means that

operators need to find new sources of revenue through new and improved user services/experiences,

as well as entirely new business models. Or, simply risk being left behind.

In reacting to this trend, operators are shifting their business strategies to become Digital Service

Providers (DSPs) in their own right, and boosting revenues through more effectively up-selling and

cross-selling of their own services, as well as developing a broader role in the digital ecosystem through

third-party partnerships.

Operators are now talking seriously about the value of “contextual knowledge”, derived from the vast

amount of data that they hold on their subscribers, in order to improve marketing effectiveness and

enhance the customer experience, as well as develop new sources of revenue.

The irony is that when it comes to Big Data strategy, the telecom industry potentially has an advantage

due to the sheer depth and breadth of data it can collect in the course of normal business: the

subscriber data that operators can access is actually often superior to that available to many internet

companies. Between their network infrastructure and their OSS/BSS systems, operators capture

continuous information about customers’ location, characteristics of their network traffic and even the

applications that they run and websites they visit. The operators have a unique business opportunity

to turn this knowledge into value-added services, both for their own subscribers as well as third parties

if they want to pursue a ‘data-as-a-service’ model through partnerships.

Operators are under huge pressure to reinvent themselves for the digital era, but the reality of ‘turning

the oil tanker’ and mounting a convincing defense against these internet players, renowned for their

innovation, agility and, most significantly, customer-centricity, is easier said than done.

“Internet players are just more data-centric in their approach. They just have a very different attitude

to data – they can’t exist without it and it is everything to their business models. For operators, until

now, it’s been a ‘nice to have’; it’s only recently that they have begun pushing to make it a part of their

future business models.” Justin van der Lande, Principal Analyst at Analysys Mason

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5 Repositioning for the future

5.1 The ‘Readiness and Vision Score’

Based on the Analysys Mason survey, we devised a ‘Readiness and Vision Score’ in order to judge how

capable and prepared operators are, both in competing against LIPs now, as well as their approach for

‘closing the gap’ in the future (see Figure 1 above).

The ‘Readiness’ score is higher if the operator respondent:

Judges their capabilities as close to LIPs already

Is already doing personalized campaigns, and is using Big Data and/or Complex Event

Processing

Is using real-time data sources and/or social network data.

The ‘Vision’ score is higher if the operator respondent:

Has made real-time, contextual marketing a high priority today

Sees good market demand for investing in sophisticated data capabilities

Does not see ‘Fear of losing customer trust/loyalty’ as a constraint

Plans to develop new business models based on customer insights.

5.2 Where do operators want to be?

We concluded that there are four broad categories of telco when it came to their Vision and Readiness

to compete in the digital eco-system:

‘Transformers’ have the vision to transform their business models and have the tools

necessary to do so

‘Head Downers’ are ‘Ready’ to compete but are focusing their capabilities on maximizing their

existing business, rather than applying them to new business models; for these operators, a

lack of ‘Vision’ does not imply lack of motivation

‘Worriers’ are keen to adopt new business models based on monetizing customer insights but

are not actually ‘Ready’ to do so

‘Unbelievers’ are not investing in the technology to generate customer insights and don’t see

the need.

Figure 1 shows a mixed picture for global operators in their response to the changing competitive

landscape. There are few ‘Transformers’ – operators with high Vision and Readiness, and the most

populated category is the ‘Unbelievers’. An Operator’s Readiness Score does not necessarily

correlate with their Vision. For instance, there are a significant number of ‘Worriers’ who recognize

the importance of pursuing new business models based on customer insights and are concerned about

© AsiaInfo 2015 18

their lack of Readiness. Meanwhile, there are those that are ‘Ready’ but are focused on applying

their technical capabilities to their existing, rather than new, business models.

6 Conclusion

Analysys Mason’s research reveals a mixed picture, in terms of operators’ Vision and Readiness in

repositioning their businesses for the digital economy and implementing sophisticated marketing

capabilities to help them get there. This is to be expected in a global survey, where regional

conditions will affect the level of priority that is given to credible and competitive customer-centric

business models. However, the survey showed that for the majority of operators, regardless of

location and local market conditions, it is the regulatory environment they have to navigate that is

stymying innovation.

“Our research has shown that the most prominent hurdle in adopting customer insights based business

models is regulation. And Europe, amongst all the regions of the world, indicated highest sensitivity

to this hurdle.” Justin van der Lande, Principal Analyst at Analysys Mason

Operators recognize that there is a need to change, and they see how sophisticated marketing

capabilities – comparable to those currently available to LIPs – will help them to monetize the latent

value found in their customer data. However, in contrast to the LIPs, operators perceive a much

higher regulatory barrier to overcome before they can begin to monetize their sophisticated customer

insights on anything like the same scale.

The asymmetry in regulatory requirements that exists between operators and internet players has

resulted in an uneven competitive landscape, and it is operators who are bearing the cost of the

resulting market distortion. So, regardless of where they are in terms of their ability to compete, they

will struggle to get there until equitable rules exist for all players in the digital eco-system.

There are signs that operators’ regulatory concerns are beginning to be addressed - slowly, but surely.

It is those operators who make customer-centricity a priority, and improve their readiness and vision

now, who will be best placed to compete effectively in the future.