telecom data monetization via big data analytics · data content for a 360 degree view of the...

TELECOM DATA MONETIZATION VIA BIG DATA ANALYTICS

Mona FahmyBusiness Systems Analyst, Enterprise Analytics - Global ITDell [email protected]

Mohamed Alaa Senior IT Business Partner, Global ITDell [email protected]

Nashwa AbolFettouhSenior IT Business Partner, Global ITDell [email protected]

2016 EMC Proven Professional Knowledge Sharing 2

Table of Contents The Analytical ‘Wisdom’ ........................................................................................................................... 3

Monetization Opportunities in Different industry Verticals ..................................................................... 4

Amazon ................................................................................................................................................. 4

Two Stories: The German Football Association (DBF) .......................................................................... 5

Story Time! Deep Dive into Telecom ........................................................................................................ 6

Monetization Opportunities ................................................................................................................... 12

Big Data Analytics in Practice! ................................................................................................................ 15

The Ethical Dimension ............................................................................................................................. 16

Conclusion ............................................................................................................................................... 17

References .............................................................................................................................................. 18

Disclaimer: The views, processes or methodologies published in this article are those of the author. They do not necessarily reflect Dell EMC’s views, processes or methodologies.


The Analytical ‘Wisdom’

For Confucius wisdom is a precious, sought quality that builds on our repositories of knowledge and

experience to enlighten our future. That belief would still be valid if we replaced Confucius's quest for

wisdom with that of data scientists to unlock data worth and insights for businesses by means of “Value

Analytics”. In the business world, learning by experience would be the most costly, especially when

learning through imitation and reflection are viable options. That’s where we need analytics.

Coupled with the technology advancement that’s slashing the costs of data acquisition, processing and

storage, opportunities are growing to leverage unprecedented value through analytics. This value is no

longer considered a luxury; it’s a key success factor for many organizations across different industries

and an enabler for competitiveness through unconventional methodologies.

This value can be realized by linking previously latent datasets with other diverse business and personal

data content for a 360 degree view of the analysis space and even broader. Data science and analytical

skills, therefore, became the key to expose hidden treasures that can uncover any untackled revenue

streams. That’s where we get the wisdom of learning from the past, predicting the future and

connecting the dots for the next competitive strategy steps.

Looks promising? Would love to try it? Would it be costly to evaluate the effectiveness of a data

analytics project on your business?

A lot of very reasonable questions can come in here… and a lot of arguments and reverse-arguments can

be made on the cost structure and estimates for analytics solutions; it won’t be a cut-throat cost nor will

it be a free meal! But at least we can claim that entire Proof of Concepts (POCs) can be built nowadays

based on open source tools and technologies running on commodity hardware before jumping into

exploratory lump-sum investments and complex Total Cost of Ownership (TCO) calculations for

commercial solutions.

“By three methods we may learn wisdom: First, by reflection,

which is noblest; second, by imitation, which is easiest; and

third by experience, which is the bitterest.” Confucius


Amazon’s Patent: ‘Anticipatory

Shipping’

In 2014, Amazon patented a

predictive analytics solution that

uses customer activity on

Amazon to predict what they

may order. Amazon is so

confident in the results of its

solution that it starts packaging

and shipping potential orders to

intermediary destination hubs

waiting for the customer order to

come in!

With that, Amazon seeks to

develop a sustainable

competitive advantage in

shipping time relying on the

power of data!

Monetization Opportunities in Different industry Verticals

Amazon

Industry gurus have already accomplished a lot through analytics and big data. In e-commerce and

online retail, Amazon, for instance has been doing it very innovatively and efficiently for years; using

data analytics algorithms to build recommender systems for products based on customer profiles,

purchase behavior history and click stream analysis creating very well-informed opportunities for up-

selling and cross-selling.

Amazon also capitalizes on this perfect understanding for the customer to provide very unique and

insightful customer service. Today, it uses item-to-item collaborative filtering on many data points such

as what users have bought before, what they have in their virtual

shopping cart or wish list, the items they have rated and reviewed,

as well as what other similar users have bought, to heavily

customize the customer browsing experience 1. On multiple levels,

they have mastered the art of service personalization!

Further, this is even going largely commercial with Amazon jumping

into the Business to Business (B2B) Big Data players’ arena through

adding big data services to Amazon Web Services (AWS). This is

creating Platform as a Service (PaaS) for data collection, processing

and sharing as well as readiness to combine with existing open data

sets that Amazon already hosts.

In all the cases above, whether it’s enhancing the chances to up-

sell/cross-sell, providing a superior customer experience or jumping

into the B2B data services space this translates directly into dollar

value saved or additional revenue earned and a gratified P&L

statement.

1 Amazon: Using Big Data Analytics to Read Your Mind, Bernard Marr, 2014


Competition: World Cup

Place: Germany

Year: 2006

Competition: World Cup

Place: Brazil

Year: 2014

The German Football Association (DBF) When Germany hosted the World Cup in 2006, the German Football

Association (DBF) has just started collaboration with SAP to develop an

analytics application called ‘Match Insights’ with the purpose of analyzing vast

amounts of data about members of the German team and their opponents.

This data was then used to measure key performance indicators, such as pass

accuracy, distance run, speed and shooting accuracy… etc.

Despite the team’s dedication to training and this developed

insights application Germany didn`t manage to win the World Cup.

Italy won the tournament by defeating France 5–3 in a penalty

shootout in the final, whereas, Germany defeated Portugal 3–1 to

finish in third place. But this was only the start!

Eight years later, DBF and German players could achieve more with the

application and they were 2014’s World Cup Champions. SAP helped to

develop more advanced analytical and mobile features so they can use

mobile phones or tablets to get more information about any opponent.

One of the main targets after World Cup 2006 was to enhance the

passing speed which the application’s analysis spotted as a

weakness. By working on this, the team was able to reduce

average possession time from 3.4 seconds in 2006 to 1.1 seconds

in 2014.

Analytics involvement had a transformational role on the football

experience for coaches, players, fans, and the media. Imagine this:

In just 10 minutes, 10 players with three balls can produce over seven million data points that the team

can analyze to customize training and prepare for the next match.

The journey to World Cup wasn`t that easy for the German team but with the help of big data analytics

they did manage to win the title 8 years later. Analytics played important role in their journey and it is

really a success story to tell!2

2 Germany’s World Cup Tactics: shaped by data, Sophie Cutis, The Telegraph, 2014


Industry Analysis &

Trends

Telecom Data

Monetization

Opportunities

Story Time: Deep Dive Into Telecom

We’ll understand the opportunities in Telecom nowadays through three phases: First, understanding the

industry trends. Second, understanding the data power. Third, through exploring what analytical

challenges are there and how they present monetization opportunities.


More and more industries are starting to realize the value proposition that analytics can bring to the

table contributing to both enhanced operational efficiencies as well as corporate and competitive

strategies. With this realization, analytics-oriented projects are getting more advocacies within

organizations and executive teams are more open to adopt and sponsor data-centric strategic initiatives

with more confidence on Return on Investment (ROI).

One good industry manifestation of this is in telecommunications service; an industry of US$ 1.5 trillion

in revenue and over 7 billion mobile users. This is a highly competitive industry with multiple providers

in each country and a high pace of change.

Let’s look at this industry’s competitive analysis with

focus on the most remarkable highlights. Porter’s 5

forces’ Model establishes a framework to guide this

analysis in the light of five driving forces.

1. Threat of new entry: Low/medium threat due

to difficulty of entering this capital-intensive

market with very high fixed costs. Access to a

telecom license could be very complex,

represnting another entry barrier. (Low).

2. Supplier power: For most markets, the large

number of Telecom equipment providers

dilutes the effect of supplier bargaining power

which is good for Telecom Operators.

(Medium).

3. Buyer power: With the many providers and

product/service offering and the inconsiderable

switching costs, Buyers have high bargaining

power in many customer segments. (Rising).

4. Threat of substitution: Subsititues from non-

Telecom providers inroduce a big risk. Internet-

based messaging and voice services provided

from Over the Top (OTT) suppliers (WhatsApp,

Skype, etc.) are decreasing Average Revenue

per User (ARPU) of Telecom operators . (High).

5. Competitive Rivalry: Rivalry is very high with

multiple operators pushed to provide high

quality services at low prices to reatin

customers. Non-Telecom competiton is also

driving the ARPU down putting great pressure

on sustaining profitability. (High).

Figure 1: Porter's Model for Competitive Analysis


A few trends are reshaping today’s Telecom provider market and challenging the traditional model of

doing business. These trends will drive the next tide of stratgic decisions jumping into the Big Data realm

and Internet of Things (IoT) connectivity world.

Profit margins from typical telecom services like voice and text are declining due to emergence of

alterantive services from non-telecorm internet-based OTT platforms. It is estimated that between 2013

and 2017 data revenue for the telecommunications sector is set to grow by US$128 billion, compared to

a US$38 billion decline in voice revenue over the same period 3.

Also, customers’ appetite for data services is increasing as a result of streaming services. Data and

content generation and consumption is growing at exponential rates. This data growth is a double-

edged sword for Telecom providers. On one side, it broadens the scope for analytics data services that

Telecom providers should have an instrumental role in. On the other side, it’s exhausting the network

infrastructure and calling for more capital investments to continue to provide a reliable quality service.

Another very relevant and significant trend is the growing interest and expectation from the Internet of

Things. According to Gartner’s analysis in 2015, IoT is at the peak of the Hype Cycle for Emerging

Technologies with 5-10 years anticipated before reaching a plateau (Figure 2). With the expectation to

live in a smarter, more connected world the role of Telecom Providers comes into play in IoT

delpoyments. Much due diligence is still required here to understand the mandates of Telecom

providers in the IoT world and the nature,usability and value of the data created.

3 Vodafone Group Plc, Annual Report, 2014

Profit margins erosion in voice and text services

Increased customer appetite for data services

Strong Competition from (OTT) providers

Internet of Things Connectivity

Network Function Vitualization (NFV)

Telecom TRENDS

1

3

2

4

5


Figure 2: Gartner Hype Cycle for Emerging Technologies

Additionally, another trend that’s recieving much attention in the Telecom world is Network Function

Virtualization (NFV). This concept aims at providing alterantive software-based implementations to

many of the existing hardware-based network functions. This is anticipated to have a huge business

impact on reducing captial expenditures and operational expenses that used to be a major pain point in

the industry. The good thing is that this presents a large opportunity to collborate with IT storage,

virtualization and cloud services vendors to accomplish.

In essence, the typical model of doing Telecom services business is no longer sufficient for sustainability

and growth. In addition to the fierce rivalry within the industry, platforms are emerging to provide

similar services at no cost and regulations aren’t in a sound position to ban this trend. Therefore, there’s

a pressing need for providers to innovate relying on the power of their data and the wave of IoT services

to control their expenditures and secure new revenue streams.


Telecom data is a valuable asset and operators are unique in the sense that they play a triple role in the

data world: data generators, data aggregators and data consumers.

Data Generators: Telecom business operations generate vast amounts of data with different structures

that can be processed to provide significant insights. Call Detail Records (CDRs) and Customer

Relationship Management (CRM) data generated from daily customer interactions are a few.

Data Aggregators: An integral part of the IT infrastructure for telecom operators is a data warehousing

and business intelligence (DW/BI) solution infrastructure. This is the basis for most of their reporting and

analytics efforts integrating data from multiple transactional sources through the typical Extract,

Transform and Load (ETL) operations with any needed preprocessing and cleansing.

Data Consumers: Within this framework, telecom operators consume aggregated data and data

generated through their mining operations from descriptive and predictive data mining models feeding

it back into their systems. In addition, they can be considered a consumer for any readily available open

or public data sets that can leverage more dimensions to their analytics space. Publicly shared social

media data is one example.

Let’s walk through a few examples to grasp the essence of telecom-specific data. One major data source

in telecom is CDR data. This source has a wealth of metadata fields that can be mined. It has detailed

records for voice and text transactions in telecom interactions like transaction parties phone numbers

(caller and receiver), routing path, duration, start and end times and the ID of the equipment producing

the record as well as many other network details.

From an analytics perspective these are used to build association and community models, predictive

models of residency, work and frequently visited locations to a KMs degree of accuracy.

One other key data source is CRM data compiling data from multiple channels of interaction with

customers: company website, email, live chat, telephone and others and analyzing it to support

customer relationship management. It has very worthy data sets that can provide effective customer

insights to support customer acquisition, retention and enhancement of the overall customer

experience.


Both data sources are ready to partner with web-based data sources, social media sources and more.

One good representation of the data sources, volumes and types in today’s digital universe is shown in

Figure 3, sourced from Teradata Inc.

This reflects the reality of today’s diverse data sources and the inferences and insights that can be built

upon. It’s the smarter digital universe with the immense content generation tools and engines calling for

untraditional approaches, technologies and skills to act upon. It’s Big Data wealth as defined!

The first step always remains to formulate the business opportunity into an analytical challenge.

Figure 3: Data Growth and Variety, Teradata Inc.


Monetization Opportunities

Being insightful of the aforementioned industry trends and with our deep understanding of the data in

hand along with other potential data sets, we can start providing clues to resolve the conventional and

emerging industry challenges as well as spotting favorable opportunities.

With value analytics, the opportunities are limitless. For instance, let’s see what In-house efficiencies can

be accomplished: Multi-faceted customer segmentation, Churn Analytics, Insights for new product

development with higher hitting rates, Network analytics and optimization…just to name a few.

A variety of commercial opportunities are there too that will be further enriched with IoT connectivity

data and metrics. As a marketing and advertising agency, wouldn’t you be interested in the results of

footfall analytics; the patterns and timings of flow of customer clusters throughout days of the

week/hours of the day? Would this Analytics-as-a-Service (AaaS) – coming from a trusted source –

impact your decisions on where, when and how to place outdoor advertising? What about real-time

advertising; what are the hit chances for a retail store sending you an offer on a product of interest

while you’re only a few steps away? What if we add to this a social media analytics dimension? How

about customers' profile and demographics? Infinite opportunities!

No more old-fashioned, costly above the line commercial techniques, a smart way of advertising is

emerging; targeting the right customer with much higher certainty of gaining customers'

attention/wallet.

It’s not a blink of an eye though to get this done, so, let’s walk in the shoes of industry leaders of various

roles for a while as we impose some questions that value analytics can guide through; Think of it as a

three-level challenge with the last portion having the highest commercial value proposition, yet, the

toughest analytical encounters!

Conventional: What signals can alert us for a possible customer churn?

Why do my customers leave my company for the competitor?

What customer value segment has the highest churn rate? What drivers explain that for this

segment?

What type of retention plan can work for each customer value segment? Do we need to go

for more personalized retention plans? Is it cost effective to do?

What network parameters need to be altered for best performance?


Each of these questions actually represents one or more BI/Analytical problems with varying complexity

for which a team of domain experts, business analysts and data scientists need to collaborate to arrive

at the proper model to use, quantify the gain and put into operation.

Many of the telecom service providers across the globe already have some put effort into addressing the

conventional and some of the emerging questions which mostly bring in-house efficiencies to the

traditional channels of revenue generation or allow for reduced expenses.

However, it’s the third class of analytical challenges that allow venturing onto the off-beaten tracks.

That’s where more investments are needed with an eye on B2B tradable ideas that can secure revenues

beyond the traditional streams. It’s a gradual development though, what was learnt from the

conventional and emerging classes is the basis to build the more advanced and complex analytics. What

started as BI with the goal of business monitoring has now developed through descriptive, predictive

and prescriptive analytics to data monetization and business metamorphosis.

Emerging: What portion of my customer-base is a dual line user? Can we measure, infer or predict what

they prefer about the other provider?

What are the demographical dimensions (known/inferred) that compose the profile of my

highest value segment customers? How can we better target customers with similar profile?

Are the members of my high-value customers ‘community my customers too? Who from my

customers are members of large overlapping communities? Who are the key influencers

within this network and cross networks? Are these key ‘influencers’ satisfied?

What combination of Telecom products and services are they interested in? Text messaging,

voice, data services, applications, etc.?

Is my combination of product/service offerings causing revenue cannibalization within the

same customer segment? What aspects of the service can we use to accomplish a split?

What network performance should be expected in this part of the day/week/month/season

according to past observations?

Golden: What type of data service Apps do my customers need/prefer? What data are they willing to

share when they register to use that App?

What services can we provide for customers to willingly register via their social media

accounts (Facebook, Twitter, LinkedIn, etc.)?

From social media, can we infer the interests of our high-value customers, aside from

Telecom services? Is it sports, cooking, science, art, news, etc? What are their major likes

and dislikes?


Figure 4: Business transformation through Analytics- EMC

Enthusiastic enough? So, how to proceed? Let’s see!


Big Data Analytics in Practice!

So how can we proceed with these analytical projects? For this, we recommend two hybrid project

lifecycles: Big data projects lifecycle that tackles the planning and implementation of the Big Data

Solutions together with the Cross Industry Standard Process for Data Mining (CRISP- DM) as the core

methodology for traditional data mining embedded within.

Big Data projects incorporate the specifics of Big Data implementation in addition to managing the

analytics project. Planning for Big Data projects should also have some key determinants to decide

upon: data source identification, data capture approaches, big data platform selection and architecture

design.

This effort needs a team with various skills to play the different roles from business understanding to

data preparation for ingestion into the big data platform and analytics then for model planning, building

and operationalizing. These roles need to include:

- Domain Experts and business users

- Business System Analysts

- Data Architects/ Administrators

- Data Scientists

- Developers

These project life cycles apply to Telecom big data analytics projects and any analytics-centered big data

projects alike. Multiple tool options exist from different vendors to handle big data capitalizing on

Hadoop project and its ecosystems. Tool options, however, are beyond the scope of this article.


The Ethical Dimension

With all chances out there, the ethical, legal and philanthropic dimensions of data acquisition,

aggregation and processing need not be compromised when dealing in B2B business schema.

Anonymized and clustered data analytics should be the key to derive trend analysis and predictions

whilst none of the individual data records should ever be disclosed or infringed.

With the complex model of interactions in the digital universe, multiple parties of the data value chain

can have claims on data ownership and rights of use and each can have their valid arguments. This is,

however, an item to be left to regulators and legislative authorities to have the final say if needed.


Conclusion

Telecom is one of the industries in a very good position to benefit from big data analytics and IoT. From

one side, data is abundant and rich for Telecom providers to develop very effective analytics projects to

leverage both increased in-house efficiencies as well as commercial solutions.

From the other side, the current market trends are pushing providers to challenge the status-quo

business models and respond to the unconventional competitive threats. This calls for big data and

analytics innovation as a savior to secure sustainability through enhanced operational efficiencies and

alternate revenue stream generation from B2B analytics services.

An analysis by-product is that opportunities are not limited only to analytics ideas. The current

challenges faced by Telecom providers and industry trends like IoT and VNF are opening very promising

channels for collaboration with IT vendors in cloud computing, virtualization and storage management

in addition to Big Data.


References

1. http://www.investopedia.com/features/industryhandbook/telecom.asp

2. http://www.smartdatacollective.com/bernardmarr/182796/amazon-using-big-data-analytics-read-your-

mind

3. http://www.telegraph.co.uk/technology/news/10959864/Germanys-World-Cup-tactics-shaped-by-

data.html

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subject to change without notice.

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Dell, EMC and other trademarks are trademarks of Dell Inc. or its subsidiaries.

http://www.investopedia.com/features/industryhandbook/telecom.asp

http://www.smartdatacollective.com/bernardmarr/182796/amazon-using-big-data-analytics-read-your-mind

http://www.smartdatacollective.com/bernardmarr/182796/amazon-using-big-data-analytics-read-your-mind

http://www.telegraph.co.uk/technology/news/10959864/Germanys-World-Cup-tactics-shaped-by-data.html

http://www.telegraph.co.uk/technology/news/10959864/Germanys-World-Cup-tactics-shaped-by-data.html

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