Allot Communication MobileTrends Report 02/2013

The Mobile
Digital Lifestyle
Segmentation
Allot Mobile Trends 02/2013

Segmenting the Mobile
Digital Lifestyle
Allot MobileTrends Report 02/2013

Analysis Mr. Yossef Arie, Department of Sociology, University of Haifa, Israel
Photography, Mr. Yossef Massa, Netanya , Israel
Allot MobileTrends Report Q2/2013 © 2013 Allot Communications. All rights reserved. Design, Mr. Guy Ruskin, Israel

Executive Summary
This study explores and outlines mobile digital lifestyles in action. The result of this research
produces useful and actionable segmentation for operators and provides a unique and important
view of subscribers’ online activities.

Marketing managers are aware that there is no “average digital user” and no single homogeneous
digital lifestyle. Thus, there is a need to identify the differences among subscribers and group
them in a way that will better reflect their lifestyle. Actionable segmentation of the digital
lifestyles of mobile subscribers can lead to successful operator marketing, customer retention
and increased revenues. Instead of defining subscribers based on the usual terms of Gigabytes
consumed, this study introduces an innovative approach: segmenting the mobile digital lifestyle
from the network operator perspective.

The diversity of data consumption is demonstrated in two dimensions:

1. Personal vs. Social online activities

2. Place (web content) and time of the online activities

This lifestyle segmentation provides a richer definition of
mobile subscribers. Data for this study is based on a sample
of over 60,000 digital users from several mobile operators
in different geographic regions. Based on analysis of their
usage records, we identified five distinct digital lifestyle
profiles; Info Seeker, Info Guzzler, Social Monitor, Social
Mingler and Digital Mover & Shaker. The last is the most
important segment since the subscribers in this category
are the drivers of the digital lifestyle. They integrate between
personal and social activities online. They create and upload
content and they react to the content of others. Like a pebble
in a pond, their activity stimulates interest and creates a
ripple effect of further activity. They don’t just participate in the online action - they create it! The
findings in this paper suggest that the more mobile operators know and understand about the
digital activities of their customers, the more effectively and efficiently they can fulfill their role
as Digital Lifestyle Provider and increase the value of their network service in the eyes of their
customers. The segments we identified are actionable allowing operators to create data delivery
and charging policies to capitalize on the data traffic these segments generate.

Introduction
Approximately two hundred years ago, Malthus recounted the term of “carrying capacity” in
order to describe the worldwide gap between the growth of the population and the growth of
food production capabilities (Malthus, 1798) “The power of population is indefinitely greater than
the power in the earth to produce subsistence for man.” Malthus argued that while population
grows in a geometrical series, the means of food production grows is in a mathematical series
and the gap is created. This description can successfully delineate the phenomenon of mobile
data use worldwide these days. There is a large gap between the growth of data delivery services
offered by services providers and the growth of data use. Global mobile data traffic grew 2.3
fold in 2011, more than doubling for the fourth year in a row. Furthermore, mobile data traffic is
expected to grow at a year over year rate of 78 % from 2011 to 2016 (Cisco, 2012).

In this context, Allot develops and markets innovative products and solutions designed to help
mobile network operators capitalize on the explosion in data traffic by providing chargeable
value and services to mobile users, and by solving data delivery issues efficiently and effectively.
Furthermore, Allot sees the “data explosion” as an opportunity for service providers to increase
the value they bring to their subscribers. This paper introduces Allot’s innovative and advanced

Allot MobileTrends Report Q2/2013 © 2013 Allot Communications. All rights reserved. 1

approach to mobile digital lifestyle segmentation and sheds light on the value of data services
to different subscriber segments.

Many service providers approach the “data explosion” through the consumption patterns of
“average digital users” i.e., light, middle or heavy data users. This classification is based on
the volume of traffic they create. However, this approach does not reflect the heterogeneity,
complexity, and flexibility of digital lifestyles. In reality, there is no “average digital user,” and no
single homogeneous digital lifestyle. Hence, there is a need to identify the differences among
subscribers and group them in such a way that will give a better understanding of digital users.
Instead of defining users based on Gigabytes consumed, this study introduces an innovative
approach for digital lifestyle segmentation and demonstrates the diversity of digital consumption
in two dimensions:

• Personal vs. Social online activities

• Place (web content) and time of the online activities
• (where and when online usage occurs)

Literature about Information and Communication
Technology (ICT) teaches us that with the advancement
of ICT, a reconfiguration of our personal and social cycles
and behaviors occurs (Dutton, 2005; Wellman, 2001, 2002;
Ling, 2008). Therefore, it is very interesting to understand
how digital personal activities (i.e., application download,
general browsing, and video streaming) and digital social
activities (i.e., email, instant messaging, social networks,
gaming and VoIP) interact in the mobile data arena.

On the same subject, literature teaches us that data users
change and rearrange their data usage patterns across
place and time (Castells et al., 2007). ICTs create new
arrangements between the “place-time” intersection in a given geographical space (Castells
et al., 2007, Green, 2002). However, instead of focusing on physical place as has been done
by previous studies, this paper emphasizes the virtual place (web content). In this context, this
study focuses and delineates subscriber activity based on the content they consume and the
time they choose to consume it.

Therefore, this paper attempts to answer the following questions:

• What types of lifestyle profiles are evident across personal
and social activities as well as content and time activities?

• What and who are the digital lifestyle influencers?

• How can we identify and leverage the distinct digital lifestyle
profiles in order to create real value for data users?

Methodology
To answer those questions, we explored several mobile operators and we researched over
60,000 subscribers, which statistically represented the data users of each one of the mobile
service providers. The data sets included the URL of the web content, the time of the sessions
and the traffic of a given site for each one of the users during December 2012. Based on these
findings we delineate the distinct classifications of digital lifestyle profiles.

It is important to note that the data collected for this report was totally subscriber-anonymous.
Subscriber identifying information such as IP address, usernames or MSISDN were not retained
in the data gathered from the mobile networks.

2 Allot MobileTrends Report Q2/2013 © 2013 Allot Communications. All rights reserved.

Findings
Table 1 presents the distribution of data usage categories among mobile subscribers during a
typical week. A subscriber using a mobile device uses on average 1.76 different categories of data
traffic. The most common category is general browsing which is used by 72% of subscribers.
Just below that are Social Networks with 29%, Video Streaming with 20% of subscribers, Online
Storage with 12% and Instant Messaging with 11% of subscribers. A smaller group uses Email
with 8%, Application downloads with 7%, P2P with 7%, VoIP with 5%, and Gaming with 5% of
subscribers (see Table 1, Graph 1)

Table 1 % Subscribers *
Data Traffic Categories General Browsing 72%
of Mobile Data Users Social Networks 29%
Video Streaming 20%
Online Storage 12%
Instant Messaging 11%
Email 8%
App Downloads 7%
P2P 7%
Gaming 5%
VoIP 5%
Traffic Heterogeneity 0.84

*Subscribers access more than one category

For the purpose of this report, we compared the percentage of subscribers using each category
of data against the volume of traffic they generated. As a result, we saw that the data traffic in
mobile networks is heterogeneous1 (of mixed makeup and varying usage). Our analysis showed
% Subscribers
a heterogeneity factor of 0.84 on a scale of 0 to 1, where 1=highly heterogeneous (see Table 1).
This heterogeneity factor of data traffic indicates a high potential for distinct segmentation.

72%
Graph 1 Percent of Subscribers
per Data Category

29%

20%

12% 11%
8% 7% 7%
5% 5%
General Browsing

Social Networks

Video Streaming

Online Storage

Instant Messaging

Email

App Downloads

P2P

Gaming

VoIP

1
Based on the Heterogeneity Measure of Blau (1977): 1 –∑Pi2 when Pi is the % of data traffic of the data category.


Overall Usage by Data Category

Graph 2 Social Networks Video Streaming
Overall Usage by
Data Category

Facebook Others
39% 32%
Others
42%
YouTube
68%

Twitter
19%

Graph 2.1 Within the Social Networks Graph 2.2 Within the Video Streaming
category, Facebook accounts for 39% category, YouTube is the overwhelming
and Twitter 19% of the content visited. All favorite, accounting for 68% of the content
others comprise 42%. visited. All others comprise 32%.

Peer to Peer VoIP

Others
32% Skype
34%

BitTorrent
68% Others
66%

Graph 2.3 Within the Peer-to-Peer Graph 2.4 Within the VoIP category, Skype
category, BitTorrent is the preferred accounts for 34% of the content visited.
destination, accounting for 68% of the All others comprise 66%. (In our study, we
P2P content visited. All others comprise did not differentiate between voice calls vs.
32%. chat or instant messaging done via Skype
or other VoIP.)


Application Traffic Volume Over an Average 24 Hour Period
After one week of monitoring the various networks, the following traffic patterns were noted.
BitTorrent, Skype, YouTube and General Browsing are active during all hours of the day. However,
Skype use is dominant during the late evening hours 22:00-0:00. General browsing is strong
during the evening hours 18:00- 20:00 while traffic to Facebook and YouTube is strongest in the
10% middle of the day 13:00-15:00.

Graph 3a
8%
Data
Consumption
Percent of Trafﬁc Volume

Patterns by 6%
Time-of-Day*

4%

2%

0%
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16.00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
0:00
1:00
2:00
3:00
4:00
5:00
BitTorrent Facebook YouTube Skype General Browsing

40%

Graph 3b 35%

Data 30%
Consumption
Percent of Trafﬁc Volume

Patterns by 25%
Day*
20%

15%

10%

5%

0%
Morning Mid Day Evening Night

BitTorrent Facebook YouTube Skype General Browsing

* These graphs show the distribution of data volume for each application over a day.


Types of Usage: Personal, Social and Mixed
Our analysis of the data traffic points to three types of subscriber activity online: personally
active, socially active, and mixed personally/socially active. It also measures the effect that each
style of activity has on the network. Ideally, 38% of the subscribers should generate 38% of the
traffic. But we see that subscriber activity can affect traffic volume on the network far below or
above the number of subscribers generating it.

Personally active (38% of subscribers) data subscribers usually access content and download
applications for personal use. Their traffic falls into three main data categories; General Browsing,
Application Download and Video Streaming.

Socially active (14% of subscribers) data subscribers interact in social networks and share
information and media such as pictures, video clips and music files. These subscribers upload
and create content that is then shared with other subscribers. Their traffic falls into six main data
categories: Email, Instant Messaging, Social Networks, Gaming, Social Video and VoIP.

Mixed personally/socially active (48% of subscribers) are the quintessential digital lifestyle
subscribers, using online data for both personal and social activities.

Table 2 Subscriber % Traffic % Network Effect
Usage Types as Percent % Traffic /
% Subscribers
of Data Subscribers and
Percent of Data Traffic Personally active 38% 17% 45%
Socially active 14% 9% 64%
Mixed Personally/Socially active 48% 74% 154%
Total 100% 100%

Graph 4a-b Usage Types as Percent Usage Types as Percent
of Data Subscribers of Data Traffic

Personal Use
17%

Personal Use
Social
38%
Mixed Use 9%
Personal/Social
Use Mixed
48% Personal/Social
Use
74%
Social Use
14%

Single-tasker, Multi-tasker
Our research showed that for each of the usage types – personal, social, and mixed – there are
two subtypes: the predominantly Single-tasker and the predominantly Multi-tasker. The Single
tasker describes the subscribers whose online activity revolves around a single data category.
Their usage pattern is serial, accessing one data category at a time. The Multi-tasker describes
subscribers whose online activity includes multiple data categories. Their usage pattern is
parallel, accessing multiple data categories at a time.

Based on the different types of use that we observed we were able to identify five Digital Lifestyle
profiles that represent five distinct segments of mobile data subscribers.


Digital Lifestyle Segments

1 2 3 4
Info Seeker Info Guzzler Social Monitor Social Mingler

• 32% of subscribers • 5% of subscribers • 15% of subscribers • 15% of subscribers
• 12% of data traffic • 5% of data traffic • 9% of data traffic • 16% of data traffic
• 75% use general web • Divided into two • Tends to access Social • Engages in pairs of
browsing distinct types: Network content personal and social
(24%), Social Video activity
• 36% of the traffic a. General Browsing and
(28%) and VoIP (18%)
is generated in the P2P (33%) • The leading pairs are
evening (compared
b. General Browsing and
• 45% of use occurs General Browsing &
to 28% for the total during the middle of Social Networks (35%),
Application Download
population) the day compared and General Browsing
(23%)
to 36% for the total & Social Video (11%)
• 44% of use occurs population
• 64% of use occurs
during the middle of
during the middle of
the day compared
the day and evening
to 36% for the total
which is similar to the
population
total population

5
Digital Mover & Shaker

• 34% of subscribers
• 58% of data traffic
• Characterized by very high data usage. This segment combines personal data activity with social data activity
Leading activity combinations are:

a. General Browsing, Social Networks, Video Streaming and Instant Messaging (31%)

b. General Web Browsing, Social Video & VoIP (15%)

• 21% of use occurs during the night, compared to 18% for the total population
• Digital Movers & Shakers are important not just because of the heavy traffic they generate but also mainly because their
activity spawns even more usage on the mobile network. The subscribers in this segment connect socially and they
upload and share the information and content that interests them.

• Subscribers in this segment are the social influencers of the Digital Lifestyle


The digital lifestyle profiles in the following table show the percent of subscribers in each segment,
the percent of total traffic each segment generates, and scores the cumulative effect their use
on the network. For example, Info Guzzlers generate the same percent of overall traffic (5%)
as their representation in the subscriber base. But Digital Movers & Shakers account for more
of the data traffic (58%) than their numbers in the subscriber base (34%). This phenomenon is
reflected in the “network effect” score of each segment. Based on analysis of this kind, mobile
service providers can tailor their offering to deliver the value that is important to each digital
lifestyle.

Table 3 % Subscribers % Traffic Network Effect Average Use
(% Traffic /
Mobile Digital Lifestyle % Subscribers)
Segmentation 1. Info Seeker 32% 12% 38% Low
2. Info Guzzler 5% 5% 100% Mid
3. Social Monitor 14% 9% 64% Mid
4. Social Mingler 15% 16% 107% Mid
5. Digital Mover & Shaker 34% 58% 171% High

Table 4
Digital Lifestyle
Segments
Info Seeker Info Guzzler Social Monitor Social Mingler Digital Total
Mover & Shaker Pop.

Percent of Total 32% 5% 14% 15% 34% 100%
Subscribers

Percent of Total Traffic 12% 5% 9% 16% 58% 100%

Average Activity Low Mid Mid Mid High

Data Categories Used App Download Browsing & App Email Browsing & IM Browsing, Social
Browsing Download Social Networks Networks, Video
Browsing &
P2P Gaming Streaming & IM
Browsing & P2P Social Networks
Video Streaming IM
Browsing, Social
Social Video Browsing &
Video & VoIP
Social Video
Online Storage

Info Seeker Info Guzzler Social Monitor Social Mingler Digital Total
Mover & Shaker Pop.

Morning 16% 15% 17% 17% 19% 18%
Mid-Day 29% 44% 44% 36% 32% 36%
Evening 37% 29% 24% 29% 28% 28%
Night 18% 12% 15% 18% 21% 18%


Graph 5 Mobile Digital Lifestyle 58% Graph 6 Segment Effect on the
Segmentation Network 171%

High

34%
32% 107%
100%
Mid
Mid

64%

14% 15% 16% Mid
12% 38%
9%
Low
5% 5%
Subscribers
Trafﬁc
1. 2. 3. 4. 5. 1. 2. 3. 4. 5.
Info Info Social Social Digital Info Info Social Social Digital
Seeker Guzzler Monitor Mingler Mover & Seeker Guzzler Monitor Mingler Mover &
Shaker Shaker

Graphs 7.1-7.5 2%
5% 8%
Data Categories 10%
23% 29%
Used by Digital 18%
Lifestyle Segments 44% 11%

75%
33%
18%
24%

Info Seeker Info Guzzler Social Monitor
Browsing Browsing & P2P Social Video
P2P Browsing & App Downloads Social Networks
App Downloads Other Pairs VoIP
Video Streaming Gaming
IM
4%
Email
5%
15%
11%

46%
54%
31%
34%

Social Mingler Digital Mover & Shaker
Browsing & Social Networks Browsing & Social Networks
Browsing & Social Video & Video Streaming & IM
Online Storage Browsing & Social Video &
Browsing & IM VoIP
Other Pairs Other Combinations


Graph 8 Digital Lifestyle Profiles by Time of Day
44% 44%

37%
36% 36%

32%
29% 29% 29%
28% 28%

24%
21%
19% 18%
18% 17% 17% 18% 18%
16%
15% 15%
12%

Info Seeker Info Guzzler Social Monitor Social Mingler Digital Mover & Total
(32%) (5%) (14%) (15%) Shaker Population
(34%)

Morning Mid-Day Evening Night

Graph 9 Mapping the Digital Lifestyle Profiles
Multi-tasker

Personal Activities Social Activities

Single-tasker

Percent of Subscribers (Percent of Traffic)


Conclusion
Global mobile data traffic has been doubling every year
since 2008 and is expected to continue to grow at a year
over year rate of 78%. Clearly, we live in an era of “data
explosion.”

This paper introduces a method for identifying different
user segments within the mobile digital lifestyle. This
segmentation is actionable and useful for operators
because it provides a unique and important view of data
use. The methodology used in this Allot MobileTrends
Report identifies the differences and similarities among the
traffic generated by mobile subscribers and groups them
according to their online behavior.

The resulting segmentation provides a richer definition of
mobile digital lifestyles and can be duplicated by any mobile
operator using Allot data monitoring and analytics solutions.

Allot Communications’ wide range of integrated solutions and outstanding solution partners
are uncovering the traffic intelligence in mobile networks around the world, and using it to drive
innovative and revenue-generating use cases for the Digital Lifestyle.

Table 5: Allot’s Leading Use Cases for the Digital Lifestyle

Info Seeker Info Guzzler Social Monitor Social Mingler Digital
Mover & Shaker

Percent of Subscribers 32% 5% 14% 15% 34%
Percent of Traffic 12% 5% 9% 16% 58%
Average Use Low Mid Mid Mid High
Data Categories Used App Download Browsing & App Email Browsing & IM Browsing, Social
Browsing Download Social Networks Networks, Video
Browsing & Social
P2P Gaming Streaming & IM
Browsing & P2P Networks
Video Streaming IM
Browsing, Social
Social Video Browsing & Social
Video & VoIP
Video
Online Storage
Digital Lifestyle Use Cases
Customer Touch Point     
Optimization
Targeted Promotion and     
Advertising
OTT Content Bundling     
OTT Premium Content     
OTT Video Caching   
OTT Video Optimization   
Service Tiering     
Application Based Charging   
Volume Based Charging    
Happy Hour  
Turbo Boost   
Bill Shock Prevention  


Glossary
Application Download Peer-to-Peer Applications (P2P) Video Streaming General Browsing
The ability to download a Applications such as BitTorrent Communication directed HTTP traffic associated with
wide range of free and paid and eMule. through video content including website browsing or other HTTP
applications from online App YouTube, Hulu, Flash Media, traffic which is not downloading
Instant Messaging (IM)
Stores, available to users of HTTP Streaming, iPhone HTTP or streaming. In addition,
Originally, instant messaging
smartphones, tablets, netbooks, Live Streaming, Mobile HTTP web browsing also includes
applications delivered real-time
etc. App Stores include Apple Streaming etc. “Social Video” applications delivering real time
text-based communications
App Stores, Google Play, is part of video streaming and updates and statistics over
between two or more users
Blackberry App World, Nokia includes video sharing web HTTP. This category contains
over the Internet. Today’s
Ovi, Palm Catalog, Windows content such as YouTube. information sites such as news,
IM applications offer a wider
Marketplace for Mobile and weather, sports, etc.
range of communication Social Networks
many others.
services including group Social networking content such Email
Online Storage messaging, media sharing, as Facebook, Twitter, Indoona, Web email sites such as Gmail,
HTTP download service, in video conferencing, voice LinkedIn, etc. Yahoo Mail, Hot Mail, SMTP and
particular from one-click hosting communication and file transfer. POP3.
Voice over IP (VoIP)
sites such as RapidShare and Instant messaging applications
Software applications that allow
Megaupload, You Send It, include Google Talk, Windows
users to conduct audio and
Dropbox, etc. Live!, Yahoo!, QQ , WhatsApp,
video communications over IP
Facebook Messenger, etc.
networks. VoIP applications
include Skype, GoogleTalk and
Vonage.
.

References
Blau, P. M. (1977). Inequality and Heterogeneity, a Primitive Theory of Social Structure. The
Free Press, New York

Castells, M. Fernandez-Ardevol, M. Linchuan, J. & Sey, A. (2007).
Mobile Communication and Society, a Global Perspective.
The MIT Press Cambridge, Massachusetts, London, England.
Cisco (2012): Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update,
2011–2016 (White Paper). Cisco, February 14, 2012

Dutton, W. H. (2005). The Internet and social transformation: reconfiguring access, in
Dutton, W. H., Kahin, B., O’Callaghan R. & Wycoff, A. (Eds). Transforming Enterprise. MIT Press,
Cambridge, MA. 375–397.

Green, N. (2002) On the move: Technology, Mobility, and mediation of social time and place.
The information society, 18, 281-292.

Ling, R. (2008). New Tech, New Ties: How Mobile Communication Is Reshaping Social
Cohesion. The MIT Press, Cambridge, Masachusetts, London, England.

Malthus T.R. ( 1798). An essay on the principle of population.
Chapter V, p 39–45. Oxford World’s Classics reprint

Wellman, B. (2001). Physical Place and Cyber-Place: Changing Portals and the Rise of
Networked Individualism. International Journal for Urban and Regional Research, 25, 227-52.

Wellman, Barry. (2002). Little Boxes, Globalization, and Networked Individualism.

Tanabe, M. Besselaar, P. & Ishida, T (Eds.). Digital Cities II: Computational and Sociological
Approaches, Berlin: Springer. 10-25.


© Allot Communications, 02.2013. Specifications subject to change without notice. Allot Communications and the Allot logo are registered trademarks of Allot Communications. All other brand or product names are trademarks of their respective holders.
About Allot Communications
Allot Communications Ltd. (NASDAQ, TASE: ALLT) is a leading global provider of intelligent broadband solutions that put mobile,
fixed and enterprise networks at the center of the digital lifestyle. Allot’s DPI-based solutions identify and leverage the business
intelligence in data networks, empowering operators to shape digital lifestyle experiences and to capitalize on the network traffic
they generate. Allot’s unique blend of innovative technology, proven know-how and collaborative approach to industry standards
and partnerships enables service providers worldwide to elevate their role in the digital lifestyle ecosystem and to open the door
to a wealth of new business opportunities. For more information please visit: www.allot.com

www.allot.com info@allot.com
Americas: 300 TradeCenter, Suite 4680, Woburn, MA 01801 USA · Tel: (781) 939-9300 · Toll free: 877-255-6826 · Fax: (781) 939-9393
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Allot Communication MobileTrends Report 02/2013

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