Data Monetization: Leveraging Subscriber Data to Create New Opportunities

Data Monetization
Leveraging Subscriber Data to Create New Opportunities
By Mike Greening, Martin Hall and Susannah Hawkins
Changes in technology and consumer demand are enabling mobile operators to learn more about
their customers than has previously been possible. The potential sources of customer data are richer
and greater in number than ever before and can deliver both revenue and cost benefits. Combined
with slowing growth in their core businesses, operators are now making significant investments in
data monetization.
July 2012

Cartesian: Data Monetization
Copyright © 2014 The Management Network Group, Inc. d/b/a Cartesian. All rights reserved. 1
Context: Why Operators Are Acting Now
The theme of data monetization has been considered by operators for some time, however it is only now
that operators are starting to focus on this activity and integrate it into their core business. The two primary
drivers have been increasing data richness and availability as well as a need to develop new revenue streams.
Driven by continuing improvements in network intelligence, increasing smartphone take-up and greater
usage of the internet on mobile devices, mobile operators have access to a wealth of end user data. In the
past, this data has taken the form of device or traffic metrics and has been analysed by network design and
management teams with the goal of improving connectivity or network service quality. Monetization
opportunities were limited as the data had little relevance beyond immediate service provision, and was
not sufficiently granular to give insight into individual subscribers or segments.
Today, the individual and social data potentially available to operators creates the opportunity to generate
new revenue streams to help offset slowing growth in the core business, while simultaneously improving
the service experience and providing benefits for end users. For example, integrating location and browsing
behaviour into operator usage data gives a personal and social context to the mobile service that has
relevance to both the operators themselves and third parties. There are a variety of different data sources
that operators can draw on to deliver benefit to their core business as well as generate new opportunities.
Figure 1: Leveraging Subscriber Data to Create New Revenue Streams
Leading operators are making significant investments in data monetization, either through acquisition or
organic growth. For example, SingTel acquired mobile advertising provider Amobee in March, citing a need
to strengthen capabilities in offer targeting, loyalty programmes and one-on-one marketing. Vodafone is
also involved in the area, investing in social media advertising start-up LocalResponse. Meanwhile,
Telefónica describes its new O2 Media unit as one of its fastest growing areas, using in-depth customer
insight to improve the relevance of marketing campaigns.

Sources of Customer Data
Operators are using a range of methods to accumulate customer insight, as shown in the figure below.
Figure 2: Sources of Customer Data for Operators
Operators have four primary sources of customer data: account information, value added services (VAS),
on-net analytics and loyalty programmes. While operators are justifiably cautious regarding the use of such
data, careful use involving opt-in where appropriate can benefit both the operator and end user.
Personal data is already available for operators with the right tools. The billing relationship provides account
information such as name, gender, home location, spending patterns and credit details, while operational
systems can be used to find device type and usage. This can provide basic demographic profiling or be used
in more specific ways such as to identify customers of a particular bank to target with a new mobile banking
app.
Existing VAS, such as mobile content sales, email and cloud storage, also provides a source of data. With
detailed analysis of what customers subscribe to and purchase, this gives ‘softer’ customer insights such as
habits, interests and activities. This enables a richer segmentation to be performed and allows operators or
third parties to proactively target users of a particular service.
On-net behavioural analytics combines customer care and network data to provide a deeper view of
customer behaviour. A range of tools may be used, including server logs for communications and usage
patterns, cell IDs to find locations frequented and deep packet inspection for browsing, social networking
and app-specific behaviour. This enables both real-time targeting of customers – for example a coupon for

those within a short distance of a store – or more detailed analysis e.g. identification of commuters based
on location throughout the day.
Finally, loyalty programmes can give insight into customer behaviour off-net – spending patterns, asset
ownership, lifestyle and travel habits – particularly when integrated with other brands. When linked to
mobile NFC technology this creates a direct link between behaviour and network intelligence. Loyalty cards
also provide a new touch point for the operator to build greater engagement, creating opportunities for
targeted points collection and redemption offers.
For example, Etisalat has launched a points-based rewards programme with partner brands to provide a
deeper insight into its customer base. This benefits customers via discounts on products and services,
provides partners with detailed segmentation data, and delivers an incremental revenue stream to the
operator through targeted promotions as well as providing churn and ARPU benefits.
Case Study: Etisalat More
Benefits to Core Business
Customer insight can be leveraged for both the core business, including up-selling and retention, and new
business opportunities, such as data use by upstream applications or profiling for third parties. To extract
maximum value from the available data, operators should look to both of these sources.
Highly targeted up-selling and cross-selling offers are enabled by detailed customer profiling and micro-
segmentation, using the data described above. This typically includes targeted offers based on subscriber
segment, and may extend to cross-selling of relevant content and introductory offers for family and friends
of current customers. It may also include more advanced up-selling, such as targeted rate plans and real-
time sales. For example, a user approaching their plan limit, given appropriate previous spending patterns,

may pro-actively be sold a temporary allowance increase. Alternatively, a user attempting to watch a video
in an overly congested cell may be offered a bandwidth boost, giving that user airtime priority in their cell.
Churn reduction techniques can also be significantly improved, increasing loyalty by ensuring marketing and
support communications are relevant to the recipient. This may include proactive outreach – for example
tailored location-based offers – or linking support systems with network intelligence to identify customers
with possible network or VAS set-up or usage issues. Enabling issues to be resolved proactively can reduce
the operating cost of customer care through linking customer care systems with individual-level network
data, reducing the number of inbound calls, as well as the need for engineering or network teams to be
involved in support queries.
As well as being made more relevant to each individual customer, retention efforts can be targeted to
improve efficiency. High risk, high value customers can be identified for specific retention programmes,
while less effort and resource is wasted on low risk customers.
Creating New Revenue Streams
In addition to benefitting the core business, there are many new opportunities to create value from
customer data. Basic customer information has value to other mobile ecosystem players such as app
developers who are interacting with an operator’s customers but do not have the same level of customer
relationship, and therefore data on the customer.
Aggregated customer data, collected by the mobile operator, can be sold to third parties to give insight into
their businesses. For example, the case study below shows the potential value flows in a free operator WiFi
service. In this case, the venue owners who have deployed the WiFi service gain value from aggregated data
on which customer segments use their venues, which locations are used and traffic patterns over a day or
week without any disclosure of information on individuals. For example, Telefónica O2 markets this type of
service as O2 WiFi, a fully managed service for venues.
Case Study: Free Operator WiFi

Increased Mobile Advertising Revenue
Mobile advertising in general can be greatly improved, and therefore increased revenue generated, with
the use of customer data. While many users can find online and mobile ads intrusive, digital marketing is
valued by consumers when it delivers timely and relevant information or promotions. The use of customer
analytics in mobile advertising platforms enables real-time rule based campaigns to deliver relevant brand
messages to consumers, and has been shown to significantly increase response rates and value of mobile
advertising when opt in and opt out are used appropriately. As well as revenue opportunities, detailed
knowledge of the customer base can be used to reduce capital costs and inform investment decisions. For
example, an operator could provide valuable insight for a retailer deciding between several locations for a
new store. Geo fencing could be used with aggregated anonymised data to assess the mix and profile of the
operator’s customers passing by each location, which in turn could be used to support the retailer make a
decision with respect to the preferred store location.
The figure below shows a comparison of response rates across a variety of advertising channels. Given
customer analytics and intelligent targeting, mobile can offer a more efficient method than traditional
methods. This is supported by a range of campaign benchmarks from O2 Media in the UK. For example 26%
of targeted customers downloaded a NatWest mobile banking app and 35% responded to a Smirnoff
nightlife exchange project in two such campaigns.
Figure 3: Advertising Response Rates1
* Direct mail, catalogue, postcard and telephone are response rate; email & internet
advertising are conversion rate based on house lists
1
Source: DMA

Cost Savings
Operators have also reported significant cost savings achieved through analysis and use of customer
information. In terms of operating costs, an operator implementing a solution to inform its customer care
systems with user-specific network intelligence reported its time-to-fix improved from 7 days to 2 days
following implementation. On the capital expenditure side, savings of 2-3% have been seen by an operator
building out a network, through to the use of detailed usage data to focus on high-traffic, high-value areas.
CapEx can also be delayed or saved in upgrade programmes by prioritising critical areas, identified using a
combination of network and customer data.
Key Challenges
Operators looking to monetize customer data face a number of critical challenges. The most publically
visible of these is the need to balance consumer privacy and control with the need to gather useful data.
Recent high profile cases have demonstrated the impact of operators or their partners missing this balance,
typically caused by insufficient communication about the level of detail being collected and its intended use.
Linked to this are requirements for data protection and anonymisation, particularly where data is shared
with third parties.
Achieving the scale needed may also be an issue, particularly for operators with smaller user bases. One
potential solution to this is to create an operator-neutral solution – such as operator-agnostic WiFi – that
gathers data from a wider base of end users.
Finally, operators are not the only type of player looking to make use of mobile customer data. Providers of
over-the-top services such as Skype, Facebook, Apple and Google are all collecting data from their respective
user bases, overlapping with operators in terms of the sources, methods and purposes of data analysis.
Conclusion
Leveraging subscriber data offers an attractive opportunity for new revenue streams, cost savings and
customer experience enhancement. However the use cases are fragmented with many different potential
customers. In order to fully realise the opportunities presented in data monetization, operators will need to
invest in and develop a range of new skills. Other players are making a move in this space and operators
need to focus on this now to ensure that they can effectively compete and maximise the return from the
rich data that they hold.

Cartesian is a specialist consulting firm of industry experts, focused exclusively on the communications,
technology and digital media sector. For over 20 years, Cartesian has advised clients in strategy
development and assisted them in execution against their goals. Our unique portfolio of professional
services and managed solutions are tailored to the specific challenges faced by executives in these fast-
moving industries. Combining strategic thinking and practical experience, we deliver superior results.
www.cartesian.com
For further information, please contact us at cartesian@cartesian.com

Data Monetization: Leveraging Subscriber Data to Create New Opportunities

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