Enhanced 360 view of customer

IBM: enhanced 360° view
A White Paper by Bloor Research
Author : Philip Howard
Publish date : September 2014
White Paper

IBM is in the vanguard for what it calls an enhanced 360° view and it is clearly well positioned to capitalise on the future growth of this market
Philip Howard

A Bloor White Paper 1 © 2014 Bloor Research
Prologue
The original version of this paper was published under the title of
“Extending a 360° view” as a generic discussion about the advantages
of implementing such a system, along with an outline of the technical
requirements for doing so. Bloor Research has been asked by IBM to
produce this customised version of that paper, which briefly considers
relevant IBM technologies and solutions in the context of the require-ments
discussed. Other than this prologue, the section that is specific to
IBM, and the conclusion, this paper is identical to the original. We have
called this paper “IBM: enhanced 360° view” as this (enhanced) is the
terminology used by IBM

© 2014 Bloor Research 2 A Bloor White Paper
Introduction
The concept of a 360° view, especially of
customers, although it potentially applies
to other things too, has been around for a
substantial period of time. The idea behind
the 360° view of customers is that the more
you know about your customers the easier it
will be to meet their needs, both in terms of
products and aftersales care, and to market
additional goods and services to them in the
most efficient fashion. Thus a 360° view helps
both in terms of customer retention and acqui-sition,
as well as up-sell and cross-sell.
The concept of a 360° view was developed
when most organisations were limited to infor-mation
stored within their relational database
systems, perhaps augmented by clickstream
data and by information available from third
parties, such as SIC codes, or location-based
data. Today, we live in a very different world
where there are far more sources of data
that can be used to inform the organisations
about customers. For example, we have call
logs and emails that provide details about
our previous interactions with that customer.
Moreover, customers avidly pursue the use
of social media (LinkedIn, Twitter, Facebook,
bulletin boards and so forth) and are keen to
express their likes and dislikes, their prefer-ences,
and their prejudices in these formats.
These new types of data, whether internal or
external, offer the possibility of expanding the
concept of what might be termed the tradi-tional
360° view into an extended 360° view.
This is sometimes referred to as 720° view but
this is geometrically invalid as a concept so
we will focus on the former terminology: the
extended 360° view.
In this paper we will discuss why we believe
that extending the traditional 360° view makes
sense and we will give some uses that demon-strate
why the extended 360° view repre-sents
an opportunity, both for those that have
already implemented a 360° view and for those
that have not. We will also discuss some of the
technological implications of moving to, or
implementing, an extended 360° view.

The argument for an extended 360° view
There are two major generic use cases for
an extended 360° view: one for marketing,
and one for call centre and other operatives
working at the point of service. In either case
the traditional 360° view of the customer is
useful but it is a blunt instrument. You can
ensure that you have information that is as
accurate as possible about your customers,
and it can span different divisions within your
organisation, so that the information you hold
is holistic. However, that information is not
detailed enough to give you a complete picture.
In the case of marketing, you cannot accurately
target individual customers. What typically
happens in most organisations is that struc-tured
customer data is analysed inside a data
warehouse or data mart and, as a result of
this analysis, customers are categorised as
belonging to particular segments of the market
according to their income, lifestyle, educa-tional
background and so forth. Marketing
efforts are then directed towards particular
customer segments. Unfortunately, such
segments are not small—in the sense that
there may be many customers in any particular
segment—which is why we describe this as
a blunt instrument: it is not the fault of the
360° view, it is simply that internal resources
such as customer relationship management
(CRM) systems, even augmented with third
party data, are insufficient to provide the
detail to create micro-segments. The ability
to use unstructured data, whether internally
collected data from call records or external
data such as social media changes all of this.
Thanks to the additional information, not to
mention opinions, which individuals make
available about themselves through conversa-tions
with your call centre or via social media,
it is possible to get to a much more granular
level of marketing. In some cases it will mean
the possibility of 1-to-1 marketing but even
where that is not the case then much smaller
segments will be possible in many instances.
As an aside we should say that Bloor Research
is not, and does not claim to be, any sort of
specialist in marketing per se; however, we
do understand that 1-to-1 marketing can
be described as the holy grail of marketing:
that you know enough about a particular
customer that you can tailor offers to him or
her that are completely specific to that person.
The extended 360° view is what makes this
possible and enables marketing programs
that improve customer retention, customer
perception and up-sell and cross-sell effec-tiveness.
It is also worth pointing out that you
need both the information from traditional
CRM systems and unstructured information
from internal sources or social media to make
this extended 360° view work. With the former
alone you get broad brush segmentation that
is not sufficiently tailored to individual require-ments,
and in the latter case you get banner
ads for flights to Barcelona for weeks after you
already went there and came back!
While information gathered in call centres may
contribute to marketing efforts and organisa-tions
may make use of the information for such
things as next best offer, there is an important
case to be made for an extended 360° view
supporting contact centre operations directly.
In particular, the emphasis in these environ-ments
is on first-time call resolution (because
it is more cost-effective for your organisation
and also because it pleases the customer).
Unfortunately, call centre operatives often do
not have access to all the facts when they are
engaging with customers. They are typically
limited to the confines of the CRM and/or
MDM system in place, and they do not have a
complete and holistic view of the customer,
which results in less than optimal response.
An extended 360° view should provide all the
relevant information about that customer that
is known by the company.

Extended 360° view use cases
The idea of an extended 360° view is relatively
new, so there are not a great many companies
with implementations in place. However, there
are several (in telecommunications, financial
services and consulting, for example) within
organisations that run customer engagement
centres. These have not typically added social
media or other external data to their existing
360° view yet, but have augmented their data
with internal data sources that are unstruc-tured.
Aside from these, the following are
vertical examples of an extended view; most,
though not all, of which have expanded their
view of customer data with external data.
A banking example
This bank was consistently losing customers
to one of its rivals. Moreover, these losses had
spiked upwards and losses had stayed high.
What it eventually discovered was that its rival
was monitoring tweets from the first bank’s
customers and whenever it found a negative
comment would reply to the tweeter offering
a free trial (and easy account transfer) to the
second bank, which was resulting in the first
bank’s losses. This is good example of using
social media in its own right: imagine doing the
same thing to your competitors, or having them
do it to you.
In response, the first bank of course started
to monitor its customers tweets for itself and,
linking this information to its CRM system, was
able a) to understand whether or not it wanted
to retain this client (he or she might, after
all, be unprofitable as a client) and then b) to
determine what compensation or next best
offer it might make to help to retain that client.
While this use case reflects one particular
bank’s experience we are aware of other banks,
across multiple continents, which are adopting
a similar approach.
A loyalty card example
This case refers to a hotel chain that has a
loyalty card programme. It too monitors social
media for comments that its members might
make but, in particular, it looks for comments
from members about staying in other hotels
that belong to other companies. Where this is
the case, and where this hotel chain has one of
its own hotels in close proximity to the one its
member stayed in, it will contact the member
to say, “Did you know that we have a hotel just
down the block from where you stayed?” and
perhaps offer an incentive.
A home improvement store example
This company runs a chain of home
improvement stores and it uses the extended
360° view not just with respect to customers
but also with regard to employees. In the
former case, it monitors tweets for comments
that suggest that the tweeter might be inter-ested
in home improvements (they mention it
specifically, they have just moved house, and
so on) and, when they get a positive result,
they compare this with their master data
management (MDM) system, where their 360°
view of customers based on internal, struc-tured
data is stored. If the tweeter is known
to the company then it will fire off relevant
marketing offers to that individual.
A financial services example
This company provides retirement savings
plans and other financial products. To make
its representatives more efficient and effective
during conversations with clients, the organi-sation
is consolidating information from dozens
of internal sources—unstructured comments
from other interactions as well as structured
data—into a unified view of that customer
account. The representatives can then focus on
the conversations with their clients rather than
searching for information while they speak.

A preventative maintenance example
An example here is a Telco that is using
preventative maintenance as a way to enhance
customer retention by minimising outages,
dropped calls and the like, with a resulting
reduction in customer complaints. We also
want to make the point that the extended
360° view is not limited to customers (or
employees) and that it is not just social media
that can be used to provide relevant exten-sions.
For example, imagine that you are a
plant hire company and that you hire out large
items of construction equipment for prolonged
periods of time. Your customers will expect
you to maintain those vehicles or equipment
in good running order (much like users of
mobile networks). However, maintenance is
expensive. Historically, maintenance has been
provided every so many months or every so
many miles. It will be more efficient to use
sensors on the relevant equipment to collect
information that can be analysed for preven-tative
maintenance purposes so that mainte-nance
can be done at the optimal and most
cost-effective time to ensure that service level
agreements are met.
Note that this maintenance scenario poten-tially
extends into many other areas. For
example, when is it appropriate to get your car
serviced? Time- or mileage-based servicing is
inefficient and we can expect motor manufac-turers
(alongside garages) to start offering
this as a service. The same would apply to air
conditioning servicing or boiler servicing, or
anything else of that type. Moreover, once we
start to get into servicing of products owned
by consumers then we potentially circle back
to CRM systems, social media commentary
and so on to make further offers: for example,
perhaps you’d like your car waxed while it’s in
for a service, or how about some alloy wheels?
Extended 360° view use cases

Implementing an extended 360° view
There are two aspects to this: implementing
the base 360° view and implementing the
extensions—adding data from non-traditional
sources—to it. Many companies will already
have done the former but some will not and
we expect the concept of an extended 360°
view to encourage new companies to adopt
this approach. We will therefore summarise
the requirements for the base capability, for
the benefit of new adopters. Existing users can
skip to the section “Extending the 360° view”.
The base 360° view
There are two fundamental requirements for a
base 360° view:
• Pulling all the information about the customer,
supplier, employee, product, service or
whatever together. Usually, this will be in a
single place (a master data management
hub) but data can also be left in different
locations provided that appropriate tooling
is in place to ensure that the data in those
different locations is synchronised and
consistent. This second approach may be
implemented by means of a master data
management registry or by other means.
• All the information needs to be consistent
(the same in every location where that is
appropriate) and reliable. As far as relia-bility
is concerned this will require data
quality processes supported, preferably, by
a data governance initiative. An important
consideration is that while you want data to
be as accurate, complete, and up-to-date as
possible, this does not mean 100% accuracy.
In practice, at least as far as consumers are
concerned, perfect accuracy is not possible
(because people move house, change their
phone number, get married and so forth).
In practice, the accuracy required will
depend on the type of data and the use
case: marketing, for example, may have
less stringent requirements than use cases
involving preventative maintenance.
Note that in the previous discussions we
mentioned CRM systems on several occasions,
especially because these tend to be widely
used in marketing environments. However, it
is important to appreciate that CRM systems
are part of the MDM ecosystem: they take
data directly from, or share data with, the
MDM environment, whether hub-based or
registry-based.
Note further that a 360° view on its own does
not help much. If the point about a 360° view is
that it enables targeted marketing (or service
provision) then you also need to be able to
perform analytics against that customer data.
This means you can segment customers into
categories so that the most appropriate offers
are made to the most relevant customers.
Extending the 360° view
Extending the 360° view is essentially about
enriching that view, making it more granular
so that you can gain marketing or other
efficiencies. There are various different
sources of information that you might use
to do this. Arguably the most obvious is call
centre records: details of what the customer
said, notes made by call centre operatives, and
so on. Emails will be another rich source of
information in which customers will express
their opinions. In addition there is social
media, which we have already discussed in
some depth, but you might also want to make
use of clickstream data (how the customer
acted when he was on your web site), or how
a customer is using your services. In the case
of the latter this will depend a lot on the type
of services offered. For example, in Telco you
would want to know how individuals use their
smartphones, what apps they use, where they
travel, what restaurants they frequent, which
theatres they visit and so on. On the other
hand, in the case of smart metering, you would
want to know what patterns of consumption
each user exhibits.
The question becomes: how do you get this
data and how do you integrate it with your
existing 360° view? Internal data is not a
problem as you already possess this infor-mation,
though you will clearly need some
way of parsing emails, documents, call centre
notes and so forth to extract relevant infor-mation.
However, external data may be more
difficult to handle. Consider the restaurants
where customers eat. This may or may not
be directly relevant to you: you may be in the
restaurant business but more likely you want
to use this information to get a better handle
on your customer. If he or she hardly ever
eats at restaurants, that tells you something
about that customer that distinguishes him
or her from someone who eats regularly at
Michelin starred destinations or from those
that eat at McDonald’s. On its own this doesn’t
tell you much, but put it together with theatre

and cinema visits, holiday destinations, hotels
stayed in, overseas vacations, business trips,
tweets about favourite TV shows, and so on
and you start to build up a picture of your
customers.
The first step, as mentioned in the home
improvement example above, is that you need
to identify the customers to which the data
belongs. In other words you need matching
processes (typically part of data quality
solutions) that can associate the information
you are collecting with particular customers.
Secondly, you need to cleanse this data. There
is no point in just dumping all of this infor-mation
into your CRM or MDM environment—
on its own it means nothing—it is when it is put
together and analysed that you can start to get
a better understanding of that customer. It is
analytics here that is important in the same
way that analytics is fundamental to lever-aging
a base 360° view. In the case of internal
data such as call centre information, this will
require text analytics. However, both machine-generated
and social media data often contain
duplicated data (think re-tweets) that needs to
be de-duplicated. Textual information will need
to be parsed and transformed into a consistent
format and, in some cases, additional data
preparation will be required before the data
can be analysed. As an example of this,
consider that most statistical algorithms will
not run against data with empty (null) fields.
In other words, some significant data quality
processing will need to be applied to the data
before it can be analysed.
In so far as this analysis is concerned you
could put the data into a conventional data
warehouse or data mart for analytic purposes,
but much of the data we are discussing comes
under the popular heading of “big data”
(actually, Bloor Research prefers the term
“any data”, both because that captures the
varieties of data types that may be involved
and also because any data doesn’t neces-sarily
imply that there is a lot of it: which
there may not be). We do not need to rehearse
here the arguments for and against the use
of non-relational platforms (most popularly
Hadoop), but we can say that these represent
a low cost way of storing this sort of data while
providing a platform for its analysis. Fortu-nately,
the same sort of analytic tools that are
used for analysing and segmenting customer
data (say) in relational environments are
starting to become available to run against
Hadoop also. We should also comment that in
some circumstances interesting information
about customers may be embedded in very
large volumes of otherwise uninteresting data:
in this case it may make sense to use a stream
processing solution that can extract relevant
nuggets of information without having to store
a lot of extraneous and unwanted data.
Ultimately, then, the information that is going
to be used to augment your master data
management system is going to be summary
data that provides a detailed profile of your
customers, but without the minutiae of every
single thing that you know about them.
Perhaps the simplest way of achieving this is
by extending either the MDM or CRM record
with a series of attributes (income, education,
relevant habits [eating out, theatre going and
so on], employment and so forth) against
which a score is assigned. This can be updated
as new information is received. Based on this
information, marketing will be well placed to
identify individuals who might be susceptible
to up-sell, cross-sell and so forth, as well as
those susceptible to churn.
However, it is not only aggregated data that
will be used. In a call centre, for example, you
may want to search through relevant customer
emails or service records during a call. In this
case you will need an interface that allows
you to combine conventional data from your
MDM or CRM system with other information
that may need to be searched, navigated, or
queried (depending on the type of data) in real
time. There is no point in extending your base
view if you do not have the ability to see and
interact with this extended information as
and when you need it. In the case of search,
pre-defined indexes will often be needed for
performance reasons. Moreover, performance
in call centres is important more generally:
if the service operative is accessing multiple
sources of data about a particular customer
while he or she is on the line, then the perfor-mance
requirements for accessing that infor-mation
are stringent: callers will not hang on
for more than a few seconds before starting to
get aggravated.
Implementing an extended 360° view

Conclusion
The idea of an extended 360° view is a new one
and derives from the fact that non-traditional
forms of information can be used to extend
what was previously thought of as the single
view of the customer (or supplier, or employee)
into something more complete. From the
examples discussed in this paper there is
clearly potential for getting a better under-standing
of customers by using this approach,
and we would certainly expect companies to
get closer to (if not actually achieve) 1-to-1
marketing when adopting an extended 360°
view than with previous approaches.
So there are clear benefits to an extended
360° view. Base capabilities such as master
data management will need to be in place
and the information in these systems can
then be enhanced with facilities for capturing,
analysing or searching, and appending the
data that will be used to augment these
systems, along with a suitable visual interface
that brings all of this information together.
Fortunately, the tools and techniques for doing
this will often be in place already though they
may need to be extended to cope with unstruc-tured
information of various types (from call
centres, emails, social media and so forth), as
well as machine generated and other forms of
non-traditional data.
As we have seen, there are already companies
moving to adopt an extended 360° view. All the
evidence suggests (and there have been lots
of surveys around this subject) that companies
that use technology (such as analytics) to better
understand their customers outperform their
competitors. Early adopters of an extended
360° view will get competitive advantages
over their rivals and this will force laggards to
adopt a similar approach or lose business. As
a result, we expect the extended 360° view to
have a major impact on the market in the years
ahead. Perhaps surprisingly for such a large
organisation, IBM is leading this trend. We
say surprisingly because innovation is more
often associated with small companies than
large ones. However, in this particular case,
you need a broad software stack to cover all of
the requirements of an extended 360° view, so
perhaps it is not so surprising after all. In any
case, IBM is in the vanguard for what it calls
an enhanced 360° view and it is clearly well
positioned to capitalise on the future growth
of this market.
Further Information
Further information is available from
http://www.BloorResearch.com/update/2232

Bloor Research overview
Bloor Research is one of Europe’s leading IT
research, analysis and consultancy organisa-tions,
and in 2014 celebrates its 25th anniver-sary.
We explain how to bring greater Agility
to corporate IT systems through the effective
governance, management and leverage of
Information. We have built a reputation for
‘telling the right story’ with independent,
intelligent, well-articulated communications
content and publications on all aspects of the
ICT industry. We believe the objective of telling
the right story is to:
• Describe the technology in context to its
business value and the other systems and
processes it interacts with.
• Understand how new and innovative tech-nologies
fit in with existing ICT investments.
• Look at the whole market and explain all
the solutions available and how they can be
more effectively evaluated.
• Filter ‘noise’ and make it easier to find
the additional information or news
that supports both investment and
implementation.
• Ensure all our content is available through
the most appropriate channel.
Founded in 1989, we have spent 25 years
distributing research and analysis to IT user
and vendor organisations throughout the world
via online subscriptions, tailored research
services, events and consultancy projects. We
are committed to turning our knowledge into
business value for you.
About the author
Philip Howard
Research Director - Data Management
Philip started in the computer industry way back
in 1973 and has variously worked as a systems
analyst, programmer and salesperson, as well
as in marketing and product management, for
a variety of companies including GEC Marconi,
GPT, Philips Data Systems, Raytheon and NCR.
After a quarter of a century of not being his own boss Philip set up his
own company in 1992 and his first client was Bloor Research (then
ButlerBloor),
with Philip working for the company as an associate
analyst. His relationship with Bloor Research has continued since that
time and he is now Research Director focused on Data Management.
Data management refers to the management, movement, governance
and storage of data and involves diverse technologies that include (but
are not limited to) databases and data warehousing, data integration
(including ETL, data migration and data federation), data quality, master
data management, metadata management and log and event manage-ment.
Philip also tracks spreadsheet management and complex event
processing.
In addition to the numerous reports Philip has written on behalf of Bloor
Research, Philip also contributes regularly to IT-Director.com and
IT-
Analysis.com and was previously editor of both “Application Development
News” and “Operating System News” on behalf of Cambridge Market Intel-ligence
(CMI). He has also contributed to various magazines and written a
number of reports published by companies such as CMI and The Financial
Times. Philip speaks regularly at conferences and other events throughout
Europe and North America.
Away from work, Philip’s primary leisure activities are canal boats,
skiing, playing Bridge (at which he is a Life Master), dining out and
walking Benji the dog.

Copyright & disclaimer
This document is copyright © 2014 Bloor Research. No part of this
publication may be reproduced by any method whatsoever without the
prior consent of Bloor Research.
Due to the nature of this material, numerous hardware and software
products have been mentioned by name. In the majority, if not all, of the
cases, these product names are claimed as trademarks by the compa-nies
that manufacture the products. It is not Bloor Research’s intent to
claim these names or trademarks as our own. Likewise, company logos,
graphics or screen shots have been reproduced with the consent of the
owner and are subject to that owner’s copyright.
Whilst every care has been taken in the preparation of this document
to ensure that the information is correct, the publishers cannot accept
responsibility for any errors or omissions.

2nd Floor,
145–157 St John Street
LONDON,
EC1V 4PY, United Kingdom
Tel: +44 (0)207 043 9750
web: www.BloorResearch.com
email: info@BloorResearch.com

Enhanced 360 view of customer

Recommended

Recommended

More Related Content

Recently uploaded

Recently uploaded (20)

Featured

Featured (20)

Enhanced 360 view of customer