B U I L D I N G
YO U R O W N
DATA-D R I V E N
M A R K E T I N G
S T R AT EGY
AN EASY-TO-IMPLEMENT BUSINESS
GUIDE IN FIVE SIMPLE STAGES
• FIVE STEPS
1. HOW TO MAKE DATA A HABIT
2. DO YOU HAVE THE DATA TO ANSWER THAT QUESTION?
3. MIND THE GAPS!
4. COMMIT TO DATA QUALITY
5. MARKETING TECHNOLOGY – MASTERING THE COMPLEXITY
• CONTACT US
• FOR MORE INFORMATION
C O N T E N T S
If you’re reading this, you already know that building a data-
driven marketing strategy is an essential business priority. You
know that being able to find, analyse, organise and use the wealth
of information available to you in competitive markets is a vital
component of driving commercial success. You know it can give you a
The good news is that, now more than ever, the ability to build your
own data driven marketing strategy is within the reach of most
companies. But there are pitfalls. Without truly understanding the
data you have, the data you need, the technology you use and the
other issues considered in these pages, your data is likely to remain a
source of unfulfilled potential.
In this e-book, Occam outlines five essential stages that will set you
on the right path to implementing your own, effective data driven
Each easy to follow section explores and explains the key steps that
will enable you to make the most of your marketing potential. Think
of it as a helping hand in avoiding what, in our long experience, are
the common pitfalls, to arrive at a strategy that delivers what every
business really needs from its data: actionable insight.
Finally, a caveat. Data is a fast-changing industry. So for the latest
insights and current thinking, stay up to date here/please watch our
NOW IS THE RIGHT TIME
At Occam, we believe that now is the time
for businesses to create their own powerful
new insights by embracing data driven
marketing principles. This is fuelled by need
to meet consumer expectations of relevant
and engaging customer experiences, the
advancement of marketing technology which
is making it simpler to embrace and more
accessible from an investment perspective,
and the proof that those organisations
that can grasp the opportunities offered by
data will outperform laggards in terms of
customer retention, acquisitions and stronger
OV E R V I E W
Data is the basis of knowledge and
actionable intelligence. It can be
leveraged to reveal important insights
that support better decision-making and
through an iterative cycle of test and
learn, help build awareness that make
future actions more effective.
In a generation, the world has moved
on from selling virtually everyone the
same products and services to fulfilling
individual needs and tasks through
customised offerings. Part of the reason
for this quantum shift is data and the
insights it enables.
There are many reasons why data-driven marketing is
much more than just another buzzword.
DATA EXPANSION VERSUS QUALITY
Despite these driving forces a number of barriers can impede
organisations in their pursuit of data driven marketing, not least the
explosion in the volume and variety of data that is now accessible.
In 2013, the world’s data could have been held on a stack of iPads
stretching two-thirds of the way to the Moon. By 2020, it is predicted
that hypothetical stack could make the trip 6.5 times over. This
expansion creates problems as it is estimated that only 37% of that
data will actually help business processes, let alone marketing.
This leads on to the quality of data and its usefulness. One recent
survey found that 60% of consumers deliberately provide inaccurate
information when registering online.
Average figures show that companies lost 12% of their revenues as
a result. Clearly, consumers lack trust in handing over their data to
YOUR OWN STRATEGY
IN FIVE EASY STEPS
Against these challenges, Occam is pleased to offer a detailed five-
stage strategy for effective data-driven marketing.
To get up and running, we suggest you consider the following points
• Make data your habit: setting business goals by starting with the
question “do we have the data to answer that?”
• Look at your data landscape: audit your data landscape for
content, quality and usefulness. Then analyse the gap between
the data landscape you have and the insight you need
• Fill your data gaps: develop strategies to gather and generate the
data you need for informed decision making - and bear in mind
that today’s savvy customers will expect something valuable in
• Commit to Quality: invest in the people, processes and technology
that can deal with the issues and turn your data into a valuable
• Turn to Technology: finally, leverage technology to help you
master the beast and turn raw data into valuable insight
HOW TO MAKE DATA A HABIT
Turning data into a habit is the essential first step on any data-driven
This step requires you to decide the driving factors for all data-led
decisions by asking yourself what your Key Performance Indicators
will be? Are you looking solely at sales figures? Is acquisition your
focus above all others? Or is it just as important to address attrition
and execute better cross and up-sell strategies? What about customer
engagement and delivering exceptional customer experiences that
impact brand perception? Answering these questions is fundamental.
DEFINE WHAT IS IMPORTANT
In effect we are looking at clearly defining
the outcomes expected from any investment
in marketing activity so that a framework for
evaluating Return on Investment (ROI) can
be developed. The age old cliché of “What
gets measured gets done” is largely true,
however you must also ensure that you set
the correct KPI’s aligned to business goals in
order to respect the maxim of William Bruce
Cameron: “Not everything that can be counted
counts, and not everything that counts can be
One of the techniques we use in Occam to
help with this process is the KPI Tree. In effect
this involves identifying the top level goal and
identifying the underlying factors that influence
this. A simple example of this is shown
below. In this example the goal is to increase
the number of active Independent Financial
Advisor’s (IFA) that an organisation has on its
Number of NEW
IFA’s recruited in
Size of the TARGET
Given that the market of IFA’s is mostly out
of the control the organisation, the size of
the target market is therefore a metric the
business should track but not one it can actually
influence. The % Conversion on the other hand
is to some degree in the control of the business,
where through its marketing, recruitment and
sales efforts, it can improve its conversion of
leads to partners.
Following this process across all of the nodes
(data points) of the Tree, the process identifies
all of the core metrics and influencers that
affect the KPI’s and objectives the business is
aiming to deliver. Identifying these measures
and acknowledging the need to track and
monitor them is key part of becoming more
data driven in your marketing, because you
are beginning to use data to help benchmark
progress and validate the activities
In this case, there are only two ways to get
1. The business either increases its sales and
marketing efforts to find and win new
2. Or it boosts its efforts to retain and keep
existing IFA’s active.
For new IFA’s, there are only two ways to
increase the number the business recruits in a
1. Increase the size of the target audience
2. Or increase conversion rates.
MAKE DATA ACCESSIBLE AND VISIBLE
Outside of KPI’s and tracking progress against
ROI, for data to truly become a habit it needs
to be readily accessible. This can be in the
form of raw underlying data to help inform
a decision, or the outcome of data insights
published in dashboards and tracking reports.
Data integration is a key enabler for this to
be successful and also one of the biggest
challenges for many businesses today, along
with technology interoperability issues. A
survey run by Winterberry Group cited data
integration as being key to helping with the
move to more data-driven marketing activity
In addition, making data visible throughout
the business is essential. This raises its profile
and through good visualisation approaches can
make data readily understandable and useful.
Whilst technology is of course, important here,
ensuring the right people and processes are
geared up to support a data integration and
visibility agenda for any organisation is also
DEVELOP YOUR DATA STRATEGY
When you start to surface data across your
business, it is essential that it is trusted, well
managed and understood. Taking steps to
implement an organisation-wide data strategy
is vital to ensure that aspects such as quality,
consistency, governance and availability are
considered, managed and executed across
the organisation. The key to managing this is
to start on the areas of the business that are
directly related to the objectives you are trying
to achieve; start small, get buy-in and then
build out from there.
There are strong reasons for creating cross-
functional working groups focused around
a specific data challenge. It’s a great way of
encouraging collaboration and breaking down
internal barriers between different functions
to ensure that internal skills and expertise are
AND ON AND ON…
Evidence shows that it takes 61 days to form a habit (not the 21 days
often cited). The result is behaviours that become ingrained in day-to-
day activity. Following the principles of Tom Bartow’s model of habit
formation can be useful when tackling data driven marketing:
1. “Honeymoon” - recognise the “honeymoon” period; when things
feel easy and everything is good. Exploit this to push activities
through and gain initial traction.
2. “Fight Thru” - this is where bad old habits start to come to the
fore and things just feel difficult. To overcome this, Tom suggests
you need to recognise this stage is normal, go back to the key
questions that drove the change in the first place to remind
yourself why you started on the path and then visualise successful
outcomes to focus on the end goal, rather than the barriers and
3. “Second Nature” – if you navigate the “fight thru” stage then
eventually it will become the norm. However, disruptions from
business change and small failures knocking confidence can
undermine achieving the goal. That’s when it becomes key to
“fight thru” and continue on the path to data driven.
Taking these first steps on the path to data-driven marketing will help
you to establish the important metrics and make use of data to track
your progress and inform your subsequent actions. You’ll know when
that step has been taken because what the data tells you will become
as important as your own experience and personal intuition. You can
then weave these facets together to deliver better decision-making
that ultimately benefits your marketing activity.
DO YOU HAVE THE DATA TO ANSWER THAT QUESTION?
When making decisions in a data driven
business, the starting point is, and should
always be to ask yourself - “Do we have the
data to answer that?”
Asking this question regularly will drive the
analysis of the data landscape a brand operates
in. This in itself will provide many of
the answers. Going further, for all brands and
marketers in today’s increasingly data-driven
marketplace, analysing the data, documenting
it and maintaining a detailed Data Landscape
Definition (DLD) should be a priority aim before
any further decisions are made.
ASK YOURSELF, WHAT IS A DLD?
At its simplest, a DLD visually represents each
discrete data system, the core attributes and
entities that are present and the interactions
and data exchanges between them; offering
a holistic overview of the collective data
landscape a business has access to. Taken to
the nth degree, it can comprise an overview
of the organisation (including its divisions and
departments), a detailed description of each
system, those managing and handling data, and
the logical design of the systems and a regular
audit of the data held.
But no DLD exists in isolation and there is
no right or wrong approach. It has to be
accommodated into general business and
marketing practice and reflect the objectives
of the organisation it operates in, plus its
business goals, people and processes. These
might include: longer-term business goals; key
people; personal data processing; and industry
To develop this definition and underpin all data-based decisions
across the business, marketers and key decision-makers should
aim to answer the following four key questions:
ASK YOURSELF, WHY DO IT?
Simply put, if you don’t understand your data
landscape, then you are unlikely to understand
all your data. And if you don’t understand all
your data, you are unlikely to be executing
effective marketing, product development or
business strategies - potentially leading to some
fundamental and costly mistakes.
At its core, a DLD allows a business to
understand the big picture and ensure relevant
data is flowing freely in a timely manner to the
relevant point of use. But at the granular
level, it furnishes decision-makers with a point
of reference with which to make informed
decisions and ensure they are doing things
correctly to meet business objectives and long-
term strategic goals.
This is becoming increasingly relevant as
we edge ever nearer to the landing of the
General Data Protection Regulation (GDPR)
updates and the upcoming Privacy & Electronic
Communications Regulation (PECR) overhaul.
ASK YOURSELF, HOW TO DEFINE A DATA LANDSCAPE?
Think of your DLD as a living, breathing
representation of your data ecosystem that
continues to evolve with your business in a
format that suits its ever-changing needs. It is
easily broken down through a simple checklist:
• Start with a straightforward inventory.
Define the divisions, departments and third
parties that comprise your data landscape
and identify which are relevant to the
overarching objective you are undertaking
this activity to support.
• Identify the systems used by each division
and department. And be comprehensive!
• Identify where these systems are shared
and how information is exchanged
• For each system, define the primary inputs
and outputs and then take this further, and
define the method of data capture for each
• Understand the system entities, e.g. is the
system account, customer or email-centric?
Does it include contact details? And how
well populated is the data?
By examining each of these, the outputs can
help decision-makers to understand the internal
relationships between the systems, processes
and the data they produce and use that insight
to drive decision-making at a strategic level.
After all, the quality of your data (in terms
of relevancy and accuracy) and the integrity/
authority of your data is key to business and
The GDPR is here, noting that even with Brexit,
UK businesses who want to trade with Europe
will still need to meet a minimum standard. The
honeymoon period has started and by May 25
2018 your house needs to be in order.
There is a quiet, and growing, concern that
the direct marketing industry’s very own
‘PPI bonanza’ will be borne from GDPR.
So we should be acting now to avoid that
risk. Companies are already starting the
journey towards compliance and a detailed
understanding of a data landscape is a key
starting point to that.
Additionally, the upcoming Privacy & Electronic
Communications Regulation (PECR) overhaul
will also necessitate that organisations
understand what data they have and how it
flows within their business.
For the data aspects of GDPR and PECR, the
Data Landscape Definition can provide much
of the ‘as-is’. GDPR and PECR define the ‘to-be’.
Compliance, then, comes down to identifying,
understanding and filling the gaps.
So the question really is not ‘Why do it?’, but
‘Why wouldn’t you?’
WHY WOULDN’T YOU DO IT?
MIND THE GAPS!
Having explained how data can be made into
a habit and the important concept of data
landscapes, the next step is to find what to do
if any of the answers to the second point – “do
we have the data?” – are ‘no’.
Data gaps come in many forms, from
incomplete personal information (e.g. missing
DOBs, where you like to holiday, size of
household), to incomplete transactional data
(e.g. we know how you use one of our products,
but what about the rest?); to organisational
gaps (e.g. Customer Services know your car
breaks down on the M6 once every year, but
no-one has told Marketing).
Now more than ever, what you collect is
inextricably linked to how you collect it and as
such that is the focus of this stage.
Recently, Occam’s sister company Amaze One
researched how consumers feel about the way
brands handle their personal data. You can
explore the results here, but the picture that
emerged was one of disaffected consumers,
tired of handing over data for what they
perceive as very little in return.
WHY THE VALUE EXCHANGE MAT TERS
This imbalance in the value exchange has been
masked by the fact that brands keep asking for
data and consumers keep sharing it. So if they
are still sharing, where is the evidence that
consumers feel short-changed?
The growth in use of tools such as Google’s
preferences on Chrome and Gmail is one
pointer. Increases in adblocking, which grew
globally by 90% between 2015 and 2016, are
another. Amaze One’s research tells us that 70%
of consumers are concerned about the way
personal information is collected, while 4 out
of 5 have concerns about the way their data is
sourced, captured and sold.
Consumers, it seems, haven’t been offering
data because they feel an overbearing need
to share. They’ve been accepting the release
of a bare minimum of personal data out of
resignation – hardly the basis for a great
Creating a better value exchange matters if
we are to reverse the growing numbers of
consumers ‘punishing’ brands. It matters
because more data – and more valuable data
- comes from a better value exchange in your
data capture strategy. And it matters because,
in a potential post-GDPR world where we want
consumers to give brands permission to engage,
we need to give them better reasons to do so.
HOW TO CREATE A BET TER VALUE EXCHANGE
How do brands encourage consumers to part with more and better information willingly?
1. Start with trust
Overwhelmingly, consumers said elements
such as trust and control mattered most.
There were variances depending on age,
however, the natural levels of mistrust
(particularly among older age groups)
showed that, before offers can make an
impact, brands first need to establish these
hygiene factors. So transparency in opt-
ins, permissions and deleting data is an
essential first step.
2. Treat data as a privilege, not a right
Once permission to engage has been given,
brands should listen to what the consumer
has said and give them what they have
asked /signed up for.
Cyclical audits of the data lifecycle, from
initial collection through to profiling, should
drive content and communications and
explore opportunities to educate and build
trust as a precursor to filling data gaps.
3. One size does not fit all
What you offer in return for the declaration
of data by customers should take account
of what is most valued (by them, not
you). What works varies by age, sex and
affluence, so it is essential for brands to use
attitudinal data to create a value exchange
segmentation that can be applied to
customers and enquirers alike.
4. Avoid appearing mechanical
Every communication should have a point,
implicitly demonstrating to consumers
that you are valuing, not exploiting, their
permission to engage.
How do brands enable these strategies? They
do it by investing in the people, processes and
technology that can turn data into a valuable
asset. That will be the subject of step four in this
series which looks at data quality.
MEANWHILE, HERE ARE 3 SIMPLE DATA
GATHERING STRATEGIES THAT CAN
CREATE A BET TER VALUE EXCHANGE:
1. Make opt-ins and permissions big, bold and obvious: Transparent
opt-ins build trust and enhance feelings of control. Don’t hide them in
the small print. Make them a feature, not an afterthought.
2. Only ask for what you need: Consumers can’t feel in control of
a relationship where they’re asked for large amounts of seemingly
unrelated and irrelevant information. Relevance helps build trust.
3. Make it progressive: Build your data capture strategy in proportion
to your developing relationship. So instead of asking for everything up
front in a move perceived as a cynical data grab, start with the bare
minimum, and build the data pool over time.
COMMIT TO DATA QUALITY
Few would argue that, when it comes to data-
driven marketing, the quality of the data is an
obvious essential. But what do we mean by
Simply put, quality data is data that you can use
with confidence for its intended purpose. As
such, data quality means taking the necessary
steps to ensure that data is accurate, valid and
consistent, but also complete (you have all
that is necessary to meet the need) and readily
accessible in a timely fashion.
For marketing, the primary intended purpose of
data is to help the business develop customer
understanding. This can then be used to power
a better customer experience and, therefore,
ultimately achieve acquisition, retention, cross-
sell and satisfaction targets. Similarly, marketers
turn to data to help them identify which
marketing activities are driving the best results
when compared to the costs and investment
required to undertake them. As such, data
quality for marketing should be primarily
focused on those areas that directly impact
understanding customers and the performance
of marketing activities.
Poor quality data gets in the way of these core
marketing objectives. How can you deliver a
consistent, personalised customer experience
if you don’t trust the data and associated
customer insight that shape it? If poor quality
data leads to the wrong conclusion, how
will that experience impact on the client’s
perception of your brand? In a recent Experian
report, 75% of organisations stated that
inaccurate data was undermining their ability
to provide excellent customer experience - a
Similarly, if poor quality data prevents you from
identifying the best performing activities from
the poorest, you will continue to spend money
in the wrong places, squandering budget and
leading to difficult questions from the business.
1: IDENTIFY YOUR STARTING POINT
Take stock of the current state of your data
and the impact it is having on your business.
Start by identifying the core data you need to
support the business objectives you have by
defining your data landscape as we discussed
earlier . Once you have this, then come the
activities to audit the data those systems hold
and identify how fit for purpose they may be.
Data profiling technology is a great help here.
It assists you in identifying the key attributes
of your data, such as per cent populated,
accuracy, duplication and patterns in data error.
Alternatively, you could partner with a specialist
who undertakes this activity for you and who
may also be able to help you contextualise the
extent of the issues and opportunities in your
business by providing a comparison with others
they have worked with.
You also need to identify the benefits that
could be realised if the data issues were
addressed. This could be in terms of saving
costs (for example, eliminating constant ad-
hoc data correction), increasing revenue
(for example, through better customer
understanding delivering better targeting), or
indeed softer benefits (for example, removal of
manual, repetitive tasks that impact employee
satisfaction). Key to this activity is working with
the business stakeholders that will benefit from
better quality data.
There are a number of practical steps that you can take to
tackle the data quality challenge head on:
H O W D O W E
TA C K L E I T?
2: PLAN, PRIORITISE AND MANAGE
Once you understand the current state and the potential benefits, it is
possible to start to define and prioritise activities by comparing them
to your overarching objectives.
Categorise the data quality activities in terms of their alignment to
objectives and the costs to undertake and the benefit they yield.
Aim to focus initially on those activities that are low cost but likely to
deliver a high degree of benefits aligned to your goals.
When you have this view, you can then start to work through the
activities, but not before you determine how you will manage the
initiatives going forward. Key to this will be in getting the appropriate
technology, people and processes in place to support data quality.
• Technology: key capabilities essential to an ongoing programme
of data quality are data integration and transformation, master
data management and data profiling. In essence, find technology
that helps to make data readily accessible and allows you to
manipulate it as required by business needs. Similarly adopt
technology that allows for master definitions of data to be
agreed and then enforced throughout your business. Finally
embrace technology that will help you with the ongoing
statistical evaluation of data, essential to understand issues and
opportunities within your data..
• People: creation of a cross-functional working group comprising
representatives from the areas of the business that contributed to
the benefits review and those that are likely to be involved in the
execution of data quality initiatives. This group will be responsible
for controlling the execution of the data quality plan. An executive
level sponsor should also be assigned to give it senior level
• Process: creation of a governance framework and measurement
strategy that embeds the data quality initiatives within the
organisation and operationalises the activity.
3: DO. REVIEW. LEARN. REPEAT
Once you have a plan and a view of how to manage its execution, the next step is to start
progressing your initiatives. The essential element of this stage is to review results using the
measurement strategy within your governance framework, comparing actual impacts with those
that were expected and refining activities to focus on the elements most important to
Where marketing is trailblazing data quality
within the organisation, it’s likely that you
will face resistance and may need to rapidly
prove some value before the business commits
to fully going down the path of data quality
management. In this situation, the key is to
start small. Pick the easiest issue to solve that
offers the greatest benefit and do the minimum
needed to ensure you can control the initiative.
Then use the resulting benefits to support
your business case for a broader, more
comprehensive approach to data quality
management. After all, data quality needs
commitment. Without the organisational
structure to support quality, and without
investment in people, process and technology,
your data quality objectives can’t be met.
But with commitment and investment, every
organisation can turn the data it has into the
data it needs.
4:C O M M I T T I N G
TO Q UA L I T Y
5: MARKETING TECHNOLOGY
– MASTERING THE COMPLEXITY
Making good use of marketing technology may be a challenge to many. However, it is the final
stage in putting together and implementing an efficient date-driven marketing strategy that
produces results. You have a wealth of options.
Core technical capabilities
Three core capabilities should guide your data-driven marketing technology strategy:
1) Bringing together and optimising data for marketing
2) Transforming data into insights; and
3) Making insights actionable by enabling decisions to be made, automated and ultimately
orchestrated across channels to deliver an optimised customer experience
Underpinning these capabilities is the concept of integration. Data must be made accessible for
it to reveal insights. Insights must be made accessible to facilitate decisions, and decisions must
be made accessible to result in meaningful customer touch points. Each of these is essential for
delivering actionable insight, the foundation stone for data-driven marketing.
M A K I N G T H E
C O M P L E X A
L I T T L E B I T
S I M P L E R
Selecting technology to provide these capabilities can feel
like a daunting task. With latest counts suggesting close to
6,000 vendors of marketing technology, the choice may seem
bewildering. Yet a little common sense (and a methodical
approach) can make this complexity feel simpler.
Step 1: Assign ownership for technology strategy
Depending on the size of your organisation, this could be a single individual, or a cross-functional
working group headed by a dedicated Marketing Technologist. Either way, involving your internal
IT teams is key, as is having someone with the ability to understand the worlds of marketing and
technology. If you struggle to identify a suitable internal candidate to head this up, then consider
bringing in specialist external support to kick-start your strategy.
Step 2: Agree your guiding principles
Your guiding principles form the bedrock of your technology purchasing strategy.
Some key principles to address include:
• Do you choose a Software-as-a-Service (SaaS)/Cloud system, or host it yourself?
• ‘Best of breed’, or is ‘just enough’ good enough (i.e. will you take 70% of what you need for a
quicker implementation with reduced costs, or only settle for the full 100%)?
• Do architectural constraints exist? Legacy systems? Does IT mandate a Microsoft-based
infrastructure? What does your new technology need to integrate with?
One principle that Occam always advise our clients to follow is to focus on software that satisfies
the concept of “Loosely Coupled Open Systems”:
i) Open System technology means the vast
majority of functionality and data can be
used and accessed via other technologies.
These Application Programming Interfaces
(API) might, for example, include outbound
campaign management technology that
could instruct an email platform to send
emails, a mobile messaging platform to
send SMS and a print production system to
create and send direct mail packs. They will
then tell those systems to pass back any
interaction/response data. Open system
technology is essential for automation and
integration of different capabilities.
ii) Loosely Coupled means that integration
between applications should be achievable
in a way that allows for one application to
be swapped out for another with minimal
effort. This is essential to enable technology
choices to evolve with changing consumer
needs and behaviours. The only challenge
is that loose coupling can be extremely
hard to achieve in practice and is often
dependent on middleware that may be in
the hands of IT.
Step 3: Recognize what you already have
You’re probably already using a wealth of technology, either directly within the marketing
department, or within other areas of the business. In our experience, a technology audit often
highlights where businesses are licensing multiple technologies that provide the same core
If you have undertaken a Data Landscape Definition, then you already have a great starting point to
map out your technology. However, be sure to look outside the boundaries of marketing across the
entire business landscape as you might just find a nugget of gold.
Step 4: Categorise the good, the bad and the ugly
Every company needs a consistent way of evaluating the technology they have against business
needs (including those guiding principles). A framework such as the TIME model from Gartner can
be a useful way to tackle this activity. It compares the technical capability and business value of
software, then categorises it under one of 4 treatments:
TOLERATE: Continue to maintain. Technically
sound, but business workarounds may be
needed to operate
INVEST: Continue to maintain & enhance.
Meets technical and business needs
MIGRATE: Consider replacement/upgrade.
Meets business needs but requires technical
improvement (if feasible)
ELIMINATE: Consider decommissioning. Fails to
meet technical and business needs
Do this will enable you to understand
the key areas you need to make
changes in and also the technologies
and partnerships that represent
Step 5: Focus on your investments
Integration is the key principle underpinning
data driven marketing technology and the
3 core capabilities we believe are essential.
Yet integration can also be a problem cited
by 56% of marketers as the biggest obstacle
they encounter when pursuing data-driven
There are ways to address this. Investing in
integration Platform-as-a-Service (iPaaS)-
type propositions is one option (especially if
pursuing the “loosely couple” principle). But
if time, resource or budget constraints make
that unlikely, then once you have identified
your cornerstone investment technologies, you
could look to identify all the other technologies
that have pre-integrated with them. More
and more vendors are building ecosystems
out of connected third party applications.
For example, many BI/MI platform vendors
provide pre-built integration with marketing
applications such as CRM systems, digital
analytic platforms and social media monitoring
technologies. These make data from those
systems accessible for use in analysis and
reporting. Start by looking at your investment
technologies and identifying the integrated
options that exist across the technologies you
are looking to acquire.
MORE STEPS TO FOLLOW,
BUT YOU SHOULD BE ON THE RIGHT PATH
By following the 5 steps above, you have begun to master the complexity of marketing
technology in your data-driven marketing strategy. Subsequent stages include documenting your
requirements, evaluating and selecting suppliers, implementing the technology and mastering the
organisational change needed to unlock their potential. All daunting tasks in their own right, but
with these foundations in place you will be well set-up to succeed and have a much clearer view of
your ultimate destination.
Our experience suggests that many businesses
have only dipped their toes into the waters
of data driven marketing. Perhaps they
have become lost in the sheer volume of
information. Perhaps they have experimented
in a particular channel or business area, but
haven’t taken things further.
Yet there is so much more that businesses can
now do, so many more ways to unlock the full
benefits; a much greater opportunity to ‘do
data driven marketing properly’.
We hope this guide will encourage you to look
again at your data marketing strategy, help you
understand what you really need from it, and
give you the tools to develop a system that
matches your needs and can expand and adapt
as the future unfolds.
Because one thing is certain: the organisations
that know their data best, and can wield it most
effectively, will stand the greatest chance of
building long-lasting, trusting relationships with
C O N C LU S I O N
For over two decades, Occam has been the
one stop shop for data-driven marketing. Our
experts work with organisations to tame data -
collating it, cleansing it, improving it and turning it
into information that helps businesses know their
customers better, so their communications achieve
We don’t work alone.
Together with digital marketing, technology and
commerce consultancy Amaze, we formed Amaze One, a
new breed of CRM agency, blending Occam’s data rigour
with Amaze’s creative magic. And as part of the St Ives
Group, we partner with specialists in retail and consumer
market strategy, consultancy and market research. The
result? Every aspect of your marketing, covered.
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