B U I L D I N G Y O U R O W N

D ATA - D R I V E N M A R K E T I N G

S T R AT E G YAN EASY-TO-IMPLEMENT BUSINESS

GUIDE IN FIVE SIMPLE STAGES

• INTRODUCTION• OVERVIEW• FIVE STEPS

1. HOW TO MAKE DATA A HABIT2. DO YOU HAVE THE DATA TO ANSWER THAT QUESTION?3. MIND THE GAPS!4. COMMIT TO DATA QUALITY5. MARKETING TECHNOLOGY – MASTERING THE COMPLEXITY

• CONCLUSION• CONTACT US

C O N T E N T S

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I N T R O D U C T I O N

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 distinctive edge.

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 marketing strategy.

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 and subscribe to our bi-weekly insights. Visit www.occam-dm.com/about-us/contact-us.

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N O W I S T H E R I G H T T I M E

At Occam, we believe that now is the time for businesses to create their own powerful insights by embracing data-driven marketing principles. This is fuelled by the 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 engagement.

O V 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.

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D ATA E X PA N S I O N V E R S U S Q UA L I T Y

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 businesses.

YO U R O W N S T R AT E G Y I N F I V E E A S Y S T E P S

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 carefully:

• 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 exchange

• Commit to quality: invest in the people, processes and technology that can deal with the issues and turn your data into a valuable asset

• Turn to technology: finally, leverage technology to help you master the beast and turn raw data into valuable insight

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F I V E S T E P S

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H O W TO M A K E D ATA A H A B I T

Turning data into a habit is the essential first step on any data-driven marketing journey.

This step requires you to decide the driving factors for all data-led decisions by asking yourself what are the Key Performance Indicators? 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.

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D E F I N E W H AT I S I M P O R TA N T

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 counted”.

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 books:

Given that the market of IFA’s is mostly out of the control of 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 a 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 being undertaken.

In this case, there are only two ways to get more IFA’s:1. The business either increases its sales and

marketing efforts to find and win new partners; 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 given term:1. Increase the size of the target audience;2. Or increase conversion rates.

+

KPINumber of NEW

IFA’s recruited in term

METRICSize of the TARGET IFA

Market

+

KPI Tree

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M A K E D ATA A C C E S S I B L E A N D V I S I B L E

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 throughoutthe 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 critical.

D E V E L O P YO U R D ATA S T R AT E G Y

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 shared.

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A N D O N A N D O N …

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 challenges.

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 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.

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D O YO U H AV E T H E D ATA TO A N S W E R T H AT Q U E S T I O N ?

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 before any further decisions are made.

A S K YO U R S E L F, W H AT I S A D L D ?

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 standards.

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:

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A S K YO U R S E L F, W H Y D O I T ?

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.

A S K YO U R S E L F, H O W TO D E F I N E A D ATA L A N D S C A P E ?

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 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 marketing success.

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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?’

A S K YO U R S E L F, W H Y W O U L D N ’ T YO U D O I T ?

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M I N D T H E G A P S !

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 doif 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 by visiting amazeone.com/fair-trade, but the picture that emerged was one of disaffected consumers, tired of handing over data for what they perceive as very little in return.

W H Y T H E VA L U E E XC H A N G E M AT T E R S

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 relationship!

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.

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H O W TO C R E AT E A B E T T E R VA L U E E XC H A N G E

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 or cynical 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.

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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.

M E A N W H I L E , H E R E A R E 3 S I M P L E D ATA G AT H E R I N G S T R AT E G I E S T H AT C A N C R E AT E

A B E T T E R VA L U E E XC H A N G E :

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C O M M I T TO D ATA Q UA L I T Y

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 ‘data quality’?

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.

W H Y B O T H E R ?

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 depressing statistic.

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.

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4

1: I D E N T I F Y YO U R S TA R T I N G P O I N T

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 percent 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 ?

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2: P L A N , P R I O R I T I S E A N D M A N A G E

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 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 support.

• Process: creation of a governance framework and measurement strategy that embeds the data quality initiatives within the organisation and operationalises the activity.

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3: D O. R E V I E W. L E A R N . R E P E AT

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 the business.

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.

C O M M I T T I N G T O Q U A L I T Y

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M A R K E T I N G T EC H N O LO GY – M A ST E R I N G T H E CO M P L E X I T YMaking 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.

Three core capabilities should guide your data-driven marketing technology strategy:1) Bringing together and optimising data for marketing2) Transforming data into insights; and3) 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.

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Decisioning & Orchestration Platform

InsightsPlatform

PRESENT DATADecisioning View

PRESENT DATAInsight View

PRESENT DATAConnectors/Integrations

MANAGE DATAQuality, Identity Management, Storage

GATHER DATAConnectors/Listeners/Integrations

APPLICATION - Decisioning and Orchestration PlatformThe D&O platform leverage CDP data via an optimised Decisioning View offering real-time updated consumer interaction data. It’s designed to:

1: Orchestrate communications/interactions accross customer touchpoints, channels and media that are involved accross the entirety of the customer journey (from traditional outbound and inbound channels like email and call centre, through to interactive channels such as display ads).2: Incorporate insights such as models, segmentation from the Insights Platform and couple with machine learning to drive the optimal communication at each touch point.

It offers core capabilities to connect with content/digital asset repositories, integrate with the channel/touchpoint, manage decisioning logic, support contact optimisation and record interaction events.

UNDERSTANDING - Insights PlatformThe Insights Platform leverages CDP data via an optimised Insight View offering granular detail of the consumers interactions. It provides capabilities for:

1: Data Visualisations & Exploration | 2: Reporting & Dashboard Design | 3: Segmentation & Modelling

Outputs are made available to connected systems/touchpoints and to the Decisioning & Orchestration Platform.

DATA - Customer Data PlatformThe CDP masters good quality customer data and circulates this is in real-time throughout the engagement platform ecosystem. It’s engineered to:

1: Integrate with customer touchpoints, channels and media that are involved accross the entirety of the customer journey (from traditional outbound and inbound channels like email and call centre, through to interactive channels such as display ads) in real-time for collection and presentation of data as and when it’s needed.2: Enforce Data Quality Controls & Standardisation to ensure data is useful and accurate and can support Decisioning/Insight requirements.3: Deliver robust identity management providing a single view of the customer accross known and unknown interactions (covering PII data and other more transient data such as Cookies, Device Fingerprints).4: Collate and link all interactions to the single view of the customer to provide a complete picture of their involvement with the brand.

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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.

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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.

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Step 3: Recognise 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 features.

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

This approach will enable you to understand the key areas you need to make changes in and also the technologies and partnerships that represent cornerstone investments.

great

poor

Tech

inic

al C

ondi

tion

Business Valuelow high

Tolerate Invest

Eliminate Migrate

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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 marketing activities.

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.

M O R E S T E P S TO F O L L O W, B U T YO U S H O U L D B E O N T H E R I G H T PAT H

By following the 5 steps in this guide, 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.

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C O N C L U S I O N

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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 their customers.

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A B O U T U S

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 better results. We also help our clients to embrace marketing technology, everything from providing advanced data visualisation solutions to enabling brands to orchestrate engaging customer experiences across all their channels.

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.

G E T I N TO U C H

ROGER STEVENSBusiness Relations

T: +44(0) 7710 166583E: roger.stevens@occam-dm.com

London | Manchester | Bristol | Bath

occam-dm.com