ARE YOU REALLY A DATA-DRIVEN MARKETER?WHAT MARKETERS NEED TO CONSIDER WHEN APPLYING DATA TO THEIR MARKETING STRATEGIES.

SEPTEMBER 2017

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Data is fast becoming the fuel that not only drives decisions but also predicts outcomes. The explosion of data has given marketers endless insight into campaign performance, consumer behaviour and market trends. Technologies have appeared to manage this wealth of data and careers made out of analysing it.

The challenge for many advertisers is in harnessing this data to produce relevant insights that can be applied with confidence. For example, those that have perused cross channel and cross device attribution will tell you of the challenges of structuring data from multiple sources and then applying learnings to campaign strategies at scale.

Greater insight when planning, buying and reporting is likely to increase a return on marketing spend through a reduction in wasted ad spend and greater targeting. There is a great opportunity for marketers to use data as their competitive advantage.

Becoming a data-driven marketer can be a daunting task at times with the sheer amount of data available and the seemingly small amount that can be applied for some.

This document aims to simplify the process to becoming a data-driven marketer and identifies the key areas of investment needed.

HOW PRESENT ARE DATA-DRIVEN DECISIONS IN YOUR MARKETING STRATEGIES TODAY?

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Data-driven marketing refers to the marketing insights and decisions that arise from the analysis of data about or from consumers.

We believe that an advertisers competency in data-driven marketing is dependent on the number of data-driven decisions made and their confidence in those decisions.

We encourage advertisers to plot themselves in one of the twelve boxes to understand where they are today.

Presence of data-driven decisions in the current marketing strategies

Confidence in data-

driven decisions

LOW

HIGH

HIGH

FOUR COMMON BARRIERS TO DATA-LED MARKETING4

Common barriers

Not enough useable data

Not enough confidence

in data quality

Unsure of the useful

data available

A lack of technology or skillset to

manage data

effectively

Some advertisers are challenged with a lack of actionable data. Most FMCGs for example, do not manage their own commerce journey so lose out of valuable online ecommerce data that can be applied to online advertising. Increasing data regulation has also restricted the use of previously collected data. Uncertainty regarding future regulation has often halted plans to capture and use further data from new sources.

It is common to have multiple tracking solutions in place for tasks such as measuring marketing success. Advertisers have often been challenged to first implement, and then trust a single source of truth correctly. Multiple technologies increases the risk of discrepancy and lowers confidence in data further.

First party data isn’t the only set of data that can provide value to your marketing. Second and third party data can add great value if used in the correct way. Understanding and prioritising the data that is available and it’s quality, is also often a barrier to it’s adoption.

Managing and analysing large datasets often needs specific expertise and technology. Some advertisers do not currently have this resource internally which limits their capability with data.Hiring internally for some skillsets such as programmatic is also challenging for marketers due to the low supply of talent in the market.

FOUR PILLARS THAT ENABLE DATA-DRIVEN MARKETING5

STRATEGY

Objective Setting

Use Case Generation

TECHNOLOGY

Capability Analysis

Leveraging Existing Tech

Filling Capability Gaps

TALENT

Planning To Upskill

Leveraging External Resource

DATA

Data Auditing

Data Volume

Data Quality

HOW TO CREATE A STRATEGY FOR DATA6

By putting a data strategy in place, it allows you to prioritise the data available and how you might use it. It also identifies the current barriers to data-driven marketing and sets a plan to improve your confidence in data.

Your data strategy should show you all the practical examples of how you intend to use data to inform your marketing, the barriers to these and how you intend to overcome them. You should figure out the specific decisions you want data to inform.

Applying data to your marketing strategies doesn’t have to come with huge investment. These could be as simple as testing new third party data segments or as complex as unifying all customer data in a single customer view.

Ensure that you have listed all the practical use cases for data. Understanding the barriers to these requires a detailed analysis of the data, technology and talent required and an analysis of your capability today.

Understand your use cases and the expected

outcomes

What data is required to deliver the use cases?

What investment is required to deliver the

use cases?

Prioritise strategy based on cost, effort and impact

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UNDERSTANDING YOUR FIRST PARTY DATA SOURCES

Data-led marketing is predictably limited without the high quality, scaled data. For some advertisers, a high volume of data may come naturally. Unfortunately for many, there is little understanding of the quality data available to them.

The first step to activating first party data is to recognise and understand all the data sources available to you. The quality of data from each source will differ for every business so it’s important to think about where data is being generated at scale today and its quality. What does it tell you about your consumers and how easily can it be applied? Is it accessed through your website or through your media channels? Are these consumers higher or lower on the purchase funnel?

Aim to identify consumer behaviours across the whole purchase funnel in both online and offline sources. Work with your marketing channels and partners to understand how easily each of your data source can be integrated into them.

Understanding where your strong first party data is today will help you to prioritise your use during your data strategy.

GETTING MORE OUT OF YOUR EXTERNAL DATA SOURCES8

Number of users

Correlation with

campaign performance

Advertisers without quality data in large supply often look for external data sources to identify and capture user behaviour.

Third party data is commonly used across marketing today. This data can be used as an additional layer of targeting/ insight and typically provides data to identify a consumer demographic, intent, interest and more.

Second party data relationships, while still in its infancy as an established data source, are also an interesting source of consumer data. Advertisers and publishers with valuable data that they are not using themselves, are now packaging it into segments and selling it.

It is important to consider how the data was collected and how it has been modelled/ segmented. There is often a trade off between scale and accuracy meaning that a large audience segments may not always be the most accurate. Ensure that you are getting the best balance to suit your marketing needs when using this data.

Working closely with third party data providers to help them understand your needs will give you the best chance to find data that has the highest impact for your brand.

TECHNOLOGY CONSIDERATIONS

Technology enables data to be processed and activated. It assists your talent to deliver your data strategy.

For some advertisers, first party data is often not utilised to its full potential because it is either too difficult to track/ activate or has not added enough value to campaigns. Ensure that your current marketing technology stack is well equipped to track and activate data from your strongest data sources. Does your stack have the capability to activate your most important use cases?

It’s important to understand that not all new data technologies are needed.

Most marketing buying platforms such as DSPs, adservers and analytics platforms have data tracking capability. Additional data tools such as Data Management Platforms (DMPs) and Data Visualisation Tools should be considered using your use cases, your current capability and the additional capability required.

Once you are activating data from multiple sources across multiple channels and devices, it may be time to bring in additional technology. Some common use cases for additional data are to either centralise consumer data or assist your talent in collecting, analysing and activating data.

Ensure that you have documented the specific requirements needed from additional technology.

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Access to data from sources such as your website can be as simple as adding a tag to a webpage. However to ensure that the data being tracked from this source is relevant and of quality, someone is needed to define the data being captured and to ensure it is being collected in a compliant way.

Different types of expertise are needed depending on the task (analysing, tracking, measuring, modelling) and also dependant on the different types of technologies being used. New technologies are emerging frequently bringing with it a need for new expertise.

Some of this expertise can be notoriously difficult to find. When planning your use cases, assess the business investment needed and consider the talent that is already available in the business today. What upskilling is needed for the use case to be executed effectively?

Work with your agencies, technology providers and partners closely to understand the gaps in expertise and where support is needed specifically. Take ownership of data management where you can and put in place a plan to upskill your own talent.

TALENT IS NEEDED TO ENSURE THAT THE DATA USED IN MARKETING REMAINS ACCURATE, SCALED AND COMPLIANT

HOW TO SET A DATA-DRIVEN APPROACH IN MOTION11

Every marketer is different. Not all have a wealth of data available to them and not all advertisers need complex technologies to manage/ activate their data. Your data strategy should be led by use cases specific to you.

By completing an audit of your data, technology and talent specifically against your use cases, you can begin to understand the investment required to activate them.

Prioritise your use cases by plotting them against the investment needed and the predicted business value. This will then surface the use cases that should be activated first and those that will need more investment.

Predicting the business value of some use cases can be difficult as their impact on marketing can be difficult to attribute. We encourage a test and learn approach to data strategy so use cases with the lowest barrier to activation may be the best to activate first.

Investment

Bu

sin

ess

Val

ue

Quick wins Big Results

Nice to have Evaluate

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KEY CONSIDERATIONS WHEN INVESTING IN DATA12

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1SPECIFIC REQUIREMENTS

BUSINESS VALUE

SELECTION AND NEGOTIATION

When investing in data, technology or talent, ensure that you have documented and prioritized the key business requirements for that investment. What are the most important barriers that need resolving to activate the use cases of high value?

Build different cost & performance scenarios to understand commercial thresholds for any new investment to ensure choices bring a positive ROI.

Finally, once it’s decided that a new investment would be commercially viable, the process of selecting technology partners and negotiating commercials would commence. Ensure that you give yourself the best opportunity to find the best solution by leveraging the expertise of your external partners.

SUMMARY13

On slide 6 we outlined the importance of defining use cases. These are the practical examples of how

you will use data to inform your decisions. You will use these to audit your capability in data, technology and talent. You will also use these to

prioritise your invests and also track progress.Think carefully about the value your use cases will add to the business and set both actionable and

ambitious targets.

On slides 7-10 we consider the capability needed to make data-driven decisions. It’s important that advertisers identify their strongest data sources

and the external data sources available. Understanding the ability and limitations of your

tech stack is essential for understanding the investment needed going forward. Advertisers must

also be very realistic with themselves on their ability to attract and retain operational talent..

Once your data strategy is set, you should have a good understanding of the priority use cases and areas of investment. On slide 12, we noted the

importance of setting clear requirements, the need to justify the investment and an organised selection process. Leverage external resource and contacts to fully understand emerging technologies and trends.

Define Use Cases

Audit Capability

Activate and Invest

FINAL THOUGHTS14

Our document aims to highlight that becoming a data-driven marketer doesn’t have to be a daunting challenge. By focusing your efforts on creating a data strategy that is tailored to your needs and one

that is reinforced by realistic use cases, the task can become a manageable one.

Advertisers must recognize their strengths and weaknesses in data, technology and talent in relation to their use cases. A like for like comparison of data competency cannot exist when the data available

to advertisers, and how they use it, can be vastly different.

Our recommended next action for brands who believe they are ready to embark on their data strategy is to brainstorm their specific use cases and identify their main barriers to activation.

Understand the data use cases that will drive value for your business and their main barriers and get your plan in motion.

Good luck!

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