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Driving Growth with Measurement

Supplemental DAC Learning Lab Workbook

Driving Growth with Measurement | Supplemental DAC Learning Lab Workbook 3

Contents

DAC’s Measurement Definitions ‘Cheat Sheet’ ................................................4

Key Marketing Measurement Challenges ........................................................6

Asking the Right Measurement Questions .......................................................7

Effective Data Story Telling ...............................................................................8

Building Your Marketing Measurement Dream Team .....................................13

9 Qualities that Define True Data-Driven Marketers .......................................17

4 Rules for Measuring Customer Engagement ..............................................20

Marketing Data Fundamentals: What CMOs Need to Know..........................23

ROI: Today’s Most Misunderstood Marketing Metric .....................................28

It’s all About the Math ....................................................................................30

Using Multi-Channel Attribution to Improve Marketing Effectiveness ...........32

4 Ways to Spark Greater Analytics Adoption .................................................35

8 Ways to Leverage an Analytics Program .....................................................37

Hidden Opportunities in the Age of GDPR .....................................................41

Sample Value Dimensions for an Analytics Business Case ...........................44

Key Marketing Measurement Findings ...........................................................45

Driving Growth with Measurement | Supplemental DAC Learning Lab Workbook4

Accountability (in Marketing): Responsibility for managing marketing activities to achieve measured results and improved outcomes while contributing to long-term brand growth and enterprise value.

Analytics (For Marketing): The statistical process of transforming data into insights for making better decisions.

Analytics Culture: A shared mentality which recognizes that broadly available data-driven analytics and insights are the catalyst for more effective decision-making, where individuals consistently use analytics to evaluate business outcomes and quantify the results of marketing activities via measurement and testing.

Audience: Any group of potential or existing customers identified as the intended recipients or “targets” for a given advertisement or marketing message based on their likelihood to respond.

Customer Identity: The ability to verifiably recognize individual customers across all points of engagement, including marketing, sales and contact channels, and across devices by using multiple data points to create a single view of those customers. A critical requirement in order to develop normalized and complete customer journeys for analysis, measurement and insights.

Data: Factual information derived from various primary and secondary sources, including quantitative and qualitative values and variables, that can be collected,

DAC’s Measurement Definitions ‘Cheat Sheet’

Speaking a common marketing measurement “language” is an important success ingredient for any analytics program. If team members use different working definitions of such things as metrics, marketing ROI, customer identity or analytics in general, for that matter, it can lead to confusion, missed opportunities and diminished results.

Below is a short list of key definitions (organized alphabetically) related to marketing measurement. This list will grow as we add more. Leave a comment listing any terms you’d like to see DAC define.


analyzed and used as a basis for calculation and decision making.

Marketing Performance Measurement: Systematic analysis of marketing processes and performance with the goal of optimizing marketing effectiveness, improving customer experience and growing revenue.

Marketing Mix Modeling (MMM): A statistical analysis of aggregate sales, marketing, and business drivers data that quantifies the impact of different marketing channels and tactics (the marketing mix) on financial outcomes, resulting in insights and recommendations that can be used to optimize marketing investment allocations and predict future outcomes

Metrics: A set of measureable performance standards that may be used as the basis for determining effectiveness and accountability.

Multi-Channel Attribution / Multi-Touch Attribution (MTA): The statistical process that determines and assigns credit for revenue and other converting events to the individual online and offline addressable marketing touches across the entire consumer journey, from awareness to purchase.

People-Based Marketing: A precision marketing approach centered around using data to persistently identify and connect with the right people (individuals), at the right time, in the right place with the right message rather than marketing to devices.

Predictive Analytics: The use of data, statistical algorithms and other analytical techniques, based on historical performance, to forecast likely future outcomes based on forward-looking assumptions.

Return on Investment (ROI or MROI): The financial value attributable to a specific set of marketing initiatives (net of marketing spend), divided by the marketing ‘invested’ or risked for that set of initiatives.

Unified Analytics: A new marketing measurement standard that integrates multiple statistical techniques, such as marketing mix modeling and multi-channel attribution, in order to accurately and consistently measure the financial impact of marketing investments at all levels of granularity and across all dimensions entire value and identify the best ways to optimize customer interactions.


Key Marketing Measurement Challenges

Marketing is not seen as having a direct impact on the organization’s financial performance or other business outcomes. Functional silos within marketing seem to be operating independently and vying for the same budget dollars and attention.

With swarms of marketing initiatives always underway, no one is clear on which ones are really contributing the most to the bottom line.

Marketing budgets are set on the basis of what was spent last year, and not on what was achieved, or based on company goals for the year ahead.

It’s unclear how marketing dollars should be allocated across product lines, regions, channels, etc., causing different factions to fight PowerPoint wars to grab as much as they can.

10 TOP MEASUREMENT PRESSURES

1 Marketing expected to prove value quantifiably

2 Inability to connect online & offline components

3 Archaic attribution methods

4 Lack of visibility and accountability.

5 Need for more precise targeting

6 Data deluge has become overwhelming

7 Data comes from multiple disparate sources and is stored in too many silos

8 Some data is poor quality

9 Inability to gain insights from existing data

10 Fractured and ineffective organizational adoption

KEY BENEFITS OF ADVANCED ANALYTICS

1 Major improvements in marketing ROI

2 Ability to quantifiably connect marketing directly to revenue

3 Greater accountability and visibility for marketing in the C-suite and entire organization

4 Improved effectiveness of marketing initiatives

5 Ability to see a complete, holistic picture of all demand drivers, including online, offline and non-marketing factors

6 Understand with far greater accuracy what the impact of future marketing will be

FOUR KEY DRIVERS OF MARKETING ANALYTICS TODAY

Rapid Data Expansion: Growing rapidly; underutilized; greater disparity.

Broad Proliferation of users: More functional areas need/use analytics.

Accelerated Pace of Marketplace: Customer/product demands; less time to make decisions and adjustments.

Need to Embed Analytics Organization-Wide: Analytics in isolation don’t work; must become part of culture.


Strategic Questions

• What is the most productive allocation of limited marketing resources to drive profitable business growth?

• What measurement tools, technology, skills and data do we need to measure and improve the impact of our marketing efforts?

• How much revenue does marketing drive?

• What is the ROI of our marketing investments?

• How much marketing investment do we need to achieve our business objectives?

• What is the relative impact of our different marketing channels?

• Does marketing drive revenue across all sales channels?

• How does marketing contribute to brand health?

• What is the revenue and profit impact of investing in brand awareness?

• What is the right balance of marketing and promotions?

• What are our most profitable segments and what is the most efficient marketing allocation to target them?

• Which types of prospects and customers present us with the greatest opportunities?

• Which potential and existing channel partners offer the greatest opportunities?

• What is the return on our channel partner investment?

• How can we maximize customer profitability and lifetime value?

• What is the impact of non-marketing factors on sales?

Asking the Right Measurement Questions

Key measurement questions identify, prioritize and gain alignment behind the things that an organization hopes to learn about its ability to measure the impact of marketing.

The questions help guide selection of metrics and creation of a measurement implementation plan. Questions are also meant to help identify data and insight gaps, and to focus the measurement roadmap on helping find answers to the things that matter most. Here are some of the key questions that marketing analytics initiatives attempt to address:

Tactical Questions

• What is the optimal mix of brand and product messaging?

• How should we flight our marketing investments?

• What is the optimal frequency for media exposures by and across channels?

• Which are the most effective and efficient publishers, keywords and creative?

• What is the right balance of marketing channels for this campaign?

• When and to whom should we offer promotions and coupons?

• How effective is our loyalty program at attracting and retaining customers?

• Which customers should we target, and on which marketing channels?


Marketing organizations today are becoming increasingly adept at using sophisticated marketing measurement, data and analytics technology and methods.

They’ve added legions of data scientists and other analytics specialists to create and implement analytical models. Such models – including predictive models – help marketers assess both long- and short-term impacts, connect online and offline insights and demonstrate quantitatively how marketing is contributing to revenue growth, enterprise value and other business outcomes.

Despite those major strides, however, many brands remain poorly equipped to tell a compelling measurement story to decision-makers in the C-suite, Board or other parts of the organization.

For all of their advances in complex measurement, data and analytics, marketers can easily become too focused

on the tactical, and not nearly focused enough on the broader strategic vision.

Even for marketers who make a living – in part at least – via creative endeavors, it’s tempting to communicate analytical findings and insights through collections of data and facts. What’s missing in those kinds of presentations is context. Context makes the data relevant and tangible for others. It helps non-nerds make sense out of facts and other data that can seem like just noise.

Charts, tables, graphics and other factual data are the “what” of the story. What you also need is the “Why?” “How?” “Who?” and “So what?” These things help everyone focus on what’s really important.

This is where storytelling plays a vital role. No marketer wants measurement results to appear boring, unclear or forgettable.

Effective Data Story TellingMAKE SURE YOUR MEASUREMENT RESULTS RESONATE!

Marketing organizations today are becoming increasingly adept at using sophisticated marketing measurement, data and analytics technology and methods.

They’ve added legions of data scientists and other analytics specialists to create and implement analytical models. Such models – including predictive models – help marketers assess both long- and short-term impacts, connect online and offline insights and demonstrate quantitatively how marketing is contributing to revenue growth, enterprise value and other business outcomes.


Uninspiring Presentations Undermine Measurement Efforts

Measurement presentations often fail to inspire because the charts, graphs and other structural components are created before the presenters actually understand the underlying story themselves.

For example, many presentations tout the results of marketing initiatives across various channels without taking into account the bigger picture of how those channels – both online and offline – interact with each other.

Presenting results in silos misses the bigger story of, say, how TV or word of mouth contributed to the success of other efforts. Marketers too often approach measurement through a disjointed series of technically sound but ad-hoc ways spread across four major silos:

• Silo #1: Customer Metrics: Satisfaction, experience, segment, behavior, etc.

• Silo #2: Unit Metrics: Product sales, marketing cost per unit, pricing optimization, etc.

• Silo #3: Cash Flow Metrics: Program & campaign ROI, mix models, portfolio management.

• Silo #4: Brand Metrics Silo: Brand equity drivers, financial valuation, brand imagery attributes, etc.

Customer Metrics: This silo often looks at how prospects ultimately become customers – the so-called path to purchase or customer journey. From awareness, to preference, to trial, to repeat purchase, many companies track progress through a “hierarchy of

effects” type model that paints a picture of how things evolve from broad market, to potential, to actual revenue.

Customer satisfaction is most often measured via surveys and reported by channel and touch point. Only rarely is it correlated to specific or observed customer behaviors, which – ultimately – are more important.

The customer metrics silo might also include attitudinal data by segment (why customers buy what they buy, for example). And this data is often correlated with actual customer purchase data to create a handy segmentation model. Segments can then be monitored for “mobility” (movement of prospects or customers from one segment to another) and “velocity” (the speed at which customers move between segments).

At some B2B companies, this customer pathway can go as far as developing separate P&Ls for specific customers.

Even the best measurement results are

lost on an audience forced to endure dry,

uninspired interpretations or death by

complex charts and tables.

Unit Metrics: This silo is where many companies park their most advanced measurement, data and analytics capabilities. In part that’s because they usually have tech systems in place that provide a sophisticated ability to know exactly what was sold, where and at what price.

What you communicate about your marketing measurement findings should be interesting and impactful. And even though analytical findings are seldom simple, the story must be simple enough for top executives, team members, colleagues and other decision makers to find it compelling, so they’ll remember and apply the messages.


Most brand marketers have fairly good information on the attributes of customers who’ve bought specific numbers of units over a given time period. Yet, somewhat surprisingly, not many seem to know the true identity of the individual they actually sold it to.

With some quick math, they can calculate marketing cost per unit as a (very) rough measure of overall marketing efficiency. Further mathematical gymnastics get them to pricing optimization analysis, which in turn can provide a modicum of insight (but not a whole lot) into the value of branding.

Cash Flow Metrics: Here companies focus on just how efficient their marketing spending is based on short-term returns. Program and campaign ROI models measure the immediate impact or net present value (NPV) of profits expected to result from a given investment.

Meanwhile, companies create media mix models using fancy techniques such as regression analysis that can identify which combinations of media placements, integrated media elements and even copy or creative executions generate the most profitable response.

Armed with those insights, marketers can begin to demonstrate how they are optimizing budgets by focusing on activities and executions that provide the greatest forecast return in a sort of portfolio management exercise.

Brand Metrics: This silo is often where companies attempt to track the longer-term impact of marketing through overall brand health. Survey-based tracking studies gauge customer and prospect perceptions about the brand, including such things as its personality, accessibility and value propositions, among others.

Brand scorecards are sometimes used to monitor how these perceptions change over time within market segments and across multiple constituencies such as community influencers, employees and others.

A few brands (but more all the time) have made the successful leap forward and developed financial models that can estimate the value of the brand as a means of helping determine how marketing investments influence assets on the company balance sheet.

BUT…Such Silos Create Roadblocks

In any given silo, marketing organizations get quite good at implementing effective measurement and analytics systems. But they struggle to synthesize insights gained across multiple silos into a cohesive, holistic picture of what’s really going on. They fail to fully understand how one silo influences or explains another, or clarify the predictive drivers of the business on a broader level.

Most companies aren’t able to do this scientifically because it’s not a challenge their analytics geeks can solve with some simple equation. Problem is, each silo measures very different pieces of the marketing effectiveness picture, in very different ways.

Today, marketing’s creative juices are

needed more than ever to help make

sense of the science.

Some are short term; others long. Linking them algorithmically forces you to make major assumptions that can be highly unreliable when pitted against actual market dynamics.

Even if you can seemingly solve the problem mathematically, you’ll likely have to employ highly sophisticated statistical techniques that few people in either marketing or finance will be able to understand well enough to embrace or defend the method.


Telling your Complete Measurement Effectiveness Story

The result of all this siloed, uncoordinated measurement is that marketing gets lost trying to divine the true story of effectiveness. And while it might have been standard practice in the past to simply throw a lot of measurement spaghetti against the wall and see what sticks, today’s C-suite has little patience for the fog of complexity when it comes to tracking payback on spend.

To tell the complete story of marketing effectiveness, you must structure and organize disparate information sources so they display information in a way that allows you to present insights in a graphically related view showing how it all fits together.

It’s not a matter of “Is it art or is it science?” It’s both. The science is reflected in the measurement tools and technologies marketers deploy today to gauge success. And fortunately, repertoires of such tools are expanding rapidly.

The art has historically been defined as the creative spark of imagination behind the execution of marketing messages in words, pictures and other devices used to engage customers.

According to the experts at analytics

giant SAS, “Science shows us that it’s the

emotional side of the brain that causes

action, while the logical side is where we’ll

make sense of it later. That’s why supportive

data is so key to a story’s success.”

Today, marketing’s creative juices are needed more than ever to help make sense of the science. Even the best measurement results are lost on an audience forced to suffer through dry, uninspired interpretations or death by page after page of complex charts and tables.

Brand marketers must be able to tell a compelling story of marketing effectiveness within their own organizations.

Data Story Tellers or Data Translators Can Help

Researchers at MIT recently studied how advanced analytics technology is reshaping the practice of management. One of their conclusions was this: In many organizations, there is a persistent disconnect between data scientists and the executive decision makers they support. “That’s why it’s time for a new role: the data translator,” they argued.

Data translators – or storytellers, if you will – can help ensure that marketing organizations achieve real business impact form their measurement programs. McKinsey is also a big backer of the data translator role. McKinsey says that translators can play a key role in helping marry the technical expertise of data scientists with the operational expertise of marketers.

The need for this new role is so strong that the McKinsey Global Institute estimates demand for data translators in the U.S. alone will reach two to four million by 2026.

Anecdotally, it seems to be happening already. As this article is being written, a quick search reveals 755 “data storytelling” jobs listed on LinkedIn. Indeed.com listed 784 plus another 653 for data translator or data interpreter.


DATA STORY TELLING BEST PRACTICES

Here are seven data story-telling best practices adapted from a variety of sources, including brand marketing practitioners, analytics experts and firms such as Tableau, SAS, McKinsey and others.

1 Identify The Story: The first task – before creating fancy charts and diagrams for your presentation – is to explore the data, findings and various insights to identify the story or stories that reveal the purpose of it all. Look for connections to the outcomes you are seeking. Some marketers even create storyboards to help focus their thoughts and sketch out the findings.

2 Use A Story Structure: The best way to communicate analytical findings is through stories. And when you tell stories you should use a story-telling structure. Stories have characters that face challenges. They paint a picture of what’s broken and how the story’s “heroes” are coming to the rescue. Perhaps most important of all, stories impart lessons and get people to think.

3 Personalize the Story: Make it as close to real life as possible. Authentic stories, illustrated with metaphors and backed by fact-based findings are the most powerful. They make it easy for the audience to relate to the information. More qualitative data and findings can be used to full in around this foundation.

4 Show, Don’t Just Tell: The best stories are backed by strong visual components that include photos, symbols and other images, along with charts and graphs. Charts and graphs should communicate their main message in a single glance. But it’s also okay to include additional layers of information that come through when the graph is studied more closely.

5 Use Stats Wisely: Keep in mind that the statistical data is still the star of the show, so don’t leave it out altogether or oversimplify. The key, as the analytics experts at Tableau remind us, is to “create well-informed visual analysis, charts and graphs.” You might have to study your data for a while and try out some different ways of presenting it before you discover what’s most effective.

6 Keep It Simple: Be direct and be simple. Concentrate the story. Stick to no more than two or three main issues. And be sure to clearly define how those issues relate to your particular audience.

7 Draw Conclusions: Come to the point. Don’t force your audience to assimilate the information, analyze it and draw their own conclusions. That’s your job. Highlight the data that relates directly to the story. For example, are you comparing current state to prior state? A rate of change? A key goal-based number? Focus on what the audience needs.


This is troublesome – and not only because more than half of CMOs say they can’t make a direct connection between marketing activities and company performance. Marketing analytics adds considerable horsepower to your marketing program, from delivering a better customer experience to creating a competitive advantage. But if the team doesn’t have the skills or the time to harness insights to drive strategy, it won’t be able to deliver on marketing priorities.

More importantly, the company runs the risk of falling behind more astute and agile competitors.

Why Talent Align With Strategy

One problem is that there’s sometimes misalignment between what the CMO wants to accomplish and the ability of analytics team members to meet that vision. There are several potential reasons for this disconnect.

One is that companies fail to hire the right talent because they have a hard time determining the right combination of

technical skills and data expertise they need.

Another is that they hire talented analytics professionals, only to underutilize them. Many data scientists with advanced skills find they spend more time wrangling data than analyzing for insights.

To fix the problem, CMOs will have to reimagine the structure of their marketing analytics team. A crucial first step is to define your marketing goals and strategy and build a framework that supports it.

For example, in reorganizing its global marketing team, SAS defined a “go-to-market” framework and aligned it with corporate initiatives. This changed the way the company viewed team roles. Rather than expect marketers to be experts in everything, marketing executives at SAS recognized they needed a mix of “go-to-market leaders” and specialists. The leaders act like orchestra conductors. They coordinate activities, maintain a big picture view of the organization and connect the dots.

Building Your Marketing Measurement Dream Team

Using data to drive marketing strategy and connect with consumers is a top CMO priority. But it’s not an easy rodeo. Even though companies aren’t suffering from a lack of data, they are often missing the talent required to lasso it.

Just 1.9% of marketing leaders say their companies have the right people in place to leverage marketing analytics, according a recent CMO Survey.


ROLE: Director of Data Science and Analytics

Strategic, curious and creative, this role is responsible for leading the team, and as such, must be capable of pulling together disparate threads and asking the questions that can lead to unique insights. In addition to tracking the team’s performance, they use their considerable communications skills to ensure the analysts are delivering timely, accurate insights that decision makers need.

Their key responsibilities include:

• Driving strategic business decisions

• Setting KPIs, and

• Acting as liaison with the C-suite and department leaders across the business.

Thus, it’s critical for them to be capable of working with a wide range of personalities and functions. This is a role that touches every aspect of the business, from operations and warehousing to marketing and sales.

In hiring, look for candidates with a background in data science, an analytics mindset and who are entrepreneurial in nature. As technology changes quickly,

they also must be willing to stay hands on. Otherwise, there’s a risk they won’t keep up with new developments, which would inhibit their ability to drive results.

This role is particularly important to startup and growth-stage companies that need to build both a measurement infrastructure and a data pipeline. In hiring someone with both a data science and a technical background, hybrid talent is an economical choice.

In fact, people hired for this role often have experience running their own company. Since they already know how to build organizations from scratch, they’re more likely to be successful in getting analytics platforms up and running.

ROLE: Data Translator or Story-Teller

With a deep knowledge of the business, data translators collaborate with segment leaders and product experts. Their domain expertise enables them to translate data into the language of these decision makers, using their storytelling and data visualization skills.

Depending upon the organization, the data translator may be known as the business intelligence specialist or the analytics strategy manager. No matter what the

The Ideal Marketing Analytics Team

Every organization is unique, so the exact composition, size, scope and responsibilities of the marketing measurement team will vary. SAS, for its part, has defined four job categories to drive analytical marketing in its organization. They include digital marketer, content marketer, customer experience expert and marketing scientist. The latter includes two types of roles: data visualization analyst and segment analyst.

To perform to the highest standards, though, every marketing measurement team should look to fill the following positions with the best talent they can find.


title, key responsibilities include acting as the liaison to other departments and building reports and dashboards based on decision maker requirements.

Although detailed technical skills aren’t usually required for this role, recruiters are seeing a higher demand for candidates who also have the ability to manipulate data. That’s because data translators may sometimes need to pull data themselves to meet quick turnaround times.

Translators, then, should have expertise in programming and statistical software such as SQL, SAS, R or Python. It’s also helpful if they have an agency background. The experience of working with multiple clients and executives at once is invaluable because candidates learn how to pivot quickly and produce quality work at the same time.

ROLE: Data Scientist

Most marketing measurement teams will benefit from hiring a mix of data scientists. Senior members of the team with advanced skills will focus on analysis and communicating insights, while junior members will spend more time on cleaning data.

Marketing data scientist is an emerging title for this role, which is responsible for developing predictive models, gathering marketing and competitor intelligence, and measuring campaign effectiveness, including customer growth, churn and retention.

When hiring for senior positions, look for candidates with strong technical backgrounds and experience developing data models. Their experience will include roles as a junior analyst, in which they gathered data and created reports.

New graduates are great for entry-level positions in digital marketing and web analytics. They’ll build skills and expertise in a range of data types, including online

and offline, TV, radio, social media and web traffic. They’ll also spend a lot of time in search marketing, which helps them understand consumer patterns and behaviors. As they gain experience and get additional education – for example, a certification in programming and analytics – they’ll move into roles of increasing responsibility for developing models.

ROLE: Market Researcher

Market research data is valuable to the analytics team because it adds an important perspective: why customers act as they do. By tapping into this source of knowledge, analytics teams are likely to identify valuable new opportunities, says Bill Franks, Chief Analytics Officer for the International Institute of Analytics.

Though they focus on different things, the marketing research and marketing analytics functions go hand in hand. The market researcher will look at macro developments such as economic and industry trends, as well as what the organization’s competitors are doing.

They’ll develop and analyze the results of social media monitoring and brand equity studies. The core responsibility of this role is to dive deep into consumer insights and help the company’s executives and marketing teams understand consumer behavior.

Smaller companies and startups typically don’t have a need to fill this position at the outset. But it’s a common role within large organizations.

Look to hire candidates who have spent time at agencies, where they are able to gain a broad view of many different markets. Once onboard in large companies, they work as an internal consultant and act as a liaison across the organization’s multiple brands.


ROLE: Systems Integrator

As marketing teams become more dependent on data and marketing technology, they will benefit from having a full-time systems integrator who can ensure smooth implementation and higher data quality. Also called a data engineer, this is a crucial – and hard to fill – position that is responsible for bringing in new technology tools and ensuring they are implemented properly.

Successful candidates will have both a technical background and data science experience. Since they are expected to work very closely with DevOps and software developers, they must be able to speak the language of both.

Keep in mind, though, that there is growing demand for candidates with this kind of hybrid background. And they are such a rare find that recruiters label them unicorns.

These five core roles form the backbone

of your team, and they’ll ensure you’re

taking advantage of every data-driven

opportunity. With vision and talent now

aligned, you can increase the impact of

marketing – and prove it too.

TOP QUALITIES TO LOOK FOR IN TEAM MEMBERS

Passion for and understand value of analytics in decision-making

Curiosity and creativity to ask “what if” questions

Business acumen to understand how analytics decisions may play out

Ability to collaborate and build relationships with colleagues across the organization, from marketing and product development to finance and manufacturing

Speak the language of business and of industry experts in other departments

Understand IT including its methodologies and challenges


Even when they look outside the organization for help they have trouble finding the right experts. Only 17 percent of brands say they’re satisfied with the ability of their marketing agencies to produce real transformation and innovation from data-driven insights, according to Forrester.

A Shortage of True Data-Driven Marketers

The truth is, many marketers talk the measurement talk but can’t walk the walk. True data-driven marketers are rather rare, and have a unique combination of skills and talents. It’s not surprising, then, that more than 50 percent of CMOs are having trouble locating talent with expertise in personalization technologies. Knowledge of data analytics and media-mix optimization is also scarce.

The situation isn’t likely to get better any time soon. There are currently over 22,000 matches for “data-driven marketer” on Indeed. Gartner’s 2017-2018 CMO Spend Survey found that analytics is set to receive the greatest share of marketing budgets, coming in at

9.2 percent. As the demand for data-driven marketers increases, the competition will intensify.

Finding a candidate that actually possesses the full skill set required of a data-driven marketer can be a time-consuming process of sift through resumes of those that claim to deliver and those who actually can.

To spot their unicorn, CMOs should know what qualities they’re looking for. These are the skills that every true data-driven marketer possesses – and the ones CMOs should prioritize on their hiring checklist.

9 Qualities That Set Data-Driven Marketers Apart

1 Wide-ranging experience in data, measurement and analysis — True data-driven marketers have acquired a solid understanding of marketing analytics by working extensively with many different types of data sets. They at least understand statistics and modeling (even if they don’t model themselves) and can aggregate data from

9 Qualities that Define True Data-Driven Marketers

Today, most marketing professionals have at least some experience working with data and analytics. Yet, expertise in only a few areas – social media monitoring and web analytics, for example – isn’t nearly enough to qualify a marketer as “data driven.”

This is a problem for CMOs, who are under intense pressure to use data to deliver stronger, quantifiable results. Their teams often fall short of the expertise needed to harness company data, extract consumer insights, develop customized campaigns, and measure results.


disparate sources, analyze it and draw conclusions that lead to strategic marketing decisions.

2 Deep Knowledge of MarTech — Data-driven marketing is closely tied to technology of many flavors. There are often data silos across the organization, for example, all of which must be brought together to create a centralized marketing analytics platform. Thus, the ideal candidate will exhibit the technical competence to work closely with tech and systems managers, as well as have broad knowledge of analytical marketing tools and technologies.

3 Data-driven mindset — Beyond the mechanics, attitude is key. A true data-driven marketer will exhibit a passion for fully exploiting data and analytics to improve marketing results.

4 Curiosity — The most effective data-driven marketers are those who continuously ask questions and form hypotheses about what the data means. This curiosity leads them to present workable solutions backed by solid proof.

5 Part behavioral psychologist — At the core of data-driven marketing is the desire to understand consumer motivations and develop campaigns that leverage these insights.

6 Strategic thinking — Predictive analytics will give an edge to companies that master. What will customers likely want in six weeks? In six months? Candidates should not only understand predictive and prescriptive analytics but also know how to use them to target the right customers and capitalize on trends.

7 Creativity — Analysis meets creativity in data-driven marketing. Data-driven marketers must be capable of crafting a vision based on insights and then translating that vision into effective creative and messaging.

8 Effective Storytelling — A true data-driven marketer knows how to tell a dynamic story about analytical

insights and findings. C-suite and other departmental heads often don’t know what to do with data when it’s presented to them. Data-driven marketers must have exceptional communication skills to bridge that gap.

9 Ability to pivot —A data model might suggest one solution but can be proved wrong in testing. True data-driven marketers are agile, not afraid to pivot when tests uncover new learnings.

How To Spot Data-Driven Marketers

To identify true data-driven marketers, CMOs might need to change their interviewing approach. These tactics can help you surface the people who are truly data driven.

• Test applicants in a new way. Ask candidates to describe past projects and successes and follow up with pointed questions about their role in analyzing data and extracting insight. Another option is to test candidate knowledge directly. Show them a marketing scenario and ask what tools they would likely use.

• Expand your search beyond traditional marketers. Don’t hesitate to interview candidates with less marketing experience, especially if their resume shows they are strong in some of the other technical, analytical or statistical skills.

• Accept that you may not be able to hire a unicorn each and every time. Given the complexity of this role, hiring managers may need to take a coalition approach, forming a team that, when combined, meets the requirements of a data-driven marketer. And that’s ok. Just be sure that each member understands their role and the importance of co-workers for success.

Promoting a data driven culture and mindset across your organization will also help reinforce the measurement message on a continuing basis.


Sure, using engagement as a strategic marketing pillar makes sense. As with nearly all areas of marketing measurement, however, the approach must be framed in the right context in order to deliver useful insights you can combine with other data to improve outcomes.

First recognize there are two generally accepted engagement types: emotional engagement and behavioral engagement. The emotional variety is more popular among marketers, but behavioral is more important (the accompanying sidebar lists 15 different types of behavioral engagement).

Most marketers will do best by staying focused on evolving the behavioral relationship between customer and company, not just the emotional attachment one has to the other.

Keep in mind that creating and measuring links between marketing activities and customer actions is not a linear, one-time process. The complex relationships between many online and offline channels, and their collective

impact on a customer’s purchase decision, are far more difficult to gauge and influence than they once were.

4 Rules of Engagement

Engagement-focused marketers must develop effective methods for tracking how individual customers or segments move down one or more desired paths toward a closer commercial relationship with a product or brand. The goal is to quantifiably justify spending in areas that will influence behaviors that lead to desired outcomes such as more referrals, more prospects or more purchases.

There’s no set formula for an engagement measurement methodology. But there are specific steps marketers can take to develop a more comprehensive view of engagement and a process for capturing the most relevant insights from customer behaviors. Here are four “rules of engagement.”

4 Rules for Measuring Customer Engagement

Marketers love to use customer engagement as a key metric. These days, nearly everyone sees it as a critical measure of customer value and brand strength.

But while there’s widespread agreement that engagement should be measured (who doesn’t want to know how well customers are receiving your message and interacting with your brand?), agreement about how to best go about it usually ends right there. Ask brand marketers how they measure engagement – or for that matter, how they even define it – and answers will vary greatly.


Rule #1: Develop a Vision

Don’t even attempt to measure engagement until you determine the outcomes you hope to achieve. The objectives you set will influence how you design your campaigns and make important allocation decisions.

For example, are you trying to sell more of product X to customer segment A? Are you trying to retain more customers from segment B? The more granular your initial focus, the less daunting your task will seem.

With objectives defined, map out the different components of the purchase “funnel” (in quotes, because that’s no longer a nice neat path, but a complex mix of engagements) and the non-linear pathways that customers or prospects take to reach a given point where economic value (such as a sale or signup) is created.

How do customers find your website? What steps do they take to download a whitepaper or other collateral? At what point does the sales team begin to interact either directly or indirectly with customers or prospects? What role do channel partners play in all this?

Asking such questions will shed light on how different behaviors influence one another and lead to value-creating engagement activities.

To probe more deeply into these engagement drivers, your next step is to identify the places where you have good data and where you don’t. Look beyond traditional customer survey type data, brand tracking studies and CRM system information. For example, what web analytics are you capturing that might provide insights? Do you have access to point-of-sale data or call center transcripts?

Rule #2: Create a Methodical Testing Process

If there are areas you have little or no data, make experience-based assumptions. Then test those assumptions by constructing experimental designs. Understanding the engagement chain better can provide insights into the value of specific activities.

Armed with this information, you can begin validating previously fuzzy relationships between, for example, word-of-mouth referrals and sales, among others. Focus your tests on one or two areas at a time, replacing assumptions with facts as you go along to plug holes in your approach.

Applying increasingly disciplined, scientific techniques will help you and your team understand the net impact of specific interactions, and thus determine which levers are the most beneficial to pull.

Rule #3: Tap the Predictive Qualities of Upstream Behaviors

As data gaps begin to disappear, you’ll be in a better position to add more rigor to your predictive modeling. This might include such things as multivariate, linear or non-linear regression techniques.

For example, prospects that download whitepapers might give us 80% confidence that they will speak positively to someone else who is considering our brand. And if a customer speaks positively to three people, there’s a 75% likelihood (for example) that one of those people will engage with our brand.

But take care not to blow your measurement credibility by holding up small, isolated pieces of the engagement puzzle and declaring them predictive. Predictive models must encompass all of the drivers of engagement and, importantly, the interrelationships between them.


Trying to draw a straight line between awareness and sales, for example, make marketing (and you) more vulnerable to stakeholders who may question your allocation decisions. Finance, for one, will certainly know that awareness alone doesn’t pay the rent. (Also see Making Your Measurement Story More Compelling on the DAC site.)

Rule #4: Leverage Your Engagement Drivers

After you identify correlations between upstream behaviors and economic value, you can begin to isolate the likely drivers of even more economic behavior and focus you marketing on extending or leveraging those drivers.

For example, if you see that social media references actually make the referrer 60% more likely to repurchase, then you might want to invest more in promoting referrals among your existing customer base. Or if ecommerce activity at your website spikes when certain influencers post news about you, you might want to amp up your influencer marketing efforts.

The goal is not just to find the engagement behaviors that lead to profitable outcomes. You also want to build marketing programs that can stimulate more of those behaviors.

What it all Means

There’s no question that developing a set of useful metrics around a tricky measurement topic such as customer engagement is a challenge for any organization. That’s why it’s best to break down this measurement monster into a series of smaller, less scary components that are more easily tackled.

By nature, engagement is dynamic; change will be constant. So you’ll need ways to test, learn and adapt quickly and use insights to build the foundation for a broader engagement strategy. Guided by a solid vision to start, this type of learning approach might mean the difference between distinguished success and momentum-killing mediocrity for your marketing programs.

15 TYPES OF BEHAVIORAL ENGAGEMENT

1 Visiting a website

2 Viewing or clicking an ad

3 Downloading a document

4 Opening a marketing email

5 Requesting more information

6 Viewing a webcast

7 Visiting a store

8 Buying a product

9 Calling customer service

10 Mentioning a product/ brand in social media

11 Making a referral

12 Completing a survey

13 Rating a product online

14 Writing a product review

15 Reordering a product


One data aspect that’s sometimes overlooked, Schoen says, is the ability it gives CMOs to forecast the brand’s future and lead it forward. “It’s about understanding, through data, how the market and consumer behavior are changing, and how those changes need to be reflected in product development,” he adds.

So how are CMOs doing when it comes

to mastering data? In general, not well it

would appear.

Schoen gives them a collective C-minus, although he’s quick to add that some are doing much better. The low grade isn’t because CMOs are ignorant of the problem. “In part, it’s because the opportunity data presents to

CMOs is so vast, and the capability of technology has grown faster than even the best organization’s ability to keep up with it,” says Schoen.=

A recent CMO Council study highlighted the scope of the problem. Of particular note, 78% of marketers surveyed agreed that the CMO should be the catalyst and driver of a brand’s data-driven customer strategy, but only 19% said they currently are. This prompted CMO Council Executive Director Donovan Neale-May to warn in a subsequent interview that if CMOs don’t get better at dealing with data, their jobs could be in jeopardy.

‘Without Insights, It’s Only Information’

Data, in a broad sense, is nothing more than facts and statistics collected for reference and/or analysis.

Marketing Data Fundamentals: What CMOs Need to Know

Data is at the core of almost every key challenge that CMOs — or any marketers for that matter — face today, and will likely have a profound impact on marketers’ ability to prove their value.

“CMOs must understand how to find and reach their target customers, which is a data problem,” says Michael Schoen, general manager and VP of marketing solutions at Neustar, a global information services provider . They must also understand, he adds, how effective their efforts are in media spend, message optimization, and channel optimization, as well as the types of customers who engage with their brands and buy their products — all data problems.


The main characteristic of data is that it represents objective reflections of the real world, as opposed to opinions and beliefs.

Nevertheless, data can be incorrect, biased, or incomplete, says Dirk Beyer, head of data science research at Neustar. “The goal is to use data for objective and informed decision making and communication, and to establish accountability,” he says.

In today’s digital world, almost everything a business or customer touches produces data. Companies have access to an avalanche of data about transactions, customer behavior, supply chain activity, sales performance, product use, and more, says Rishi Dave, CMO at Dun & Bradstreet. “But that data is not relevant without insights; it’s only information,” he says. “Data needs to be captured, consolidated, and normalized, and then it can be analyzed for insights.”

Data can be categorized many different ways. Cory Treffiletti, VP of marketing and partner solutions at Oracle Data Cloud, recommends putting data into one of four basic buckets:

• Demographic data. Historically the most common data type used for targeting, often with broad generalizations.

• Behavioral data. Highlights interests, intent, and consumption. Treffiletti breaks this down into two subcategories: behavior that indicates broader interest in a topic (e.g., reading articles) and in-market data derived from behaviors farther down the purchase funnel (such as comparing features and prices).

• Geographic data. Pinpoints a user’s physical location. Common sources of this type of data include offline, IP, latitude/longitude geographic tools, and beacons.

• Purchase-based data. Derived from actual purchases.

But as Beyer says, it’s difficult to create a complete taxonomy of all the data a brand might want to use to run its business. He cites these broad categories as particularly relevant:

• Sales transactions data. “This is the ultimate outcome of the business,” Beyer says. “If the business has a direct sales relationship with the customer, this data is very granular and can include all aspects of the individual transaction.”

• CRM data. Encompasses all the data that describes a particular customer, including historical sales, data the customer has shared with the marketer (surveys, subscriptions), customer service history, and any relevant data from other sources.

Marketing execution data. Includes records of marketing interactions in each channel used, as well as earned and owned interactions with customers. This data can be at the individual customer-touch level, or it can be an aggregated level of estimated impressions in a given market. In the latter case, cost data and customer response data are important elements.

• Market conditions data. Describes the economic environment, competitors’ activity, weather conditions — anything related to the background of the marketer’s own business activity. “Marketers might not be able to influence these things, but they can help explain the business outcomes,” Beyer says.

• Supply chain data. Encompasses critical information about the organizations, people, activities, and resources involved in moving a brand’s products and services from their source of origination to the end customer. It’s important because marketing must execute within the


constraints of what can be produced and delivered. Marketing also can provide valuable feedback to product design or production and to shipment plans.

• Customer sentiment data. Often obtained through surveys and focus groups, this data helps marketers understand customer attitudes toward their products, brands, and advertising messages.

Building a Data Infrastructure

Regardless of the type of data, or the purpose for which it is being used, the effectiveness of data-based marketing initiatives is highly dependent on the quality of the data itself.

“The first thing that is required to use data

effectively is to have a data strategy,”

Schoen says. An easy mistake to make

is to develop a strategy based on low-

quality data. “This is definitely one of

those GIGO [garbage in, garbage out]

areas,” he says. “You may have the best

process, strategy, and algorithms for your

data-based decision making, but if the

data is wrong, you’ll end up doing the

wrong thing, making wrong decisions,

targeting the wrong consumers.”

There are many ways to define data quality. D&B starts by asking four questions:

• Is it accurate?

• Is it complete?

• Is it timely?

• Is it globally consistent?

“If you can answer yes to all of these questions, you likely have taken the steps to ensure your data quality,” Dave says. He adds that it’s imperative for marketers to have a process in place to maintain quality because “data becomes outdated literally within minutes. If you aren’t constantly cleansing your data and appending it with trusted third-party data, the quality will rapidly decline.”


Data quality is Oracle Data Cloud’s primary concern because it is fundamental to the success of its customers’ and partners’ businesses, Treffiletti says. Its data quality assurance process focuses on six attributes:

• Seek out qualified data that has been verified across multiple sources.

• Look for large data sets from your choice of data providers.

• Consumer data across all connected devices provides the most holistic view.

• Ability to activate a robust array of IDs, including cookies, registration ID, and device ID assets.

• Ensures the company has access to a large pool of media partners to activate a data strategy.

• Does this data type make sense for your marketing initiative? Does it align with your KPIs and target audience?

Sources of Data

Another important filter for parsing data quality is source of origin — whether it is first-, second-, or third-party.

First-party data is information that marketers obtain directly from customers through their interaction with the marketer’s brand. Loyalty card programs are a common source of this kind of data, and it’s generally the most valuable. It includes personally identifiable information (PII) such as name, address, phone number, email address, and, often, an extensive transaction history.

Second-party data typically comes from publishers. “At Neustar, we think of second-party as any intermediary between the brand and the consumer,”

Schoen says. For example, if Starbucks were launching a sports drink, it might be interested in ESPN.com data on users who view, say, track and field content. The data is relevant and presumably accurate, but it may lack PII and is not owned by the marketer.

Third-party data comes from an unrelated vendor, is invariably associated with anonymous digital identifiers, such as cookies, and rarely includes PII. It’s most often used for building and understanding marketing campaign audiences. Third-party data providers often use modeling to replicate small-audience attributes in larger samples, which can dilute accuracy.

Marketer confidence is generally highest in first-party data (especially of the sort generated by loyalty card programs) and lowest in third-party data.

Second-party data can vary considerably. Schoen offers an example: “If Starbucks were doing a marketing partnership with Safeway to target consumers who purchase coffee in Safeway stores, it would likely have a high level of confidence. It’s still second-party data, owned by Safeway, but Starbucks knows that Safeway has high-quality data about what its consumers are buying. They might have less confidence doing the ESPN deal because ESPN doesn’t have the same kind of PII on its users.”

Identity As Key Metric

From a marketing perspective, the single most important data attribute may well be identity. Records of customer interactions often contain PII as well as semi-anonymous identifiers such as cookies and Ad-IDs.

“Being able to tie these identifiers together, so that a more complete picture of the customer and the marketer’s interactions with him or her can emerge,


establishes an identity,” Beyer says. Identity is critical because it’s the way that everything gets connected, and it’s often a way marketers determine data quality.

One of marketing’s biggest challenges today is not only knowing and understanding customers across each of their individual devices (the average person uses five devices and has more than 25 IDs), but connecting them to one consistent message. An effective customer graph connects all those identities to form a single view of an individual. This allows marketers to seamlessly reach their audiences across multiple channels and accurately measure campaign performance.

Connecting the Dots

But data’s greatest value to marketers may be its ability to improve customer experience. “Customer experience is the ultimate strategic differentiator in the foreseeable future,” says Wilson Raj, global director of customer intelligence at SAS. “The only way you can differentiate is by delivering a unique customer experience that’s based on a solid, connected business strategy driven by marketing analytics.”

To maximize data’s value, marketers should view customer experience as one strategy across all media. “Keep it focused on the customer and the context in which the customer operates,” Raj says. “Use your data to maintain that focus, continue to enrich your existing data with new sources, and align the marketing process with the customer journey.”

BEST PRACTICES FOR DATA MANAGEMENT

Here are a few data best practices from the experts interviewed for this article:

• Have a well-defined customer data strategy. Determine the problems that need solving, such as customer acquisition, improved customer lifetime value, reduced churn, and increased engagement.

• Grow data capabilities incrementally. Start with projects where success can be documented with clear metrics, and build on wins.

• Eliminate data silos. Make sure all departments across the organization are using the same networks and software solutions, or at least ones that are completely compatible with each other. Relevant data should be accessible to any department that can use it, not considered the “property” of the originating department.

• Set data-based analytic triggers to ensure brand activity and contextual relevance throughout the customer journey.

• Align data to campaign KPIs.


Invoking ROI to gauge marketing impact can severely distort the true value that marketing is delivering for your organization. Sure, it’s hard to have a marketing measurement conversation without ROI coming up. It is, after all, one of today’s most used marketing metrics. ROI calculations are a handy yardstick to show top management how marketing measures up.

But is “return on investment” really an accurate way to measure marketing effectiveness? Sadly – and perhaps even shockingly to some – the answer is often no.

The concept is certainly honorable. Marketing should show a return on the investments it makes – just like any other department. Linking marketing to financial performance and business results is absolutely critical.

It’s just that most people who use ROI in a marketing context probably aren’t using it correctly, or really mean something else, says Dominique Hanssens, professor of marketing at UCLA Anderson School of Management and a globally renowned marketing scientist.

ROI’s roots, he points out, are in evaluating one-time capital projects. “But is marketing a one-time capital project?” asks Hanssens. Clearly not.

We regularly talk about marketing “investments” which sound likes a good thing. We want everyone to view marketing’s budget as something that will pay dividends. But – technically speaking – marketing outlays are still an expense, no matter what we choose to call them. In CFO-speak, marketing costs are a P&L item, not a balance sheet item.

As a result, notes Hanssens, marketers rarely mean ROI when they say ROI. Nevertheless, “plain” ROI can certainly be an important interim metric for marketers. But it falls well short of helping us understand marketing’s contribution to business goals, or how those contributions can be improved.

In part, that’s because ROI is too limited. To truly gauge and improve marketing effectiveness, for example, we must factor in the strategic intent of all marketing investments a company makes. ROI doesn’t really do that.

The Rub over ROI

We’d all love to quantify marketing performance with a single number. But ROI is a ratio, and ratios

ROI: Today’s Most Misunderstood Marketing Metric

Here’s a thought not much discussed much among marketers: ROI might not be the right way to measure the impact of your marketing. In part, that’s because we routinely misunderstand and misuse ROI in all manner of ways.

This is how and why.


are not what matter most here. What does really matter? Net cash flows, says Hanssens. Performance measures such as net profit, for example, are derived by subtracting various costs from revenue. ROI is different. You get it by dividing net revenue by cost.

How, then, can marketers compare ROI on different marketing investments, such as a television ad campaign versus a paid search campaign? As it turns out, you can only make an accurate ROI comparison if the spending amounts are the same. ROI, you see, changes at different spending levels. It is not only a function of the medium, but also of the investment in that medium.

And it’s also critical to know that maximum ROI does not necessarily produce maximum profit. Oops! Blame the concept of diminishing returns. Many marketers might think that the highest ROI corresponds to the best spending level. Unfortunately, that’s not so.

Because of the diminishing returns effect

(among other things), ROI can rise while

the rate of sales growth drops.

After a certain investment level, marketing effectiveness declines. But that doesn’t mean you stop investing. Profits may still rise, albeit not as fast.

Should you stop spending when ROI drops, even if you continue to produce bigger profits? Most likely not. The point at which you’d stop or make a change depends on the return of the last incremental amount spent, not the overall ROI. This “incrementality” is a key ingredient that many marketers miss – here and in other measurement areas such as attribution.

This introduces what’s known as “return on marginal

investment” – or ROMI. And “marginal” return as compared an average return is what can make all the difference for accurately interpreting results and making decisions on future spending.

So if you must use a return measure to gauge marketing effectiveness, ROMI may be better. The only thing you really need to know is whether ROMI is positive or negative. Or, put another way, are you underspending in a given category…overspending…or getting it “just right” (where ROMI is zero)? And the determining lever is how much you spend.

Tracking Complex Interactions

An impressive ROI attached to a specific activity probably means little or nothing if broader marketing goals aren’t being met. Focusing solely on dollars-in (“I”) compared to dollars-out (“R”) ignores a complex web of interactions that happen in between.

Only by analyzing as many of those intermediate processes as possible can we gain useful insights into what’s working and what’s not, and alter allocations to achieve better results.

The core takeaway bears repeating:

If you’re satisfied with only for a

seductively simple measure such as

ROI, you may severely distort the true

value that marketing is delivering for

your organization.


It’s all About the Math

What’s sometimes lost in the rush to install marketing measurement is this: The cornerstone of successful marketing analytics is the math behind it. If you don’t have the math right, by definition your attribution will be wrong, and by extension your allocations and attempts to optimize your investments will also be wrong.

Effective marketing resource allocation depends on accurately attributing revenue to different marketing investments online as well as offline and at point of purchase.

Early versions of marketing analytics included traditional forms of measurement that we’ve had for decades, such as media mix models, agent-based models, digital attribution and simple correlations using Excel spreadsheets. Today’s more advanced analytics tap rely on predictive analytics and other marketing science to help companies reallocate billions of advertising dollars while realizing double-digit sales lifts with zero additional spend.

Advanced analytics in marketing can hone in on hundreds of a given company’s business drivers, from pricing, distribution and online reviews, to social media chatter,

advertising and hard sales data to uncover critical insights about what’s really driving results, and what to do next in the real world. The allocation step is where you put what you’ve learned from attribution and testing into play. Then you can quickly measure outcomes, validate models by running real-time tests, and make course corrections to optimize allocations and results.

Spotting ‘Quant Quackery’

Surprisingly, some brands are still using largely discredited simple marketing mix econometric methods, or for online marketing “last click attribution.” Such models aren’t looking at the total ecosystem, nor are they measuring the precise impact of, say, TV on search, or search’s impact on retail sales. Simply plugging offline spend into digital marketing analytics models doesn’t achieve “cross-channel” analytics.

Using flawed models is like crediting a single movie theater for an Academy Award winning performance, or trying to win a football game with just eight players on

Marketing and math haven’t always meshed. Marketing was long considered a creative endeavor somehow divorced from the rigor and transparency of science-based business process. The very definition of analytics – “the scientific process of transforming data into insight for making better decisions” – caused tension in marketing-dom. But no longer. Better analytic methods, new data-driven technologies and C-suites calling for greater marketing accountability have changed all that.


the field. Unfortunately, while the measurement buzzword “attribution” is everywhere these days, many of the solutions trying to solve for this challenge are sub-par. Yet models that play without all the pieces are little more than what we might call quantitative quackery.

For example, to get a true, holistic view of what’s going on and thus make better business decisions, you need all forms of digital data (search, social, mobile, etc.) in the analytics. Without it you’re missing a rich vein of information about consumer behavior. And remember, even if you spend a large part of your budget offline, that doesn’t mean online behavior – the consumer’s “digital life” – isn’t influencing the decision-making process.

Relying heavily on old-school data

“samples” is another problem.

Samples may still have a place, but part of big data’s beauty is the ability to use all the data from online and offline marketing and sales channels, plus external factors (such as the weather or unemployment), not just samples that are far more error-prone.

Still more quant quackery occurs when marketing analytics focus on attribution for only a small piece of the overall enterprise. To achieve accurate results you have to factor in enterprise-wide relationships. Think of it an advanced form of the old connect-the-dots exercise, only in this version you have to include dots for activities and outcomes you might not be able to actually see, but which exist nonetheless in the data.

Quantifying Marketing’s Business Impact

The right math behind the analytics is essential to bringing data to life for marketing organizations, thus allowing for faster insights and better decision-making.

This includes such things as:

• Quantifying the long-term impact of brand advertising (brand equity).

• A holistic approach that includes all online and offline methods and channels.

• Deploying the latest technology, not simple regression models.

• Transformational thinking that takes marketing analytics beyond simple “research project” status toward enterprise-wide adoption.

Consider a major auto industry player that’s a superstar in the world of marketing analytics. This company’s cross-functional analytics team has the daunting task of making sure the company spends its $1+ billion marketing budget as effectively as possible while contributing to business goals, achieving the best ROI and increasing shareholder value.

They do it with advanced analytics that allow the company to run continuous marketing strategy simulations under a wide range of complex variations. These simulations employ cross-media attribution insights that help the company predict with greater accuracy than ever how changing the amount spent in one marketing area will likely impact the performance of advertising elsewhere, and what this all does for the bottom line.

Using advanced analytics, this giant global marketer has also been able to coordinate local and national marketing and dealer incentive budgets, and simply by shifting allocations generate tens of millions of dollars in new revenue from the same spending level.

Almost any company can deploy more advanced analytics by focusing on methods that avoid the above pitfalls. But one thing is sure: Businesses that don’t will be left behind.


Using Multi-Channel Attribution to Improve Marketing Effectiveness

Five core advancements are changing the attribution landscape:

1 Market- and consumer-level integration

Consumer-level attribution and market-level modeling – including online, offline and non-media factors – are merging. As this happens, what Forrester Research has called “adaptive marketing” is replacing old planning and measurement processes, transforming how major brands deliver on marketing goals and connect with customers. Integration of attribution and mix modeling tools and functionality is making the old campaign mindset obsolete and enabling real time optimization.

2 New capabilities to measure and predict brand impact

Unlike older attribution approaches that can’t incorporate longer-term brand advertising impacts, advanced tools can begin to factor brand impacts alongside shorter term,

consumer level impacts. That’s significant since failing to account for brand can throw your marketing measurement and ROI calculations off by a wide margin.

3 Improved predictive accuracy

Model refinements have greatly improved predictive accuracy. The best models don’t merely analyze historical information; they examine relationships between market factors and have the ability to look forward. They account for all paid/owned/earned influences and analyze customer behavior across a full range of both online and offline activities that ultimately lead to purchase. Most importantly, they can transform those insights into simulations that answer what-if questions.

4 Programmatic connections

For some companies, one of the most dramatic refinements is the ability to integrate attribution insights with programmatic buying. In essence, this turns an attribution

Multi-channel marketing attribution – the science of assigning value to each marketing or brand touch point across all online and offline channels – is evolving quickly and becoming more effective all the time. But many companies – including some major brands – still use antiquated tools and ineffective methods.

Slowly, advancements in attribution tools, technology and tactics that combine digital attribution with mix modeling, optimization and the offline world are writing a new chapter in smart analytics.


platform into the ‘brains’ of a programmatic effort, helping inform everything from how to allocate spending across DSPs, to which creative works best and where.

5 Improved insight adoption

Accumulated experience with advanced attribution at scale for major global marketers has also produced learnings and refinements in how insights are applied internally to make better decisions, faster.

The most successful adopters are

recognizing that analytics is not a marketing-

only solution.

At one large hospitality company, early attempts at digital attribution proved to be entirely inadequate to the needs of a major global marketing organization. Prior efforts had relied on notoriously inaccurate ‘last touch’ attribution, and marketing’s impact was difficult to measure or compare across multiple tactics, channels, brands, geographies and messages.

One gaping hole was the company’s inability to measure the impact of long-term branding effects and, for example, to accurately predict whether it should spend more on advertising its entire portfolio, or focus on individual hotel brands. Companies with large portfolios often struggle to understand how to achieve both brand and enterprise objectives. Planning, allocation and measurement all take place at separate times and locations, causing major inefficiencies.

Now the company is deploying more advanced marketing analytics to gain new customer insights, merge digital and offline into a single view, and deliver results that are being noticed at the highest levels of the company. They’ve connected several digital data, CRM, targeting and web analytics solutions to provide real-time recommendations

for programmatic buying, plus planning and optimization capabilities. Cost-savings from the planning process alone were substantial.

Adding new capabilities to track brand advertising impacts is providing the company with insights into the best way to allocate marketing investments across portfolios and specific hotel brands. The key to revealing these vital interactions is the new ability to comprehensively, accurately and continuously link market- and consumer-level insights with predictive capabilities that tell them where marketing investments will yield the greatest impacts.

By employing these advances, the company has gained the ability to not only understand halo effects, but can now translate that knowledge into spend allocations and specific messages down to the individual level. One key refinement is the ability to interpret results across different time frames, as brand equity takes more time to determine than typical digital-only attribution.

Meeting New Competitive Threats with Analytics

Business-to-business technology companies are also achieving dramatic results by adopting and scaling sophisticated analytics programs. Consider a $3 billion, US-based leader in mobility, virtualization, networking and cloud services. Since the company first embarked on its analytics journey in 2012, it has by its own description “completely transformed the way we work.”

For several years, the company was growing rapidly at an annual rate of over 25%. But growing competitive pressures caused it to seek new ways to capture competitive advantage. Analytics provided an answer.

The company was using a patchwork of CRM systems but had little evidence of real return and only a sketchy understanding of whether its marketing investments were paying off. With annual media spend growing 5-15% and


market share under pressure, the company got more serious about analytics.

“Applying B2C modeling approaches to a purely B2B business was groundbreaking at the time,” says one marketing VP at the company. “The ability to optimize marketing spend across the entire mix delivered bottom-line impact, including a 5% lift in sales with no additional marketing spend in the first quarter of use.”

Multi-Stage Methodology

This B2B tech brand took a multi-stage approach with four key goals:

• Examine the full marketing ecosystem – including both external (non-controllable) and internal (controllable) factors – to better understand marketing’s contribution to sales.

• Understand the interplay between all touch points in the path to purchase. Importantly, the model they use identifies both marketing’s direct and indirect Leaving out the latter can result in underestimating impact.

• See how traditional media ignites online activity and how online and social media amplify traditional media.

• Analyze each pathway through the customer funnel and link them in a system of models.

Some findings confirmed what the company expected. But others were surprises that challenged the company to make significant and sometimes counter-intuitive changes. The company began to account for diminishing return effects and reduced customer acquisition costs by shifting some funds from above-the-line channels such as TV and radio and into digital.

Once market-level analytics tools were in place, customer-level attribution was used to reveal the interplay of individual

digital investments. The company could then assess results against an independent library of response curves that serve both as a starting point for evaluating unexplored media, and a benchmark on the company’s own progress.

Now they’re able to evaluate spend down to individual products and geographies. Product-specific insights also clearly reveal halo effects. In addition, geographic insights have helped the company understand for the first time if and when it is overspending in the U.S., for example, and how it can optimize investments elsewhere.

And like many organizations, this company had self-imposed constraints on where it could spend its marketing budget. They’d set floors and ceilings for allocations based largely on gut feel and experience. But attribution modeling has changed that. Instead of imposing arbitrary limits, the company now plans its allocations based on insights derived from the data.

Improving Programmatic Effectiveness

Refinements in attribution are also providing new insights into ‘digital body language’ – the aggregate of all the digital activity you see for a given customer. With those insights, brand marketers can reap big benefits from continually optimizing their programmatic efforts. For example, brands that use numerous DSPs have been able to boost returns by 10% or more by syncing attribution tools with programmatic systems to achieve greater efficiencies.

The latest refinements in integrated tools and modeling methods allow companies to account for variables such as changing vendors or publishers, along with the effects of changes to creative and placement levels. And the software tools let them simulate the impact of changes and make mid-course corrections – on a weekly, daily or (soon) a real-time basis.


4 Ways to Spark Greater Analytics Adoption

McKinsey, for one, has framed the move toward greater marketing measurement as the intersection of three sectors, where “value capture” exists at the point of overlap between three areas:

• Big data,

• Predictive & optimization models, and

• Organizational transformation.

For those who get it right, opportunity

abounds in the form of new abilities

to segment customer populations,

fact-based support and greater

transparency for strategic decisions,

and a way to quickly apply test-and-

learn techniques to explore war game

scenarios, among others

Like many organizations, however, yours may be scrambling to simply stay upright against hurricane force changes. And throwing money at analytics won’t by itself help achieve the business goals you seek.

Experience of successful analytics driven marketing organizations suggests that the war against institutional lethargy can only be won with organizational change.

Here are four suggestions that can help spark greater analytics adoption:

1 Create an Analytics “SWAT” Team

To succeed, companies must embrace fact-based decision-making across the entire organization. But it’s not news that such efforts can meet strong resistance. The Rx is to create a neutral “Analytics Innovation and Transformation” team. This team reports to the CEO, has buy-in from the entire C-suite and is tasked with disrupting institutional lethargy so that the organization can begin to capture analytics value.

A common measurement mistake that many companies make is that they spend big on marketing analytics but they don’t worry enough about how to actually apply insights across the organization.

Increased spending must be matched by a commensurate effort of those analytics to drive better decision-making.


2 Re-position Paradigms

A simple starting point is the realization that analytics is no longer the exclusive purview of the data nerds, but is (or should be) a core organizational competency. To get there, C-suite seat holders might need reminders of what’s going on “out there” in the fast-expanding universe of marketing measurement.

A senior executive at a global packaged goods company recently led a group of other executives at his company on a pilgrimage to Silicon Valley for immersion training in the latest and greatest developments from social media, data technology, analytics and more. They visited several of the major players, along with a few small disrupters that are making sure “business as usual” is gone for good. Changing the paradigms of your team members in this way, among others, can help shake things up.

3 Make Marketing Lead

CMOs (preferably in concert with finance) must be key players in any analytics transformation team. Maybe, as some have suggested, CMO should really stand for “Change Management Officer”. Everyone’s feeling the heat. Accountability (through transparency) has never been such a hot topic among marketers. There’s an urgent need for many CMOs to strengthen their hand within the organization by taking the lead around fact-based decision-making.

4 Articulate Answers and Tell Good Data Stories

A key to bringing cross-functional teams on board is to simply articulate the answers you are seeking, and where the data to provide those answers will come from. Some divisions may be sitting on valuable data assets without even knowing it. By identifying that data and setting up a system to collect, protect and use it, doubters can be won over.

Fact-based decision-making is only

partly about math and computational

power. It’s an organizational

challenge as well. Marketing is

now a war of information, insight

and asymmetric advantage gained

by applying advanced analytics.

The organizations most adept at

integrating this change will win.


As their programs grow and mature, analytics-driven marketers are finding new freedom to focus on turning newfound customer insights into efforts that expand relationships, convert sales opportunities, build customer and shareholder value and support a wide range of other business outcomes.

Here are eight ways that companies are applying their increasing measurement mastery to expand the influence of their marketing analytics programs:

1 Use Insights to Create NewCustomer Value and Enrich Product/Service Offerings

Advanced marketing analytics technology doesn’t just plug you into big data. It also connects you to big analytical models and big visualization to generate customer-centric insights you can use to innovate

customer-facing products and services and create customer value. It works because through analytics and identity building you now know a great deal more about your customers than you ever did before.

For example, with cross-channel attribution you gain a clear picture of what influenced a customer – in both the online, offline and non-media worlds – to ultimately make a purchase. That knowledge helps you make better decisions about how to allocate marketing dollars. But it also is valuable intelligence you can use to suggest product, service and feature enhancements to other parts of your organization.

2 Becoming More Predictive

Many marketers still measure most of what they do through a rear view mirror. But innovation is not

8 Ways to Leverage an Analytics Program

You’ve spent a great deal of time, effort and money to build and refine a marketing analytics program. But it’s not simply about crunching data and teasing out useful insights. Once you have valuable information to inform decision-making and optimize marketing efforts, your efforts can be put to good use in a variety of ways.

Most companies use analytical insights to inform spending decisions across channels and tactics, and to help with planning. But brands with some of today’s most successful marketing analytics programs are increasingly seeking ways to leverage analytics in other ways.


backward looking. It has both feet firmly planted in the future. Marketers with high analytics IQs are learning to leverage the predictive component of today’s top analytics technology to peer into the future more accurately than ever.

And – importantly – today’s analytics

technology lets users quickly simulate

possible outcomes before putting money

on the line. You can use those forward-

looking insights to test possibilities and

change direction on the fly as new insights

become available. The old “campaign”

mentality of set-and-forget is dead.

3 Perfect Programmatic

In the headlong rush to let machines do their ad buying, companies are channeling huge and growing sums into programmatic purchases. But just because machines are handling ad placements at lightning speed doesn’t mean the process is efficient and effective at producing the desired results.

Some of the best data-driven marketing organizations are combining attribution technology with media buying engines into a new approach to match their media spending to the analytical insights they are generating. This includes such things as frequency capping, eliminating retargeting after a customer has made a purchase and selecting cookies to target based on propensity to convert.

4 Merge Brand Measures with Direct Response

A few brands are experimenting with using analytics to optimize marketing on both a long- and short-term time horizon at the same time. In the past, brand campaigns were always considered long-term. Their impact was difficult to measure and took place cumulatively over time.

Direct response was different, especially in the digital realm. It showed quick results that were easier to quantify. But measuring only one or the other doesn’t provide an accurate picture of what’s really going on. Cross-channel attribution technology changes that and opens new doors to innovation. And companies focus on what’s easier to measure, which hurts long-term success.

5 Build New Internal Alliances

Once you’ve established marketing’s value quantitatively, you can begin growing your influence in other areas of the company. For example, connecting marketing more closely with finance is proving particularly helpful at some companies where analytics have brought marketing and finance much closer together.

In one such case, the CMO of a major financial firm enlisted help from his finance department to bring new analytical discipline to the marketing team. That, in turn, created greater respect and buy-in for marketing’s efforts to become more data-driven and connect marketing investments to business results.

The analytics team at a major tech company provides clear and simple presentation materials in customized packages targeting one senior marketing and finance execu tive at a time. The team also reaches its internal marketing community by inserting key insights into executive keynotes, videos and training sessions.


6 Refine Strategic Approaches to Analytics Technology

Some marketers are “reverse engineering” their overall strategies by better refining how analytics can play a role. For example, they keep these areas in mind when developing strategy:

• Business fit: Can it handle complex product portfolios and advise on next dollar investment across media, marketing, product and service opportunities? Will it provide both a top-down and bottom-up view in one place to bring together media measurement and direct/addressable marketing?

• Data Integration: Can it pull in and analyze both internal and external data from all online and offline channels?

• Tool functionality: Is the software accessible and useful across groups and decision-making levels? Can users easily share results with others, from managers to the C-suite?

• Optimization approach: Can it rapidly predict results from different scenarios?

7 Combine Stats with Common Sense

Marketers moving up the analytics maturity scale understand it’s not enough to get interesting insights. They need to validate learnings to know if they’re right by testing and confirming results in the market via A/B tests, closed-loop attribution and other tactical tools. The most successful companies combine stats and sensibil ity. In other words, they strike a balance between marketing reality and statistical fit. To check sensibility of results they:

• Check for acceptable measures in marketing contribu tion, source of volume change, and relative effectiveness of each touch point.

• Conduct sanity checks against known business truths and existing knowledge.

8 Stay Agile

As the CMO of one successful firm says, “We literally make decisions day by day based on performance. For example, we have created the agility to shift dollars from national TV to local markets when a specific designated market area is outperforming.”


Hidden Opportunities in the Age of GDPR

How GDPR Is Playing Out

Europeans are exercising their rights under GDPR at a faster rate than expected. In a recent SAS survey of almost 2,000 U.K. and Irish Internet users, 27 percent said they have already activated their GDPR rights over personal data, and 56 percent plan to do so within the next year.

Respondents also reacted strongly when asked about their data being shared with third parties. More than half (54 percent) reported that doing so would cause them to take steps to protect their information.

The Facebook/Cambridge Analytica data debacle has motivated many people to reexamine the use of their personal data. The study reports that social media companies will be hit the hardest, with 44 percent of survey participants saying they have or will exercise their right to have their data removed from social media sites.

The numbers are smaller – but still telling – when asked about other industries:

34 percent said they would do so with retailers,

30 percent for banks, and

30 percent for insurance companies

The advent of GDPR seems to have actually accelerated webpage load times, and the difference is dramatic. Catchpoint reported in July that the U.S. version of USA Today was loading in 10.22 seconds versus the European versions at an average of .57 seconds. The difference, experts say, is most likely due to the removal of third-party features, such as ad servers, Google services and analytics and social media plugins.

Brian Byer, vice president and content and commerce practice lead at Blue Fountain Media, says that some companies are taking this opportunity to do some much needed website housekeeping. “We have clients who

The General Data Protection Regulation (GDPR) is now a fact of life. What will the impact on marketing analytics be long-term? Is this the end of people-based marketing and the dawn of a new, more stringent data privacy world?

While most of the focus has been on the scramble for GDRP compliance, much less attension has been on subtle new opportunities that will emerge from GDPR for organizations that see and leverage them.


are amazed at the quantity of tags that have been built up over the years and are presently unused. As a result, our GDPR clients are taking a hard look at these and removing the speed bumps.”

A Trust-Building Opportunity

It’s easy to look at GDPR as an obstacle. But many savvy marketers are also seeing it as an opportunity to build trust with customers. By respecting their privacy concerns, brands have the chance to strengthen loyalty and obtain the most useful data. In the SAS survey, respondents said they are most trusting of organizations that promise they will not share data with third parties (38 percent) or misuse their data (37 percent).

Implementing GDPR consent requirements

is also an opportunity to acquire flexible

rights to use and share data.

“Don’t lose sight of the fact that implementing GDPR consent requirements is an opportunity for an organization to acquire flexible rights to use and share data,” says Lydia Clougherty Jones, research director at Gartner. If data and analytics leaders involve themselves in the right way, Jones says, they can use GDPR to gain greater access to data and create new uses for that data.

Organizations can also realize cost savings from GDPR compliance. It’s a chance to clean house, streamline your data collection processes and obtain higher quality information. By removing old records, or records of unengaged customers, time is better spent on the real customer.

GDPR’s data portability feature creates another opportunity for some companies. Individuals can obtain

and use their personal data for their own purposes across different services and request that their data be transferred to a new controller. This could create more parity in the marketplace, benefiting smaller or newer companies, as new customers are able to switch services more easily.

Strategies for a New Era

Go lean. Identify which data points are most important to your marketing analytics strategy. Then, says Gerry Widmer, partner and CEO at Zesty.io, “really focus on the 10 or 15 pieces of data that actually contribute to improving the customer experience. Once you know what data you need — and what you need it for — it suddenly becomes a lot easier to inform the end user about why you’re collecting their data.”

Pare back.

Determine if you are inadvertently collecting data that 1) you don’t need and 2) could you put you out of compliance. For instance, you might be capturing things such as IP addresses, location data and images of a person – all of which is protected data under GDPR. If you don’t need this information, jettison it.

Dive into your toolbox.

It’s often easier to focus on what’s lost rather than the opportunities that remain. One outcome of GDPR is that more marketers are turning to contextual advertising. Research from ad tech firm Sizmek found that 87 percent of marketers said they plan to increase contextual targeting in the next 12 months, while maintaining personalized advertising where possible.

Hone stories.

Robert Rose of the Content Marketing Institute, sees GDPR as “the biggest opportunity in more than a decade for content marketers to become strategic.” The


key, he says, isn’t to give up on using personal data to optimize content and marketing strategies, but to gather better information. “Data given, rather than scraped or gathered unwillingly, is simply more valuable as a marketing asset,” he emphasizes. “Providing valuable content-driven experiences where the data is given willingly, trustingly, and actively is the way to not only comply but to thrive in this new business environment.”

Empower users.

Generational differences create opportunities too. The SAS survey found that younger users (18- to 24-year-olds) are more willing to exchange personal data for incentives. These users reported that they are less likely to activate their data rights if they can receive a satisfactory incentive. They are willing to exchange data permission for financial rewards (35 percent), free merchandise (24 percent) or more personalized services (17 percent) – all much higher than in other age groups.

What the Future Holds

Concerns about data privacy aren’t going away. GDPR may be leading the way, but Rashmi Vittal, vice president of marketing for SAP Customer Data Cloud, says it’s just the beginning.

“Enforcement is only getting started, and

we’re already seeing new privacy mandates

such as California’s AB 375 (California

Consumer Privacy Act, or CCPA) and the

European Union’s (EU) ePrivacy Directive

push the issue of using data with integrity to

the forefront,” Vittal says.

Bottom line?

Data transparency is here to stay. But it’s an opportunity for marketers and marketing analytics professionals to hone their strategies, create stronger, more trusting relationships with customers, and cull the most useful data from users.


VALUE DIMENSIONS INCLUDES BUT IS NOT LIMITED TO

Financial / ROI • Marketing forecasting and optimizations • Data-driven measurement of marketing contribution and overall ROI • Trade-off analysis and what ifs

Fact-Based Decision Making • Ability to identify key material drivers of demand and financial performance • Analytical framework enabling continuous performance monitoring and improvement • Relative return on channel/media with prescriptive analytics • Faster decision-making process • Improved demand forecast accuracy • Foundation for global portfolio management efficiencies • Foundation to measure impact of brand health/equity changes on demand

Subject Matter Expertise • Collection, organization and transformation of key organization data • Advanced technical staff utilizing proven methods to build the “best” model • Architecting and translating model and data to align to business context

Business innovation • Exploratory analysis to identify hidden opportunities • Single repository of institutional knowledge

Operational Efficiencies • Best practices in data and modeling • Improved data availability and a more disciplined marketing measurement platform • Foundational models that allow for quick updates and for rapid learning • Standardize analytical plan, reports and presentation templates • Accelerated time-to-value for new staff

Political and Diplomatic • Perception of a higher levels of accountability for marketing business impact • Alignment of high priority business questions • Regularly scheduled marketing performance reviews

Sample Value Dimensions for an Analytics Business Case


Key Marketing Measurement FindingsSpending on marketing analytics is forcased to increae more than 200% in the next three years.

The use of marketing analytics in decision making has increased over time but dipped in over the last year. B2C companies are the biggest users.

A lack of trained professinals as well as tools/processes to measure the impact of marketing analytics are the biggest obstacles to using marketing analytics within companies.


Marketing analytics spend shows fluctuation but no consistent increases over past 6 years.


Percent of marketing budget spent on marketing analytics by key dimensions.


Companies use of marketing analytics continues to fluctuate: B2C companies biggest users.


Process, people, and insight failures largest disrupters of marketing analytics use.


Contrubution of marketing analytics to firm performance shows no improvement over time.

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