Media Outlook 2019: Spell Marketing with a ‘D’

The January marketing calendar in New York has included for the past decade or so a certain can’t-miss event of the Direct Marketing Club of New York. In 60 fly-by minutes, 100-plus advertising and marketing professionals hear a review of the previous year in marketing spend, a media outlook for the current year and macro-economic trends driving both.

The January marketing calendar in New York has included for the past decade or so a certain can’t-miss event of the Direct Marketing Club of New York. In 60 fly-by minutes, 100-plus advertising and marketing professionals hear a review of the previous year in marketing spend, a media outlook for the current year and macro-economic trends driving both.

Bruce Biegel, senior managing director at Winterberry Group, keeps everyone engaged, taking notes and thinking about their own experiences in the mix of statistics regarding digital, mobile, direct mail, TV and programmatic advertising.

“We will be OK if we can manage the Shutdown, Trump, China, Mueller, Congress and Brexit,” he noted, all of which weigh on business confidence.

Suffice it to say, marketing organizations and business, in general must navigate an interesting journey. Biegel reports estimated U.S. Gross Domestic Product (GDP) growth of 2.3 percent in 2019 down from 3 percent in 2018, while total marketing spending growth in 2018 had dipped below its historic level of exceeding two times GDP growth.

In 2019, we are poised for 5.3 percent growth in advertising and marketing spending a slight gain from the 5.2 percent growth of 2018 over 2017.

Watch the Super Bowl, By All Means But Offline Dominance Is Diminishing

Look under the hood, and you see what the big drivers are. Offline spending including sponsorships, linear TV, print, radio, outdoor and direct mail will spot anemic growth, combined, of 0.1 percent in 2019. (Of these, direct mail and sponsorships will each post growth of more than 3 percent, Winterberry Group predicts.)

But online spending growth display, digital video, social, email, digital radio, digital out-of-home, and search will grow by 15.5 percent. Has offline media across all categories finally reached its zenith? Perhaps. (See Figure 1.)

Figure 1.

Credit: Winterberry Group, 2019

Digital media spend achieved 50 percent of offline media spend for the first time in 2018. In 2019, it may reach 60 percent! So who should care?

We do! We are the livers and breathers of data, and data is in the driver’s seat. Biegel sees data spending growing by nearly 6 percent this year totaling $21.27 billion. Of this, $9.66 billion will be offline data spending, primarily direct mail. TV data spending (addressable, OTT) will reach $1.8 billion, digital data $7.85 billion, and email data spend $1.96 billion (see Figure 2.)

Figure 2.

Credit: Winterberry Group, 2019

Tortured CMOs: Unless She’s a Data Believer

Marketing today and tomorrow is not marketing yesterday. If marketing leadership does not recognize and understand data’s contribution to ad measurement, attribution and business objective ROI, then it’s time for a new generation to lead and succeed. Marketing today is spelled with a D: Data-Driven.

Unfortunately we don’t have all the data we need to manage Shutdown, Trump, China, Mueller, Congress and Brexit. That’s where sheer luck and gut instincts may still have a valid role. Sigh.

Understanding and Leveraging Big Data for Audience Insights

Big data can be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions — it isn’t just data for data’s sake. But how big is big data?

Data mining, big data
Creative Commons license. | Credit: Flickr by KamiPhuc

Big data can be defined as extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions — it isn’t just data for data’s sake. But how big is big data? Some data management platforms (DMPs) have over 900 million consumer profiles globally with 10,0000 different data points associated with it. To get even close to that amount of data from consumer surveys, you’d have to run about half a billion surveys.

So while big data has been most impactful to programmatic advertising and media, it also provides a lot of opportunity for other marketing efforts and market research. Due to the insight it provides into consumer behavior, as budgets continue to shrink and the speed of decision making continues to increase, big data is necessary. 

The 4 V’s of Big Data

Today, big data is bigger than ever with more people engaging and utilizing tools that offer big data integration. However, incorporating big data is a marathon, not a sprint and companies have to take the right steps before making the leap. If you’re not already using big data, before getting started, you’ll want to familiarize yourself with the 4 V’s of big data:

  • Volume: the amount of data available
  • Variety: the different types of data
  • Velocity: how frequent, real-time, or up to date the data is
  • Veracity: how accurate and applicable the data is

The biggest challenge when it comes to data is the “veracity” of it. Because there is so much and such a variety of data, it can be difficult to assess its accuracy and application to your business. Discerning the signal from the noise is where most innovation teams will spend their time interpreting the data. In other words, veracity helps to filter through what is important and what is not, and in the end, it generates a deeper understanding of data and how to contextualize it in order to take action.

Data Veracity: The Most Important “V”

Data veracity, in general, is how accurate or truthful a data set may be. In the context of big data, however, it takes on a bit more meaning. More specifically, when it comes to the accuracy of big data, it’s not just the quality of the data itself but how trustworthy the data source, type, and processing of it is. Removing things like bias, abnormalities or inconsistencies, duplication, and volatility are just a few aspects that factor into improving the accuracy of big data.

Unfortunately, sometimes volatility isn’t within our control. The volatility, sometimes referred to as another “V” of big data, is the rate of change and lifetime of the data. An example of highly volatile data includes social media data, where sentiments and trending topics change quickly and often. Less volatile data would look something more like weather trends that change less frequently and are easier to predict and track.

The second side of data veracity entails ensuring the processing method of the actual data makes sense based on business needs and the output is pertinent to objectives. Interpreting big data in the right way ensures results are relevant and actionable. Further, access to big data means you could spend months sorting through information without focus and without a method of identifying what data points are relevant. As a result, the velocity of data and agile methods come into play here — big data should be analyzed in a timely manner, as is difficult, otherwise the insights would fail to be useful.

Big data is highly complex, and as a result, the means for understanding and interpreting it are still being fully conceptualized. While many think machine learning will have a large use for big data analysis, statistical methods are still needed in order to ensure data quality and practical application of big data for better marketing activation. For example, you wouldn’t download an industry report off the internet and use it to take action. Instead you’d likely validate it or use it to inform additional research before formulating your own findings. Big data is no different; you cannot take big data as it is without validating or explaining it. But unlike most market research practices, big data does not have a strong foundation with statistics—luckily, integrating it with survey data can help.

Integrating Big Data With Survey Data for Market Insights

While big data can answer when, where, and what, it can’t answer why. Integrating primary research, particularly with an agile methodology that can keep up with the velocity of big data, can help to analyze and connect the dots — easier said than done.

The obvious benefits of using big data in marketing includes gaining a better understanding of people, content, and media. By combining big data with survey data, you can identify a market opportunity, understand your target audience, and incorporate findings into your messaging and creative execution. Infusing survey research with big data also means the volume of questions that need to be asked are reduced as big data provides more answers. So our understanding of consumer behavior is going to grow exponentially over time as we bring these two worlds together. The ability to incorporate big data to use fewer questions can also deliver more speed and value.

The result is unique audience intelligence. The benefits of this approach mean removing the guesswork during activation, letting the audience identify the opportunity for you, creating more effective messaging, and ultimately increasing value on ad purchases. There are certainly challenges to infusing survey research with big data before organizations can reap the benefits. Since DMP’s were originally meant for advertising, they haven’t been made research-grade, so oftentimes there are errors or inconsistencies in the data related to an audience. However, there are solutions out there — and more to come for sure — that will be able to overcome the challenges and provide an accurate depiction of audience insights through big data.

Programmatic Advertising Is Running Amok

Having spent many years in the direct marketing business, I’m usually amused by examples of target marketing gone awry. My personal favorite happened when I was on Amazon purchasing a cell phone bracket for my bicycle.

Target stock imageHaving spent many years in the direct marketing business, I’m usually amused by examples of target marketing gone awry. My personal favorite happened when I was on Amazon purchasing a cell phone bracket for my bicycle. Amazon’s algorithm generated this suggestion:

Amazon wants Chuck to be a pirateNow I don’t know how frequently the pirate boots and the tri-corner hat are bought together with the cell phone mount, but I have to say that the combination was tempting for a few minutes.

The fact remains that direct marketing is not perfect. Many years ago, I made a donation to my alma mater, Rutgers College. The student on the phone asked if I wanted to designate my gift to a particular part of the University, and when I said, “No,” he said, “Well I’m in the Glee Club and we could sure use the money. Will you designate to the Glee Club?”

“Sure,” I said.

For decades now, I’ve been getting mail addressed, “Dear Glee Club Alumnus.” One day, I will attend a Glee Club reunion, certain that many people will remember my contribution to the tenor section.

While these harmless examples of imprecision are humorous, there’s nothing funny about the current exodus of major advertisers from the Google ad network and YouTube. Programmatic ad placement is a boon to target marketing, but like most direct marketing, it’s not perfect.

Major advertisers are in a tizzy over how to control where their ads appear … and the Google ad network is scrambling to get control over placement, as they should be. Advertisers need to protect their brands from appearing in an environment that can harm them.

Just a few examples: Ads for IHOP, Cinnamon Toast Crunch, “The Lego Batman Movie,” “Chips” and others have recently popped up among nude videos from everyday users or X-rated posts from porn-star influencers. Ad Age 3/6/17

A Nordstrom ad for Beyonce’s Ivy Park clothing line appeared on Breitbart next to this headline: NYTimes 3/26/17

Chuck's take on Nordstrom appearing on BreitbartHere’s a great attempt at an explanation for this juxtaposition:

“What we do is, we match ads and the content, but because we source the ads from everywhere, every once in a while somebody gets underneath the algorithm and they put in something that doesn’t match.  We’ve had to tighten our policies and actually increase our manual review time and so I think we’re going to be okay,” Schmidt told the FOX Business Network’s Maria Bartiromo. Fox News 3/23/17

Appearing next to hate speech is particularly problematic for brands:

Google-displayed ads for Macy’s and the genetics company 23andMe appeared on the website My Posting Career, which describes itself as a “white privilege zone,” next to a notice saying the site would offer a referral bonus for each member related to Adolf Hitler. Washington Post 3/24/17

The Wall Street Journal reported Coca-Cola, PepsiCo Inc., Wal-Mart Stores Inc. and Dish Network Corp. suspended spending on all Google advertising, except targeted search ads. Starbucks Corp. and General Motors Co. said they were pulling their ads from YouTube. FX Networks, part of 21st Century Fox Inc., said it was suspending all advertising spending on Google, including search ads and YouTube … Wal-Mart said: “The content with which we are being associated is appalling and completely against our company values.”
Ads for Coca-Cola, Starbucks, Toyota Motor Corp., Dish Network, Berkshire Hathaway Inc.’s Geico unit and Google’s own YouTube Red subscription service appeared on racist videos with the slur “n–” in the title. Wall Street Journal 3/24/17

And as difficult as it is for the ad networks to control, brands have their own challenges trying to protect themselves from undesirable placements. Different departments running different campaigns with different agencies cause ads to appear on corporate blacklisted sites. BMW of North America has encountered that issue because its marketing plan does not extend to dealerships. While the company does not buy ads on Breitbart, Phil DiIanni, a spokesman, noted that “dealerships are independent businesses and decide for themselves on their local advertising.” NYTimes 3/26/17

Clearly our technology’s ability to target has outstripped our ability to control it. And while it remains to be seen what controls will be put in place, it’s likely that, as always, target marketing won’t be perfect.

Customer Value: Narrowcasting vs. Broadcasting

The traditional model for customer acquisition has essentially been a broadcast approach, reaching a large audience generally descriptive of the customer base. Contrast this with what is sometimes described as “narrowcasting.”

Virtually every brand we’ve met with in the last few months is hungry for new customers: The war for the customer is on. For more on growing your customer base, consider reading “Bigger is Better: How to Scale Up Customer Acquisition Smarter,” which is an article we published recently about how to grow your customer base.

Many organizations are hooked on customer acquisition. That is, in order to hit sales plans for the organization, new customers will be required in large numbers. It’s about as easy to kick the “acquisition addiction” as it is to kick any other for most brands. Try going without coffee suddenly, and see how your head feels. It’s not very different from reducing a business’s dependence on customer acquisition as a means to achieving revenue and profit targets.

Organizations that need ever larger numbers of new customers to achieve growth goals eventually will find the cost of acquiring incremental net new customers can become prohibitive.

Broadcast vs. Narrowcast
The traditional model for advertising and customer acquisition has essentially been a broadcast approach, reaching a large audience that is generally descriptive of the customer who a brand believes to be a fit. Contrast this with what is sometimes described as a “narrowcasting” strategy. Narrowcasting uses customer intelligence to understand a great number of discrete dimensions that a consumer possesses and can leverage statistical methods to validate the accuracy and predictiveness of targeting customers through these methods.

The chart below, depicting the value of customers acquired through traditional broadcast capabilities upfront and over time helps illustrate why “broadcast” strategies for customer acquisition alone aren’t enough.

Research for Mike Ferranti blog

Broadcast Acquisition Strategies Lack Focus on Customer Value
Large numbers of customers have been acquired in a trailing 13-month window – lots of them. The challenge is this cohort of customers has been acquired without adequate consideration of the right target.

Consider the fact that the target customer value of average or better customers is around $500. In the example above, the marketer has acquired a large number of customers who are lagging in their economic contribution to the business. While the customer acquisition metrics may look good, this was a large campaign and produced several hundreds of thousands of customers over its duration – the average value of those customers is quite low indeed.

Low Customer Value Manifests Itself, Even if Acquisition Volume Is High
When sales targets are rising, it becomes harder to justify the high cost of customer acquisition if the customers previously acquired are underperforming. This leads to a very common bind marketers are placed in. The only way to “make the number” is to acquire more and more.

The most competitive and high quality businesses steadily acquire and have a robust customer base whose economic contribution is materially higher. Consequently, profits are higher, and we have a fundamentally better business.

Oftentimes, “broadcast” advertising approaches define the target with a single criteria like age, income or geography. This can be effective, especially when the media is bought at a good value. However, “effective” is almost always defined as “number of customers acquired.” This of course is a reasonable way to judge the performance of the marketing – at least by traditional standards.

There is another way to measure the success of the campaign that is only just beginning to be understood by many traditional “broadcast” marketers: customer value. The chart above shows that this cohort of acquired customers had relatively low economic value.

Root Causes of Low Customer Value
What are the causes of low value? It would be fair to start with the ongoing marketing and relationship with the customer. Bad service could keep customers from returning. Poor quality could lead to excessive returns. Over-promotion could drive down value. Getting the message and frequency wrong could lead to underperformance of the cohort. These are all viable reasons for lower value that need to be rationally and methodically ruled out prior to looking elsewhere.

Therefore, if operational issues are not clear – either through organizational KPI tracking, or simply by monitoring Twitter — then a marketing professional needs to start looking at three things.

  1. The Target (and Media)
  2. The Offer (and Message)
  3. The Creative

Given the target is historically responsible for up to 70 percent of the success of advertising, this is the first place a professional data-driven marketer would look.

Target Definition Defines the Customer You Acquire, and It Drives Customer Value.
A fact that is often overlooked is that target definition means not just focusing efforts and advertising spent on consumers who are most likely to convert and become customers, but it also defines what kind of customers they have the potential to become.

In conversations with CMOs, we often discuss “the target customer” or the “ideal customer” they wish to introduce their brand to. The descriptions of course vary by the brand and the product. Those target definitions are often more qualitative in nature. In fact, only about 30 percent of CMO’s we engage with regularly are focused on using hard data to define their customer base. While these are helpful and create a vocabulary for discussing and defining who the customer is, those primarily qualitative descriptors are often sculpted to align with media descriptors that make targeting “big and simple.”

“While simplifying is good business, when simplicity masks underlying business model challenges, a deeper look will ultimately be required, if not forced on the organization.”

While we would not refute a place for those descriptors of a valued consumer, they do fall short of true target definition. Ideally, the process of defining the customer who a brand wishes to pursue must begin with a thorough inventory of the customers it already has, and a substantial enhancement of those customer records which provides vibrant metrics on affluence, age, ethnographic, urbanicity, purchasing behaviors, credit history, geo- and demo-graphics, net worth, income, online purchasing, offline purchasing and potentially a great deal more.