Do consumers really understand “native advertising” labeling?

There’s no question that “native advertising” – paid editorial content – has become a popular “go-to” marketing tactic. After all, it’s based on the time-tested notion that people don’t like advertising, and they’re more likely to pay attention to information that looks more like a news article than an ad.

Back in the days of print-only media, paid editorial placements were often labeled as “advertorials.” But these days we’re seeing a plethora of ways to label them – whether identified as “sponsored content,” “paid posts,” or using some kind of lead-in descriptor such as “presented by …”

Behind all of the verbal gymnastics is the notion that people may not easily distinguish native advertising from true editorial if the identification can be kept somewhat euphemistic. At the same time, the verbal “sleight of hand” raises concerns about the obfuscation that seems to be going on.

These dynamics have been tested. One such test, conducted several years ago by ad tech company TripleLift, used biometric eye-tracking to see how people would view the same piece of native advertising, that carries different disclosure labeling.

The results were revealing. Here are the percentages of participants who saw each ad, based on how the content was labeled:

  • Presented by” labeling: ~39% saw the content
  • “Sponsored by” labeling: ~29%
  • “Promoted by” labeling: ~26%
  • “Brought to you by” labeling: ~24%
  • “Advertisement” labeling: ~23%

Notice that the content that was labeled “advertisement” was noticed the least often. This provides yet more confirmation that people ignore ads.  When advertisers used softer/fuzzier terms like “presented by” and “sponsored by,” they achieved a bigger lift in the content being noticed.

It comes as little surprise that those same “presented by” and “sponsored by” labels are also the most potentially confusing to people regarding whether the item is paid content. And when people find out the truth, they tend to feel deceived.

Members of the Association of National Advertisers look at it the same way. In an ANA survey of its members conducted several years ago, two-thirds of the respondents agreed that there should be “clear disclosure” of native ads – even if there’s a lack of consensus regarding who should be responsible for the labeling or what constitutes “clear” disclosure.

Asked which labeling describes native ad disclosure “very well,” here’s what the ANA survey found:

  • “Advertisement”: 62% say this labeling describes native ad placements “very well”
  • “Paid content”: 37%
  • “Paid posts”: 34%
  • “Sponsored by”: 31%
  • “Native advertising”: 12%
  • “Presented by”: 11%
  • “Promoted by”: 11%
  • “Branded content”: 8%
  • “Featured partner”: 8%

Considering that the findings are all over the map, it would be nice if a universal method of disclosure could be devised. But the language that’s agreed upon shouldn’t scare away readers, since in so many cases native advertising isn’t directly pitching a product or service.  Labeling such content “advertising” would be as much of a misnomer as failing to divulge the company paying for the placement.

My personal preference for adopting consistent labeling language among the options above would be “Sponsored by …”  What’s yours?

Roads to … nowhere?

Google Maps admits its business listings are riddled with errors and outright fraudulent entries.

The news reports hit fast and furious this week when the media got wind of the millions upon millions of “faux” business listings on Google Maps, thanks to a new Wall Street Journal exposé.

It’s true that there are a ton of map listings displayed by Google on search engine results pages, but the latest estimates are that there are more than 11 million falsely listed businesses that pop up on Google searches on any given business day.

That number may seem eyebrow-raising, but it’s hardly “new news.” Recall the reports that date as far back as a half-decade — to wit:

  • In 2014, cyber-security expert Bryan Seely showed how easy it was to use the Internet’s open architecture to record telephone conversations and create fraudulent Google Maps listings and locations.
  • In 2017, Google released a report titled Pinning Down Abuse on Google Maps, wherein it was estimated that one in ten fake listings belonged to actual real-live businesses such as restaurants and motels, but that nefarious third-parties had claimed ownership of them. Why do this? So that the unscrupulous bad-actors could deceive the targeted businesses into paying search referral fees.

Google is owning up to its continuing challenges, this week issuing a statement as follows:

“We understand the concerns of those people and businesses impacted by local business scammers, and back in 2017 we announced the progress we’d made. There was still work to be done then, and there’s still work to be done now.  We have an entire team dedicated to addressing these issues and taking constant action to remove profiles that violate our policies.”

But is “constant action” enough? Certain business trades are so riddled with fake listings, it’s probably best to steer clear of them altogether.  Electricians, plumbers and other contractors are particularly sketchy categories, where roughly 40% of Google Maps listings are estimated to be fraudulent entries.

The Wall Street Journal‘s recent exposé, published on June 24th, reported on a search its researchers conducted for plumbers in New York City.  Of the top 20 Google search results returned, only two actually exist where they’re reported to be located and accept customers at the addresses listed.  That’s pretty awful performance even if you’re grading on a curve.

A measure of progress has been made; Google reports that in 2018 it removed some 3 million fake business listings. But that still leaves another 11 million of them out there, silently mocking …

Marketing AI and Machine Learning Come Into Better Focus

Artificial intelligence and machine learning are two phrases that have become regular currency in the marketing world over the past several years. It isn’t hard to figure out why, as both AI and machine learning have the potential to help marketers make sense of the ever-increasing volume (and complexity) of raw data that’s become available in increasing amounts, thanks to the digitization of “everything.”

Some people use the two terms interchangeably, but that isn’t exactly right. According to Thorin McGee, director of content at Fast Capital 360, the distinction is subtle yet significant:

  • AI is when you develop an algorithm that allows a computer to “think” for you towards achieving a goal.
  • Machine learning is when you let a computer create an algorithm to find ways to meet the goals you give it, based on large pools of data.

Put the two together, and you have the ability to gain some really deep insights into what your data is actually telling you, thereby improving decision-making success.

On the data front, this great potential is tempered by some significant challenges. Christopher Penn, chief innovation officer of marketing data and analytics consulting firm Trust Insights, characterizes them as the “5 V’s” of data:

  1. Volume — There’s so darned much of it.
  2. Variety — More kinds of data are being churned out.
  3. Velocity — Data is coming at us faster than ever.
  4. Veracity — If data isn’t verified, it can do more harm than good.
  5. Value — In raw form, data isn’t particularly useful. Like oil, data needs to be refined to be of value.

If getting your arms around data seems like trying to hug a stream of water, you aren’t alone in thinking that. Many companies are pretty adept at using data to identify what happened — and maybe even to diagnose problems and why they happened.  But it’s less easy to predict what will happen based on data … and even harder to use data to determine with confidence what should happen.

The biggest challenge — but also the one with the biggest potential payoff — is tapping machine learning to process and use data in forging future business as you wish it to be.

To date, very few companies have come all that close to becoming AI-powered enterprises. But it’s where we’re headed in the coming decade.  It represents one of the biggest opportunities for differentiating one company from another.  But it will require a disciplined and concerted effort:  talent acquisition (developers and data scientists), tapping outside vendors, along with taking available open-source code and building upon that to implement the appropriate marketing technologies.

Oh, and committing to a multi-year initiative and budget even after all of those other pieces are in place.

Surveying the current landscape, are there particular entities that you see as on the leading edge in applying AI and machine learning to their marketing endeavors? Please share your observations with other readers.

Chief Marketing Officer: The most thankless job in the corporate world?

Few people I know would claim that being the Chief Marketing Officer of a company is a job without risks. Indeed, numerous articles in the business press point to an average length of tenure in a CMO position that is often measured in months rather than in years – indeed, the shortest length of time among all C-level jobs.

And now, a recently completed survey of CMOs  underscores just how wide-ranging the reasons are for those employment characteristics. Branding consulting firm Brand Keys tested a number of issues to see which are the ones that keep CMOs “awake at night.”

Three-quarters or more of the respondents to the Brand Keys survey reported that every factor presented was significant enough to cause them to lose sleep.  Leading the list with near-universal high-alert concern is ROI factors. Other factors of concern to nearly every respondent in the survey are big tech and data security issues.

Listed below is how each of the factors tested by Brand Keys turned out with CMOs in terms of “losing sleep” over them.

90%+ lose sleep worrying about:

  • ROI/ROMI factors
  • Big data, big tech and big security issues
  • Establishing trust with customers
  • Innovation, AI, technology and marketing automation developments
  • Consumer expectations regarding privacy and transparency

80%-90% lose sleep worrying about:

  • Managing social networking
  • Creating relevant advertising content
  • Successfully deploying predictive consumer behavior analytics/technologies
  • Dealing with consumer advocacy and social activism
  • Developing long-term strategies that align with corporate growth goals
  • Having the ability to engage with audiences – not just find them

At the “bottom” of the pile … 75%-80% lose sleep worrying about:

  • “Democratization” of the digital world and protecting brand equity within it
  • “Political tribalism” and its effect on brand reputation
  • Being relevant when tweeted about
  • Keeping consumers engaged with the brand
  • Creating better cross-platform synergies for marketing campaigns
  • Creating an “unlearning curve” to move away from legacy marketing metrics
  • Creating marketing synergies among different generational/age cohorts
  • Being replaced by the chief revenue officer

This last worry factor – losing their job – seems almost preordained given the tenuous circumstances more than a few CMOs deal with in their positions.

… and likely made more so because CMO’s are quick to be blamed when things don’t go well, even if they aren’t in the strongest position to effect the changes that may be needed. “Responsibility without authority” is the stark reality for too many of them.

What are your thoughts about the dynamics faced by CMOs in their companies?  Whether you speak from personal experience or not, I’m sure other readers would be interested in hearing your views.

 

KISS and tell: Testing the notion that the world’s strong brands are “simple” ones.

When you think of strong brands, the notion of their “simplicity” might seem a bit surprising. And yet this is the contention of Siegel+Gale, a leading brand strategy firm.

S+G has gathered together its research findings in an annual ranking it calls the World’s Simplest Brands.  These are the brands that deliver best on their promise with simple, clear, intuitive experiences.

Howard Belk, the company’s CEO and chief creative officer, explains it this way:

World’s Simplest Brands quantifies the substantial monetary value of investing in simplifying.  Now in its eighth year, our study reaffirms an increasing demand for transparent, direct, simple experiences that make peoples’ lives easier … the data prove that simplicity pays.”

In order to research brand simplicity, S+G queried ~15,000 people living in nine countries (the United States, India, China and Japan plus several European and Middle Eastern nations) to evaluate well-known brands and industries on their perceived simplicity.

Among the findings in its most recent annual evaluation, S+G reports that political, economic and cultural uncertainty coupled with shifting customer expectations are contributing to a heightened desire for simplicity.

The value of simplicity manifests itself in a number of ways; two key ones are:

  • A clear majority of people (~64%) are more likely to recommend a brand that delivers simple experiences.
  • A majority of the survey respondents (~55%) report that they are willing to pay more for simpler experiences.

S+G calculates that companies which fail to provide simple brand experiences forego nearly $100 billion in sales revenues collectively.

Based on its research, S+G ranks the Top 10 World’s Simplest Brands, as well as a Top 10 ranking for brands in the United States. Netflix, ALDI and Google top the worldwide rankings:

  • #1. Netflix
  • #2. ALDI
  • #3. Google
  • #4. Lidl
  • #5. Carrefour
  • #6. McDonald’s
  • #7. Trivago
  • #8. Spotify
  • #9. Uniqlo
  • #10. Subway

Explaining how several of the key brands made it to the pinnacle, S+G reported the following:

  • Netflix achieved top spot for the first time, thanks to its ease of experience allowing users to stream, pause and resume viewing without commercials or commitments.
  • ALDI scores well because they surpass big-box competitors with their clear communications, affordable prices, and premium private-label products.

U.S. Brand Simplicity Rankings are Different

Not surprisingly, S+G’s Top 10 list of the simplest brands looks different from a purely American perspective, with just four brands ranking in the Top 10 on both the USA and world lists. Here are the top-performing brands based on just American respondents:

  • #1. Lyft
  • #2. Spotify
  • #3. Amazon
  • #4. Costco Wholesale
  • #5. Subway
  • #6. Google
  • #7. McDonald’s
  • #8. KFC
  • #9. Southwest Airlines
  • #10 Zappos

What’s also interesting is what kinds of brands aren’t showing up on the Top Ten lists. News and social media industry participants aren’t ranking well – think platforms like Facebook, Twitter and LinkedIn and broadcast networks like CNN, NBC and ABC.

Also failing to show up are brands operating in industries that are steeped in complexity – fields like car rental services, insurance services and the worst one of all, TV/cable and other telecommunications brands.

The S+G report concludes by stating companies and brands “benefit greatly by keeping it simple for customers … or [they] suffer the consequences.” Moreover, companies that are operating in highly competitive marketplaces can cut through and rise to the top based on their brand simplicity.

More information about the Siegel+Gale research findings can be accessed here.

What about you?  Which brands would you classify as particularly noteworthy in their simplicity appeal? Please share your thoughtss with other readers.

Is third-party marketing data on life support?

As a marketing professional for the better part of four decades, I can’t imagine any of us doing our jobs without soaking up as much data as possible to help with our decision-making.

And data accessibility is miles ahead of where it was when I first entered the marketing field.  Back in the day, “finding data” meant hitting the reference libraries to access government or other reporting – especially if you were lucky enough to be located within a reasonable distance of one.

There was the phone for real-time information-gathering … and also the FAX machine for quick receipt of “facts in brief” — not to mention the “wait-and-wish-for” mail and package delivery services.

If it was insight you needed from customers or prospects about a new industry or business venture, primary research was always an option — if you had the money and the time to allocate to the effort.

As for “first-party” data, that was available as well – but how often were we at the mercy of the bureaucratic machinations of in-house IT departments to get even basic data requests processed in a timely way?

All of which is to say that marketers have always used data – but the quantity wasn’t as great, while the timeframe of data acquisition was at a snail’s pace compared to today’s reality.

But now, after having become quite spoiled at the availability of all sorts of information, might it be that we’re regressing a little?

In particular, third-party information purchased in bulk, often from data aggregators, seems to be where the backsliding is occurring.

Consider ad targeting and building audiences: We have access to valuable first-party data thanks to website analytics and studying the results of our own e-mail campaigns.

There’s no question that the first- and second-party data which marketers are able to access are highly valuable in that the information creates efficiencies in campaigns and drives higher conversion rates. But theoretically, the ability to layer on accurate third-party data would take things even further.

There’s also been third-party behavioral data from three big behemoths — Google, Facebook and Amazon – that can be used for MarComm targeting purposes. But of those three platforms, just one of them allows third-party data to be made publicly available to end-users.

This poses challenges for the suppliers that aggregate and sell third-party data, as the quantity and quality of their information isn’t on the upswing at all.

Fundamentally, finding a good source for third-party data entails understanding what sources each data aggregator is using and the methodology it employs to collect the data.  Factors of scale, quality, reputation and price also come into play.

But despite best efforts, when testing third-party data for MarComm campaigns and lead-generation efforts the results are often pretty ugly — the data loaded with inaccuracies and basically terrible for efficiency metrics.

It doesn’t help that with the rise of Amazon as yet another “walled garden” of data, the “open web” represents a ever-smaller portion of the total ad spend — and hence also a decreasing amount of the third-party data that’s available to end-users.

With the veracity of third-party data becoming more suspect, it’s had an interesting effect on data management platforms, which are now focusing more on the actual messages themselves and not the “personas” of the people receiving the messages or how they were identified and targeted.

Is it possible for third-party data to provide good information to AI systems — intelligence that can verify and augment the value of the first-party data? If leading ad platforms can use such third-party data to enhance the accuracy and value of what they sell to advertisers, there still may be valuable material to work with.  As it stands, though, I’m not sure that’s the case.

What are your experiences?  Please share your perspectives with other readers here.

Programmatic ad buying in the B-to-B sector: The adoption rate grinds to a halt.

Each year, Dun & Bradstreet publishes its Data-Driven Marketing & Advertising Outlook report.  The report’s findings are based on a survey of marketers in the business-to-business sector.  Among the questions asked of marketers is about the advertising tactics they utilize in support of their sales and business objectives.

A look at D&B’s annual outlook reports over the past several years, an interesting trend has emerged: The adoption rate of B-to-B companies being involved in programmatic ad buying has plateaued at somewhat below 65% of firms.

In fact, you have to go back to 2015 in D&B’s reports to find the proportion of companies involved in programmatic advertising running significantly below where it is now.

That being said, those firms that are involved in programmatic ad buying are planning on allocating additional funds to the effort. The most recent survey finds that ~60% of the respondents involved in programmatic advertising plan to increase their spending in 2019.  That includes ~20% who plan to allocate a significant dollar increase of 25% or greater.

Another interesting finding from the 2018 survey is that there appears to be slightly less interest in display and video programmatic ad placements – although display remains the most commonly run ad type.

Where heightened interest lies includes one category that should come as no surprise – mobile advertising – as well as several that might be more unexpected. Social media advertising seems like it wouldn’t be a very significant part of most B-to-B ad buyers’ bag of tricks, but two-thirds of respondents reported that programmatic advertising in that sector will be increasing.

Another interesting development is that ~17% of the respondents reported that they’re stepping up their programmatic buying for TV advertising – which may be an interesting portent of the future.

Lastly, the survey revealed little change in the types of challenges respondents face about programmatic ad buying – namely, how to target the right audiences more effectively, how to measure results, and the need for better technical and operational knowledge for those charged with overseeing programmatic ad efforts inside their companies.

More information and findings from the 2018 D&B report can be viewed here.