Need for Media Profiles Lists? Unilever agrees.

[click to read article]

[Source and credits: AdAge]

On Monday, we wrote:

Media profiles are key to Business Intelligence and Advertising.

Unilever launches “Trusted Publisher” list.

AdAge writes: “Unilever is launching a Trusted Publishers network that goes beyond the standard audience-verification, anti-fraud and brand-safety guidelines of most marketer ‘whitelists.'”

“Both the publishers and the criteria will be continuously re-assessed”

“We’re aiming to have as many publishers as possible, but they need to go through these selection criteria,” DiComo says. Both the publishers and the criteria will be continuously re-assessed, he says, “because the space is moving so quickly.”

Unilever’s initiative is logic.

Knowing: 4 in 10 brands deliver ads on unsafe sites – Cision

Consequently: 70% implement black or white lists…

… but 64% fear negative impacts on performance.  71% fear to not achieve reach while delivering to the right audience in the right context. – Cision & Digiday

Consequently, the solution is to:

1/ Have marketers define their white lists themselves for each campaign, each brand. And align in each country.

2/ Have those lists automatically built, maintained and updated, directly serving the trading desk.

Bottom line: Keep your Brand Safe, let TrustedOut manage your white lists.

Watch a demo.

Contact us:

 

 

Media profiles are key to Business Intelligence and Advertising.

Example of a taxonomy DNA: BHG.com
Mouse over to zoom

Click to zoom

Media profiling:
Collection and Classification.

TrustedOut profiles media via data collection to gather intangible data and content classification to evaluate expertise and perception.

Data collection.

From a domain, here for our example, bhg.com, TrustedOut will collect a lot of intangible data such as: its name: Better Homes and Gardens, the owner, here, Meredith Corporation, the organization type, here it’s a Private company, find if content orientations are declared, here, no political, no religious orientations, the online traffic, the revenue, the number of employees, etc, etc…

All those informations are important when defining what the analyst, the CMO, the ad agency, trust, and want to base intelligence, fully ensure brand safety, understand who’s receptive to a message, a promotion, etc…

Content classification.

Content classification is used primarily for our taxonomy and to understand how an information source is perceived, like “spotted as” fake news, junk science, conspiracy theory…

Here’s how it works:

Mouse over to zoom

Let’s have look at BHG.com’s taxonomy.

As described above, our AI-operated taxonomy permanently assesses where the site is good at, meaning non only, the subjects covered, but also, the level of expertise.

Another view of BHG.com’s taxonomy is below with the top classification level and a drill down on this top level, here: People.

Our taxonomy tells us BHG is Specialized in:

People
People › Entertainment And Leisure
People › Entertainment And Leisure › Gardening
People › Entertainment And Leisure › DIY (Do It Yourself)
People › Lifestyle
People › Lifestyle › Food And Beverage (yes! BHG has a recipes section)
People › Lifestyle › Decoration And Design And Architecture Specialized
People › Lifestyle › Home

and BHG covers the following:

People › Society › Family
People › Culture And Arts › Museum And Exhibition
People › Lifestyle › Feminine
People › Culture And Arts › Movies
People › Sports › Gymnastics And Fitness And Yoga
People › Sports › Horse Riding
People › Education › Preschool And Primary School
Sciences › Medicine And Health › Personal Health
Industries › Transportation › Bus

… and BHG has a Limited coverage in:

Sciences › Human Sciences › Sociology

Machine learning operates our Taxonomy and online perception to keep our database of media profiles, unbiased, universal and always up-to-date.

Importance for Business Intelligence:
No trust, no Intelligence.

Say you are in the food market and want to understand how some cuisine types are perceived amongst specialized publications in America:

To feed your intelligence tools, such as Digimind (demo here), your Corpus will look like this.

16,000+ sources (49k new articles abstracts a day) will ensure you analyze, and thus base your strategic decisions, on content you define.

Would you have thought Better Homes and Gardens would be part of your Corpus? At first, Home and Garden does not sound like Food and beverage specialist, does it? (well, if you are looking for Chicken recipes, it’s here).

This anecdote is to point out the need for both an unbiased and universal classification and a depth of expertise from the content you will base your decisions on.

It is critical you trust the right, and all the right, publications to trust any intelligence coming out of those publications. Depth and width.

No Trust, No Intelligence.

Importance for Advertising:
Brand Safety and Budget Optimization.

Here, you want to advertise your new product to the US Food Market. Keeping your brand safe will be your top priority… After all, you will pay to increase your business, not ruin the brand reputation it took you years to build.

Brand Safety is top priority for CEOs and CMOs.

For your online ad campaign, the trading desk of your advertising agency will define the query, with, amongst other things, desired and not-desired keywords, to select the content you trust compatible with your brand.

But a page can match all those criteria but be published on a site not safe or compatible with the advertiser’s brand. You must also select the publications you trust compatible with your brand.

Otherwise, your brand is at risk. And advertisers know and fear it:

4 in 10 brands deliver ads on unsafe sites – Cision

The only solution for the CMO to be certain to keep brands safe: Define himself the lists of publications he trusts compatible with her/his brands.

Consequence:

70% implement black or white lists… – Digiday

But while the vast majority is using lists, the vast majority is unhappy with the solution. As of today:

… but 64% fear negative impacts on performance.  71% fear to not achieve reach while delivering to the right audience in the right context. – Cision

Consequently, the solution is to:

1/ Have marketers define their white lists themselves for each campaign, each brand. And align in each country.

2/ Have those lists automatically built, maintained and updated, directly serving the trading desk.

Bottom line: Keep your Brand Safe, let TrustedOut manage your white lists.

Let’s go back to our US Food Market example. Our marketer, here, wants to build a white list of US based publications, specialized in Food and Beverage and also wants them to be in business for more than 3 years, not politically, nor religiously oriented and, of course, not spotted as fake news, hate news or junk science.

Corpus looks like above and now more than 9,000+ sources are immediately available to be imported or live feeding your trading desk.

Trading desk runs its query within the perimeter of the white list. Best of both worlds, search and directory.
Brand is now totally safe.

Run more where it returns more.

In addition, Media Profiles bring ad budget optimization.

By adding media profiles to a campaign report, marketers and agencies can surface media with the best ROI and thus, increase budgets where return is optimal.

BHG works best for your campaign? Let’s get more of this profile and spend your ad budget where it makes the most sense.

Questions? Shoot!

 

New demo page showcasing TrustedOut and BI, Ads and PR

A new demo page has been added to TrustedOut.com

The scenario

 

ACME is a sport car maker launching a new model extensively using Artificial Intelligence (AI). ACME has 2 main countries, US and France and wonder what market to test first.

1. Corpus Intelligence for Business Intelligence: Market selection.

New corpus, the CMO (or Marketing Manager) defines 3 conditions to be necessary.
a. Where are the publications? We said France and the United States
b. What should these publications be about? ACME wants to grab how AI is perceived from publications covering Politics, for regulations, Law, for any legal aspects, Tech, to gauge technology used and perceptions and, of course, Transportation, for anything car related.
c. Want to be safe from any toxic content? Of course, no fake new and no junk science TrustedOut classification knows how gauge the expertise level of a source and how sensitive to the news the taxonomy should be. At this stage, we want generalist publications by setting the expertise level to “Covered” Here is the corresponding query for our Corpus, which we are going to name “ACME AI in new model”.

Go to the demo page >

2. Corpus Intelligence for Brand Safety & Campaigns. White listing.

ACME’s CMO wants to check if Pure Player Media (media only available online) is a good target. After all, Pure Players should be more reactive and not having to sync print, for example, that can be daily, weekly or monthly, with immediate online publishing. Let’s go back to TrustedOut and change the Corpus as follow: a. Where are the publications? We now want to limit to France. b. Select Pure Players? We want media where “out of digital” is set to None to only get those not publishing on any other support.

Go to the demo page >

3. Corpus Intelligence for Coverage & Content Analytics. PR campaigning.

Digimind gives us the key concepts to write our Press Release: European Union/Commission and Neuronal Networks. With the Corpus we have what publications to target, with those key concepts we have how to write a Press Release that will interest those targets.

Go to the demo page >

Questions? Shoot!

Presentation to the GESTE (major online editors in France)

We were very proud to be invited to present at the latest GESTE (major online editors in France) event about “Trust and labelization“.

The presentation.

Click to run presentation

 

The Table of Content.

The problem:
Distrust in media.

The consequence:
In decision-making, only what is trusted in can be trusted out.

The point:
Distrust is general, trust is personal. No universal list.

The logic:
Trust is about reputation. Media Brands are about reputation.

The solution:
Industrial Profiling Media Brands.

The application:
Easy querying, live feeding.

The technology:
Machine learning, Web crawling, big data and microservices to self-feed, self-grow and daily validations

Our live taxonomy:
Permanent machine learning, customizable sensitiveness & specialty depth,
enterprise mapping.

The opportunity:
BI, Ads & PR

Question? shoot!

 

Get information from Traditional Media, have conversation on Social Media. Not the other way around.

Misinformation and biases infect social media, both intentionally and accidentally

This highly recommended article from The Conversation exposes 3 types of bias identified by Indiana University. Hereafter are our takeaways.

1/ Bias in the brain

More information means less quality content shared

“Cognitive biases originate in the way the brain processes the information that every person encounters every day. The brain can deal with only a finite amount of information, and too many incoming stimuli can cause information overload. That in itself has serious implications for the quality of information on social media. We have found that steep competition for users’ limited attention means that some ideas go viral despite their low qualityeven when people prefer to share high-quality content.”

Beware emotions in headline trap

“One cognitive shortcut happens when a person is deciding whether to share a story that appears on their social media feed. People are very affected by the emotional connotations of a headline, even though that’s not a good indicator of an article’s accuracy.”

What matters is where it’s coming from.

“Much more important is who wrote the piece.”

TrustedOut foundation: profile who’s behind to evaluate your trustworthiness appreciation and the path to greater trust in media:

Optimism and method for greater trust in media.

2/ Bias in society

Like seeks like (“Birds of a feather flock together”)

“When people connect directly with their peers, the social biases that guide their selection of friends come to influence the information they see. …social networks are particularly efficient at disseminating information – accurate or not – when they are closely tied together and disconnected from other parts of society.”

“Us vs Them”

“The tendency to evaluate information more favorably if it comes from within their own social circles creates echo chambers that are ripe for manipulation, either consciously or unintentionally. This helps explain why so many online conversations devolve into “us versus them” confrontations.”

We are right. Distrust in fact-checking

“…during the 2016 U.S. presidential elections [analysis] shows that Twitter accounts that shared misinformation were almost completely cut off from the corrections made by the fact-checkers. When we drilled down on the misinformation-spreading accounts, we found a very dense core group of accounts retweeting each other almost exclusively – including several bots. The only times that fact-checking organizations were ever quoted or mentioned by the users in the misinformed group were when questioning their legitimacy or claiming the opposite of what they wrote.

3/ Bias in the machine

Getting more of the same. Accurate. Or not.

“The third group of biases arises directly from the algorithms used to determine what people see online. Both social media platforms and search engines employ them. These personalization technologies are designed to select only the most engaging and relevant content for each individual user. But in doing so, it may end up reinforcing the cognitive and social biases of users, thus making them even more vulnerable to manipulation.”

Illusory truth effect. Repeat until it feels true.

“For instance, the detailed advertising tools built into many social media platforms let disinformation campaigners exploit confirmation bias by tailoring messages to people who are already inclined to believe them. Also, if a user often clicks on Facebook links from a particular news source, Facebook will tend to show that person more of that site’s content. This so-called “filter bubble” effect may isolate people from diverse perspectives, strengthening confirmation bias.”

Popularity bias. More clicks makes it feel more true.

“Another important ingredient of social media is information that is trending on the platform, according to what is getting the most clicks. We call this popularity bias, because we have found that an algorithm designed to promote popular content may negatively affect the overall quality of information on the platform. This also feeds into existing cognitive bias, reinforcing what appears to be popular irrespective of its quality.”

Get information from Traditional Media, have conversation on Social Media. Not the other way around.

Unsurprisingly, and somewhat reassuring, numbers from Reuters/Oxford (hereafter for the US) show trust in social media are the lowest with 13% vs 34% for news overall and the highest at 50% with News/Media I use. (we developed this with this post “While distrust is general, trust definition is personal.“)

Related posts:

Saving journalism. [updated 2/19/19]

Top 2019 predictions: Privacy and Transparency

TrustedOut’s market

Questions? Comments? Contact us!

 

 

Older people share more fake news.

Age predicts behavior better than any other characteristics (even party affiliation )

Researchers at New York and Princeton Universities, through their recent surveys, are saying older users shared more fake news than younger ones regardless of education, sex, race, income, or how many links they shared. [source: The Verge]

7 times more fake news sharing

“But older users skewed the findings: 11 percent of users older than 65 shared a hoax, while just 3 percent of users 18 to 29 did. Facebook users ages 65 and older shared more than twice as many fake news articles than the next-oldest age group of 45 to 65, and nearly seven times as many fake news articles as the youngest age group (18 to 29).”

Profiling media sources…

“It won’t be easy: how to determine whether a person is digitally literate remains an open question. But at least some of the issue is likely to come down to design: fake news spreads quickly on Facebook in part because news articles generally look identical in the News Feed, whether they are posted by The New York Times or a clickbait farm.”

… to build trust.

Profiling sources so limit fake news spreading is similar, in logic, to profiling sources to limit misleading intelligence. We call it “Corpus Intelligence” and will focus on B2B solutions. In production end Q1 2019.

TrustedOut’s market

In its latest report on Social Intelligence, Forrester writes, straight right from the beginning:

Enterprises Are Still Not Using Social Intelligence To Its Full Potential” 

“Social Listening Platforms’ Current Offerings All Look Alike

Each social listening platform provider emphasizes its unique applicability and use across the enterprise. But each vendor also parades a roster of features and functionalities that largely look the same from one to the next. Buyers will struggle to distinguish major differences between each vendor’s current offering because social listening platforms all rely on the same data sources as the foundation of their platforms. … most vendors in this evaluation tap into the same third-party aggregators such as webhose.io for web content, LexisNexis or Factiva for news, … Social listening platform shoppers may find the breadth of data sources an important selection factor, but the discernment of data differentiation becomes increasingly difficult when all vendors source from the same well. ”  – Forrester, Q3’18

We couldn’t agree more.

Even more if you add the current crisis of distrust in content. Magnified but far, far more complex than just some fact checking to feel better with fake news being fixed. No, fake news are just the tip of the iceberg. The issue of trust in news and information in general is to, first understand who is talking before listening to anything they say, and then, ultimately taking any action.

The immense problem today is to not profile who is talking and thus, the trust you can put in the publisher, before spreading and commenting which means adding your intrinsic support.

Intelligence needs data.

Nothing new here. AI with its deep and machine learning, needs data. Analysts need data… any kind of intelligence needs data.

Social Intelligence needs data.

Forrester makes a point by saying there is no differentiation of the offer because there is no differentiation of the data used for the Social Intelligence. Of course, we agree and that’s the foundation of TrustedOut: providing profiled media sources. Let’s have a look at the 3 references mentioned by Forrester:

Webhose.io, Factiva and LexisNexis are all about articles. We believe Media is what matters.

Webhose.io claims to be “Data As A Service”, provides articles. Factiva (Dow Jones) does the same but claims to be curated by (lots of) humans. LexisNexis does the same but focused on legal.

We totally respect those three and in no way, are we judging them. We are just saying they, all three, take an “article” approach. You could also get your articles by the author name but none is scoring them.

None of them is focusing on the media itself. TrustedOut does. Here is why:

Trust is based on a reputation. An article does not have a reputation.

An author may have a reputation but is temporary and linked to a matter.

A publisher brand definitely has a reputation and its values guarantee stability.

Bottom line: Your Trust is based on the publisher brands you value.

This is why TrustedOut is an AI-Operated profiling media database offering our clients to define their trust via sophisticated queries (65+ fields and 400+ categories) because ONLY you can define your own trust. No-one can tell you what you trust.

The distrust fix is in giving you the tools to define what you trust.

Update: Digital Content Next wrote recentlyConsumer trust is a vital and a key differentiator for publishers in a competitive environment. Fostering trust, prioritizing consumer rights and offering transparency of data practices is more important than ever before for premium publishers.”. This could be from us.

Company sizes.

LexisNexis has 10,000+ employees and $2.8 Billions in revenue, 5M users and is available in 175 countries.

Factiva was bought by Dow Jones in 2006 for $160 Millions when their revenue was $290 Millions, used by 1.8M users and 80% of the Fortune Global 500.

Webhose.io is a younger independent company out of Tel Aviv claiming 35,000 registered users, $5.5M in revenue and 115 languages.

Market is growing fast.

$4B in 2018 and according to BusinessWire: “The Global Social Media Analytics Market size is expected to reach $11.6 billion by 2023, rising at a market growth of 28.6% CAGR during the forecast period.”

Various Sector Demands is growing fast.

“Asset managers double spending on new data in hunt for edge” – Financial Times

“Investment groups have more than doubled their spending on new digital information sets and data scientists in the past two years… Asset managers last year spent a total of $373m on data sets and hiring new employees to parse them, up 60 per cent on 2016, and will probably spend a total of $616m this year, according to a survey of investors by AlternativeData.org, a trade body for the industry. It forecasts that overall expenditures will climb to over $1bn by 2020″

So, demand is fast growing…. better use content you trust.

Social vs Traditional Media Analytics.

Do they compare? Are they opposed? Is one already over? 

Yes, Social Media have changed and are changing Business Intelligence. But, while Social Media are definitely newer than traditional media, does it mean, one should be considered and not the other?

How do Social Media and Traditional Media compare?

According to Wikipedia: Social media outlets operate in a dialogic transmission system (many sources to many receivers). This is in contrast to traditional media which operates under a monologic transmission model (one source to many receivers)”

We agree.

Monitoring and Listening apply to both Social and Traditional Media.

“Social monitoring is identifying and responding to individual brand mentions on social media. Social listening, on the other hand, is collecting data from those social mentions and broader customer conversations, and pulling insights from them so you can make better decisions for your customers… Social monitoring is reactive. … where social listening, which is proactive,… allows brands to take those short-term interactions and build them to glean insights for a long-term strategy. … Through social listening, you can also unearth trends among your industry, competitors, and consumer experiences. You can then make necessary changes to stay ahead of the curve and keep customers happy.”- Sprinklr

We agree. For both. Monitoring and Listening apply to Social and Traditional Media. Monitoring is the PR/Alert and Listening is the Intelligence/Analytics part. Matter of fact, the Sprinklr post goes on with metaphors:

“There are many metaphors you can use to make this distinction clearer. Social monitoring is the trees; social listening is the forest. Social monitoring is the pixels; social listening is the picture. Social monitoring is the bandaid; social listening helps you find the cure.”

We agree again. All apply to both Social and Traditional Media.

Social and Traditional Analytics are both mandatory.

From the Public Relations Society of America (PRSA): “Therefore, thinking about both (social and traditional) as steps within finding, converting and keeping customers seems like a mentality shift we all need to make. This has already proven true… the smartest tactics from advertising, public relations, marketing and editorial together, regardless of the type of media. In essence, [clients] are merging social and traditional rather than thinking of them separately.”The Digital Research and Analytics group, a subset of Ketchum 

The mandatory need to profile what you are listening to.

The very same way you want to understand who is talking in Social Media Listening, you must profile the Traditional Media you are analyzing to understand who is talking in Traditional Media Listening.

Intelligence In makes Intelligence Out.

Not knowing the profile of the media you are using for your analytics means not knowing what comes out of your analytics tools.

In other words, not profiling what you feed your tools with, means you are totally wasting your time and money.

All intelligence processes are made or broken by the quality of what they are fed with.

Would you trust, and make decisions based on a survey where you don’t trust the sample used for that survey? (Here’s the wikipedia page on Survey methodology explaining sampling) and thus…

Corpus Intelligence makes Intelligence trustworthy.

TrustedOut Full Overview | Business cases: Content orientations | Media metrics impact | Country comparisons

Update: Traditional news media are back

in the just released Edelman Trusted Barometer

  • The number of respondents who consume traditional news weekly or more, and share or post news content several times a month or more, has increased by 14 percentage points from 26% to 40%.
  • Those who consume traditional news weekly or more has risen by 8 percentage points from 24% to 32%.
  • Inversely, the number of people who say they consume traditional news less than weekly has dropped by over 20 percentage points from 49% to 28%.

Trust in traditional media also continues to increase. According to the survey, trust in traditional media in the U.S. and Europe is higher than trust in search and social platforms. An earlier study from Gallup shows a similar rebound in media trust overall in the U.S.

 

Top 2019 predictions: Privacy and Transparency

In this Forbes article, 12 C-level leaders share their predictions for 2019.

Top predictions, results of a lesson learned the hard way: 2019 will be the year of Privacy and transparency.

Hereafter are our favorite parts from the article:

In 2019, Marketers Will Strike the Right Balance of Personalization and Privacy.  Lynne Capozzi, CMO, Acquia

“… 2019 will be the year that marketers not only prioritize data privacy, but they start to get the balance right — offering the appropriate amount of personalization and privacy to build customer relationships based on trust. Consumers will continue to challenge brands to do so — otherwise they’ll move on. …”

Transparency Will Make Much Bigger Cracks Within the Digital Ecosystem as CMOs Prioritize Tech Partners.  Mike Pallad, President, Undertone (cross-platform synchronized digital marketing for the world’s most prominent brands)

“…In the coming year, the demand for transparency will finally force marketers to choose only the tech partners that most empower them to understand the reach, frequency, and impact of their campaigns (across all of their digital partners), allowing them to spend in the most intelligent ways….”

CMOs Will Stop “Going with Their Gut” And Truly Harness Data to Make Informed Decisions.  Matt Sweeney, President of Xaxis North America

86% of US brand marketers plan to invest in outcome-driven media over the next 2 years. In 2019, CMOs will make strides toward outcome-driven media, allowing them to tie their media metrics more directly to their business goals. … By truly harnessing their data, CMOs will no longer need to go with their gut instincts when making media investment decisions. They will be more agile with their budgets and media strategies, using data to deliver better returns and deliver the best consumer experiences.”

Better media, greater profiling.

Our takeaway here is two folds:

An opportunity for an improved trust in better media.

In our previous post, Optimism and method for greater trust in media., we wrote “to improve media should have Journalists to defend themselves and improve with more accuracy, more transparency and less bias with recognized sources and countered partisan perceptions led with their media brand values.”

This prediction confirms our reading of Gallup and Axios.

A strong need for media profiling.

As media strengthen their brand values and, at the same time, Marketers will get less intrusive customer data, they will rely, even more, on analyzing the media pulse within their well defined audiences. This is the market purpose of TrustedOut.

As this profiling must not be biased and permanently updated, only an AI-operated profiling can deliver this. This is the tech foundation of TrustedOut. 

Of course, do not hesitate to reach out if you have any questions.

 

While distrust is general, trust definition is personal.

Here are 3 interesting facts (US data): 1/ people are spending more time following the news, according to Pew Research Center, 2/ distrust in news is severe and growing with 72% believing traditional major news sources reporting news they know to be fake, or purposely misleading according to a poll from Axios and SurveyMonkey and finally 3/ Trust in news depends on which news media you mean according to the Media Insight Project.

As the content you use makes your education, your opinions and, most importantly, your decisions-making, defining your trust is mandatory. This is the foundation of TrustedOut. We call it Corpus Intelligence. First targets: the $4B spent in text analytics ($10+B by 2023) to make this intelligence trustworthy and also everyone concerns with Brand Safety to help them define precisely their trusted brand perimeters.

PS: Must read article (This chart is coming from it): ‘My’ media versus ‘the’ media: Trust in news depends on which news media you mean