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”.
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.
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.
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 TheNew 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.
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.
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.
To pursue on our Behind Business Cases with this 3rd example, we didn’t have the same questions with the field “Country” we had with “Out of Digital”. Country filters are obvious, in particular for marketers, but what didn’t expect was how on eye-opener it would be.
Import an existing Corpus into TrustedOut
Compare countries within the same Corpus
To discover the countries have very different profiles in particular, their taxonomies.
Comparing Apples to Apples
To avoid comparing Culture, Politics and Entertainment with Business, Society and Tech, TrustedOut can align those two countries and have you compare… apples to apples.
In this 2018 Gallup survey, a clear split between political parties is noticeable, but even more interesting is crossing Presidential elections with this chart.
Losing raises trust.
In America, when you lose a Presidential election (Democrats and Republicans), your trust in media goes up the year after.
2016. Donald Trump (Republican).
Winners: Republicans. Down the election year. Flat the year after.
Losers: Democrats. Down the election year. Up the year after.
2012. Barack Obama (Democrat).
Losers: Republicans. Down the election year. Up the year after.
Winners: Democrats. Flat election year, Flat the year after.
2008. Barack Obama (Democrat).
Losers: Republicans. Down the election year. Up the year after.
Winners: Democrats. Down election year, Down the year after.
2004. George H. Bush (Republican).
Winners: Republicans. Down the election year. Flat the year after.
Losers: Democrats. Down election year. Up the year after.
2000. George H. Bush (Republican).
Winners: Republicans. Flat the election year. Down the year after.
Losers: Democrats. Down election year. Up the year after.
Interesting.
Now, do Political orientations within your Corpus of content impact analytics?
Today, your company is using analytics tools and thus, corpuses of content. Are you sure you know what you are feeding your intelligence tools with? Importing your existing corpuses to TrustedOut will help.
Let’s take an example of two corpuses, one for the USA and on for France, and ask TrustedOut if you are comparing apples to apples.
Now, let’s click on that “Corpus analytics” button to discover…
… the US corpus top category is “Cultures & Arts” while the French on is “Business”.
Back in July, for the 5th event at the BPI (Public Bank of Investment, the largest sovereign bank in the world) on Silicon Valley vs France, we ran TrustedOut on on the corpus used for France for this event and check if political orientations would influence analytics.
And yes it did, as this slide shows:Later on, as we were working on TrustedOut User eXperience, we decided to apply this to our customer journey. In other words, show case this business need of viewing a content orientation impact on your decision-making. Here it is in TrustedOut:
