Microsoft Endorses Controversial Kids Safety Bill

• by Wendy Davis  @wendyndavis, Yesterday

Microsoft on Tuesday endorsed the Kids Online Safety Act, a controversial bill that would broadly regulate how social media platforms display ads and content to minors the platforms know or should know are under 17.

The bill “is a tailored, thoughtful measure that can support young people to engage safely online,” Microsoft vice president and general counsel Brad Smith said in posts on LinkedIn and X, formerly Twitter.

“We must protect youth safety and privacy online and ensure that technology – including emerging technologies such as AI – serves as a positive force for the next generation,” Smith wrote, adding that the Kids Online Safety Act “provides a reasonable, impactful approach to address this issue.”

Microsoft's endorsement comes on the eve of a Senate Judiciary Committee hearing regarding online sexual exploitation of minors.

The Kids Online Safety Act would require social platforms to take “reasonable measures” to prevent potential harms associated with social media use when displaying material to users the platforms know or should know are 16 or younger. The proposed law would task the Federal Trade Commission and state attorneys general with enforcement.

Some youth advocates including Fairplay support the bill, arguing it will help protect teens from eating disorders, online bullying and other harms.

Bill co-sponsor Senator Richard Blumenthal (D-Connecticut) has also attributed adolescents' mental health issues to social media platforms.

“Record levels of hopelessness and despair -- a national teen mental health crisis -- have been fueled by black box algorithms featuring eating disorders, bullying, suicidal thoughts, and more,” Blumenthal stated last year, when he and Senator Marsha Blackburn (R-Tennessee) reintroduced the bill.

But numerous digital rights organizations including the Electronic Frontier Foundation oppose the proposed law, arguing it would violate the First Amendment.

One concern is the measure could prevent teens from accessing content protected by free speech principles. In general, the First Amendment prohibits the government from suppressing a range of content that law enforcement officials like attorneys general might consider harmful -- ranging from photos associated with eating disorders, to “hate speech,” to material discussing drug use.

COMMENTARY IAB: Consumers Willing to Pay 12 Times What Advertisers Do for Digital

COMMENTARY

IAB: Consumers Willing to Pay 12 Times What Advertisers Do for Digital

• by Joe Mandese  @mp_joemandese, Yesterday

The Interactive Advertising Bureau (IAB) released findings of a remarkable study during its annual conference this morning, showing that consumers would be willing to pay $163.50 per month to continue receiving digital media they currently receive for free, because it is ad-supported.

While that finding was intended to demonstrate the worth that free, ad-supported digital media has for American consumers, it suggests digital media publishers are leaving a lot of money on the table -- more than 12 times what they are currently deriving from advertising.

If I divide IAB estimates for 2024 U.S. digital ad spending by the U.S. population and then divide that by 12, it reveals that digital publishers currently derive only $13.48 monthly from the average American's advertising value. That's 93% less than what they would yield if they simply charged consumers directly based on what they told the IAB they would be willing to pay to continue receiving currently free websites and apps.

Interestingly, even charging Americans $163.50 per month would seem to be a bargain for them, given a second finding from the just-released study: The dollar value they'd be willing to accept to stop using the internet.

While it's unclear whether that value is just for accessing ad-supported digital media, or for what they also pay directly for access and/or premium services, the IAB found the average American would require $37,619 annually -- or $3,135 monthly -- to stop using the internet.

In other words, the average American would be willing to pay nearly half the value of what they believe digital media is worth to them, which is more than 12 times what advertisers currently pay publishers to defray the cost of consumers accessing their digital media.

Needless to say, these numbers are highly theoretical and are based on a comparison of consumer perceptions to actual industry economic data, but the reality is it kind of proves the opposite of what the IAB was trying to demonstrate, according to the study: "This gap reinforces the need for advertising to keep the Internet free and open."

Oddly, the IAB study was intended to demonstrate the underlying "data value exchange" -- presumably meaning that American consumers understand their identity and behavior data is the proxy the digital ad industry uses to monetize their value -- but the study never actually asks consumers how much they believe their data is worth to digital publishers, advertisers and agencies.

If I take a conservative 50% of digital ad spending going into working media buys, with the balance being eaten up by various data-related targeting, identity resolution, and ad servicing costs (you know, the digital "ad tax"), then the theoretical value of consumer data is something like $6.75 per month, per capita.

Mind you, this is just some gross, back-of-the-envelope analysis, so don't hold me to any of these figures.

But I figured it might be worth your while to put the IAB's consumer value research findings into some real-world terms.

I'd be curious to hear what some of you think the real value exchange actually is.

COMMENTARY Is That a Paradox in Your Pocket, Or Are You Just Glad to Watch Some Video?

COMMENTARY

Is That a Paradox in Your Pocket, Or Are You Just Glad to Watch Some Video?

• by Joe Mandese  @mp_joemandese, 3 hours ago

There's an ironic paradox in new research released today that speaks volumes about the future of technological adoption, and especially how we research it.

The Pew Research Center finds that smartphone penetration has exceeded household broadband penetration. That's not the paradox, but it's one part of it.

The paradox is that Pew also announced it is changing the methodology it uses to research consumer adoption of technology for reasons related to it.

Instead of using telephone-based surveys, it has begun using a web and snail mail-based surveying method managed by consumer research giant Ipsos. The reason: few people answer their phones anymore.

And while that is an insight worthy of a 3.0 column in itself, it speaks to a fundamental shift not just in consumer technology, but in how consumers use the technology they adopt. Or don't.

Ever since landline telephones reached a critical mass of U.S. households in the 1950s, consumer researchers utilized the so-called "random digital dial" method to survey Americans about all sorts of things, including their media usage.

In fact, that phone methodology was more or less Madison Avenue's gold standard until about 15 years ago, when it started weaning itself from it due to the erosion of its efficacy.

Between do-not-call list registries, the acceleration of mobile phone adoption, and underlying shifts in consumer media usage behaviors, random digit dialing was becoming less and less effective.

This may seem quaint by today's "Big Data-plus" approach to consumer media and technology research, but it's worth reading a MediaPost op-ed written by the esteemed Josh Chasin back in 2009 to remember that perspective.

Back then, Chasin was Chief Research Officer of Comscore, and until recently he was Chief Measurability Officer of VideoAmp, but he's always been a forward-thinking media researcher.

I'm not sure why Pew's methodological shift took so long -- or why it coincides, paradoxically with its finding that smartphones are now the dominant way Americans access digital media -- but I'm reprinting part of a Q&A that Pew provided along with today's release so you can understand the rationale in their own words.

As important as Pew's finding as well as its survey methodology shift are for the next-generation of media technology, I'd like to focus the rest of today's weigh-in on an attendant part of it: how technology changes consumer behavior, not just because of what it can do, but because of the unintended ways people end up using them.

Think about the landline phone, as an example. When American households first got access to them, people were thrilled to answer them anytime they rang. Over time, that novelty wore out, and Madison Avenue played its own significant role in that, because, you know -- telemarketing.

So what once was a ubiquitous lifeline connection to American households quickly eroded into both a personalized media connector/player as well as a means of screening unwanted inbound communications.

The truth is that the smartphone has evolved to become many, many other things -- and certainly anything that "has an app for that" -- but an equally significant game-changer is its increasing role as a media hub.

We have evolved from the landline phone to the cable/MVPD "triple play" to the broadband-only one, and soon, to a smartphone-only one.

That last part is my prediction, but it is informed by a conversation I had with my nephew Josh Lovison about 15 years ago when he was then leading the mobile practice in the IPG Media Lab.

We were having a conversation about smartphone penetration and I was gushing about the explosion of apps, the app economy, and how apps were altering the behaviors of how consumers used their phones.

"Those things are important," Josh said holding his own smartphone up in front of me, adding: "But the most important thing is how the microprocessing power is expanding inside this device."

Josh went on to explain that smartphones would soon become more powerful -- and have greater bandwidth -- than the gateways we were then using to connect to digital media inside our homes: cable broadband modems.

That was one of a handful of lightbulb moments that have gone off in my own head during my years covering media, several of which came from Josh, but ever since then I've been waiting for the day when I could swap my wired household connection for a mobile wireless one.

I'm still on the "contact me when it becomes available" waitlist for Verizon's wireless mobile household modem as soon as it comes into my rural Connecticut neighborhood, and I can't wait to write a column about that once it gets "installed."

In the meantime, my Verizon-powered handheld devices are getting pretty close, and I find myself increasingly streaming large video files that I otherwise would have watched on my WiFi-connected TV screen.

To explain the thinking behind the change in its consumer survey methodology, the Pew Research Center provided the following Q&A between Managing Director of Internet and Technology Research Monica Anderson and Research Associate Colleen McClain.

Monica Anderson: We see this research as foundational to understanding the broader impact that the internet, mobile technology and social media have on our society.

Americans have an array of digital tools that help them with everything from getting news to shopping to finding jobs. Studying how people are going online, which devices they own and which social media sites they use is crucial for understanding how they experience the world around them.

This research also anchors our ongoing work on the digital divide: the gap between those who have access to certain technologies and those who don’t. It shows us where demographic differences exist, if they’ve changed over time, and how factors like age, race and income may contribute.

Our surveys are an important reminder that some technologies, like high-speed internet, remain out of reach for some Americans, particularly those who are less affluent. In fact, our latest survey shows that about four-in-ten Americans living in lower-income households do not subscribe to home broadband.

Why is your team making the switch from phone surveys to the National Public Opinion Reference Survey (NPORS)?

Colleen McClain: The internet hasn’t just transformed Americans’ everyday lives – it has also transformed the way researchers study its impact. The changes we’ve made this year set us up to continue studying tech adoption long into the future.

We began tracking Americans’ tech use back in 2000. At that point, about half of Americans were online, and just 1% had broadband at home. Like much of the survey research world, we relied on telephone polling for these studies, and this approach served us well for decades.

But in more recent years, the share of people who respond to phone polls has plummeted, and these types of polls have become more costly. At the same time, online surveys have become more popular and pollsters’ methods have become more diverse. This transformation in polling is reflected in our online American Trends Panel, which works well for the vast majority of the Center’s U.S. survey work.

But there’s a caveat: Online-only surveys aren’t always the best approach when it comes to measuring certain types of data points. That includes measuring how many people don’t use technology in the first place.

Enter the National Public Opinion Reference Survey, which the Center launched in 2020 to meet these kinds of challenges.

By giving people the choice to take our survey on paper or online, it is especially well-suited for hearing from Americans who don’t use the internet, aren’t comfortable with technology or just don’t want to respond online. That makes it a good fit for studying the digital divide. And NPORS achieves a higher response rate than phone polls.

Shifting our tech adoption studies to NPORS ensures we’re keeping up with the latest advances in the Center’s methods toolkit, with quality at the forefront of this important work.

Anderson: Are the old and new approaches comparable?

McClain: We took several steps to make our NPORS findings as comparable as possible with our earlier phone surveys. We knew that it can be tricky, and sometimes impossible, to directly compare the results of surveys that use different modes – that is, methods of interviewing.

How a survey is conducted can affect how people answer questions and who responds in the first place. These are known as “mode effects.”

To try to minimize the impact of this change, we started by doing what we do best: Gathering data.

Around the same time that we fielded our phone polls about tech adoption in 2019 and 2021, we also fielded some surveys using alternate approaches. We didn’t want to change the mode right away, but rather understand how any changes in our approach might affect the data we were collecting about how Americans use technology.

These test runs helped narrow our options and tweak the NPORS design. Using the 2019 and 2021 phone data we collected as a comparison point, we worked over the next few years to make the respondent experience as similar as possible across modes.

Anderson: What does your new approach mean for your ability to talk about changes over time?

McClain: We carefully considered the potential for mode effects as we decided how to talk about the changes we saw in our findings this year. Even with all the work we did to make the approaches as comparable as possible, we wanted to be cautious.

For instance, we paid close attention to the size of any changes we observed. In some cases, the figures were fairly similar between 2021 and 2023, and even without the mode shift, we wouldn’t make too much of them.

We gave a thorough look at more striking differences. For example, 21% of Americans said they used TikTok in our 2021 phone survey, and that’s risen to 33% now in our paper/online survey.

Going back to our test runs from earlier years helped us conclude it’s unlikely this change was all due to mode. We believe it also reflects real change over time.

While the mode shift makes it tricker than usual to talk about trends, we believe the change in approach is a net positive for the quality of our work. NPORS sets us up well for the future.

NBCU Returns to Radio City for Upfront Presentation

• by Wayne Friedman , 3 hours ago

NBCUniversal is returning to its usual place in Radio City Music Hall on the first Monday of the traditional TV upfront week (May 13) for its upfront presentation.

This follows news that Warner Bros. Discovery will also return with a traditional live presentation at the Theater at Madison Square Garden. 

A year ago, TV upfront presentations were impacted by the actors' and writers' strikes. As a result, actors who were respecting picket lines did not appear at the high-profile events.

“As consumers' viewing habits continue to shift, the opportunity to connect with audiences across platforms is more valuable than ever,” said Mark Marshall, chairman of global advertising/partnerships at NBCUniversal, in a statement.

For Telemundo -- its Spanish-language TV network/platform -- NBCU plans a night “celebration” after the day’s upfront event on Monday. 

Last year, NBCU experienced a major disruption with the sudden departure of its most senior advertising executive, Linda Yaccarino, who departed the company to become CEO of X/Twitter, just before the live NBCU upfront event.

After two years of the COVID-19 pandemic (2020 and 2021), NBCUniversal returned to its in-person upfront presentations at Radio City in 2022. 

NBCU will stage another of its annual technology events -- One24 -- its fourth annual conference, this year at 30 Rock's Studio 8H, home of “Saturday Night Live” on March 20.  

Paramount Global will avoid a gala, star-studded presentation at its usual spot in Carnegie Hall in favor of a series of intimate meetings with buyers and clients, now for  a second year

Media Marketing Glossary

 For many of subscribers to LeNoble's Media Sales Insights are also TV clients who are System 21 stations, I thought a glossary of terms might be helpful for each or you in this ever-changing industry. Please let me know it what you see is helpful by sending me a quick note to drphilipjay@gmail.com or a text to 3038887666. Philip Jay LeNoble, Ph.D.

Welcome to System 21© 2024+

System 21© is an academic approach to media marketing directed to the preservation, growth and ongoing success of local businesses and their communities across the United States.

According to General sales Managers, Directors of Sales and Local Sales Managers, and client commentaries, System 21© provides new businesses expansion and revenue growth while endowing many visible economic benefits to local businesses and communities across America. The results of which increases hiring, reduced unemployment, and amplifies economic advantages to those local communities served.

Each media company yearly licensed to use System 21©, will enjoy the advantage of supporting their media marketing team with its graduate level core curriculum from coursework including merchandise management, retailing, managerial accounting, competitive positioning, organizational and interpersonal communication to sales promotion, creative copywriting and psycholinguistics, (how language affects behavior).

The curriculum empowers System 21© Portfolio Managers to present a secured and market tested service to their clients unlike other media training companies operating across the U.S.  

Each media company licensed to use System 21© affords their local-direct clients with modern technological media enhancements including search engine optimization and search engine marketing to better enhance local businesses’ website activity including increased consumer engagement and acquisition incorporating extra potential online consumer purchase opportunities.

Annual local-direct branding campaigns and spike months promoted by System 21© Portfolio Managers enables local businesses to increase their brand’s awareness, recognition and total market acceptance.

Centered upon the partnership television and radio companies have with each of its local-direct business clients, businesses will have the potential to increase their objective reach among their current core consumer base while increasing new business utilizing digital and mobile behavioral targeting established from search and purchase history. 

System 21© provides ongoing training throughout the year empowering Portfolio Managers’ media marketing expertise to greatly benefit local-direct clients towards their development, growth, expansion, financial benefits and increased employment opportunities.

Since 1993, local businesses sampled, informed System 21© Portfolio Managers they saw an increase in their brand’s recognition along with month-to-month and annual revenue growth.

An additional benefit to media companies using System 21©, local business semi-annual and annual renewal rates were 87%.

 

The following Glossary of Terminology will help to understand all the nuances in the field of media marketing with its complex language of marketing and advertising,

 

 

Media Marketing Glossary

In learning System 21©, it is important as a Portfolio Manager, to learn, understand and teach the language of the media marketing business to local-direct clients to help gain their trust and to become a complete resource to each.

To maximize marketing effectiveness, marketers must be able to understand consumer attributes and how different audiences interact with their brand across channels and devices, so they can optimize budgets and create relevant experiences that drive meaningful business results.

Nielsen Visual IQ’s glossary provides definitions of common terms associated with marketing effectiveness. Some of them may be new to you, and we want to demystify words you’ve heard but haven’t had a chance to clarify.

You’ll find definitions for some of the most important terms, including current vocabulary associated with data collection and activation, multi-touch attribution, people-based marketing, predictive analytics, and other related concepts.

Addressable Channels: Marketing and media channels where individual, user-level data (such as cookie data) is available to track touchpoints in the consumer journey.

Algorithmic Attribution: A multi-touch attribution methodology that uses machine-learning to calculate and fractionally assign credit for a given success metric to the influential marketing touchpoints and dimensions (campaign, placement, publisher, creative, offer, etc.) along the consumer journey, as well as to predict the outcome of future marketing spend allocations.

Audience Attributes: Demographic, behavioral, interest and intent qualities that characterize an individual. Examples include: gender, age, occupation, income, lifestyle interests, purchase intent and more.

Audience Segment (aka Segment): An identifiable group of individuals who share similar characteristics, needs or behaviors and who generally respond in a predictable matter to a given marketing or media stimulation.

Baseline (aka Brand Equity): The intrinsic value and level of performance against a set of key performance indicators (KPIs…aka... Key Performance Indicators) derived solely from a brand’s recognition in the marketplace. It provides an initial value from which to evaluate the impact of incremental marketing investments.

Brand Engagement: The measurement of the extent to which a consumer has a meaningful interaction with a brand (visits a landing page, views a video, downloads content, etc.) when exposed to a brand marketer’s middle- and upper-funnel marketing or media.

Channel: A digital and/or offline marketing category. Channels can be classified by paid, owned and earned, as well as addressable versus non-addressable.

Constraints: Factors associated with specific channels or tactics that limit the extent to which the media spend invested in them can be changed during the optimization process, and then the specific range of values in which media spend can vary to ensure the recommendations produced by an attribution solution can realistically be put in market given those factors. For example, the available inventory of branded search terms in a paid search marketplace is a constraint that limits the ability for increase spend on those terms more than 60% and decrease spend on those terms more than 100%.

Consumer Journey: A chronological sequence of all the marketing and media touchpoints experienced by an individual user.

Container Tag: A delivery mechanism for pixels that eliminates the need to place multiple data tracking codes on a website.

Cookie: A unique piece of code that is placed on a user’s browser by a tracking technology the first time that user accesses a given digital asset. Each piece of information a cookie tracks is called an “event,” and events are transmitted to ad servers or data collectors via codes called tags. For example, each time a user searches for an item online, it is recorded in their browser and later retrieved by ad servers to remember their behavior and better customize their search queries.

Cost: The price that is paid for media.

Cost Per Thousand (CPM): The standard pricing model for many media channels (e.g. online display, video, etc.). Alternative pricing models are flat rate, pay per click (PPC) or cost per acquisition (CPA).

Cost Reconciliation: The process of associating actualized cost data with paid media channels and their publishers to accurately calculate efficiency metrics like cost per acquisition.

Cross-Device Mapping: The process of matching a single user to two or more connected device ID’s associated with that user. This process may also be referred to as “cross-device matching,” “cross-device pairing,” or “cross-device bridging.” There are three different approaches to this process that can be used individually or together to maximize accuracy and scale: first-party deterministic, third-party deterministic, or third-party probabilistic.

Data Management Platform (DMP): A data warehouse technology that centralizes and deduplicates first, second and third-party data sources to generate audience segments that can be used for marketing and media audience targeting and creative optimization.

Device ID: A unique identifier that can be used to identify a mobile device.

Dimension: A characteristic or feature associated with a marketing or media touchpoint. Each touchpoint may include over a dozen dimensions. For example, an online display ad may include the campaign, placement, publisher, ad size, creative, line of business, etc.

Earned Marketing: The free, publicity generated marketing produced by a brand’s fans. Examples include: Facebook likes, retweets, online reviews, word of mouth, etc.

Engagement Score: A KPI metric used to measure and optimize brand marketing campaigns. The score is typically a compilation of a set of events known as brand engagement activities, such as: first time website visits, rich-media ad interactions, video completions, etc.

Even Weighted (aka Linear): A rules-based attribution model that distributes equal credit for a given success metric to each touchpoint experienced by a user.

Exogenous Factors: Factors external to the dimensions associated with marketing, including: seasonal factors (weather, holidays, etc.), economic factors (interest rates, gas prices, etc.) and competitive activities (changes to media tactics, new product launches, etc.) that lie outside of marketers’ immediate control and may impact marketing effectiveness.

First Click/First Touch: A rules-based attribution model that gives 100% of the credit for a given success metric to the first touchpoint experienced by a user.

First-Party Data: Consumer data that a brand produces at no cost. This data is typically collected from direct contact with the company’s customers, and includes site analytics data, CRM data, etc.

First-Party Deterministic: : One of three cross-device mapping approaches. It may also be referred to as “brand authentication” since this approach uses the brand’s own first-party, authenticated user data. User authentication may include a website login, app interaction, email interaction, etc.

Halo (aka Halo Effect): A value that quantifies the degree to which different channels lift, or in some cases, cannibalize each other. For example, as the result of a halo effect, a marketer may see an increase in branded search queries or conversions while running an online display campaign.

Impressions: In digital media, impressions are a measure of the number of times a media advertisement or marketing message is served. In offline media, impressions are a measure of the number of times an ad or message may have been seen.

Incrementality: A desired outcome (revenue, sales, leads, brand engagement, etc.) gained from a marketing activity that would not have been generated without that marketing activity.

Inter-Channel: The affinity between tactics used within one channel and those used within another, such as which online display ads drive searches on which keywords.

Intra-Channel: The affinity between different tactics used within the same channel, such as which non-branded keywords drive searches on which branded keywords.

Key Performance Indicator (KPI): The metric that a marketer uses to judge the success of a marketing initiative.

Last Click/Last Touch: A rules-based attribution model that gives 100% of the credit for a given success metric to the last marketing touchpoint experienced by a user.

Lookback Window: The defined period of time for which an ad can be expected to influence a user to convert or engage with a brand after exposure.

Marketing Mix Modeling: A statistical modeling attribution approach that uses summary-level data from both non-addressable channels and addressable channels to infer the relationships between different channels and tactics and deliver recommendations for optimization. The summary-level data that feeds a marketing mix model may include counts of individuals who were exposed to and/or took action upon various marketing initiatives; the particular date, time or location from which a marketing message was viewed; as well as exogenous factors, such as economic, seasonal and competitive data that have an impact on performance, such as interest rates, the weather, new product launches, etc.

Match Rate: The number of user ID’s associated with a marketer’s touchpoint data that also match a set of user ID’s associated with another data provider (such as a cross-device data provider), divided by the total number of user ID’s associated with the marketer’s touchpoint data. Match rate is expressed as a percentage.

Model Validation: The process of verifying the accuracy of attribution measurement and the optimization recommendations it delivers by comparing the predicted results with the actual results.

Movement Data: Location-based data that represents the anonymous offline movement and visitation patterns of consumers.

Multi-Touch Attribution: A rules-based or algorithmic attribution methodology that leverages individual, user-level data from addressable channels like online display and paid search to calculate and assign fractional credit of a success metric to the influential marketing touchpoints and dimensions (campaign, placement, publisher, creative, offer, etc.) along the consumer journey to measure past marketing performance.

Non-Addressable Channels: Includes channels like broadcast TV, radio, print, out-of-home, in-store displays, etc., where marketing messages are delivered to individuals who cannot be identified at a user-level.

Owned Marketing: All the communication assets that a marketer creates and has control over without having to make per-unit investments in order to expose them to consumers. Examples include a brand’s website, blog, mobile website, etc.

OTT Over-the-Top, commonly known as “OTT”, is a term used to refer to video transmitted via the Internet and bypassing traditional cable or linear distribution. Viewers/consumers can stream OTT video through a variety of devices such as connected TVs (CTVs), desktops, tablets and phones, both at home and “on-the-go.”

Paid Media: All of the advertising assets for which a marketer pays in order to expose them to consumers. Examples include TV commercials, print ads, online display ads, paid search, retargeting, etc.

People-based Marketing: The process of combining various, disparate IDs associated with a single consumer (e.g. cookie ID, device ID and offline ID) into a single, anonymous unique identifier for the purpose of understanding consumer attributes and behaviors within the context of marketing performance measurement.

Personally Identifiable Information (PII): Any data that can be used to identify a specific individual. Most marketers prefer to use non-PII data in order to protect consumer privacy.

Pixel: A transparent gif file or clear image placed within websites, ads and emails to track digital activity (e.g. impressions, website visits, etc.) at a user level. A pixel may also be referred to as a tag.

Point of Diminishing Returns: The point at which increased investment in a particular piece of marketing or media results in a decrease in the overall return (assuming all variables remain fixed). For example, the point of diminishing returns for a PPC campaign occurs when increasing the budget results in a decline in conversion rates and an increase in cost per acquisition.

Publisher: A media vendor that owns or manages media inventory, such as ad space, made available for purchase by marketers.

Response Channels: Any channel that enables a customer or prospect to initiate a desired action in response to exposure to a marketing or media stimulation. Some marketers call these “sales channels” or “revenue channels.” Examples include an eCommerce website, mobile website, traditional retail store, call center and more.

Rules-Based Attribution: A subjective multi-touch attribution methodology that distributes credit for a success metrics across one or more marketing touchpoints using a prior defined or arbitrarily assigned weight. Examples of rules-based attribution models include first click, last click, u-shaped and even weighted methods.

Scenario Planning (aka “What-If?” Scenarios Planning): The use of predictive analytics to forecast future performance and produce customized marketing and media plans containing the optimal mix of channels and tactics needed to maximize marketing return while also accounting for constraints.

Second-Party Data: Consumer data that is shared between trusted marketing partners. Examples include “intent to purchase” data that is shared between an airline and a hotel chain or “online shopping” data that is shared between a retailer and a manufacturer.

Stimulation Channels: Any channel whose assets produce a marketing or media impression with a customer or prospect. Some marketers call these “impression channels,” “communication channels,” or “marketing channels.” Examples include paid search, online display, TV, radio, out of home, email, direct mail and more.

Tag: A piece of code placed on a website or an ad by which user-level data is collected. A tag may also be referred to as a pixel.

Taxonomy: Predefined, hierarchical classifications and naming conventions of touchpoint dimensions, KPIs and other brand-specific categorizations. (Marketing Metrics and Key Performance Indicators (KPIs) are measurable values used by marketing teams to demonstrate the effectiveness of campaigns across all marketing channels) Taxonomies differ across marketing organizations and ensure that the analyses, insights and recommendations map directly to a brand’s unique jargon and business goals. (In marketing attribution, taxonomy refers to hierarchical classifications and naming conventions for touchpoint dimensions. These are the elements of your marketing and advertising initiatives such as creative, placement, keyword, publisher and more.)

Third-Party Data: Consumer data that is collected by an external source from the marketer that intends to use it.

Third-Party Deterministic: One of three cross-device mapping approaches. It may also be referred to as “third-party authentication” because this approach uses a third-party common login across devices (e.g. Google, Facebook, Twitter, etc.).

Third-Party Probabilistic: One of three cross-device mapping approaches. It may also be referred to as “third-party inferred match” because this approach requires partnering with a cross-device technology vendor to make an inferred match across multiple devices to a single user. The connection is made using consumer behavior, relationship-based patterns, Wi-Fi linking, geo-clustering, as well as other variables.

Time Decay: A rules-based attribution model in which the percentage of success metric credit gradually builds while leading up to the last touchpoint in the consumer journey.

Touchpoint: Any media or marketing interaction to which a consumer is exposed. Touchpoints can include a wide range of interactions, from seeing a television commercial to conducting online price comparisons on a comparison shopping engine site, to clicking on a display ad or search result.

Trafficking: The process that places advertisements within media inventory.

Tribal Knowledge Factors: Any internal knowledge that lies within a company and may impact marketing effectiveness. Examples may include mergers and acquisitions, pricing changes, product launches, etc.

U-Shaped (aka Position based): A rules-based attribution model in which the majority of success metric credit is assigned to the first and last touchpoint experienced by a user, and the remaining credit is distributed evenly to the touchpoints in between.

Unique Identifier/Unique ID: The mapping of multiple, disparate, anonymous user IDs associated with a unique user to a single, anonymous user ID that identifies the unique user across platforms, channels and devices.

Yield Curve: A line that plots the maximum number of theoretical “what-if?” marketing and media spend scenarios available to a marketer based on historical marketing performance data for purposes of forecasting marketing performance.

Knowing your client’s business and their distinctive selling advantage(s) enables them to compete more constructively against oncoming or existing category similars. The game is always about competitive positioning or re-positioning. Helping them reach their primary target through behavioral marketing and an ongoing partnership with your media company elevates their survival as the highly competitive 21^st century digital environment impacts ever community.

Philip Jay LeNoble, M.B.A. Ph.D. 303-795-3662

 

The language we provide, above brightens your vocabulary and enhances your esteem with your local-direct business clients. Begin by using it in everyday conversations with your colleagues and then ...with your local-direct clients.

Happy New Year

 

 

 

 

 

 

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COMMENTARY

Would You Pay $100 To Watch the Super Bowl?

• by Richard Whitman , Columnist, 6 hours ago

Is it just a matter of time before the Super Bowl becomes part of a subscription package? 

A few years ago that would have been unthinkable. But earlier this week word surfaced that streamer Peacock’s first ever NFL playoff game earlier this month generated 2.8 million in new sign-ups over a three-day period. 

And now a survey from Stagwell’s Harris Poll and Front Office Sports reports that 64% of NFL fans and 49% of all U.S. adults would pay for a subscription to watch the Super Bowl. 

The poll was conducted earlier this month using a nationally representative sample of 2,100+ adults. 

So it seems there may be lot more upside from streamers that want in on the NFL action. 

The poll also found that 61% of NFL fans and 45% of all US adults say they would be likely to pay for a subscription to a streaming service to watch an NFL playoff game. 

If the NFL were to make a postseason matchup a pay-per-view event, 57% of NFL fans and 42% of US adults surveyed say they would likely pay a one-time fee to watch. 

I can remember talking to network TV types a few years back about putting the Big Game on some kind of pay tier. No way, they said at time, with a few even suggesting there’d be rioting in the streets.  

That may seem extreme, but fans are very passionate about their teams.  

But the industry has done a fiendishly good job of softening up the fan base—we're all now accustomed to paying lots of monthly or one-time fees for streaming services and other packages more tailored to our interests.  

I wonder how much the Super Bowl would be priced as pay-per-view event? The Harris Poll found that over half of fans (53%) willing to pay a per-game fee (the Super Bowl wasn’t mentioned specifically in this context) would pay $10 or more. And 17% would be willing to pay $20 or more. 

What about $100 for the Big Game? That sounds reasonable, doesn’t it? I know people who have paid that much for a pay-per-view boxing match that lasted five minutes.  

The Big Game by contrast is a four-hour extravaganza with a wildly entertaining (sometimes) half-time show and dozens of over-the-top multimillion-dollar commercials.  

I don’t know about you, but count me in.