Is There a Future for Research? An Interview with Jeff Boehme

I have known media veteran, Jeff Boehme, from our days at NBC in the 1980s and since then, he has had a varied and interesting media career path. “I’m a veteran of local broadcast rep firms, NBC, ABC, NCC Media, Nielsen, Kantar Media, Rentrak and Comscore,” he explained where he concentrated on audience evaluations and processes for media currency acceptability. He has some strong opinions about where media is today and the role that research and data plays in it.

Charlene Weisler:  What role should data play in media today?

Jeff Boehme: Data always played a critical role in media. Content is now distributed on more types of technology than ever. Virtually all of these digital devices collect usage information and have been enabled in the marketplace by a multitude of companies. Content providers have taken advantage of technology by supplementing their traditional distribution infrastructure with streaming capabilities through over the top (OTT) platforms. Brand marketers realize the potential of reaching customers with far greater efficiency and effectiveness through addressable advertising across multiple platforms and content.

But defining the benefits of efficiency and effectiveness is not a standardized process; there are real issues surrounding the massive data sets collected from these digital devices and becoming ubiquitous as media currency. Ultimately data can and should be leveraged to maximize the effectiveness of the three basic pillars of brand advertising – creating awareness, reinforcing equity and driving purchases.

Weisler: What types of data are most important and what is currently missing?

Boehme: Over five years ago we understood the remarkable advantages of ‘big data’ expressed as the three V’s - volume, velocity, and variety. The sheer scale of anonymous, passively-collected user information provides much more statistically sound results than traditional small panels and surveys. However, most every big data set is incomplete and may not include essential data elements required for currency acceptance, making traditional tools still necessary to supply missing data points. I would add there should be a few more Vs to consider – the validation of the data (how accurate it is) and the ultimate V – its value. The value of the data ultimately answers the questions posed by the brand and can be accepted as currency on all sides of the ecosystem with confidence.

The good news is we now have more data than ever before - the bad news is that there are significant inconsistencies with the sources, collection techniques, methodology, standards, transparency and importantly – conclusions. All major cable MSOs are offering their tuning data to a variety of companies, as are virtually all connected TV (CTV) manufacturers. I have seen significant disparities on results depending on whom and how a company processes, manages, applies statistical corrections and matches census segments.

Weisler: Should age and gender still form the basis of currency?

Boehme: While age/gender metrics are still valuable criteria of value for brands and media, they have been supplemented with more relevant information including major census breaks and product usage. It was only in the late ‘70s when automotive brands finally looked at the data and revealed that women were the dominant influencer in car purchases. This transformed the industry in terms of understanding the real consumer, how to design new vehicles (think mini-van) and media investment placement strategies. Currency options now include actual auto ownership household impressions based on ‘auto intenders’ created by matching massive tuning and car ownership.

It really wasn’t until 1987 when Nielsen launched their people meter service that age/gender metrics became the de facto currency. However, many brand marketers learned that age/gender weren’t enough to efficiently plan or buy media – specifically for high spending categories such as automobiles. Most consumer purchaser data sets available today are household-specific and include information more relevant than just age/gender. Knowing that a household has pending lease expiration for a BMW is more valuable than simply counting adults 25-54.

Weisler: What is your opinion of the general state of attribution?

Boehme: Channeling Sergio Leone’s epic masterpiece western film “The Good, the Bad and the Ugly” - The Good is we now have a plethora of consumer-based intelligence and media companies are able to use attribution techniques to see a finer view of the customers’ behavior across screens and determine what components of media campaigns work (or don’t). The Bad is the complexity of data, multiple data sources, missing data points/deprecation and differing methodologies. The Ugly is there doesn’t appear to any consistent standards – resulting in significant outcome discrepancies.

Last year, CIMM completed a study on attribution which found the inconsistency of key television attribution inputs, not technology, is the main cause of variance in outcome measurements. They compared eleven different providers and determined, “more stringent media measurement standards are required to ensure attribution results that are consistent and comparable from provider to provider, with exposure data, more than occurrence data having the biggest impact on outcome results.”  I agree with their findings and with their report’s other recommendation requiring additional standardization, such as commercial IDs similar to Ad-ID, for identifying ad occurrences and in defining exposure and reach.

Weisler: What do you think is the most important issue facing Research at this time?

Boehme: Most research groups are a cost entry on a ledger, requiring investment without a direct responsibility for cash flow. Many successful researchers have learned to move quickly, adopt better data skill sets and provide actionable input into a sales process and discover how their company can be more profitable. Many companies see data scientists as a replacement for the research process but smart companies see the value of both, with complementary skill sets and valuable disciplines. The simplest distinction may be that the data scientist determines what could be accomplished with data and the researcher helps define what should be done with the data.

Weisler: Where do you see the Research function at media companies in the next five years?

Boehme: Data science has helped us improve our capabilities with disciplined scientific and technology-enabled approaches, beyond traditional research processes. However, Research is still a vitally imperative function as it is responsible for the objective analysis of the data with the clear communication of insights, business implications and recommendations. We have all witnessed the perils of utilizing large datasets without sufficient oversight in its contextual use case. Ultimately the most successful companies will discover research and data science are opposite sides of the coin – connected they bring greater value.

This article first appeared in www.Mediapost.com

 

 

Nielsen’s Take on the Accuracy of Big Data

While many programmers have found that set top box data is immensely useful, Nielsen offers caution. Their take, as per a recent Nielsen NewsWire blog post, is that set top box data alone does not give the full picture of who is watching. They advise that this data should not be used in a vacuum because it undercounts certain demographics – especially young and diverse viewers.

According to Nielsen, “At a time when Hollywood is moving for more on-screen diversity and inclusion in TV programming, the study found, using real data, that this could have implications when it comes to programming decisions.”

Here are some highlights from that post:

1. The difference in delivery systems, especially Over-The-Air, skews return path data.

 Nielsen notes that some Americans don’t have the income to spend on premium entertainment content; others opt for over-the-air (OTA) programming in light of improving digital technology. Widespread technological advancements have fueled a steady growth of broadband-only (BBO) homes as well. The combination of OTA and BBO homes have swelled in the U.S. from 15 million homes in 2014 to nearly 28 million homes in 2018. Considering 41% of the consumers in those 28 million homes are multicultural (either Hispanic, African-American or Asian) and 10% are a younger demographic (18-24), an uncalibrated RPD sample would significantly under-represent these audiences and skew the total audience measurement.

2. Set Top Box Data Undercounts Hispanics and African Americans

Nielsen reports that compared with official U.S. Census estimates and the Nielsen national panel, RPD-capable homes under-represent Hispanics by 33%, Spanish-language dominant Hispanics by 49% and African Americans by 34%.

3. The Implications On An Actual Program Ranking is Stark

When looking at Fox’s Empire, for example, diverse audiences made up 75% of the program’s viewers in December 2018, driving ratings success when using a representative panel. However, using set top box data, these multicultural audiences were undercounted. “The differences,” noted Nielsen, “are not to be discounted. Looking at a rank among 25-54 year old viewers, Empire ranked 16th using Nielsen’s representative panel, but dropped to 38 in RPD-only homes. Conversely, Empire ranked third among OTA homes.”

Data silos continue to be a vexing problem in media. Taking return path data on its own will not give programmers a full picture of who is watching.

This article first appeared in Cynopsis.

Using Advanced Technologies in Advanced Television. Interview with dataxu’s Sandro Catanzaro

There is a lot of talk about the introduction of AI and machine learning (ML) to better understand human behavior. Now, as these protocols move into the media industry, we are finding more companies employing AI and ML to better target consumers over time. 

Sandro Catanzaro, Founder and Chief Innovation Officer at dataxu describes himself as a serial entrepreneur. He was one of the co-inventers for dataxu’s real-time optimization algorithm, based on research he did at MIT. Here is an overview of his company, the world of advanced and addressable television and the data work being done in the industry.

Charlene Weisler: What is your definition of advanced TV? Is it the same as addressable?

Sandro Catanzaro: Advanced TV is TV advertising that is purchased on an impression basis using advanced audience data and software automation, creating additional value for both the buy and sell sides of the transaction. Addressable TV is a form of advanced TV where households are targeted on a one-to-one basis via cable and satellite set-top boxes, Smart TVs and OTT devices, but not all advanced TV is necessarily addressable. Other forms of advanced TV may be based solely on automating the purchase process, but still displaying ads on a one-to-many broadcast basis.

Charlene Weisler: What are the challenges in advanced TV?

Sandro Catanzaro: One of the primary challenges in advanced TV right now is the ability to target and provide attribution for OTT campaigns. This form of TV is accessed by the viewer via internet enabled televisions and streamed either live or on-demand. The connected nature of OTT makes it very similar to digital video, but as these ads run on actual TV screens and not traditional digital devices (PCs and mobile phones), the typical digital markers (cookies and mobile IDs) are not available for identification, making advanced targeting difficult.

Charlene Weisler: How can they be overcome?

Sandro Catanzaro: This issue can be overcome through new forms of identity management made possible by cross-device graph technology. A device graph is the unification of several otherwise separate devices, such as a laptop, mobile phone, tablet and smart TV under one unique household. In the real world, these devices don’t exist in a vacuum; they are linked through ownership and usage and can be used to understand the full context of a person’s digital footprint. By including smart TVs and OTT devices in a device graph, marketers are able to leverage advanced audience data, built using legacy digital IDs, to enable addressable targeting on televisions, even though these legacy IDs are not present.

Charlene Weisler: What metrics do you use?

Sandro Catanzaro: Advanced TV is typically purchased on an impression basis as opposed to ratings, but it is possible to provide traditional TV metrics such as GRP. However, we find that marketing professionals are also frequently leveraging more detailed metrics to prove success, such as lift studies which compare conversion rates of exposed populations versus control groups. This is made possible through addressable forms of advanced TV and device graph technology, where specific viewers are directly targeted and others are intentionally excluded, in order to compare their actions across all devices, and in the real world, after having viewed the ad.

Charlene Weisler: What is dataxu?

Sandro Catanzaro: dataxu is a software company that helps marketing and media professionals use data to improve their advertising using AI to optimize ROI on marketing investments. dataxu ingests first-party data (e.g., customer purchase information), matches it up with many other kinds of data across devices and identifiers and creates a customer machine learning classifier for each campaign that invests more budget into what’s driving acquisition and less into what isn’t. We offer three products: TouchPoint™, our demand side platform (DSP); OneView™, our identity and data management platform; and ClearSight™, our advanced analytics and data visualization product.

Charlene Weisler: Where does it reside in the ecosystem? Who are your competitors?

Sandro Catanzaro: Sandro Catanzaro: Some of our products, such as TouchPoint™, compete with other DSPs, including The Trade Desk or MediaMath, but our analytics, cross-device identity management and advanced TV capabilities stand alone.

Charlene Weisler: How will GDPR impact your side of the business?

Sandro Catanzaro: dataxu has always placed the highest value on transparency, quality and privacy. We have had a dedicated task force for GDPR since it was announced. We expect GDPR to have an impact on the entire industry, not just dataxu; however, we know we are prepared and well-positioned to face the challenges coming. We were fully prepared when GDPR came into effect; we are active in industry-wide schemes (such as the IAB Technical Consent Program), are a registered vendor with the IAB Europe and are members of self-regulatory organizations [e.g., Network Advertising Initiative (NAI), Digital Advertising Alliance (DAA) and European Digital Advertising Alliance (EDAA)] to make sure our customers are best placed for success under the new rules.

Charlene Weisler: What does the future of TV buying look like 3-5 years from now?

Sandro Catanzaro: In the next three to five years, we expect more and more people to follow the growing trend of taking their viewership to connected devices, not only leading to more innovation surrounding those devices, but creating a larger pool of inventory for advertising to be served. We expect that in this time period all major TV programmers will have made their content available via connected devices and most will be providing marketers access to this advertising via programmatic channels. The largest portion of TV buying will still operate through legacy methods, such as upfronts, but advanced TV will soon constitute a much larger piece of that pie in the near future.

This article first appeared in www.Mediapost.com
 

Coming Full Circle in Research by Making Big Data Smaller. An Interview with Maru/Matchbox’s Bruce Friend

“Technology-enabled research is a trend in the marketplace,” noted Bruce Friend, President of Global Media and Entertainment, Maru/Matchbox. 

Harnessing the power of technology in the pursuit of research insights is something at which Friend excels thanks to his previous work at OTX and Vision Critical. Now, much of his effort focuses on “building capabilities that are platform-based [unlike] a traditional research company, which still tends to be very focused on one-off, ad hoc studies, employing different survey-based methodologies.” 

Friend helps clients create single source panels that leverage a company’s own consumer assets, essentially connecting behavioral data from their DMP and other data assets with survey-based demographic and attitudinal data to track over time. “Furthermore, we build custom panels where our clients own the panel members and all of the data collected on those members. So, we are helping them create their own insights platform and program that has tangible asset value.  This value then continues to grow, as the panel size increases and we collect more data on the members,” he explained.

Charlene Weisler:  So how has the custom research business changed?

Bruce Friend: We are seeing that companies want to work with research partners in an ongoing relationship - always on, always delivering in terms of data and in terms of thinking. That is where we are heading. We are doing so not only by continuing to partner with Vision Critical, which spun off the research consulting part of thir business to become Maru/Matchbox 2 ½ years ago but by also acquiring new companies that complement the part of the business that we are concentrating on as well as enable us to do things on a standalone basis, as well as on private panels and communities. 

Charlene Weisler: And the media landscape overall is changing.

Bruce Friend: It’s been an interesting time in this “pending merger world” that the media industry is in now. We are seeing (content) companies building out full capabilities to support the process from script-to-screen. And beyond script-to-screen, really – being able to control the entire ecosystem from the standpoint of developing content, marketing it, distributing it, continuing to build franchises and monetize the businesses going forward. It is also about the platform – not just the content anymore. Its about how we get the content to the consumers in different ways. We see that even from companies such as Amazon (Prime Video), Twitter (TV, Video), Facebook (Watch) and others, who are adapting to video being a new and  increasingly dominant content form. It always seems that it all comes back to video and we are certainly seeing that more and more in the online space.

Charlene Weisler: Yes video is important. But more people are talking also about voice.

Bruce Friend: Yes, obviously voice activation is going to be the norm. In the not too distant future, I see companies conducting surveys through Alexa and Google Home. There are certainly some privacy issues around that sort of thing and I am sure there will be ways of working through that. Probably by creating panels of homes where people will opt in. In addition to voice, audio is also making a resurgence. We currently work with four or five companies that are very audio-focused.  Just like with video, audio is finding many new areas where it can exist and thrive. The emergence of podcasting is only going to continue. It is an indication of where our business is heading, where people want to listen to what they want when they want to listen to it – just like video.

Charlene Weisler: In creating panels from a clients’ own dataset, it sounds like you are able to fully leverage first-party data. Is this a trend? And what if a company doesn’t have a lot of first party data?

 

Bruce Friend: We are leveraging both first and third-party data. Certainly there are many resources for third-party data. Obviously the first-party data is better because it is essentially a 100% match rate as we recruit customers directly from the client’s database to become panel members. But in some cases some people don’t have first-party data. In those cases, we look for third-party data matching opportunities. We also run our own panel here in the U.S. and in Canada – both have around 250,000 members – so we can leverage them, as well as look to match more data sources into them. Our panels can also be used to look at communities outside of the company’s own panel. If you want to look at competitive viewers and competitive distribution services for example, you can then leverage our panel in addition to your own. Clients don’t always want to talk to just their own customers. 

Charlene Weisler: Do you see any evolution in how online communities are being built and used?

Bruce Friend: When communities started, they were about better, faster and cheaper. Communities were the start of agile research. The client could control and use the platform as a DYI tool. Most of these panels were 5,000 to 15,000 members. The trend is now not to have these smaller siloed communities within one company and across different brands, but to build a mega-community or an enterprise-wide community. We can now put all of these communities together with an organization with 50,000 to 150,000 members across the organization. Going bigger is better and when you then connect your DMP or other specific first-party data. You then have a very powerful asset with enough scale to do some very interesting things with the data and with surveys on top of it to give you more strategic insights. Communities used to be “light tactical” research – most people were not using communities for very strategic work. What we are finding now with some of our larger clients, who have made the effort to build out bigger panels, some as large at 175,00 members, is that they can now do more strategic work on them. As a result, we are seeing budgets move from more traditional research into platform-based panel offerings, such as ours, where clients can better leverage their own big data. 

Charlene Weisler:  So where do you see research going?

Bruce Friend: I see this model where companies tie into technology with an embedded community where you can talk to someone today, talk to them again a week from now, and on an ongoing basis. The company owns the panel asset, is building out that asset, that asset really has (data) currency to them while they can still conduct large survey studies within the panel. But it’s really about the creation of a resource that links behavioral data, attitudinal survey data, qualitative data, etc. into an ongoing relationship in an ongoing data stream. Automation will drive a lot of this, as well as will A.I. We are making big data smaller, more contextual and more understandable because we are looking at data that is in a panel and is more representative of the audience or subscriber base that the client has. 

Charlene Weisler: Sort of bringing the data science and ethnography elements of research back together.

Bruce Friend: Yes. I feel that we are coming full circle, back to where we were years ago when I first entered the industry. At that time research and big data lived harmoniously within the same insights departments, and that must happen again in my opinion. Otherwise, companies today that haven’t already moved to effectively consolidate their research and data science teams into one, and build business intelligence assets that support their entire organization, run the real risk of rapidly falling behind their competitors that have.

This article first appeared on www.MediaVillage.com