Connecticut College Magazine

Chris O’Hara ’90 of Salesforce is helping companies ethically navigate the evolving world of digital marketing and consumer data.

BY DOUG DANIELS

There’s an iconic scene in Steven Spielberg’s Minority Report (a sci-fi thriller) in which Tom Cruise walks through a shopping mall, a steady stream of jabbering digital billboards scanning his retinas and instantly plying him with personalized advertisements that call his character out by name and even reference past purchases.

That movie is set in the year 2054. But today, we already have much of the technical capability to create the type of intrusive consumer experience Spielberg’s film envisioned, generating unprecedented opportunities for businesses and organizations to reach their customers, clients and audiences with precision-targeted strategies. This can be a double-edged sword, offering consumers more-personalized marketing and advertising and introducing them to products they love while also presenting a potential slippery slope into privacy violations and misuse of online data.   

“The challenge is striking a balance,” says Chris O’Hara ’90, P’24, VP of global product marketing in the Data and Identity Group at Salesforce, a cloud-based software company that provides customer relationship management services. “People really want personalization, but they also want privacy. And now, either because of new state-level laws or because of voluntary company policies, organizations are looking at how to start collecting data from consumers in a more responsible way and still offer that personalization.”

Salesforce, with its headquarters in San Francisco, has grown exponentially since its founding in 1999, with 60,000 employees and more than $21 billion in revenue for the 2021 fiscal year. And as the world’s digital reliance continues to spread, Salesforce and its subsidiaries are expected to see steady revenue growth in the years to come.

Tech giants like Apple and Google are dramatically changing the way they use and share personal data, and many companies are now scrambling to adjust to the new challenges to marketing and advertising as their access to that data becomes increasingly limited.

But while the privacy landscape is certainly changing in the U.S., not everywhere in the world is following suit. While Europe led the way with its highly restrictive General Data Protection Regulation, or GDPR, Japan has taken a different approach. There, billboards have been equipped with facial recognition cameras so they can identify a person’s gender and age as they’re walking past and instantly tailor ads to them, not unlike the scene in Minority Report. The technology is intended to be anonymous, but it isn’t a giant leap to totally personalized advertising, and that raises enormous privacy concerns. Facial recognition technology is becoming so common that many of us unlock our phones with it now. Beyond the business and advertising implications, there are also questions about data obtained by government and law enforcement entities. The FBI alone has access to nearly half a billion images for facial recognition uses.

Traditionally, data from various websites is aggregated and profiles are built based on site visits. Lots of the browsing data on laptops, and desktops are captured via “cookies,” or little snippets of code that identify users and store their data. Mobile phones and other web-connected devices (even some refrigerators) also capture behavioral data. Widely available for purchase on open marketplaces, this consumer data can include credit scores, online behavior, a person’s specific interests, particular websites they’ve visited—all information collected and shared almost entirely without the knowledge of the precise individual. If you have ever looked at a pair of shoes online and then noticed ads for those exact same shoes that follow you to a series of other websites, such as news sites, that is part of what cookies do.

A big development in data regulation came in 2018, when California passed the California Consumer Privacy Act, requiring more transparency and limits relating to data use. That’s when you may have started noticing the ubiquitous pop-ups on websites asking you to opt in or out of a company’s data collection or cookies policy.

Google has said it plans to enforce new policies by next year, depriving marketers of the ability to purchase the type of data they’ve long treasured. Exactly how consumer relationships are built moving forward and how marketers and advertisers ply their online trade will most likely involve a blend of new tactics and strategies that will replace the use of third-party cookies, which have been a central component of online advertising for years.

“One idea is something called FLoC—Federated Learning of Cohorts,” O’Hara explains. “The way that would work is Google will identify somebody as belonging to one or multiple cohorts, which are basically groups based on people’s interests. So you might be in the fashionista cohort, or the traveler cohort, or the outdoors-enthusiast cohort, and you’ll be assigned this random number and Google will build these various FLoCs as long as there are enough people who meet the criteria.”

This type of group-level web tracking would represent a significant shift for how advertisers identify new potential customers. Instead of companies targeting the browsing history and hyperspecific consumer data about individuals, they’ll now be forced to settle for information about various cohorts as a whole.

What will these new privacy rules mean for big publishers and big marketers, and how will they collect and use data responsibly?

CHRIS O’HARA ’90

Say, for example, you are identified as a European-travel intender and antique-thimble collector. Instead of having your personal information shared, you would simply be assigned anonymously to a larger group that could include thousands or millions of antique thimble collectors. No individual-level data that could violate privacy would be included.

“The advertising industry is pushing back on this idea of cohorts, because obviously they’re used to doing things a certain way, and it has sparked a really big, raging debate about what the future is going to look like and how it’s going to work,” O’Hara says. 

“What will these new privacy rules mean for big publishers and big marketers, and how will they collect and use data responsibly? Advertisers need to learn to use Google, Facebook and Amazon in new, effective ways that are still responsible and ethical.”

One important ongoing debate, O’Hara says, revolves around whether companies such as Facebook, Twitter and Google are objective “platforms” for content or they should be considered “publishers,” since they do exercise some editorial control and create their own algorithms and other structural elements that can control who sees what information.

O’Hara has been working in data-driven marketing and advertising from the earliest days, when the concept seemed more science fiction than concrete business necessity. An English major at Conn, his passions resided in creative writing, philosophy and drama. But a post-college job as a writer and editor for a cigar magazine introduced him to emerging marketing and advertising strategies just when the internet was beginning to broaden its reach, in the 1990s.

He quickly discovered he had a knack for sales and advertising and began learning about data-driven tools that would later prove revolutionary. He joined Salesforce four years ago, when it acquired Krux, the startup where O’Hara led data strategy, for $800 million. Krux, a data management firm, helped clients in the marketing industry better understand, analyze and target the customers who visited their websites and apps so that they could improve engagement and enhance customer relations through the use of far more precise ad targeting. 

He and the co-founders of Krux wrote Data Driven, a book that examines the ways in which new data is transforming marketing. His latest book is titled Customer Data Platforms: Use People Data to Transform the Future of Marketing Engagement. The extensive line of marketing products Salesforce offers includes software for marketing emails, mobile ad campaigns, social media advertising and analytics, making it a one-stop shop for digital marketing. O’Hara’s role involves marketing and strategy for the products related to data. 

“My job is to meet with clients and discuss data strategy with them and determine how to better understand people through their data,” he says. “This way, I can help them build better experiences across different touchpoints for their customers.”

A big part of what O’Hara does for clients at Salesforce is coordinate customer data in ways that improve customer service and incorporate more efficiency. For example, few people have been spared the misery of speaking to a call center representative who has a boilerplate script yet no knowledge of your history with the company or how to resolve your individual issue. Large companies often have disparate databases where customer info is stored. Salesforce helps consolidate data and use it more effectively so that when somebody contacts a call center, the person on the other end of the phone instantly has access to the caller’s order history, their interests and the marketing campaigns they’ve already been exposed to. The customer doesn’t have to start from scratch with each new person they speak to, repeatedly providing order numbers and other details.

“It may sound simple, but that’s really what creates value,” O’Hara says. “That’s what makes people more likely to stay as a customer, and more likely to spend more money, and it makes them happier and more satisfied. And that’s really, from a broad perspective, the type of stuff we’re working on.”

O’Hara contends that the Covid-19 pandemic has accelerated and forced changes in data collection. While some companies may have been reluctant to embrace a “digital first” model, the pandemic has left them with little choice. The challenge remains to find balance when it comes to privacy and managing data. Prior to Covid, people at least had some control over their marketing experience, in the sense that they could choose to log on to Facebook or Instagram, or to use a phone app to make purchases. But during the pandemic, to help ensure social distancing, many daily tasks—like getting coffee—have transitioned into the digital-first approach O’Hara mentions.

“I love that I can choose to place a coffee order with Dunkin’ Donuts through their iPhone app, go pick it up at the drive-through, and not have to exchange any money to get my order,” O’Hara says.

“But that’s my choice, and I know [Dunkin’ will] be using that data to suggest new items for me to try, for example. Where things cross the line is if I’m just walking by a Dunkin’ Donuts, having never opted in to anything, and my mobile phone lights up within 50 feet and says, ‘Hey, Chris, take 20 percent off a large coffee and a donut.’

“That’s an invasion of privacy, and at that point, you’re just like Tom Cruise walking through that mall.”

{This originally appeared in Connecticut College’s CC Magazine, Summer 2021 issue)

Paleo Adtech Podcast

Chris O’Hara is V.P. of Global Product Marketing at Salesforce, focusing on the data and identity suite of products including Audience Studio (a DMP) and the Salesforce CDP. A well-known speaker, pundit and author, Chris has written eight titles including six on culinary pursuits (listen to the episode for more on this fascinating jaunt in his personal journey), “Data Driven” with Krux co-founders Tom Chavez and Vivek Vaidya and “Customer Data Platforms: Use People Data to Transform the Future of Marketing Engagement,” co-written with Paleo Ad Tech co-host Martin Kihn. The latter is the #1 book on the hottest category in marketing technology today. It’s also one of the only books on the category, but let’s not quibble.

After a smoke-filled start as cigar review editor at Smoke magazine, Chris held various sales roles at publishers including MediaBistro, at the time a thriving content and job search site for media mavens, before finding his way to ad tech via start-ups such as Traffiq and nPario. The latter was an early DMP/CDP that provided the data spine for WPP agencies and Xaxis. It was launched by ex-Yahoo and SAS execs in 2010.

An encounter with Krux co-founder Tom Chavez while writing a position paper on DMPs for eConsultancy led to a position as head of DMP marketer sales for that pioneering platform. Meanwhile, Chris was a prolific writer for industry publications such as AdExchanger and his own blog, The Devil’s Work, a reference to “idle hands” (we think). Krux was acquired by Salesforce in 2016, bringing Chris to his current bivouac.

In this frolicsome episode, Marty and Jill follow Chris up the dot-com boom and back down again, as his family grows and he’s out there “hustling” for ad sales, perfecting his writing and pitching voice, and earning his ad tech pedigree. He shares what it’s like to work in a decommissioned building, how long it takes an ex-Russian Army officer to eat to a large steak, and why it’s time to break up with the third-party cookie. Don’t miss it.

Measuring Digital Marketing Success

According to Blue Kai, I’m a tech-savvy, social media-using bookworm in the New York DMA, currently in the market for “entertainment.” At least that’s what my cookie says about me. Simply by going to the Blue Kai data exchange’s registry page, you can find out what data companies and resellers know about you, and your online behavior and intent.

In this brave new world of data-supported audience buying, every individual with an addressable electronic device has been stripped down to an anonymous cookie and is for sale. My cookie, when bounced off various data providers, also reveals that I’m male (Acxiom), have a competitive income (IXI), three children in my family (V12), a propensity for buying online (Targusinfo) and am in mid-management of a small business (Bizo). I’m also in-market for a car (Exelate) and considered to be a “Country Squire,” according to Nieslen’s Prizm, which is essentially a boring white guy from the suburbs who “enjoys country sports like golf and tennis.” Well, I’m horrible at tennis, but everything else seems to be accurate.

As a marketer, you now have an interesting choice. Instead of finding “Country Squire” or “Suburban Pioneer” on content-specific sites they’re known to visit, now I can simply buy several million of these people, and find them wherever they may be lurking on the Web. This explains why you suddenly see ads for BMWs above your Hotmail messages right after you looked at that nice diesel station wagon on the VW.com Web site.

Today’s real-time marketing ecosystem works fast and works smart. But, how do you decide whether to buy the cookie or the site?

Most marketers insist that audience buying is meant for performance campaigns. This is largely a pricing consideration. Obviously, if I want to sell sneakers to young men, it makes sense to buy data and find 18-35-year old males who are “sneaker intenders” based on their online behavior and profile, and reach them at scale across the ad exchanges. Combined data and media will likely be under a $4 CPM, and probably less since both the data and media can be bid upon in real time. For most campaigns with a CPA south of $20, you need to buy “cheap and deep” to optimize into that type of performance. It sounds pretty good on paper. There are a few problems with this, however:

What are they doing when you find them? 
OK, so you found one of your carefully selected audience members and you know he’s been shopping for shoes. Maybe you even retargeted him after he abandoned his shopping cart at footlocker.com and dynamically presented him with an ad featuring the very sneakers he wanted to buy, and you did it all for a fraction of a cent. The problem is that you reached him on Hotmail and he’s engaged in composing an e-mail. What are the chances that he’s going to break task and get back into the mind-set of purchasing a pair of sneakers? Also, what kind of e-mail is he composing? Work? A consolation note to a friend who has lost a loved one? Obviously, you don’t know.

Maybe you reached that user on a less than savory site, or perhaps on a social media site where he’s engaged in a live chat session with a friend. In any case, you have targeted that user perfectly—and at just the wrong time. This type of “interruption” marketing is exactly what digital has promised us it wouldn’t be.

Perhaps a better conversion rate can be found on ESPN.com, or a content page about basketball, where that user is engaged in content appropriate to the brand.

How do you know where the conversion came from?
Depending on your level of sophistication and your digital analytics tool set, you may not be in the best position to understand exactly where your online sales are coming from. If you’re depending on click-based metrics, that is even more true. As a recent comScore article points out, the click is a somewhat misleading metric. Put simply, clicks on display ads don’t take branding or other Web behavior into account when measuring success.

Personally, I haven’t clicked on a display ad in years, but seeing them still drives me to act. Comparing offline sales life over a four-week period, comScore reports that pure display advertising provides average lift of 16 percent and pure SEM provides lift of 82 percent—but search and display combined provide sales lift of 119 percent. So you simply can’t look at display alone when judging performance—and you have to question whether you’re seeing performance lift because you’re targeting, or because your buyer has been exposed to a display ad multiple times. If it’s the latter, you may be inclined to save the cost of data and go even more “cheap and deep” to get reach and frequency.

How do you value an impression?
Obviously, the metric we all use is CPM, but sometimes the $30 CPM impression on ESPN.com is less expensive than the $2 RTB impression from AdX. Naturally, your analytics tools will tell you which ad and publisher produced the most conversions. Additionally, deep conversion path analysis can also tell you that “last impression” conversion made at Hotmail might have started on ESPN.com, so you know where to assign value.

But, in the absence of meaningful data, how do we really know how effective our campaign has been? I believe that display creates performance by driving brand value higher, and some good ways to measure that can now be found using rich media. When consumers engage within a creative unit, or spend time watching video content about your brand, they’re making a personal choice to spend time with your message. There’s nothing more powerful than that, and that activity not only drives sales, but helps create lifetime customers.

For today’s digital marketer, great campaigns happen when you understand your customer, find them both across the Web and on the sites for which they have an affinity—and find them when they are engaged in content that’s complementary to your brand message. Hmmm . . . that kind of sounds like what we used to do with print advertising and direct mail. And maybe it really is that simple after all.

Chris O’Hara is svp of sales and marketing for Traffiq. He may be reached through his blog at Chrisohara.com.

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The New CMO Lexicon: Redefining “Identity” and “Marketing”

Today’s consumers are highly demanding. They expect curated movie recommendations from Netflix, one-click restaurant reservations from OpenTable, on-demand limousine service from Uber, limitless housing options from AirBnB and the world of commerce available 24/7 from Amazon Prime. It’s a great time to be alive for a consumer, but perhaps the worst possible time for the CMO of any other company. Just think, Uber doesn’t own cars. They are a technology company built from the ground up to deliver personalized service at scale to consumers—that’s what today’s marketing is all about.

 

Only a few short years ago, CMOs had a difficult, but simpler, remit: build the brand and the consumers follow. Absolut vodka was about as undifferentiated a product as anything on the market, but great packaging and a clever ad campaign made it a power brand. It thrived because the world still worked on the principles of How Brands Grow, Byron Sharp’s 2010 book. Sharp posited that a marketer needed two things to succeed: availability in the consumer’s mind and availability of the product at the shelf. Brands like P&G’s Tide control lots of mindshare with mass media budgets, and P&G ensures it is widely available at every supermarket so a consumer can easily choose between it and Wisk at the “moment of truth.”

That system is dying rapidly, as mass media channels become fragmented into thousands of websites, apps, streaming media channels and experiences we don’t even understand yet. As a marketer, you can’t “buy eyeballs” today like you used to. This paradigm is largely responsible for the ever-shrinking average CMO tenure (from 44 months last year to only 42 months today). CMO’s must be prepared to insert themselves along steps of the consumer journey that move from channel to channel, and also have the ability to capture each tiny piece of digital exhaust that consumers’ gadgets and gizmos throw off, helping to inform their understanding of how they engage with a brand.

To make it clear, here’s a chart:

OLD CMO	NEW CMO
Rents access to people	Owns people data
One-to-many marketing	One-to-one engagement
Big bets on limited channels	Small bets on dozens of channels
One “big tent” message for many	Dozens of messages for segments
Panel-based attribution	Real-time feedback
Agency defines strategy	Marketer owns strategy, agency executes
Ad-focused	Experience-focused

Yesterday’s CMO would “buy eyeballs” with big TV and print campaigns, and use subscriber information as a proxy for targeted reach. Today’s CMO wants to own cookies and mobile keys so they can have a one-to-one conversation. Yesterday’s CMO looked at the performance at the end of a campaign, and optimized for the next one based on results from a survey. Today’s CMOs crave access to real-time performance data so they can optimize at run time. Things couldn’t be more different.

In this new norm, what should CMOs do to ensure they stay ahead of the curve? They have to change the way they think about consumer identity and how that impacts their work as marketers–and redefine the way they think about “marketing” in general.

Identity beyond IDs

A few months ago, I wrote that “identity is the new basis of competition” in marketing. That’s still true—you can’t build meaningful cross-channel experiences if you can’t tie people together with their devices. To that end, I recently was invited to an internal town hall with marketers from a large beauty company where the CMO announced that they just now eclipsed over 500 million addressable IDs in their data management platform. Her staff started clapping. Why? Because these weren’t known buyers, just cookies and mobile IDs—but they represented the ability for marketers to connect and build experiences for anonymous people who interacted with their website, mobile app or an ad. That has real, tangible value.

But, devices don’t buy things, people do. Just because you have a good device graph with billions of cookies, e-mail addresses and mobile keys doesn’t mean you have a good view of the people behind that information. Identity data must be augmented with data from systems of engagement to formulate a true view of the consumer. Every click, download, article read, and video view throws off digital exhaust that is filled with scraps of information that machines can use to paint a truer picture of a consumer’s identity. When marketers start valuing all data as a financial asset, they are starting the process of turning IDs into people.

For those of us in the industry, we can be forgiven if we think the world revolves around display, social, mobile and video advertising. We’ve gotten really good at delivering personalized digital experiences in real time, and we have a Lumascape full of clever technologies that are moving the needle for brands that are trying to reach connected consumers. CMOs must think outside the Lumascape, and connect these important addressable touchpoints to mass channels like TV, radio and print in order to deliver personalized experiences at scale.

More than  Marketing

The problem is that our definition of marketing often misses the concept of touch points that can exist separately from marketing. These touch points can include interactions between salespeople and potential customers, what happens when a product is returned, conversations on community sites and forums where customers talk to each other about a brand, and also within the e-commerce experience when a consumer is making a purchase. These are arguably more valuable interactions with consumers than a digital banner ad or email because these are either people that are existing customers or those about to buy. They’re incredibly valuable to a brand and don’t involve the traditional notion of “marketing” whatsoever.

Let’s take a look at an example. I fly Delta because I love their app, and they reward my loyalty with special phone numbers so I can reach someone no matter how hairy things get throughout my travel experience. Every time I interact with their website, app and service representative is an opportunity for Delta to market to me—and also an opportunity for the brand to learn more about the way I fly and what matters most to me as a consumer. Getting it consistently right keeps me loyal, but getting even slightly wrong brings me one step closer to tweeting #DeltaStinks. Not fair, but that’s representative of brand relationships today.

To be successful, CMOs must expand their definition of identity. “Identity” is more than just an ID. It’s what is formed after capturing every possible insight from every interaction. And “marketing” is not just about cross-channel messaging, it’s about creating great consumer experiences with every touchpoint that happens including sales, service, commerce and more.

It’s a great time to be a data-driven marketer.

[This article originally appeared in AdExchanger on 10.16.2017]

Location/Mobile Data Panel at DM16

Watch to explore the possibilities of using mobile location and beacon data to better segment, target, and analyze consumers. Featuring FreckleIOT, Ticketmaster/Live Nation, and PlaceIQ.

Best Practices in Digital Display Media (Interview)

Digital display is remarkably complex. Standard campaigns can involve multiple vendors of different technologies and types of media.

Today, eConsultancy launches Best Practices in Digital Display Advertising, a comprehensive look at how to efficiently manage online advertising. We asked the author, Chris O’Hara, about the report and work that went into it.

Why did you write Best Practices in Digital Display Media?

In my last job, a good part of my assignment was traveling around the country visiting with about 500 regional advertising agencies and marketers, large and small, over three years. I was selling ad technology. Most advertisers seemed extremely engaged and interested to find out about new tools and technology that could help them bring efficiency to their business and, more importantly, results to their clients. The problem was that they didn’t have time to evaluate the 250+ vendors in the space, and certainly didn’t have the resources (financial or time) to really evaluate their options and get a sense of what’s working and what isn’t.

First and foremost, I wanted the report to be a good, comprehensive primer to what’s out there for digital marketers including digital ad agencies. That way, someone looking at engaging with data vendors, say, could get an idea of whether they needed one big relationship (with an aggregator), no data relationships, or needed very specific deals with key data providers. The guide can help set the basis for those evaluations. Marketers have been basically forced to license their own “technology stack” to be proficient at buying banner ads. I hope the Guide will be a map through that process.

What was the methodology you used to put it together?

I essentially looked at the digital display ecosystem through the lens of a marketer trying to take a campaign from initial concept through to billing, and making sure I covered the keys parts of the workflow chain. What technologies do you employ to find the right media, to buy it, and ultimately to measure it? Are all of these technologies leading to the promised land of efficiency and performance? Will they eventually? I used those questions as the basis of my approach, and leveraged the many vendor relationships and available data to try and answer some of those questions.

What’s the biggest thing to take away from the report?

I think the one thing that really runs through the entire report is the importance of data. I think the World Economic Forum originally said the “data is the new oil” [actually, the earliest citation we can find is from Michael Palmer in 2006, quoting Clive Humby] and many others have since parroted that sentiment. If you think about the 250-odd technology companies that populate the “ecosystem,” most are part of the trend towards audience buying, which is another way of saying “data-driven marketing.” Data runs through everything the digital marketer does, from research through to performance reporting and attribution. In a sense, the Guide is about the various technologies and methodologies for getting a grip on marketing data—and leveraging it to maximum effect.

There’s an explosion of three letter acronyms these days (DSP, DMP, SSP, AMP, etc) that marketers are still trying to sort out. Do we need all of them? Is there another one around the corner?

I am not really sure what the next big acronym will be, but you can be certain there will be several more categories to come, as technology changes (along with many updates to Guides such as these). That being said, I think the meta-trends you will see involve a certain “compression” by both ends of the spectrum, where the demand side and supply side players look to build more of their own data-driven capabilities. Publishers obviously want to use more of their own data to layer targeting on top of site traffic and get incremental CPM lift on every marketable impression. By the same token, advertisers are finding the costs of storing data remarkably cheap, and want to leverage that data for targeting, so they are building their own capabilities to do that. That means the whole space thrives on disintermediation. Whereas before, the tech companies were able to eat away at the margins, you will see the real players in the space build, license, or buy technology that puts them back in the driver’s seat. TheBest Practices in Digital Display Advertising Guide is kind of the “program” for this interesting game.

To learn more about the Best Practices in Digital Display Advertising Guide, download the report here.

Same Turkey, New Knife

The way the ad tech world looked pre-DSP...and pre-DMP

Technology may still capture the most advertising value, but what if publishers own it?

A few years ago, ad technology banker Terence Kawaja gave a groundbreaking IAB presentation entitled, “Parsing the Mayhem: Developments in the Advertising Technology Landscape.” Ever since then, his famed logo vomit slide featuring (then) 290 different tech companies has been passed around more than a Derek Jeter rookie card.

While the eye chart continues to change, the really important slide in that deck essentially remains the same. The “Carving up the stack” slide (see above), which depicts how little revenue publishers see at the end of the ad technology chain, has changed little since May 2010. In fact you could argue that it has gotten worse. The original slide described the path of an advertiser’s $5 as it made it’s way past the agency, through ad networks and exchanges, and finally into the publisher’s pocket.

The agency took about $0.50 (10%), the ad networks grabbed the biggest portion at $2.00 (40%), the data provider took two bits (5%), the ad exchange sucked out $0.35 (7%), and the ad server grabbed a small sliver worth $0.10 (2%), for a grand total of 64%. The publisher was left with a measly $1.80. The story hasn’t changed, and neither have the players, but the amounts have altered slightly.

While Kawaja correctly argued that DSPs provided some value back to both advertisers and publishers through efficiency, let’s look ahead through the lens of the original slide. Here’s what has happened to the players over the last 2 years:

• Advertiser: The advertiser continues to be in the cat bird seat, enjoying the fact that more and more technology is coming to his aid to make buying directly a fact of life. Yes, the agency is still a necessary (and welcomed) evil, but with Facebook, Google, Pandora, and all of the big publishers willing to provide robust customer service for the biggest spenders, he’s not giving up much. Plus, agency margins continue to shrink, meaning more of their $5.00 ends up as display, video, and rich media units on popular sites.

• Agency: It’s been a tough ride for agencies lately. Let’s face it: more and more spending is going to social networks, and you don’t need to pay 10%-15% to find audiences with Facebook. You simply plug in audience attributes and buy. With average CPMs in the $0.50 range (as opposed to $2.50 for the Web as a whole), advertisers have more and more reason to find targeted reach by themselves, or with Facebook’s help. Google nascent search-keyword powered display network isn’t exactly helping matters. Agencies are trying to adapt and become technology enablers, but that’s a long putt for an industry that has long depended on underpaying 22 year olds to manage multi-million dollar ad budgets, rather than overpaying 22 year old engineers to build products.

• Networks: Everyone’s talking about the demise of the ad network, but they really haven’t disappeared. Yesterday’s ad networks (Turn, Lotame) are today’s “data management platforms.” Instead of packaging the inventory, they are letting publishers do it themselves. This is the right instinct, but legacy networks may well be overestimating the extent to which the bulk of publishers are willing (and able) to do this work. Networks (and especially vertical networks) thrived because they were convenient—and they worked. Horizontal networks are dying, and the money is simply leaking into the data-powered exchange space…

• Data Providers: There’s data, and then there’s data. With ubiquitous access to Experian, IXI, and other popular data types through 3^rd party providers, the value of 3^rd party segments has declined dramatically. Great exchanges like eXelate give marketers a one-stop shop for almost every off-the-shelf segment worth purchasing, so you don’t need to strike 20 different license deals. Yet, data is still the lifeblood of the ecosystem. Unfortunately for pure-play segment providers, the real value is in helping advertisers unlock the value of their first party data. The value of 3^rd party data will continue to decline, especially as more and more marketers use less of it to create “seeds” from which lookalike models are created.

• Exchanges: Exchanges have been the biggest beneficiary of the move away from ad networks. Data + Exchange = Ad Network. Now that there are so many plug and play technologies giving advertisers access to the world of exchanges, the money had flowed away from the networks and into the pockets of Google AdX, Microsoft, Rubicon. PubMatic, and RMX.

• Ad Serving: Ad serving will always be a tax on digital advertising but, as providers in the video and rich media space provide more value, their chunk of the advertiser pie has increased. Yes, serving is a $0.03 commodity, but there is still money to be made in dynamic allocation technology, reporting, and tag management. As an industry, we like to solve the problems we create, and make our solutions expensive. As the technology moves away from standardized display, new “ad enablement” technologies will add value, and be able to capture more share.

• Publisher: Agencies, networks, and technologists have bamboozled big publishers for years, but now smart publishers are starting to strike back. With smart data management, they are now able to realize the value of their own audiences—without the networks and exchanges getting the lion’s share of the budget. This has everything to do with leveraging today’s new data management technology to unlock the value of first party data—and more quickly aggregate all available data types to do rapid audience discovery and segmentation.

 The slide we are going to be seeing in 2012, 2013 and beyond will show publishers with a much larger share, as they take control of their own data. Data management technology is not just the sole province of the “Big Five” publishers anymore. Now, even mid-sized publishers can leverage data management technology to discover their audiences, segment them, and create reach extension through lookalike modeling. Instead of going to a network and getting $0.65 for “in-market auto intenders” they are creating their own—and getting $15.00.

Now, that’s a much bigger slice of the advertising pie.

[This post originally appeared in ClickZ on 2/1/12]

The Data Driven Agency

Three ways you can supercharge your digital media agency with data

Today’s digital media agency has access to enormous amounts of data, but using it effectively is what is going to make the difference between the shops of the future and the also-rans. Delivering data-driven insights is the key to being a 21st century agency. Here are three ways you should be working with data to secure your future:

Visualize it

How much time are you and your colleagues spending collating data, building reports, and formatting spreadsheets and PowerPoint decks for your clients? Most of the agencies I have worked with over the years admit to dedicating an embarrassingly large amount of (highly expensive) time towards these menial tasks. It’s not that getting your clients the data they need is not worth the time, it’s simply that there are now so many automated ways to deliver the data without burning salary.

To paraphrase former agency head and Akamai leader David Kenny, if you are doing things with people that you can be doing with computers, you have already lost. Why spend time formatting Excel spreadsheets and populating PowerPoint report templates with data, when you can be spending salaried employee time selling more services, optimizing campaigns, and delivering great strategy and creative?  Today’s automated ad management solutions and DMPs offer powerful ways to port both audience and ad serving reporting data into a single interface, to get instant access to key metrics such as frequency to conversion, churn rate, and channel attribution.

Ask yourself if the cost of such a system is more than the cost of the time your employees you have been spending building reports—and, ultimately, more than the cost of your eventual demise, should you ignore the changes afoot in your business.

Aggregate and Activate it

Think of all the data you have access to from a digital media standpoint. If you are helping clients execute a digital media campaign, you have traditional serving data from your demand side server, such as DFA. You probably also have engagement data from your rich media ad server. If you have access to your clients’ website pages (or at least tags there), you have site-side data, including conversion event data. If you are using an audience measurement tool, or are doing audience-specific buying through a demand side platform, you also have audience measurement data. Great. What are you doing with all of it? Moreover, what kind of data does your client have that you can help them add to activate the common advertising data types I have just described?

Let’s take the example of an agency using an audience measurement reporting tool, alongside an ad server report. In this case, it is possible that the analyst knows that the highest frequency converters for his travel campaign belong to a popular PRIZM segment, and he may also know that visitors to a popular travel site are three times as likely to engage with his rich media ad creative. Now what? Obviously, the right move is to buy more of the audience segment and double up with guaranteed advertising on the travel site. But what about audience overlap?

How can the advertiser reduce ad waste by ensuring that members of his audience segment that he is securing for as little as $2.00 CPM on exchanges are not overrepresented on the premium site for which he is paying $18.00 CPM? Plus, how many members of that audience are also already registered as customers? If you are not deploying a DMP to aggregate your clients’ CRM (first-party) data alongside the site-side and ad serving (2^nd party) data and the purchased (3^rd party) data segments, then there is going to lots of duplicated uniques in your audience. Smart data aggregation creates ad activation through waste reduction, lifting conversion rates, while lowering cost per conversion. Getting an effective universal frequency cap across digital channels is very difficult, but every dollar not wasted on duplicate impressions is another dollar that may be spent finding a new audience member. Reducing waste adds reach—and performance, which every client likes.

Compare it

As a digital media agency, you’ve run hundreds, perhaps even thousands of campaigns, producing thousands of data-rich reports for your clients. How much of that knowledge are you leveraging? Although you might know the top travel sites and audience segments to reach “moms of school-age children in-market for a beach vacation,” how readily available is that knowledge? Is it sitting inside your Media Director’s head, or hidden in various documents that don’t talk to one another? How about access to normative campaign data? How quickly can you find out how certain sites performed against similar KPIs without doing hours of research?

Like or not, advertisers want to know how their campaigns are performing against known standards, and it’s gotten a lot more complicated than beating a 0.1% click-through rate lately. Knowing how your last 10 travel campaigns performed—from which guaranteed site buys succeeded, to which audience segments performed, to which creatives elicited the highest CTR—is just step one. Having that data available for quick reference means that every new campaign can start from an advanced performance level, and your media people don’t have to recreate the wheel every time you receive an RFP.

Today’s smart DMPs also feature the ability to leverage your data to an even greater extent, especially for audience buying. Why limit yourself to pre-packaged audience segments that do not include your client’s first-party data? Today’s more advanced DMPs give marketers the ability to create audience segments on the fly, building discrete segments from data that includes available third-party data—but also first-party data, such as registration details, transactional records, and signals from hosted social media listening solutions. It’s the difference between buying from an ad network and creating your own.

Summary

Buying into portals’ site sections was the first phase in the effort to bring contextual and audience relevance to ad buying. Networks followed, offering packaged audiences at scale. Then bidded exchange buying came, offering pre-packaged audience segments at the individual cookie level. Today’s best practices include marrying all available data types to give marketers the ability to create their own targeted buys, and modern data management platforms are helping the largest advertisers automate what they have been doing since the first direct mail piece went out: finding targeted audiences. Leveraging today’s DMP technology can not only help you find those audiences more easily, but help you understand who they are, why they respond, and help you find them again.

Chris O’Hara is head of strategic partnerships for nPario, a DMP with clients that include Yahoo! and Electronic Arts, among others. A frequent contributor to industry publications, this is his first column for The Agency Post. He can be reached through his blog on www.chrisohara.com

[This article originally appeared in The Agency Post on 1/25/12]

Know Your Audience

Using Audience Measurement Data to Optimize Digital Display Campaigns

These days, advertising and data platforms are giving marketers a wealth of information that can be used to validate their strategies, and optimize their digital campaigns for better performance. There is a lot of data to sort through—some more useful than others. Sometimes, good campaign optimization comes down to the basics: Understanding who your audience is, and why they are doing what they are doing.

Let’s look at a real life example of a digital display campaign, run through the digital ad agency of a popular mattress retailer. The agency wanted to test new inventory sources for the campaign by running broadly on general interest sites, evaluating the demography of audiences that showed purchase intent, and optimize over the course of the campaign to maximize impact.

A theory being tested was that older audiences, who report more difficulty sleeping than younger demographic groups, would respond more favorably to the retailer’s online display ads. Campaigns were initially skewed to sites that over-indexed against audience composed of 50 and older.

Figure 1: Age of Ad Viewer, by Impressions.

As Figure 1 shows, a bulk of impressions during the discovery portion of the campaign were delivered to visitors aged 46-65 years of age, which was the desired demographic. After analysis of those who viewed or clicked on a display ad, and then went on to purchase, the audience composition was remarkably different. As shown in Figure 2, the bulk of conversions came from those aged 18-45.

Figure 2: Age of Mattress Purchaser (Conversions).

The agency adjusted the ad buy to heavy up on sites that over-indexed for a younger audience, and opted out of buys tailored to the older demographic. As wasted impressions were trimmed down in the overall plan, conversion rates increased dramatically. Testing and validating your instincts with data on an ongoing basis is the key to success in digital display advertising. The mattress retailer, who experienced better sales from older store visitors (offline), found a more responsive younger audience online. Although it seems obvious, having the initial data means being able to smartly allocate marketing capital, and having access to ongoing data means not having to rely on old insights in a changing marketplace.

Another offline theory the mattress retailer sought to validate was the mattress life cycle. After collecting brick and mortar sales data for years, the retailer knew that the average life of a mattress was approximately 7 years, and that the single greatest life event influencing the purchase of a new mattress was moving. Therefore, it made sense to target audiences based on length of residence (>7 years), and target content around buying or renting a new home.

Inventory was bought from a wide range of home-specific and moving sites, and measured using Aperture audience measurement populated with data sets from Experian, IXI financial, V12 demographic, and Nielsen PRIZM data.

 

Figure 3: Length of Residence, by Impressions.

Figure 4: Length of Residence, by Click.

As Figures 3 and 4 amply demonstrate, the mattress retailer was targeting the bulk of impressions towards individuals reporting over seven years residence in a single location, and clicks among that group indexed the highest in aggregate. That data validated the approach of buying into sites with a strong audience of self-reported homeowners. However, a deeper look into audience data revealed a strong distinction between renters and buyers.

Fig 5Comparing Impressions and Conversions by home ownership status.

As noted in Figure 5, although the bulk of impressions in the campaign were served to homeowners, renters were the ones buying the most mattresses. This learning did more than any other data point to drive campaign optimization.

Naturally, the next step in the campaign optimization process was to focus inventory delivery to sites that promised a concentrated audience of home renters. Sites such as ForRent.com, ApartmentGuide.com, and Renters.com were added to the optimization plan.

More insights came as the Aperture data was collected. Despite purporting to have a heavy concentration of renters, two of the more popular sites actually index much higher among homeowners, as shown in Figure 6. It looked as though homeowners that were looking into renting made up the majority audience—a fact that helped the retailer tailor specific messaging to them.

Figure 6: In this example, a media site aimed at renters, over-indexes against current homeowners.

Figure 6: In this example, a media site aimed at renters, over-indexes against current homeowners.

For this particular campaign, the ability for the retailer to validate certain audience assumptions using real demographic data was critical, as well as the ability to leverage the distinction between two types of potential customers: home owners, and renters. Additionally, getting real audience metrics beyond a publisher’s media kit or self-declared audience information enabled the retailer to craft its creative and messaging in a highly specific way that increased conversions.

When it comes to audience validation and campaign optimization, here are three keys:

• Know Your Data: In today’s technology-driven marketing world, knowing how to leverage the data available to you is critical to both understanding and targeting your audience. Make sure your marketing investment decisions are driven through the analysis and usage of 1^st party data, including registration data for demographic modeling; 2^nd party data, such as ad server and search data for behavioral modeling; and 3^rd party data, such as available audience segments from providers like Nielsen and Datalogix, for audience validation, matching, and lookalike modeling. Data is not just about buying audience segments for targeting; it’s about trying to get a 360-degree view of your ideal customer.

• Choose the Right DMP: There are DMPs for every marketer, so be careful to choose the right one. Big Data needs call for pure play DMPs that can stitch together highly disparate data sets that include all data types, and make both insights, audience segments, and lookalike modeling available in real-time. Marketers looking to buy from a variety of 3^rd party audience segment providers should choose a data marketplace such as Exelate, or be willing to access a more limited number of data sources inside a DSP such as AppNexus.

• Leverage Audience Measurement: Finally, there is a lot that audience segments can bring to the table in terms of audience insights. Understanding the audience composition of who saw, clicked on, and converted after seeing your campaign gives you the ability to learn about your target customers, their online behaviors, and (most importantly) find more of them. Your DMP should have the ability to marry audience and campaign data to give you a highly granular level view of your best (and worst) performing audience types—down to the creative level.

Learnings from this case study, and other valuable information, can be found in my upcoming “Best Practices in Digital Display Media,” coming in January 2012 from eConsultancy.com.

[This article originally appeared in ClickZ on 1/4/2012]