Category Archives: Media Agencies

Sep 17 2024
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Ad Technology, Artificial Intelligence, Marketers, Media Agencies, Online Media, Publishers

How AI is actively transforming advertising

by Eric Picard

Artificial Intelligence (AI) and its subset, Machine Learning (ML), have been integral to the advertising industry for decades. These technologies have transformed how businesses connect with consumers, optimizing many of the dollars spent and ensuring targeted engagement. But as AI evolves, particularly with the advent of Large Language Models (LLMs) and generative AI, we’re entering a new era where smart automation is a reality. Let’s explore how AI, ML, and LLMs are reshaping advertising, untangling the complexities of these technologies, and understanding their distinct roles and applications.

To begin with, it’s important to differentiate AI and ML. AI is the broader concept of machines performing tasks that typically require human intelligence, such as decision-making and pattern recognition.

Machine Learning (ML) is a subset of AI, and has been the engine that powers many of the smart decisions in today’s advertising landscape. At its core, ML is about teaching computers to learn from data, much like how we learn from experience. There are several approaches to ML, each with their own unique applications and strengths in advertising.

One of the most prevalent techniques is supervised learning. This approach involves training an ML model on a dataset that includes both inputs and the correct outputs—sort of like coaching a sports team with the playbook in hand. In advertising, supervised learning is often used for predictive targeting. By analyzing historical data, these models can forecast which segments of an audience are most likely to respond to a specific ad campaign. This allows advertisers to allocate resources more effectively and maximize return on investment.

Unsupervised learning, on the other hand, is a bit like sending a detective into a room full of clues without any instructions. The model explores the data, finding patterns and relationships on its own. This technique is ideal for audience segmentation, helping advertisers discover new and potentially valuable consumer groups based on shared behaviors or characteristics. It’s akin to discovering hidden subcultures within a larger community, providing insights that can drive more personalized marketing strategies.

Reinforcement learning is another fascinating ML approach, where models learn by trial and error—similar to training a pet with rewards and consequences. In the dynamic world of real-time bidding, reinforcement learning algorithms adjust bidding strategies on the fly, learning which actions yield the best results. This adaptability is crucial in environments where market conditions and consumer behavior can change rapidly.

It’s also worth mentioning neural networks, a type of ML model inspired by the human brain’s structure and function. These networks are particularly powerful in tasks involving complex pattern recognition, such as image and speech recognition. In advertising, neural networks can enhance programmatic buying by evaluating vast datasets to identify subtle patterns in consumer behavior, enabling more precise targeting and personalization.

While these examples illustrate some of the common ML techniques used in advertising, the field is vast and continually evolving. Each method brings its own set of tools to the table, contributing to a more nuanced and sophisticated advertising ecosystem. As ML technology advances, its role in crafting more targeted, efficient, and engaging advertising experiences will only grow, pushing the boundaries of what’s possible in the digital marketing space.

Now, let’s address the role of LLMs in advertising. LLMs, such as GPT-4o, are advanced AI models that excel in understanding and generating human-like text. They are not primarily designed for data analysis or real-time decision-making—which are traditional ML use cases—but rather excel in tasks that involve language processing and text-based interactions. LLMs are being leveraged to automate processes that require a nuanced understanding of language and context, such as drafting personalized ad copy, facilitating customer service interactions, and enhancing chatbots.

In media buying and selling, LLMs are being applied to automate complex processes that traditionally required human intervention. By programming LLMs to think with a specific viewpoint and set of instructions, they can streamline tasks like scheduling and orchestrating campaigns, crafting and refining ad messages, and even generating comprehensive reports. These models act more as strategic partners, assisting human teams in managing and executing processes efficiently.

The application of ML in advertising remains robust, focusing on data-driven decision-making. ML algorithms excel in predictive targeting, analyzing vast datasets to identify optimal audiences, and optimizing bids in real-time to maximize ad spend efficiency. These capabilities are essential for real-time bidding environments and dynamic pricing models, where decisions must be made swiftly and accurately based on ever-changing data inputs.

Generative AI, closely related to LLMs but with distinct applications, is making significant impacts in creative advertising. While LLMs are adept at processing language, generative AI models are designed to create new content—be it text, images, or even video. In advertising, generative AI can automate the creation of ad visuals or video content, generating variations tailored to different audience segments. This capability not only enhances creativity but also accelerates the production process, allowing for rapid experimentation and iteration.

The distinction between generative AI and LLMs is important. While both can be used in the creative process, LLMs focus on language and dialogue, whereas generative AI extends to producing varied forms of media content. Together, they offer a comprehensive toolkit for advertisers looking to innovate and engage audiences more effectively.

As AI continues to advance, ethical considerations around data privacy and algorithmic bias abound. Transparency in how AI systems operate and make decisions is essential to maintain consumer trust. Balancing automation with human creativity and insight is also crucial. While AI can handle data-driven processes, human expertise is irreplaceable in crafting compelling narratives and understanding the subtleties of consumer emotions.

Looking ahead, the fusion of AI technologies promises even more sophisticated advertising solutions. We can anticipate AI models that predict consumer needs with remarkable precision, integrating seamlessly into the consumer journey. This future of advertising is not just about efficiency—it’s about creating meaningful, anticipatory experiences that resonate with consumers on a personal level.

By automating routine tasks and augmenting human capabilities, AI is enabling advertisers to deliver more personalized, effective, and efficient campaigns. The key to success will be embracing AI as a partner, leveraging its strengths while preserving the creativity and empathy that only humans can provide.

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Sep 16 2024
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Ad Ecosystem, Ad Serving, Ad Technology, Marketers, Media Agencies, Online Media, Online Privacy, Programmatic, Publishers

Programmatic Ads in 2024

A privacy-centric new world

By Eric Picard

I’ve been writing about advertising technology and digital advertising since 1999. Every five to seven years we go through a new wave of transformation. The current wave is the Privacy wave, and it’s transforming the way that the infrastructure of the ad technology space functions. Previously we were reliant on digital identifiers, generally enabled by cookies on the web, and AdIDs in the mobile app space, but today these mechanisms have been deprecated or are in the process of being deprecated. This has decimated a huge swath of the industry that relied on these IDs to make all sorts of decisions, including linking together multiple tracking methodologies – to power 3rd party data. That industry has shrunk significantly, since users are becoming impossible to track that way.

This transformation is largely driven by increasing consumer demands for privacy and stringent regulatory requirements. Today, the focus has shifted towards privacy-centric methodologies, with first-party data taking center stage. In this comprehensive overview, we’ll explore the current state of programmatic advertising, delve into the innovative strategies employed to maintain effectiveness while upholding privacy, and highlight the strategic implications for advertisers.

Understanding Programmatic Advertising Today

At its core, programmatic advertising is the automated buying and selling of online ad space, allowing brands to target specific audiences at scale. This process involves real-time bidding (RTB), where ad inventory is bought and sold on a per-impression basis in a few hundred milliseconds. Programmatic platforms use sophisticated algorithms to analyze vast amounts of data, enabling advertisers to reach their desired audiences with precision.

Without cookies and AdIDs, the whole industry is in the process of retooling.

The Role of First-Party Data

First-party data refers to information collected directly from consumers either through brand-owned channels such as websites, apps, and loyalty programs on the advertiser side, or through the publisher’s relationship with the consumer. This data is gathered with explicit user consent, making it both reliable and compliant with privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

The importance of first-party data cannot be overstated. It provides a comprehensive view of consumer behavior, preferences, and demographics, enabling advertisers to create personalized and relevant ad experiences. By leveraging first-party data, brands and publishers can build direct relationships with their customers. This data can be used in many valuable ways, but importantly can be used to deliver the correct advertising to consumers, and to measure effectiveness of advertising.

Privacy-Centric Targeting Methodologies

In response to the growing emphasis on privacy, advertisers have adopted several innovative targeting methodologies that prioritize user rights while maintaining effectiveness:

Contextual Targeting: In a world where user data is increasingly protected, contextual targeting offers a privacy-friendly alternative. This method involves placing ads based on the content of the web page rather than individual user data. For example, an ad for hiking gear might appear on an article about nature trails. By focusing on the context rather than the person, advertisers can maintain relevance without compromising privacy. Contextual has been around forever, but new approaches to contextual are being used in much more sophisticated ways. What’s old is new again.

Cohort-Based Targeting: Google’s Privacy Sandbox initiative popularized the concept of privacy-first approaches to targeting users, their first broadly discussed version was called Federated Learning of Cohorts (FLoC), which groups users into cohorts based on similar browsing patterns. Google has reiterated and revised their approach several times, and hasn’t really gotten traction. But that’s mostly because they’re Google, and the industry isn’t sure they trust the approach they have come up with. While Google’s approach isn’t gaining traction, the overall approach of building privacy-safe cohorts of users for anonymous targeting is sound, and may well get cracked at the industry level sooner or later. This approach allows advertisers to target clusters of users with shared interests, removing the need for individual tracking.

Identity Solutions: With the decline of third-party cookies, identity solutions have emerged as a viable alternative. These solutions use hashed identifiers like email addresses or phone numbers to create a persistent identity across different platforms. The success of these solutions depends on user consent and robust data protection measures, offering a way to recognize users while respecting their privacy. While these approaches work technically, getting to scaled anonymous identity is a real challenge that still is being overcome.

Data Clean Rooms: These secure environments allow brands and publishers to collaborate and combine their first-party data without exposing user identities. Data clean rooms enable advertisers to gain insights and enhance targeting precision while adhering to privacy regulations. By facilitating data collaboration in a controlled setting, clean rooms offer a solution to the challenges posed by privacy laws. Note that while this approach overcomes the legal issues, it’s still not quite clear that consumers will be accepting of the approach as it becomes normalized and written about in the press.

Beyond Basic Targeting: Curated Audiences and Inventory

Publishers lost a lot of ground in the first wave of programmatic advertising, which pushed all the power to the media buyer. This created a long period of data asymmetry where publishers didn’t know why an advertiser was buying any given impression – and the knowledge of their own audience was ignored by the buyer. Things have changed with the loss of 3rd party data, cookies and IDs.

The industry has responded by rallying around the sell-side of the market for the first time in many years. The outcome is what is being called Curated Audiences and Curated Inventory. Effectively the sell-side of the market has access to an immense amount of information on their side of the fence, about the consumers visiting their sites, and about the behavior of those consumers. This all is Publisher First Party Data, and is able to be blended with other data sources by the Publisher to create large pools of inventory that are curated to the needs of the buyer. Vendors and the publishers themselves have found ways to build high scale and highly effective targeting and optimization technologies based on these approaches, and package the inventory into programmatic deals (PMPs):

Curated Audiences: Publishers can create pre-defined audience segments based on aggregated data insights and consumer behaviors. These segments are crafted using a combination of consented user data (the consumer said the publisher can use it) and non-personal data, ensuring compliance with privacy regulations. By curating audiences, advertisers can buy advertising at scale that allows them to reach relevant consumers without relying on intrusive data collection methods.

Curated Inventory: Publishers can package their ad inventory using their own data and intelligence, often with the support of sophisticated external data providers. This approach allows advertisers to target ads to appropriate audiences without using personal data. For instance, sophisticated geo-targeting can be employed to deliver regionally relevant ads, enhancing user engagement without compromising privacy. Some vendors use decades old approaches in new ways, using census and other forms of data that allow demographic, psychographic, and other types of targeting by overlaying the location of the user when they receive the ad against known information about that location. For instance, assuming someone on a golf course at 3PM is likely a golfer.

These curated solutions provide advertisers with powerful tools to reach their desired audiences while navigating the complexities of privacy regulations.

Strategic Implications for Advertisers

The shift towards privacy-centric methodologies is not just a technical adjustment; it’s a strategic imperative. Advertisers must align their programmatic strategies with these approaches to maintain consumer trust and comply with legal requirements. This involves investing in data infrastructure, nurturing direct customer relationships, and staying informed about regulatory changes.

Moreover, as privacy becomes a selling point, brands that demonstrate a commitment to safeguarding user data can differentiate themselves in a crowded marketplace. Transparent data practices and clear communication about how consumer data is used can build loyalty and encourage engagement.

Overcoming Challenges in the Programmatic Space

While the transition to privacy-centric advertising offers numerous benefits, it also presents challenges. Advertisers must adapt to new technologies and methodologies, requiring investment in training and infrastructure. Additionally, the loss of third-party cookies necessitates a reevaluation of measurement and attribution models, as traditional methods may no longer apply.

To overcome these challenges, collaboration within the industry is crucial. Brands, agencies, publishers and technology providers must work together to develop standards and best practices that prioritize privacy while delivering effective results. By fostering an ecosystem of transparency and cooperation, the industry can navigate the complexities of programmatic advertising in 2024 and beyond. The IAB Tech Lab is a great example of an industry organization working to drive this kind of collaborative adoption and roll out.

The Future of Programmatic Advertising

As we look to the future, programmatic advertising will continue to evolve in response to technological advancements and changing consumer expectations. The integration of artificial intelligence (AI) and machine learning will enhance targeting capabilities, allowing for even greater personalization while respecting privacy. Additionally, the rise of connected devices and the Internet of Things (IoT) will present new opportunities for advertisers to engage with consumers in innovative ways.

Ultimately, the key to success in programmatic advertising lies in embracing privacy as a core value. By doing so, advertisers not only comply with regulations but also foster trust and loyalty among their audiences. In 2024 and beyond, the challenge will be to innovate continuously while keeping consumer privacy at the forefront of every decision. As the landscape continues to evolve, staying informed and adaptable will be essential for advertisers seeking to thrive in this dynamic environment.

The current state of programmatic advertising is characterized by a delicate balance between effective targeting and stringent privacy requirements. First-party data has emerged as the linchpin of modern advertising strategies, offering a path to personalization that respects user privacy. By adopting innovative targeting methodologies and maintaining a strong commitment to privacy, advertisers can successfully navigate the programmatic landscape, building lasting relationships with their audiences in the process.

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Apr 03 2017
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Ad Ecosystem, Ad Technology, Economics, Marketers, Media Agencies, Online Media, Predictions, Publishers

The Fifth Wave Of Ad Tech: Privileged Programmatic

By Eric Picard (originally published on Adexchanger.com Friday, March 10th, 2017)

The first seven years of the programmatic revolution were driven by three major efforts.

It began with the creation and propagation of the massive new infrastructure needed to support real-time bidding. That was followed by the connection of all demand to all supply in the programmatic infrastructure. New ad products, formats and platforms then emerged, built on top of this new infrastructure.

This was a significant revolution – what I’ve called the third and fourth waves of ad technology. We’re now entering a fifth wave: privileged programmatic.

As the programmatic ecosystem matures, we’re seeing massive adoption of these new tools and technologies by the largest advertisers and media agencies now spending at scale. During the first seven years or so, many ad networks procured and resold media and some large marketer early adopters broke ground – many of which are now reaping the dividends.

But the very largest budgets are now coming into programmatic, and the game is changing. To illustrate the change, let’s talk about the historical evolution that the financial markets went through as they hit their maturity threshold during the rise of electronic trading.

Lessons From High-Speed Traders

In the highly recommended book “Dark Pools: The Rise of the Machine Traders and the Rigging of the US Stock Market,” by Scott Patterson, there is a clear narrative that will start to feel familiar to those working in programmatic media.

When the electronic markets were created, the early winners were typically hedge funds established and managed by the same humans who built the electronic market infrastructure. They knew that traders that responded the fastest to auctions could get significant advantage over other participants. Thus began the high-speed trading (HST) revolution. High-speed traders made millions of dollars a day on high-volume trades at very high speeds.

As the market matured, large traditional stock market players entered the electronic trading business and had their lunch eaten by the upstart high-speed traders. They found this to be unacceptable. The basic logic was, “If I’m spending billions of dollars a year on your electronic exchange, I need some privilege that gets me ahead of these little upstarts who have ‘know-how’ but are tiny players compared to me.”

The biggest players went to the exchanges and demanded privileged bidding mechanisms to allow them to win in the auction even if another player bid higher or bid first. They removed the advantage built in by the high-speed traders.

Nobody warned the HST companies. Within weeks in some cases, many simply went out of business. They had no idea what happened, but knew they suddenly weren’t winning in the auction. Eventually a few found out that the unpublished bid mechanisms that allowed them and the large brokerages to win in the auction had been uncovered and made available more broadly. But most of the damage was already done.

Privileged Programmatic

Privilege in an auction environment is not necessarily a bad thing. Much like the RTB exchanges in advertising, the electronic markets were seen as the great equalizers – fair unbiased auctions – but the reality is that the HST companies had their own type of advantage based on infrastructure knowledge. A real business argument can be made that buyers spending vast amounts of money should be able to negotiate for privilege with the sellers. That’s exactly what is happening in programmatic advertising.

Have you noticed that many of the biggest early players in programmatic have come upon hard times? Suddenly algorithms that were designed to provide advertisers with performance while still stripping off big dollars via an arbitrage model stopped working. Why?

Over the last few years we’ve seen the massive adoption of new privileged mechanisms in programmatic. Whether we discuss private marketplaces (PMPs), header bidding, first-look or programmatic guaranteed, they are predictable artifacts of the maturation of the programmatic marketplace. And don’t let any early knowledge you’ve gathered on these mechanisms create a false sense of comfort – PMPs from three years ago often look nothing like the configuration seen today. These mechanisms are not created equally.

For publishers, this maturation is very good news. Many large publishers viewed programmatic as a “rush to the bottom” in the early days and now see programmatic mechanisms bringing balance back to the marketplace.

Many publishers expressed frustration as programmatic created for the first time in digital media an information asymmetry that favored advertisers. Publishers had no idea why advertisers bought media from them over the open exchange, and now with these privileged mechanisms, the conversation has moved back to media buying and sales teams are empowered to negotiate and structure deals that drive customer value.

The hallmark of the first seven years of programmatic was a bottoms-up reinvention of buying based on data-driven decisioning and performance – and the biggest lever on performance was price of inventory. Early adopters were astounded to find their desired audiences for a low cost on the exchanges, even at the same publisher sites where they were simultaneously executing direct buys at much higher prices.

But those same savvy early adopters who realized huge discounts by buying the same users on the same publishers over the open exchange saw the writing on the wall. They recognized that prices were rising on the best users as the competition in the auction rose – since unsurprisingly, the same users seemed to be of interest to all advertisers in the same sector.

The savviest advertisers went directly to publishers and made PMP deals to access inventory with mechanisms that gave them advantage over their competition – which is also known as privilege. By putting their PMPs in increasingly higher priority within the ad server, setting up fixed-rate, variable and hybrid-rate deals and using new tools like header bidding, the most knowledgeable buyers stayed ahead of the competition. Publishers saw that these new mechanisms drove much higher CPMs, in many cases higher than direct buys, and importantly gave them insight into why advertisers bought from them. Eventually, the very most desirable audiences on the largest and best publishers evaporated out of the open auction.

The market is tipping over on itself – with open auctions being relegated more and more to purely direct-response advertisers that are not selective about which publishers their advertising runs on. For large brands, especially those spending large budgets, which also tend to be those that care deeply about running ads on high-quality publishers, things have gotten a lot more sophisticated.

Programmatic is no longer about low-cost inventory; it is now the infrastructure for transaction where the buyer and seller are handshaking and establishing connections to the consumers that brands need to reach. Programmatic is the mechanism to bind together the new tools that empower the advertiser to take control of their audiences and apply real science to the art of advertising. Publishers now can gain insight from working through these mechanisms rather than being left in the dark.

Sophisticated publishers already know this – and are driving programmatic elements or line items in their core I/Os as part of their direct business. On the buy side, the trend is for agencies to blow up their trading desks and embed programmatic buyers into direct buying teams.

This is a clear wake-up call for publishers that are still not treating programmatic as part of their direct sales or which haven’t changed sales compensation to remove channel conflict. Same for advertisers and media agencies who are segregating their programmatic buyers from their direct buyers.

Deal design has gotten extremely sophisticated, and the trend is toward increased sophistication, not simplification. If you are driving programmatic sales at a publisher and your deals are very one-dimensional, you’re probably missing something.

If you’re buying programmatically today and haven’t analyzed the core audiences you’re reaching over the open exchange, broken out by publishers that you’re also buying directly from, you’re behind your competitors.

And if you’re a marketer, question your media partners about all of these things. You have time, but not very much.

4 Comments

  1. Gerard March 10, 2017
    In effect, were seeing “networks” appear that advertisers use. Yes! ad networks are back but the publishers are acting like their own middleman. The buyers can now group together publisher networks and create their own ecosystem of their own choosing. Tis a fun time to see the same philosophy repeat itself
    • Eric Picard March 13, 2017
      Gerard, it’s not quite the same thing, neither philosophically nor structurally. Ad networks were arbitrage mechanisms designed to extract money from the ecosystem. This is a direct relationship between buyer and seller. The seller and buyer pay only technology fees and deal with the negotiation costs.
  2. R.J. Lewis March 15, 2017
    Eric,Great piece. We definitely live in interesting times. The pace of change is such that the costs of both technology, talent, and training to keep up, may out weigh the benefits gained. Are we as an industry encouraging brands to sit on the sideline and wait for the dust to settle? Also, and most importantly, what do you see as wave six?

    Reply

    • Eric Picard March 20, 2017
      Hey R.J. This is a trend that is finally culminating after a long incubation. I don’t see it as a time for anyone to sit on the sidelines – this is the holy grail we’ve all been waiting for: Publishers are empowered to sell and build value-based relationships with buyers. Advertisers get value from their customer data investments and the ability to intelligently decide who to reach, at what frequency, and how much to pay for that exposure. Wave six? I just got Wave five out to you – let’s start there.
Feb 05 2015
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Ad Ecosystem, Economics, Marketers, Media Agencies, Online Media

The New Premium: How Programmatic Changes The Way Advertisers Value Inventory

By Eric Picard (Originally Published on AdExchanger.com Thursday, February 5th, 2015)

Five years ago, if I told anyone in our industry that I wanted to buy or sell “premium” inventory, we’d all picture the same thing: inventory that was bought or sold directly between a media buyer and publisher’s salesperson. Maybe it would be home page inventory or a section front, a page takeover or rich unit. Or perhaps it would just involve a specific publisher that we agreed equated to “premium.”

New programmatic technologies are radically changing how we think of inventory overall, especially the term “premium.” Inventory is no longer one- or two-dimensional – the definition has become much more complex. It is a multidimensionally defined set of attributes that includes traditionally “publisher-controlled” inputs, such as page location, dimensions of the creative, category and content adjacencies. But today there are additional overlaid attributes that flesh out the definition.

Advertisers can bring their own data to the dance, which we’ll hesitantly call “first party,” and overlay additional data sources, which we’ll hesitantly call “third party.” And beneath the surface level attributes are underlying components that can be much more dynamic. These components can help predict how effectively an impression can drive a campaign’s goals or outcomes.

Programmatic buying platforms historically were tied to open exchange inventory, but increasingly, they are used as primary buying platforms across open RTB, private marketplaces, direct publisher integrations and even to support direct buys. This more holistic approach ultimately leads to a “programmatic first” point of view, as the new inventory definitions being rightly demanded by advertisers become their starting point on media buys. While RTB “only” represents 20% to 40% of budgets today, it’s clear that the rapid growth of programmatic will drive these broader inventory definitions across the buyer-seller boundary.

Achieving Symmetry

Publishers are embracing the newly empowered media buyers, allowing them to bring their own data for direct buys. They are also allowing buyers to connect directly to their ad servers for programmatically enabled direct buys and buy-side inventory decisioning in real time. For the past few years, the asymmetry of information in programmatic – publishers had no idea why advertisers bought their inventory on the exchange – has been a sore point.

Publishers point out that if buyers work with them, they can open paths to the inventory, inclusive of audiences, that buyers are looking for on the exchange. As we see more collaboration between buyers and sellers on these points, pockets of highly valuable inventory that were lying dormant inside the publisher’s ad server (dare we say “premium”) will suddenly open up.

To use a mining analogy, publishers previously sold unrefined chunks of ore to media buyers, who found a variety of metals inside, but only some of it was valuable to them. So buyers started buying inventory through other marketplaces that allowed them to use their own tools and data to locate the chunks of ore that contained the metals they cared about. Now publishers are saying, “If you’re willing to pay us what you think that metal is worth, we can find more of it than you’re getting on those secondary marketplaces. But you have to work with us to get access to it.”

This new approach is both exciting and refreshing. The industry is getting over old suspicions and reluctance to share information. The asymmetry is becoming more symmetrical, and everyone involved gets more value. Days are still early, and only the most advanced players are figuring out how to make this work, but it won’t be long before this new way of defining “premium” is the standard.

Evolving Definition

How do we define “premium” in this new programmatically enabled world? Premium inventory matches the advertiser’s holistic goals, inclusive of where the ad will run – publisher, category, page location or format – and the multidimensional profiles of anonymous users behind the impressions, including first- and third-party audience data definitions, as well as geographic, demographic and other data elements provided by publishers and other parties. The advertiser believes the premium inventory will help fulfill their goals and drive outcomes that they desire.

That’s a mouthful, eh? How about this: Premium inventory matches the goals of the advertiser well enough that they’re willing to pay a premium for access.

Dec 13 2014
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Ad Ecosystem, Marketers, Media Agencies, Publishers

How private marketplaces actually work

By Eric Picard (Originally Published on iMedia – December 13, 2014)

Recently Ricardo Bilton wrote an article for Digiday about the difficulties that publishers have had embracing private marketplaces (PMPs). The validity of his article is arguable, and he called out a few of the buy-side platforms as causing some of the difficulty — despite the massive and growing volumes those platforms are actually driving in the PMP world. So instead of rebutting his article, let me define how these things work, and what the scope and difficulties are in making use of private marketplaces, but also what benefits can come from them.

Before we get into it, let’s talk about complex vs. complicated. They actually mean different things. Complex implies that the difficulty of embracing something is unavoidable — some things are just complex, have lots of moving parts and lots of opportunities to implement. Complicated implies that the difficulty is avoidable, and could be designed around. Private marketplaces today are both complex and complicated. We need to remove the complications.

History

Back in the dark ages of the programmatic world, let’s say 2008, publishers were wary of the newly emerging programmatic landscape. In order to convince them to put their inventory into the proto-exchanges that existed, the concept of a private exchange or private marketplace evolved. Keep in mind that up to that point, mostly the inventory that flowed on the exchanges came from ad networks daisy-chaining their inventory together. But as publishers began participating, and SSPs entered the scene, these private marketplace mechanisms were rolled out to support publisher concerns about yield optimization — and especially cherry picking and cream skimming — buying strategies that were major concerns for publishers in those early days.

As a result, the first private marketplaces were fairly simple to understand, and were nice ways for publishers to get their feet (or at least their toes) wet in the programmatic space. The basic concept was simple: Publishers could expose some or their entire inventory to an exchange or SSP. They could hand-pick which advertisers were invited to come into the private marketplace. Only those invited to have access could bid on the inventory.

The problem with this early approach was that it missed out on some very important fundamentals of exchange-based buying and selling. One important fundamental is bid density: For every impression that is exposed to an auction, you need as many bidders (buyers) as possible competing for that inventory in order to have the price reach a reasonable amount — especially in a second price auction.

What is a second price auction?

It’s a pretty simple idea, really — if three people participate in a second price auction for an Apple, all three people put in the highest price they’re willing to pay for that apple. Person A bids $1.00. Person B bids $1.50. Person C bids $0.50. Person B would win the auction, but only pay $1.01 for the Apple. The reason for a second price auction rather than a first price auction (in the example above for a first price auction, person B would still win, but would pay $1.50) is to encourage the bidder to put their true price into the auction. Second price auctions are generally understood to have less “gaming” of the auction — since the high bidder is protected from overpaying.

But in a world where only one or two advertisers are bidding on the same impression, there’s often no second price to use. So private marketplaces by nature are problematic when it comes to bid density — and many early private marketplaces ultimately failed to succeed. There are mechanisms that can be tried — for instance using a first price auction for private marketplaces — but of course this can lead to rampant gaming of the auction — and rarely will a buyer put the actual price they’re willing to pay into a first price auction. Another mechanism is price floors –which protect the publisher from having the impression fall on the floor for close to nothing — but often a PMP price floor in those days became a price, rather than a floor due to the lack of bid density.

As our industry evolved away from the original exchanges and toward real-time bidding, a whole host of new complex issues were uncovered — but also amazing new capabilities. One of the key things that this drove in the PMP world were new innovations like dynamic floor pricing — where the SSP or even the publisher ad server was able to analyze demand across the ad server, the SSP, the PMP, and the open exchange and set the floor on a per-impression basis.

As publishers got over their initial fear of programmatic selling, they began to put their inventory into the open exchange and blend the private and open bids into the same auction. Publishers quickly realized that they needed to give the buyers that had private marketplace access a set of preferences so that they would continue in the PMP rather than bounce out to the exchange. This led to all kinds of mechanisms — across various systems that have brought us to our modern programmatic landscape for private marketplaces.

Private marketplaces today

Private marketplaces today are very confusing. They’re both complex and complicated. There’s no clear and simple definition of a PMP that means exactly the same thing to everyone because there are so many ways to implement one. And depending on your ad server, your choice of SSP and/or exchange, and the buyer’s DSP, it’s fairly impossible to know in advance how the PMP will instantiate itself. Literally if you took five impressions of a PMP and reviewed them, each could be delivered completely differently from the others.

For publishers looking to start using private marketplaces today — without any legacy configurations or expectations, there are some benefits of having waited. Today PMPs are really about giving the publisher control over the way their preferred customers get treated by the auction. As everyone knows, when you have a big customer, who spends a large amount with you annually, you probably want to give them some discounts and benefits for working with you. Private marketplaces today are evolving into sets of controls for protecting the relationship with the buyer, and often for giving them either a discount, or giving them better access and control over the inventory they want to buy. It is the latter scenario — giving the buyer control — that makes some publishers very nervous, but is the real benefit of the PMP in today’s market.

In this scenario, the publisher lets their big spending customers get some additional control over defining the audience and the inventory that they have access to. Sellers frequently will bundle this additional control with a larger overall buy, or with a high minimum CPM, or with a high minimum overall budget. And publishers are finding that this approach makes everyone on all sides of the deal much happier. Everyone wins, as long as the complexity required to pull this off is embraced.

One of the biggest innovations in the programmatic world, and one that causes a lot of the complexity behind the issues this space has been saddled with, is the Deal ID. Deal ID was supposed to solve many problems in the programmatic space, but they have added another layer of complexity. The trick is to embrace the complexity without structuring things in such a way that they become unnecessarily complicated.

What is deal ID?

It became clear that while RTB was a vastly superior way to buy and sell ads than anything else we’d seen as an industry — there were touch-points between the old systems and the new systems that were confusing. Nowhere was this confusion worse than when a buyer wanted to execute a guaranteed deal over the RTB infrastructure.

But that Deal ID mechanism has now been used in much more flexible ways than its original driving intent. Think of a Deal ID as a way to prioritize a buy against supply. And the features for how you prioritize the bid vary by ad server, by exchange, by DSP, and by SSP. Sometimes the combination of each of those things leads to a different set of capabilities.

If you’re feeling confused, you’re getting the picture. This isn’t simple stuff. But that’s okay, because with complexity comes opportunity. Here’s a complex, but powerful scenario that Deal ID opens up:

All of these bids are Deal ID bids — prices are CPM:

  • Advertiser A sets up a dynamic bid that lands at $5 for the impression. Publisher floor prices this advertiser at $7.
  • Advertiser B sets a dynamic bid at $6 for the impression. Publisher floor prices this advertiser at $4.
  • Advertiser C sets up a dynamic bid for $17 for the impression. Publisher floor prices this advertiser at $20.
  • Advertiser D sets up a dynamic bid for $1 for the impression. Publisher floor prices this advertiser at $3.

In the above scenario, Advertiser B would win the auction, and pay $6 CPMs for that impression. Since one of the Deal ID bids won the auction, the impression never makes it to the open exchange. If for some reason the publisher had set the floor price for advertiser B at $7, then this auction would have flowed through to open exchange, and then advertiser C would have likely won the auction (assuming nobody in the open exchange bid higher than $17). Advertiser C would end up paying whatever the next highest bidder was willing to pay, plus $0.01.

How was that for complex? Want it to be more complex?

Some ad platforms can support a Deal ID with a dynamic bid, or a fixed price with a priority cascade. So while price mattered a lot in the example above, if one of those bids (even the low bid) was a fixed price, it would have won the auction at the fixed price. That’s how you give your preferred advertisers ways to find their preferred audience while giving them a fixed price. The trick is to negotiate well on the price on both sides so everyone gets what they want.

So while Deal ID is just one mechanism that may or may-not be part of a private marketplace, the two concepts are becoming somewhat inextricably linked together. What is a private marketplace today? It’s a complex set of interacting tools, systems, mechanisms, and approaches that can be used to give the publisher control over the prioritization of their supply against the demand represented over the exchange. Easy to understand? No. Easy to configure? No. Easy to execute against? Not yet. But worth using? Absolutely!

This complexity means power, but the complexity leads to confusion and complications. So when we have people who aren’t practitioners writing articles about very complex systems and how they are used, and then going to sources for quotes about adoption of these complicated scenarios, the answer is going to either be vague (not quotable) or clear (not accurate.) And these clearer quotes, which aren’t really very accurate in many cases, paint a picture of the space that looks like it isn’t working.

Private marketplaces give publishers control over the prioritization of buys coming from the programmatic channel. As an industry, we’re still figuring private marketplaces out — but vast and growing dollars are being spent over them in the meantime, and those buyers and sellers willing to take the time and effort to understand the complexity are winning. Yes, we need to make the execution of private marketplaces less complicated. It would be nice if we could also make them less complex, but only if we don’t lose the power that comes with the complexity. And in the meantime, the channel is growing and productive.

Dec 01 2014
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3rd Party Data, Marketers, Media Agencies, Targeting

How To Use RTB For Targeted Reach Instead Of Retargeting

By Eric Picard (Originally published on AdExchanger)

I was recently told by an executive in a position to know that 70 to 80% of revenue in the RTB space comes from retargeting. I found that stunning because it basically tells us that the RTB space is incredibly immature. If the vast majority of revenue in the space is retargeting, then nearly all the spending comes from ecommerce companies.

That means we have huge upside in this space because ecommerce companies certainly don’t make up anything near the majority of advertising spending.

Nearly 90% of advertising spend “all-up” is done on a targeted reach basis. In other words, the advertiser has come up with an ideal marketing persona (or series of marketing personas – many brands have five to 10 defined marketing personas) and their media plan is designed to reach people matching that persona. Using old-school methods, such as Nielsen or comScore, they find publishers with audiences matching their marketing personas, and that’s where they’ll buy impressions.

The problem is that this is extremely inaccurate, and wastes budget by spreading it across the whole audience that visits this publisher. On one hand, it’s wasteful because it pushes the message on audiences that don’t match campaign goals. On the other hand, it’s OK if there’s some “waste” in media spending because there’s value in getting the message in front of slight target mismatches.

Case in point: I don’t have cable at home. We watch Hulu, Amazon Prime and Netflix when we consume TV content. But recently, while traveling, I saw a few hours of TV in my hotel each night. I was shocked by the vast number of pharmaceutical ads on broadcast television – especially on the news (which I hardly watch anymore).

Most ads related to conditions I’m not facing today – so in a sense those ads were wasted. But should I ever contract one of those conditions, I’ll likely remember those products exist. Or should one of my close friends or loved ones get stricken with those conditions, I’ll recall that a medication exists and engage in conversation with them.

So yes – this broadcast brand strategy certainly does have some value. As I’ve said before: There’s value in the fact that I know Dodge Ram owners are “RAM Tough.”

On the other hand, we can be much more precise now than in the past — if you can find the data. And if you believe in the methodology that created the data, there are ways to more precisely reach your target personas and target audiences of all flavors.

Find The Right Tools

Using demand-side platforms and social media marketing tools, including the self-service tools within Facebook, it’s now possible to find your target audience in a variety of ways. You can be very narrow or very broad. You can control exactly which sites on which you’ll reach that audience, or you can simply specify on which sites you don’t want to reach your audience.

For brands that are very particular about running ads only on approved content, there is the white list – a specific list of domains matching against publishers that you specifically approve to run ads on. This does limit scale, but there’s no limit on the size of the white list you can create.  And there are vendors like Trust Metrics that you can use to build a custom white list for you, which both hones the targeting to sites that match your brand safety metrics and massively reduces fraud.

Or if you want, you can use private marketplaces to execute buys only on the sites you specifically negotiate with for access to their audiences over RTB. This has a lot of value for pharma and marketers that are extremely sensitive to running ads on sites that match their brand values.

If you want to specify a tightly targeted user base, one that is so targeted that it limits the audience size to only a few thousand users, you can do that using tools like Facebook’s advertising that lets you specify many different elements and tells you how limited the size of your audience is.

Or there are tools like Optim.al, which hones the audience and offers ways to expand or contract it. Or tools that let you find audiences similar to your targeting with less targeting but greater impression volume. (Disclosure: My company Rare Crowds does this.) Or you could use MediaMath’s built-in features to automatically find the right audience that performs best for your campaigns.

Nearly every company playing in the RTB space has functionality designed to meet the needs of advertisers that want to reach specific audiences, not just retarget people who visited your website or who are existing customers. There is the potential to reach people you haven’t reached before, find new customers and prospect for them.

The biggest growth sector for RTB this year is clearly going to be brand advertisers and those that use RTB for targeted reach — just like 90% of all media spending.

Nov 15 2014
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Ad Ecosystem, Ad Technology, Marketers, Media Agencies, Online Media, Publishers

Programmatic buying: The FAQ every marketer needs

By Eric Picard (Originally Published in iMedia – November 15, 2014)

I was at the ad:tech conference in New York last week, and in one of the sessions, three different people asked about programmatic. They didn’t ask any nuanced questions. They effectively asked, “What is programmatic?” They were embarrassed that they didn’t know, but after the first person spoke up, others in the room were emboldened.

For someone so steeped in the programmatic space, this took me by surprise. Certainly, I thought, no one in our industry doesn’t know what programmatic is. Adding to my consternation was that this specific panel was focused on SEO — and I figured that anyone working in search must be in the know on what was happening in programmatic. So I walked around and asked people for the rest of the conference what they knew about programmatic, just so I could see how out of touch I was from the mainstream. While most people were relatively up to date, I was surprised by the lack of general knowledge and the amount of misinformation there was out there.

So I figured it was time to step back and go over the very basics in this classic frequently asked questions (FAQ) format.

What does the term “programmatic” mean?
The term “programmatic,” which I’ve been told I coined back in 2009, really just is the umbrella term for automated buying and selling of media. While this is how I use the term, and what the market generally tries to use it to mean, many people use it to refer just to one part of the “programmatic ecosystem” — real-time bidding (RTB).

What are ad exchanges?
Much like in the finance world where stocks, commodities, and derivatives are sold over “exchanges,” we now have mechanisms to sell advertising over exchanges. Think of this as an auction-based mechanism to sell ads. Most exchanges are second-price auctions, meaning that whoever bids the highest for an ad wins the ad impression but pays the price (sometimes plus one penny) that the second-highest bidder was willing to pay. And nearly all of these exchanges have moved to RTB. Ad exchanges typically perform the function of providing liquidity to the marketplace, letting supply and demand match fluidly. Ad exchanges are not typically where the dollars accumulate; they’re a relatively inexpensive conduit through which demand and supply flow.

What is real-time bidding?
RTB is an auction-based mechanism for media buyers to bid on advertising at the impression level, as the ad impression takes place. When the ad impression takes place, a call is made to the exchange, which submits the impression to all bidders (participants with seats on the exchange). Those bidders have a very short time — usually less than 100 milliseconds — to respond to the auction with their bids. Unlike in the world of paid search, where all the demand for ads sit within the ad system of the search engine, ad exchanges federate out the auction, meaning that each bidder contains its own demand and only submits what it chooses to the exchange. This makes the exchange more of a clearing mechanism, rather than the revenue-generating mechanism that the paid search auction is.

What value does an advertiser or media buyer get by using RTB?
RTB enables a media buyer to specify exactly what their goals or outcomes are and look only for ad inventory that matches against those goals. Sometimes those goals are performance based; sometimes they are audience based. In other words, buyers can specify what audiences they want to reach and buy only those ad impressions that match. This is very different from the experience of buying from publishers directly, where the publisher specifies the inventory definition. Over RTB, the buyers specify the inventory definition and only buy what they want.

Are exchanges only available for banner ads?
RTB and programmatic exchanges are not in any way limited to one inventory type. Pretty much any available media inventory (ironically except for paid search) is available this way. Display, mobile, video, social, and even some traditional media such as television, radio, and print are either already available over exchanges or will be soon.

How do I buy ads on an exchange?
Buying mechanisms for ad exchanges are typically referred to as demand-side platforms, or DSPs. Some ad networks also enable exchange buying but in some cases are not transparent about this (i.e., they might be buying ads on the exchanges and reselling them to their customers). DSPs are available from companies like MediaMath, Turn, DataXu, The Trade Desk, AppNexus, and others.

How do publishers sell ads over exchanges?
Publishers that are quite large can sometimes offer their inventory directly over an ad exchange. Some even have their own. But most publishers use an aggregator of one kind or another — either an ad network or a specialty platform called a supply-side platform (SSP). SSPs are kind of the inverse of a DSP and have specialized software for managing supply on the publisher’s behalf. Some exchanges are incorporating the functionality of SSPs directly such that publishers don’t need a separate vendor to support this need. And some SSPs are beginning to behave as exchanges on their own.

Can I buy directly from publishers programmatically?
Yes, many publishers make their inventory available over the exchange, and most DSPs can specify publishers they wish to include in a buy. Many publishers also have rolled out “private marketplaces” using either ad exchanges or supply-side platforms. These private marketplaces are kind of like private ad exchanges where the publisher makes its inventory available only to specific buyers. These have all the benefits of RTB to the buyer but give the publishers more control over floor prices they want to set — or even fixed rate deals they want to support with specific buyers or advertisers.

Can I execute direct buys, or guaranteed buys, programmatically?
Yes, there’s a whole subset or category of the programmatic ecosystem that is appropriately called programmatic direct. Solutions in this space are less well defined, as it is newer. But the general goal is to provide more automation to the buying and selling of media. These buys can happen over display, mobile, video, social, and even television, radio, and print. The ecosystem has vendors supporting the needs of buyers and sellers independently — and a few that are hybrid solutions. Companies in this space include Bionic Ads, Shiny Ads, Yieldex, iSocket, BuySellAds, and others. Many DSPs are now plugging into the programmatic direct inventory sources as well, allowing one-stop-shop buying of both RTB and direct inventory.

Is programmatic replacing more traditional ways of buying and selling media?
Yes. Interpublic Group, one of the biggest agency holding companies, has stated that it wants to move 50 percent of its media buying to programmatic methodologies by 2015, and ultimately do that across all media types. In public and private conversations across the industry with executives at both marketing and media agencies, the zeitgeist is definitely moving in this direction. Publishers were the holdup until the last few years, when they started to see the benefits of programmatic selling on their own. Many publishers are finding that programmatic selling provides higher yield, either because their cost of sales are lower or because the inventory is being used more efficiently.

Nov 04 2014
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Ad Ecosystem, Ad Technology, Economics, Marketers, Media Agencies, Online Media, Predictions, Publishers, Startups

The Digital Advertising Industry Needs An Open Ecosystem

By Eric Picard (Originally published on AdExchanger Tuesday, November 4th, 2014)

Thanks to amazing new offerings from Facebook, Google, Amazon and others on deeply connected identity and tracking solutions, we are seeing two major developments. For the first time, connected identities across entire populations are available for targeting, tracking, reporting and analytics. But these identity pools exist within walled gardens, siloed to just one provider.

From a tactical and strategic point of view, I completely understand why companies create these walled-garden identity solutions. And to some extent, they will open their walls – metaphorically allowing outside vendors and partners to enter through checkpoints, accompanied by security and wearing clearly labeled badges. Nobody can fault a company like Facebook or Google for being careful about allowing entrée to their walled gardens. The potential for a PR backlash is significant, and that could cause the overall value of their offering to decline. So yes – it’s good to be cautious.

But it does create a significant issue for every publisher outside the top five or so because their first-party data pool is limited to the activity on their own site or apps. They don’t get access to cross-site activity, nor do they have a way to compete with the efforts of the biggest players on their own. It will be hard for publishers – even the large ones – to resist the momentum that will build to plug into these walled gardens, forcing publishers to effectively commoditize themselves in exchange for access to identity, targeting and analytics data.

I’ve long been a proponent of open approaches in the ad-tech space, including open source, open architecture or open APIs. I also am a big fan of well-considered and coordinated industry or consortium efforts. I believe that efforts like OpenRTB, which is pushing for an open API standard for real-time bidding, will be key to helping the industry grow.

Open efforts like this help ensure that the biggest players don’t create huge competitive moats like we saw with paid search, where Google AdWords’ creative, functionality and APIs became the effective industry standard. As a result, any time Google makes any change, all other paid search players must immediately copy Google because of its massive dominance in this area.

Even the biggest players should support these open initiatives because regardless of any disproportionate boost one or two players may get, we’re in a massive growth phase and an open approach has proven a better way to expand industries and sectors. Building significant traction is easier with scale – and by pooling scale, the whole space has the opportunity to accelerate growth.

That said, it’s highly unlikely that Google and Facebook will take a completely open approach on their key initiatives. For one, they have enough scale to catalyze efforts and markets on their own. But more importantly, it’s not in their self-interest to be open. Remaining closed gives them opportunity to maintain control and position in the market while marginalizing smaller players in the ecosystem.

I predict that we will see more industry consortiums created around areas like identity, directly in response to the very large walled gardens that are being built now. It’s really the only way that everyone else in the industry can protect against commodification and ensure a level playing field.

Oct 11 2014
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Ad Ecosystem, Marketers, Media Agencies, Online Media, Predictions, Publishers

Programmatic’s place at the top of the marketing funnel

By Eric Picard (Originally Published in iMedia – October 11, 2014)

For decades, modern marketers have developed significant marketing plans with detailed analysis of target audiences. Often before products are designed, significant amounts of market research have been developed and applied against the product or service development process.

When a brand decides to spend millions of dollars to create a product or service, it typically then spends tens to hundreds of thousands of dollars on market research and product planning to get ready to launch it.  And then hundreds of thousands to millions of dollars to market the product.

Most of that market research and product strategy folds over into the marketing plan. And as part of that process, typically very detailed marketing personas are created — sometimes a handful, sometimes more than a dozen. These marketing personas are decomposed into the marketing plan and drive many of the media mix decisions that are used to divvy up budget among channels. And often these do get distributed to the media agency as part of the marketing plan’s translation into media planning and strategy.

But in my experience, it is fairly common that by the time the media buyer gets the media plan from the planners, the marketing personas have been stripped off. And this is even more true when we bring programmatic media into view. As an example, consider a conversation I had this past year with a media buyer at a major trading desk.

This trading desk handles the media buying for a major home improvement retailer. And when I talked with the trading desk buyer about how the company approaches this customer’s media buys over its DSP partner, the buyer looked a little puzzled. To that person, it was about only two things:

  • Buying the “home improvement” segment
  • Setting the rest of the budget to optimize spend against CPA on its web pages and letting the DSP figure the rest out

The problem with this approach is that it’s extremely one dimensional — and loses much of the value that exists within the systems used. It’s like using an F-16 to commute to work. Or an aircraft carrier to run to the store.

I haven’t seen the marketing plan for the client, but I can imagine (having seen a lot of them over the years) that the retailer has several different ones. I’ll make up a few that probably exist in part, and explain how I’d have approached the campaign using a DSP.

Persona 1: Reggie is a 28-year-old single male who lives in a major metropolitan area in a condo that he owns. He makes more than $50,000 a year and mostly shops at the client’s stores to buy décor items, fans, DIY project materials, and probably will buy things like air conditioners, painting supplies, hand tools, etc.

Personas 2 and 3: Sophie is a 35-year-old stay-at-home mother who lives in the suburbs of a major metropolitan area and is married to Tim, a 35-year-old executive who works in the city and commutes. Together they own a house that is more than 4,000 square feet and has at least half an acre of land. Tim is a weekend DIY warrior, who takes on various home improvement projects. He’s likely to take on light construction projects, buying building materials, painting materials, plumbing and electrical, and lots of landscaping tools such as riding mower, blowers, etc. Sophie is an avid gardener who buys numerous plants and gardening materials, and takes frequent courses on design and gardening at the store.

Persona 4: Arthur is 65 years old. He is retired, lives in a modest home in the suburbs, which he owns outright. He is in the process of getting ready to sell the house as he and his wife are looking to move to a smaller place or a retirement community. But he has three adult children who own homes nearby, and he frequently putters and does projects around their houses. He’s likely to buy building and painting materials.

Although I just made up these personas, they’re fairly typical of the kinds of personas I have seen over my career — if anything, they’re a bit light. Additional information that would typically accompany a persona includes the numbers of each of these personas that exist in each DMA in the U.S., perhaps even broken down by ZIP code within each DMA. And then marketing teams typically will use whatever tools are at their disposal to begin matching against mechanisms like PRISM clusters and do some media mix modeling about how to reach these audiences.

At the handoff to media agency partners for digital media, the planners at that point begin using various tools to determine what sites have traffic that matches their target audiences, and an overall media plan and strategy is devised.

Once the plan is handed off to media buyers and their trading desk partners, the thinking is usually quite distilled. Buyers going directly to publishers will send over an RFP that simplifies the media plan (they may also send the media plan) for sending to publishers. They then wait to hear back regarding what inventory is available. The trading desk partners typically decide what audience attributes align against available data segments for their goals.

Now let’s go back to the example I used above about the trading desk with a major client in the home improvement retail space. Given its customer personas, I’d have recommended a few other ways to engage and find audiences.

Perhaps it could target users who own homes of a certain size or homeowners who have been in their home for a certain number of years. It could target each of these segments by age and geography. It could differentiate both creative and offer by each of these. It could vary what products to highlight in its advertising based on some of the criteria, such as age, gender, and other elements. It could target households with children differently than households with adult children not living in the home. It could even target based on the age of children, assuming parents of college age students might be moving kids into apartments or dorms at the end of summer or fall. Or it could target urban apartment dwellers with fans in the summer and suburban homeowners with leaf blowers in the early fall, snowblowers in the late fall, and lawnmowers in the early spring.

In programmatic, we far too often fall into the trap of only feeding the portion of the purchase funnel that is focused only on CPA at low costs of media plus data. As a market, we need to expand how we see programmatic media and really try to dig into the market for data and the use of sophisticated DSP platforms.

May 10 2014
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3rd Party Data, Data Collection, Media Agencies, Online Privacy, Targeting

The 7 types of targeting you need to know

By Eric Picard (Originally Published in iMedia – May 10, 2014)

For as long as people have been buying ads, they have been targeting their desired audiences. The science behind this obviously has changed over the years. In the beginning — say, back in ancient Greece — it was as simple as putting the name of your pottery shop on a few of your clay pots. This evolved to more location-based models over the millennia, of course, and today we can geo-target your mobile device. End of story? Not quite.

As we think about the evolution of targeted advertising over the past 50 years, there are panel-based “currency” data providers such as Nielsen, Arbitron, and others. These services allow buyers to place ads on specific published content across numerous media, with an understanding of the overall audience breakdown that views this content. Buyers can place their ads on content where their desired audience makes up some percentage of the audience that consumes that content. By doing this across a certain number of publications or shows, they can be relatively confident that they are reaching a certain number of members of their target audience.

This is easy when you’re selling a product or service that has a very broad audience — say, toothpaste. But when you have a very targeted customer you’re trying to reach, it can be much more difficult. Other than niche publications clearly aligned with your target customer — say, knitting magazines or websites — it has been hard to find enough touchpoints to reach prospective customers easily.

That has changed significantly over the last few years. Let’s focus on digital media for our purposes. The core types of targeting available today include the following.

Panel-based data

Panel-based data is the most broadly used today, from providers such as Nielsen, comScore, and others. These panels are used as described above — to understand the overall audiences that consume content provided by a publisher. This “whole milk” approach works well for brand advertisers that have large audiences that are easy to find.

Geography

This category includes geo-targeting and geo-derived information such as Nielsen PRIZM clusters that merge information about households in specific geographies. This is much more important today than in the past, given that mobile devices offer information about where audiences are at the moment of the ad delivery, thereby taking location-based advertising to new heights. In mobile devices, this matters a lot, as some of the mechanisms available on the web are either not available on mobile, or much less available due to technical limitations related to cookies.

First-party audience data

First-party audience data is available from either the advertisers directly (data they have about their existing customers) or from publishers directly (data they have about their individual audience members). First-party data is derived either from explicitly provided information or from observed behavior.

On the advertiser side, this is typically CRM data; generally these are either customers or prospects with whom the advertiser has had direct contact. Perhaps the person in question has purchased from the advertiser before, or perhaps that person has signed up for a newsletter. In the case of e-commerce, perhaps the user has visited the site but hasn’t purchased, in which case a click-path analysis might derive some information about the person’s interests.

In the case of publishers, this information can be captured through registration (which actually tends to be much more accurate than professionals believe; as it turns out, many people don’t put in fake information) or from observed behavior (users who read financial news get put into a finance bucket to be targeted when consuming other kinds of content).

Third-party audience data

Third-party audience data is available from numerous providers. Typically these data points are derived from observing the behavior (anonymously) of the end users as they’re moving across numerous websites. Sometimes this data is derived from other sources, such as credit card activity matched anonymously to users via cookie matching.

Third-party retargeting data

Third-party retargeting data is available from numerous providers. These companies will typically place targeting tags on both the advertiser and publisher websites and then link those together in order to execute media buys. Because the provider needs to have matched cookies on both the advertiser and publisher websites, typically these services run as ad networks, since they need to close the loop directly. But there are providers that allow advertisers to create their own retargeting cookie pools and reach their customers and prospects over ad exchanges and through their own direct publisher relationships. This is frequently being referred to as second-party targeting.

Look-alike targeting

Look-alike targeting is available from numerous providers as well, which enables the buyer to provide the look-alike vendor or network with a pool of cookies or data definitions. The providers will then find matching audiences who “look like” the users you’ve provided to them. This allows the buyer to get value similar to retargeting campaigns, but for much larger audiences.

Custom micro-segmentation

Custom micro-segmentation is available from a few providers. This enables the buyer to specify extremely targeted audiences that are orders of magnitude more targeted than what is available over the open market and that match their ad campaign goals exactly or much more closely. This type of targeting can be used for brand campaigns or for performance.

The types of targeting above are broad bucket definitions, and there are now literally hundreds of thousands, if not millions, of available targeting segments on the market. Vendors should be more than happy to educate buyers (and sellers) on the opportunity and methodologies behind the data segmentation. I highly recommend that one or more buyers within every buying group become an expert in the types of available segmentation and the data models involved.

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