Pioneered in the early 2000s, programmatic advertising generally refers to the use of software and data to purchase and or sell ad space, as opposed to more manual and direct forms of buying and selling. While nearly any form of ads can be transacted this way, programmatic is now the dominant means of digital ad buying and selling. Close to 84% of all US display ad spending is now transacted this way, according to eMarketer.

 

Digital marketplaces where thousands, if not millions of web publishers can place ad space up for sale. Among the largest ad exchanges are Google’s AdEx, OpenX and Xandr Marketplace.

One of the driving ideas behind ad exchanges is to establish a neutral platform designed to equally serve both buyer and sellers. However, many have pointed to the inherent conflict that arises when a media owner such as WarnerMedia runs an exchange such as Xander. Similarly, Google not only provides technology for its exchange, but is in the business of selling ads on its own properties (such as YouTube) as well as ads across thousands of publishers via its Google Display Network.

The practice of attempting to gauge the impact of each individual interaction a customer has with a brand, ideally determining which ad or channel had the most impact toward driving the desired outcome, such as a sale. For example, an apparel retailer may run ads on Google search, Facebook, YouTube and display ads across the web. Attribution modeling could help that brand determine the efficacy of each.

Each programmatic ad auction is governed by different rules and buying volume which can affect how buying and selling plays out. For example, whether winning bids are based on first price or second prices, the number of advertisers, the competitiveness of the inventory, whether some brands employ pricing floors or not, the availability of the data, the level of transparency - all impact how an individual auction functions. All of these will influence what kind of bidding strategies brands will employ in an individual auction.

Various companies employ different auction models, such as first price auctions (where the highest bidder wins and pays the price it bids), second price auctions (where the winning bidder pays a price closer to the next highest bidder) and so on. Some auctions, such Google’s search auction, factor in ad prices and potential click through rates.

This tactic refers to a publisher helping brands reach its audience on other sites across the web, as well as lookalike audiences on other platforms.

Historically, when ad space was bid on by multiple bidders in an auction, the winning buyer would pay only a penny more than the price offered by a runner up (the second price, in this case). However, more recently programmatic exchanges have shifted to a first-price auction model - not unlike an auction at Sothebys - you simply pay whatever your winning bid price was. However, that can end up costing brands more than they had in mind. So via bid shading, ad buying platforms can automatically help brands adjust their bids in real time so that they win, but don’t overpay. They might split the price difference, for example, or pay a lower price based on historical rates.

This is theoretically good for brands, but it also can limit the transparency they have in the prices they pay. In other words, the buyer gets a discount, but with some DSPs, they don’t know how that discount gets calculated. However, some companies, including Beeswax, have begun making these fees transparent.

Given that they help execute millions of transitions, DSPs and SSP are able to see numerous macro trends regarding a given ad market. Some companies collect this data, analyze it, and use it to build their own data pools, or even products. Information that is passed in the bid request. For example, while executive thousands of buys for multiple home furnishing companies, an ad tech company could collect bidstream data, a put together a package for advertisers looking to reach highly responsive furniture shoppers, for example.

A product released by the ad tech company IPONWEB in 2013, Bidswitch serves almost as a universal adapter for DSPs and SSPs/exchanges. The idea behind Bidswitch is that brands or publishers can plug into multiple sources at once without having to repeat integration steps while centralizing targeting parameters, data flow and reporting.

This form of attribution modeling attempts to gauge the value of different marketing channels - such as search, social, mobile web, OTT - and how they collectively and individually impact consumers.

Google’s Chrome browser accounts for roughly two-thirds of the global browser market. In early 2020, following a similar move by Apple’s Safari browser, Google announced plans to eliminate third party cookies from Chrome within two years. While cookies don’t have the tracking prominence they once did in the pre-mobile era, Google's move has caused much of the ad industry to scramble for targeting alternatives.

 

Ads that rely on third party cookie data for targeting purposes.

This is a fraudulent activity typically used to game affiliate marketing companies into paying commissions. For example, a person may buy something online, and a fraudster will make it appear as if they’ve made that purchase right after visiting a site with an affiliate link for that product. Fraudsters place cookies for those sites on consumers’ machines without their knowledge by using pop-ups, shady ads and other mechanisms.

As 3rd party cookies are gradually eliminated from digital marketing, brands are being forced to use a variety of other signals to target consumers with ads, Increasingly those signals include first party data, broad anonymous data sets, as well as contextually-based targeting techniques.

As cookies are gradually phased out of digital marketing, brands are having to make adjustments in how they use ad tech buying tools such as DSPs. In the absence of cookies, brands can employ their own first party data, segment data, or macro audience data to bid on ad space using DSPs.

As the digital advertising world braces for the elimination of the cookie over the next few years, there are various cookieless targeting initiatives underway aimed at helping digital media retain its targeting power. Many in the industry are advocating for the  use of more direct identifiers to replace cookies, such as hashed email addresses or wireless numbers. Yet on the other end of the spectrum, many are predicting that advertisers will begin targeting much broader groups of web users via probabilistic data and more use of sophisticated data science.

In addition, companies such as LiveRamp, The Trade Desk and Google are attempting to develop new cross-industry cookie alternatives.

(Also “cookie apocalypse”) This is the industry nickname for the moment that Google eliminates the use of third-party cookies in 2022, rendering many cookie-reliant marketing techniques and businesses instantly ineffective.

While many brands rely on existing algorithms provided by ad tech partners or giants like Facebook and Google to guide their optimization and decision making, many sophisticated marketers - particularly heavy direct-response brands - have built custom ad algorithms based on their proprietary data sets and unique needs. For example, there may be very specific audiences onne brand may want to bid aggressively for, or sources of inventory that a marketer may have a reason to avoid.

Advertisers typically use all sorts of measures to optimize their digital campaigns, from click-through-rates to conversion-rates to brand favorability surveys. Many advertisers have also created unique customized ad optimization criteria based on their unique goals or creative messaging priorities.

Many marketers have developed attribution models designed specifically for their distinct KPIs and marketing nuances. For example, a brand that primarily focuses on driving brand lift may heavily weight awareness media on TV, and give less ‘credit’ to transaction ad vehicle like search, whereas an e-commerce brand may have far more data to employ, and may be entirely focused on driving people through a traditional purchase funnel -thus requiring a highly customized attribution model. One brand may see lots of value in driving people to their website - and would thus weigh that action higher - while another may care far more about email signups, etc.

Definitions across the industry vary, but generally speaking, OTT - ‘over the top’ refers to video content that is delivered to TVs minus a cable box. Think about when a Spectrum cable subscriber for instance, would start using Hulu to watch a show, rather than their set top box, they were going over the top. CTV, or connected TV, commonly refers to content consumed via apps on smart TVs or through devices like Rokus or Amazon Fire. There is some debate over these definitions.

 

A growing number of brands have either partnered with ad tech companies or worked on their own to develop customized Demand Side Platforms. These buying platforms often contain features that are built specifically for individual advertisers, and even their own custom algorithms that can be used to bid on particular audiences or to identify unique patterns or behavioral segments that advertisers may care about.

This refers to software and tools developed exclusively for the buying side of the ad business to purchase inventory programmatically. Demand Side Platforms (DSPs) can essentially help advertisers sift through thousands if not millions of potential ad impressions across sites and apps across the web—all in one centralized platform. The term ‘DSP’ can refer to the software itself or the companies that sell and manage the actual software.

Brands use Data Management Platforms (DMPs) to manage, store and process data from a wide range of sources, and to get it from place to place. But DMPs are not inherently designed for media transactions. Conversely DSPs are ad buying platforms, designed specifically to assess and purchase and inventory across a range of media types and partnerships. DSPs do employ data, but they are not DMPs.

Exchange bidding is similar in concept to header bidding in that it levels the playing field among potential advertisers bidding on inventory. But, instead of the bidding taking place on an individual site, the auction occurs within an ad server. This is often seen as Google’s answer to header bidding, as the company now handles all the transactions between SSPs and publishers

In a perfect world, this arrangement reduces the complexity - and resulting latency inherent to header bidding. However, some industry insiders are uncomfortable with the idea of Google hosting such auctions, while the company is also commonly a bidder itself via the Google Display Network.

Typically first party data refers to registration data collected by marketers, or login data collected by publishers. However, publishers can also employ first party cookies, which allow them to create profiles of audience groups, such as auto or tech enthusiasts.

When impressions go up for bid, in a first price auction the advertiser, via a DSP, ends up paying the full price of whatever they bid, regardless of whether they could have paid a much lower price to beat the competition. If you bid $5 for an ad impression, and everyone else bids $1, you pay the full five bucks.

Why you can have multiple bids for the same impression - The good part about header bidding is that for publishers it opens up the ad marketplace to more bidders, and no one single bidder has any clear advantage - theoretically. Would you rather have one person making an offer on your house, or 10? However, the flip side is that putting every single bid up to multiple advertisers causes a lot of volume for the publisher and its ad tech partners to manage at any given moment - potentially driving up costs while taxing sites.

Historically, when various ad tech companies would bid on potentially running an ad on a publisher's site at a given moment, each one would have to ‘get in line’ essentially, as auctions took place within an ad server (usually Google’s) and bids would go out to one potential buyer followed by another. Companies such as Criteo or Google could arrange deals with publishers that would assure they’d be first in line in this process.

By the mid 2010s, companies began to develop a solution called header bidding. By plugging in code on a publisher’s header, each outside source of ad demand was placed on equal footing. The bids take place before there is a call to an ad server. Companies ranging from AppNexus to Index Exchange have built commonly-used header bidding ‘wrappers’ to facilitate demand.

Short for ‘Identity for Advertisers,’ IDFA is a randomly generated device identifier employed by Apple for devices using iOS. IDFA was designed to help app developers determine which ad channels are generating leads, downloads, etc. But third parties such as attribution companies and ad networks had been taking to collecting IDFA data and using it to build their businesses. This use case was not exactly what Apple had in mind. In mid 2020, Apple announced it would be phasing out IDFA, which impacts the entire mobile app marketing ecosystem, from gaming companies to third parties Facebook. Without IDFA, brands and app companies will be in the dark when it comes to measuring the specific efficacy of mobile ads. Apple has extended the deadline for this move until early 2021.

Incrementality is a tactic which aims to help advertisers isolate the impact of different ad budgets and partnerships, and then helps them determine the value of adding more budget and/or new partnerships. For example, a heavy social media advertiser might want to calculate how many more people it would reach by adding a new platform to its buy – and what that new reach would cost relative to the rest of the media plan.

Revenue that is driven by an ad campaign that can be determined to be additive to a company’s typical business during a period of time.

A growing number of marketers have elected to build their own internal teams of programmatic buying experts and analysts. These marketers can work directly with ad tech companies, using tools such as DSPs and DMPs, and buy media on their own, without needing an ad agency. More advanced advertisers have even developed custom software and proprietary algorithms.

 

In-stream video ads  appear prior to or within streaming video, including pre-rolls, midrolls and post rolls.

A number of companies use IP addresses to target people in specific locations - also known as ‘geofencing’.

The act of targeting consumers using IP addresses - anonymous identifiers tied to specific devices, including desktop computers and smart TVs.

IP targeting in advertising enables brands to match up targeting data with a specific device or even physical address - usually anonymously. IP targeting can be used to target ads to specific households via cable boxes  - such as households that have just purchased a new car, for example. Or brands often use IP targeting to reach people in a particular location, such as in the vicinity of car dealerships or quick-serve restaurants.

IP targeting generally costs more than broader based digital ad targeting, since brands use this technique to target very narrow specific sets of people, and typically the performance of these campaigns is more effective than normal.

The general practice of using unique, anonymous device identifiers -often a randomly-generated number or triangulated data segments - to target individuals or households with ads.

Companies such as Canopy and Accelerant specialize in helping brands match up specific IP address data with ad targeting tools.

A more blunt form of attribution modeling which provides the entire credit of a successful ad conversion to the last ad a person clicked. This tends to discount other ad exposures, and heavy favors - or over-credits - search ads.

Publishers and advertisers often want more in-depth information on programmatic ad campaigns beyond just the number of bids one and the prices they pay. In order to better understand why various auctions are won or lost, or why certain campaigns perform better than others, both parties like to dig into log-level data. This often includes basics such as location data, time stamp. Log level data can be provided by ad serving companies or DSPs. However, in most cases this information is only available from  a small subset of overall auctions. Some ad tech companies, including Beeswax, are able to supply customers with 100% of their log-level data. Overall, the driving idea behind exposing log-level data  is to gain as much transparency as possible, particularly regarding where a brand’s budget is being spent.

Pioneered by ad tech companies such as Quantcast, lookalike modeling seeks at taking small sets of targeting data and anonymously identifying larger audiences or sets of data sharing similar characteristics. For example, retailers might seek a larger group of tech enthusiasts based on a small set of recent shoppers.

Traditional attribution modeling requires people to set rules. That often means that subjective humans set up rules regarding what matters and what doesn’t (search ads are worth X, ads shown over a week ago are less valuable, and so on).

Increasingly ad tech companies are employing machine learning (ML) for attribution. Theoretically, ML can help attribution models objectively figure out over time what criteria should be used to assign value to various signals- and machines can pull data from across the web to do so, meaning the intelligence employed is not limited to a particular campaign.

If attribution modeling focuses on measuring the impact of specific ads of channels, multi-touch attribution (MTA) models attempt to employ more sophisticated analysis aimed at examining how the various elements of the overall campaign did and did not complement one another to create a desired outcome.

Starting in 2015, a number of big media companies, including Dish, AT&T, Sony and Google began launching live TV packages delivered via the Internet. These so-called ‘skinny bundles' including SlingTV and FuboTV, have been aimed at the growing number of consumers who are either opting to drop pricey cable packages or who have never subscribed to a cable bundle. However this category has had mixed success; products such as Sony’s PlayStation Vue have shuttered, for example, and cord-cutting overall continues to accelerate in the US.

Video that is delivered to TV screens via the internet, often via apps. This can include non-ad supported services such as Netflix and Disney+ as well as ad-supported services such as Hulu.

Companies such as Teads and Unruly Media deliver video ads to text pages. These video ads only stream when a person scrolls down to a particular part of a web page or app. The idea behind outstream ads is to create valuable video inventory within primarily-text environments, since the supply of in-stream video can be limited.

Private marketplaces (PMPs) are not unlike an invite-only ad exchange, where a distinct set of advertisers might be invited to bid on inventory from a single large publisher. Usually a pricing floor - a minimum ad price - is set, and then advertisers bid for space.

Media buyers employ a variety of criteria to determine what price to bid on an ad and when - but in most cases, these prices are static or based on a definitive set of instructions (like say, “if a new mom who’s in the market for an SUV shows up on this site- bid $5”). Rather, in real time, bidding dynamics can change based on the availability of target audiences, competition for inventory, historical pricing and ongoing changes in performance, among other factors.

Some marketers want to purchase specific ad space on specific sites, but prefer to use programmatic tools. Often these deals include a fixed rate, rather than auction-based bidding. They are designed to be easier to manage, and reserved for premium inventory.

Various forms of fraud have plagued the digital ad industry ever since programmatic advertising took off in the late 2000s. By nature, programmatic platforms are more open than direct sales, and are thus susceptible to nefarious characters. Plus, this industry has been largely unregulated, while also being quite opaque to the average person.

Estimates regarding how much money ad fraud is costing advertisers vary widely. The fraud-tracking firm WhiteOps, as part of a joint study with the Association of National Advertisers, reported that brands lost $5.8 billion to fraud in 2019, down 11% from 2017. Yet another company, Cheq, estimates that $35 billion was being sucked out of the ad and economy in 2020.

Among the types of fraud out there:

-Fraudsters can use malware to infect consumers computers, and use those computers to generate traffic that looks human - without consumers every knowing

-Fraudsters can set up bogus websites, use bots to send fake traffic to those sites, and they sell ad space on those sites to advertisers

-Fraudsters can also use exchanges to mimic more valuable ad inventory. For example, they can sell legitimate ad space on a small blog, but disguise it as coming from the New York Times. Similar techniques can be used to sell ‘fake’ connected TV ads or even mobile ads targeted to a particularly valuable location 

Some fraud detection companies estimate that advertisers lose billions of dollars each year thanks to fraud.

While many marketers have handled some aspects of their advertising output without the help of ad agencies, there has been a decided push to bring more of this activity in-house starting in the mid-2010s. This has been driven in large part by the widespread availability of programmatic buying platforms as well as marketing cloud software - and the importance of customer data. Marketers increasingly don’t want to hand off their most precious consumer data to agencies that may come and go. Thus, many have built specialist programmatic buying teams and licences software from DSPs or other companies. The most sophisticated brands even build their own algorithms for ad buying purposes.

Similar to programmatic direct deals, but in this case ad buyers have the option to buy inventory at a negotiated price, but can chose not to - as the inventory has not been reserved.

A few large firms on both the buying and selling side of the ad industry have built full stack - i.e. sets of products geared to service multiple aspects of programmatic transactions. For example, Google has its own ad server, DSP and multiple exchanges. SImilarly AT&T’s Xandr features multiple tools for both buyers and sellers. The theoretical advantage of stacks is that they are built to work together, using common data sets and processes. But they also theoretically give one company a great deal of market power and access to pricing data, causing potential conflicts of interest that have more recently come under scrutiny.

The growth in header bidding and open bidding has dramatically increased the number of potential buyers and sellers for each ad that is served, meaning that ad tech companies are processing and paying for thousands if not millions of birds that go nowhere. Generally QPS filtering is an agreement between SSPs and DSPs to limit the sheer volume of queries per second. In other cases, QPS filtering can be performed on a per-customer level as with products such as Beeswax’s BaaS™platform.

(Also called “self-service DSPs”) A growing number of ad tech companies are offering either white label DSP products or self serve DSPs that don’t require big contracts, custom integrations or huge spends. These can be employed by big advertisers that have brought programmatic buying in house, or even small brands that only require DSPs on occasion. Facebook and Google’s self serve ad buying products are technically custom DSPs.

When impressions go up for bid, the winning bidder pays a rate that is just above the second highest bid. This theoretically saves advertisers from significantly overpaying.

Some companies, when licensing SaaS products, will require a custom set of tools and services -they are the ‘single tenant’ of that particular piece of software. In other cases, multiple companies will employ the same software - i.e. a ‘multi-tenant architecture.

This is a practice via which media buyers attempt to take more control over their ad supply chain through demanding more transparency, cutting more direct deals with publishers and overall looking to eliminate as many middlemen as possible. In other words, media buyers attempt to utilize the most direct path to inventory as possible. This is both an attempt to provide brands with more visibility and control over their ad placements, while also reducing needless fees and latency.

Brands typically employ supply path optimization to reduce the number of ad tech partners they purchase from, eliminate unnecessary steps in an individual ad buy, and to increase transparency. If the ultimate goal is to create a direct as path as possible to desired inventory, advertisers often designate specific supply partners for SPO, such as preferred publishers, exchanges and SSPs.

While the goal of supply path optimization is ultimately to provide the most direct, efficient and cost effective path to ad inventory, there is potential for waste in this buying approach. For example, brands can still end up bidding on the same impression via different exchanges, even if they are actively trying to avoid duplicative queries and multiple middlemen.

Similar to DSPs, Supply Side Platforms (SSPs) refer to software and tools designed for the selling side of the ad business, such as web publishers or large media organizations. SSPs can be used to corral and package inventory from across an array of sites and apps and make it purchasable for DSPs or individual advertisers. For instance, an SSP might pull together ad space featuring thousands of impressions targeted to recently married college graduates - all in milliseconds.

Third party cookies are small pieces of code are automatically placed on users' desktop computers and laptops by web browsers or publishers, and are used to anonymously recognize and track these users. Cookies help publishers recognize registered subscribers, for instance, or help companies like Amazon remember frequent customers – not requiring them to log in during each visit. Cookies are often frequently used in ad targeting. However cookies are generally ineffective on mobile devices.

The TV industry has spent the past several years trying to match digital media’s ability to track consumers, and more importantly directly-proved business outcomes from TV ad campaigns. It’s inherently more challenging for a number of reasons -  TVs don’t employ cookies, people don’t log into individual TV networks, people don’t shop via TV screens, etc. But the industry has been making big strides. Companies such as Data+ Math and Neustar have built models designed to both connect digital user data with TV audience data, and to track the effect of TV ads. For example, a packaged good marketers might use several of these partners to target frequent big box retailer shoppers, manage ad exposures across multiple screens, and they attempt to match up that audience information with real purchase data from loyalty cards used in stores.

Companies such as iSpot pull data from TV manufacturers which can tell advertisers exactly which ads individual households were exposed to- and that data can also be tied to other identifiers and conversion metrics.

There are a growing number of tactics brands can use to target consumers without cookies, including more sophisticated forms of contextual advertising, more ‘probabilistic’ targeting using broad data sets aimed at isolated like-minded users, as well as brands’ own proprietary data.

For many years, digital ad impressions were tracked, bought and sold based on the number ads served, regardless of whether those ads were completely visible on a web page or app, or if those ads were even fully served at all. But starting in the mid 2010s, advertisers began demanding to only pay for ads that are fully viewable, and a number of vendors emerged, such as Moat, promising to track how much of an individual ad is viewable on a web page. Eventually the Media Rating Council established a benchmarked for transactions in the industry - at least 50% of display ads must be visible for one second - two seconds for video.

While several tech giants manage large, cross industry ad exchanges, companies such as Crystall.io, SmartHub , and Ubidex enable companies to create private, proprietary ad marketplaces for specific sets of marketers and publishers.

While some ad tech companies such as Criteo specialize in re targeting specific audiences across thee, companies such as AdRobin, StackAdapt and SmartyAds enable ad agencies to build their own retargeting products via licensed technology.