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Each month, Basis Technologies’ Programmatic 101 series tackles a different facet of programmatic advertising—from best practices for buyers, to competitors in the space, to trends you should know.
Isn’t it strange that, in the age of Alexa and robot waiters, the majority of advertisers have to wrangle nine separate platforms to manage a typical ad campaign?
It’s true that our industry has seen great advancement in the realm of automated media buying, namely via the adoption of programmatic advertising. But along with programmatic’s many benefits has come the need for more talent, platforms, and integrations. The marketing landscape is fragmented and complex, yet few brands and agencies have adopted technology designed to manage that complexity.
Enter the missing link: Advertising automation. What is it, what benefits does it offer, and what does it look like in practice? Read on to find out.
The phrase “advertising automation” describes a variety of technologies and methodologies designed to streamline the entire lifecycle of a campaign. That’s right—the entire lifecycle! These tools encompass everything from planning and buying, to optimization and analytics, and even reporting and financial reconciliation.
What does advertising automation look like in action? Here are a few examples:
The cumulative effect of these automated solutions creates organizational agility, cost-efficiency, and time savings for brands and agencies. It also makes marketers’ jobs more enjoyable by eliminating frustrating manual tasks.
Now that we know what advertising automation is and what it looks like, let’s dig a little deeper into the benefits it offers. The combined impact of tools like bid shading, machine learning, and automated reporting produce two main benefits: Time savings and cost-efficiency.
Advertising automation technologies save time by eliminating the need for humans to perform tedious and error-prone tasks, handing those tasks off to technologies that can execute them quickly and without error. Marketing teams that invest in advertising automation platforms are more efficient: Case in point, Forrester Research found that agencies who implement Basis see, on average, a 35% increase in digital team efficiencies.
Take, for example, the consumer expectation for personalized media. According to Salesforce, 70% of consumers report that a company understanding how they use products and services is very important to winning their business. Yet delivering personalized customer experiences in the age of ever-increasing media fragmentation and complexity is a tall order. Marketers must build creative variations, target audiences precisely, and deeply understand cross-channel campaign performance. Here are a few examples of how the process of personalization is streamlined in an advertising automation platform:
First, marketers can use dynamic creative optimization (DCO), combined with customer data, to automatically generate thousands of ad variations. Once the campaign is live, they can leverage automated reporting to quickly communicate performance to stakeholders. Best of all, workflow automation unites the entire campaign process—planning, negotiating, buying, reporting, analytics, optimizations, and billing—into one platform, removing the need for marketers to waste time switching between systems as they balance the nuances of a personalized approach.
Beyond freeing up time for marketers to tackle more high-level, strategic tasks, advertising automation increases cost-efficiency for brands and agencies in a variety of ways.
Consider the impact of the following automated solutions across a campaign’s lifecycle: First, automated bidding solutions assess the optimal dollar amount to spend on each impression. While the campaign is running, AI-powered budget pacing monitors budget spend and alerts advertisers of significant over- or underspend. Simultaneously, built-in protections automatically safeguard against ad fraud. Together, these and many other aspects of holistic advertising automation ensure precision in campaign bidding, performance, and optimization.
The cost-efficiency offered by advertising automation extends to reducing the high costs of employee turnover as well. Organizations that implement advertising automation reduce the number of hours marketers must spend each week on tedious, manual tasks, and provide the opportunity for strategic, fulfilling work. In turn, organizations retain their best marketers for longer periods of time.
For over twenty years, Basis Technologies has been on a mission to create a better industry for advertising professionals via advertising automation. Our team is constantly working on new platform tools and features that eliminate manual tasks and boost efficiency for our partners.
Want to learn more about how automation will shape the future of digital advertising? Check out our guide, Meeting the Moment with Advertising Automation.
Each month, Basis Technologies’ Programmatic 101 series tackles a different facet of programmatic advertising—from best practices for buyers, to competitors in the space, to trends you should know.
With the introduction of programmatic media, brands and advertisers suddenly had access to an overwhelming number of options. Whether it be the rolodex of publishers to work with, the various data points available for targeting, or the sheer number of key performance indicators (KPIs) they could measure, there were a lot of decisions to make—and brands and advertisers needed help sifting through all the options.
Bid automation software was developed to answer this call for help. New algorithms and software were created to take on some of the manual work initially required of media buyers, quickening and streamlining the process of programmatic bidding.
Read on to learn what automated bidding is and what benefits it offers, and to explore a few examples of what automated bid management looks like in context.
Manual bidding is when a buyer sets the price of how much they are willing to bid on an impression. Generally, the buyer will use historical performance or will manually pull reports each week to assess eCPM, or effective cost-per-thousand impressions.
Automated bidding strategies, on the other hand, allow technology to decide how much a brand should bid to achieve their goal. The technology can ingest various data points in real time and use that information to update the bid price. That doesn't mean that bidding is out of the media buyer's hands, by any means—in fact, it gives them back the time they would have spent manually assessing reports to better control and optimize high-level strategies.
The biggest advantage of automated bidding strategies is their time- and cost- efficiency. With bidding automation, buyers don’t have to manually pull reports and analyze the data to confirm if their bid is set at the right price. Some automated strategies even ingest high-quality data that isn’t readily available to brands or advertisers—such as historical campaign performance, auction insights, or time of day—adding an extra layer of intelligence and depth to their decision-making.
To illustrate what bidding automation looks like in practice, here are a few examples of automated bidding strategies available within Basis:
Algorithmic optimization (AO) seeks out which inventory is performing best for each tactic included in a campaign. Marketers can set the algorithm to focus on either placement-level or domain-level data. After a learning period, AO adjusts the bid prices depending on how a particular domain and/or placement is performing. It can either increase the bid or stop bidding altogether on under-performing domains and placements.
Machine Learning Optimization (MLO) is a model-based solution that leverages artificial intelligence to optimize towards a campaign’s desired KPI. MLO analyzes data from more than thirty tactic parameters, at the brand level, and dynamically creates models in real-time that determine how much a tactic should bid on each impression based on the likelihood that it will result in the desired outcome—whether that be clicks, conversions, video completions, or viewability.
Bid Shading utilizes an algorithm to analyze historical bid data and determine a minimum price that is lower than the default CPM bid, while maintaining a high probability of winning the auction. Bid shading provides time savings and cost efficiencies that make it a no brainer for programmatic.
Bid Multipliers allows a single tactic to submit various bids based on how the parameter is performing. For example, let’s say a buyer notices that desktop inventory is leading to more conversions. Bid multipliers allow buyers to set rules so that when a desktop impression appears, the technology will automatically bid higher to ensure the impression is won. In the same vein, buyers can also decrease bids for inventory, domains, frequency, or creative if they are under- performing.
Put simply, automated bidding strategies leverage technology to help buyers make informed decisions about their bids. As a result, buyers can:
Curious to learn more? Check out our guide, Meeting the Moment with Advertising Automation, to learn about the many variations of automation, and how they serve to improve the lives of media professionals.
Each month, Basis Technologies’ Programmatic 101 series tackles a different facet of programmatic advertising—from best practices for buyers, to competitors in the space, to trends you should know. Check out last month’s post to learn about the art and science of cross-device targeting!
The emergence of demand side platforms (DSPs) in 2007 triggered a renewed focus on buying audiences, rather than websites. Suddenly, brands could follow consumers across websites. Now, we're even able to track users across all their connected devices.
Having access to audience data is a targeting goldmine. However, when that data isn’t used strategically, it can lead to waste, high CPMs, and poor campaign performance. Below, we’ve put together a little cheat sheet for how to approach KPIs and targeting based on campaign objectives.
If the objective is to increase awareness and engagement, you want to expose a brand or product to as many people as possible. This is the first step to driving consumers through an action or purchase cycle. The focus should be on reaching as many people as possible while starting to build up first-party audiences—a method of understanding your customers that’s increasingly important as the industry moves away from third-party cookies.
When planning for how to measure the success of an awareness or engagement campaign, viable KPIs include the following (hint: these KPIs also illustrate the type of creative mediums brands should be developing):
With an emphasis on reach and scale, programmatic buyers should focus on targeting tactics that are as broad as possible. Common targeting tactics include prospecting, demographic targeting, and native.
Unlike the early days of marketing, where consumers acted in a linear fashion, the path to purchase today can be quite circular—with customers moving from awareness, to lead, back to consideration, and then back to awareness. What we know for sure is that prospects who visit a brand’s website are more likely to convert than those who don’t, so marketers must be hyper-focused on driving quality site traffic as efficiently as possible.
When determining if a traffic or consideration campaign is effective, buyers use a cost-based KPI such as:
With the introduction of cost-based KPIs, it’s important to ensure a campaign has the right balance between refined targeting (such as contextual targeting (a great cookieless option that’s having a moment), hyperlocal, behavioral targeting, private marketplace deals (PMPs) or custom site lists (CSL)), and email targeting and cost. Keep in mind that options like PMPs, CSLs, and email tend to have higher CPMs.
If a brand has utilized the above objectives effectively, a conversion campaign’s focus is to drive an online or offline action from an audience that’s not only aware of the brand, but has already engaged.
As you can imagine, the KPIs for a conversion or action objective are focused on efficiency. But unlike traffic and consideration, the conversion objective is focused on driving a specific outcome. Recommended KPIs include:
By the time a brand gets to the conversion and action phase, all audience testing/building is complete— allowing brands to really focus in on their targets. These tactics include retargeting (whether it be display/video/location/search or click) and first- party audiences (CRM lists, lookalike modeling, etc.).
Now that we’ve covered the basics, it’s important to acknowledge that there will always be targeting tactics that can be used across objectives. For example, one could argue that retargeting benefits all KPIs—or that prospecting can be used as an audience-builder for awareness objectives but can also be used to drive down overall eCPMs for a conversion-based campaign.
The questions marketers must ask are: why am I using this tactic? What is its purpose in supporting my objective? Will the scale or cost of this tactic impact performance? When KPIs or targeting mixes are held to this level of scrutiny, marketers are well on their way to driving success for clients.
Looking for more programmatic training? You’ve come to the right place: Check out Basis Technologies’ free Programmatic Advertising Course!
Each month, Basis Technologies’ Programmatic 101 series tackles a different facet of programmatic advertising—from best practices for buyers, to competitors in the space, to trends you should know. Check out last month’s post to learn about the rise of the private marketplace (PMP)!
Voice-activated speakers, smart watches, connected TVs, tablets, and cell phones: With more devices than ever with which to reach consumers, why do they feel harder than ever to track?
According to a 2020 survey, the average American has access to more than 10 connected devices in their household. As our device ownership grows, advertisers are tasked with understanding consumer journeys across platforms, channels, and devices that don’t exactly speak to each other.
How do they do it? Enter cross-device targeting.
Cross-device targeting refers to the process of targeting one user across their multiple devices. You may be familiar with cross-device targeting if you've ever done sequential creative messaging, layered third-party audiences onto CTV inventory, or wanted to expand the reach of a hyperlocal campaign.
There are two main methods to tap into when it comes to cross-device tracking: deterministic and probabilistic data.
Deterministic data relies on a one-to-one match between devices through login data. Any company that requires a consumer to log in can create a deterministic data set—because no matter what device a user logs in with, their login information remains the same. While deterministic data is highly accurate, its scale is limited, as it relies on one data point to match consumers to devices.
Probabilistic data, on the other hand, is based on a variety of data points such as device type, operating system, browsing history, and IP address. Data companies use advanced audience models to see if they can confidently predict a group of devices—or device graph—that belongs to the same user. Probabilistic data isn’t limited in terms of scale, making it most advertisers’ first choice when it comes to cross-device targeting.
There are countless benefits to reaching a consumer at the various devices they interact with throughout their days, but here are the big three:
When advertisers elect to use cross-device targeting, they get access to more touchpoints (and more impressions) with highly qualified users. For example, let’s say you’re working on an e-commerce strategy and know that if a potential customer lands on your website they are fifty percent more likely to convert. With cross-device, you can re-engage with customers via multiple devices, ensuring that your brand is top of mind and increasing the probability that they'll convert.
The ever-increasing complexity of our industry makes it quite difficult to track a potential customer from the first ad to the last ad they're served. Walled gardens, a lack of cookie data, and the surge in screen ownership all make it difficult for marketers to answer questions like:
When advertisers tap into cross-device tracking, they can use device graphs to better understand a consumer’s action path. Understanding that customer journey is priceless information for marketers because it leads to smarter planning and optimizations.
One of the most powerful tools digital advertisers have is the ability to serve the right ad, in the right place, at the right time. Advertisers can take advantage of a device graph to control how their brand story is being told and customize their messaging depending on what part of the funnel a user is in.
Here are some common scenarios where cross-device targeting often makes sense:
Of course, each strategy is unique, and there are plenty of ways to get creative with cross-device targeting. In other words, now that we've covered the basic science of this marketing tool—the art is up to you!
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Want a deeper dive into how marketers can track consumers across devices? Check out Basis Technologies’ Data Essentials certification.
Each month, Basis Technologies’ Programmatic 101 series tackles a different facet of programmatic advertising—from best practices for buyers, to competitors in the space, to trends you should know. Check out last month's post to learn about the evolution of programmatic bidding strategies!
According to eMarketer, 2020 was the first year that that the percentage of programmatic spend was higher in private marketplaces (PMPs) than in the open exchange. By the end of 2022, PMP spend will represent 17% of total programmatic digital display ad spending, compared to 13% being spent on the open exchange.
eMarketer's findings make it clear that the landscape is changing. But why are advertisers funneling more digital spend in PMPs than in the open exchange? Isn’t the benefit of programmatic supposed to be efficiency and scale, not one-to-one deals? To understand this shift, let's dig into the differences between the open exchange and PMPs.
Our discussion hinges on two types of real-time bidding (RTB) transactions: the open exchange and PMPs.
The open exchange is a public real-time bidding auction that any buyer or seller can participate in. With access to multiple exchanges, scale is usually not an issue, and bids remain relatively low since there's so much inventory. Advertisers can layer first-, second-, and third-party data to target very specific audiences. This type of transaction is most commonly used when a brand's goal is to find their customer wherever they go on their web journey.
A PMP deal, on the other hand, is an invite-only marketplace where publishers make their premium inventory available directly to select buyers. Unlike the open exchange, the scale for PMPs can be quite limited, due to either inventory or the the low match rate of third-party audience data.
To recap, the open exchange is like general admission at a concert, while PMP deals are like VIP tickets backstage. If the main benefits of the open exchange include scale and cost-efficiency, what are the situations in which PMP deals make more sense? Read on for four examples:
Whether you sell health insurance or pet accessories, advertisers need to "fish where the fish are." PMP deals allow brands to cherry pick their site list, while still using the automation of RTB, and tapping into first-party data that isn’t reliant on cookies.
Since PMP deals can be customized based on the advertiser’s needs, they offer more control over inventory. For example, let’s say you're a cannabis brand looking to run specific high-impact units across a specific publication. You can work with the vendor directly to ensure that you only bid on ad slots that are available.
PMP deals are characterized by greater transparency and control, which means that ad fraud is less of a concern. All of these factors ensure increased brand safety.
PMP deals are often compared to the site direct buy of traditional advertising. The difference is that since this inventory can be bought programmatically and has a dynamic CPM, advertisers can side-swipe site direct minimums while maintaining an overall efficient eCPM.
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Now that we know a bit more about PMP deals, let's return to our original question: Why are advertisers spending more programmatic dollars in PMPs than in the open exchange?
In short, the shift is because PMP deals speak to programmatic's past of direct buying, its present need for transparency, and its future reliance on cookieless targeting solutions.
Hungry for more? Check out Basis’ Programmatic Advertising Essentials Certification to gain a fundamental understanding of the programmatic ecosystem and how PMP deals play a part in it.
Each month, Basis Technologies’ Programmatic 101 series tackles a different facet of programmatic advertising—from best practices for buyers, to competitors in the space, to trends you should know.
One of the most complex aspects of programmatic buying is the ability to decide how much you’re willing to pay for an impression. As the advertising industry has evolved, so too have the bidding models and strategies available. Below, we dive into the evolution of bidding models, from waterfall auctions and header bidding to first- and second-price auction models and bid shading.
Before we get into the different bidding methodologies, let’s get clear on the meaning of real-time bidding, or RTB. Contrary to common misconception, RTB is not synonymous with programmatic!
RTB refers to a buying method used to purchase a single impression via an auction-based system in real-time. Programmatic, on the other hand, is a broad term that represents the automated technology infrastructure that enables media buying.
So, you can use RTB to buy inventory programmatically, but there are other ways to buy inventory that are not in real-time, such as programmatic direct and preferred deals. In other words, RTB is always programmatic, but programmatic encompasses more than just RTB.
Now that we’re clear on what RTB entails, let’s talk about the various models under the RTB header.
When RTB was first introduced, inventory was sold to one ad network at a time. The term ‘waterfall auction’ describes the norm for bidding during this time.
In a waterfall auction, publishers reach out to ad networks one by one, giving priority to larger networks and networks that have yielded more revenue for a publisher in the past. This means that major players like Google Ad Network receive more exposure to auctions, even if there are other networks willing to pay a higher cost.
Header bidding was introduced to address the inequitable nature of waterfall auctions. In the header bidding model, all buyers bid at the same time. This model provides an equal playing field for all buyers despite their size or revenue yield. Today, header bidding has almost achieved the level of an industry standard.
First-price and second-price bidding models are yet another variable to be considered in RTB. In the first-price bidding model, the buyer pays the exact amount they placed for their bid. For example, let’s say Advertiser A bid $4.00, Advertiser B bid $3.00, and Advertiser C bid $3.75. Advertiser A would win the auction because their bid was the highest, and they would pay $4.00. Since buyers have no idea what their competitors are bidding, this method leads to higher bids.
In the second-price bidding model, the buyer pays $0.01 more than the second-highest bid. Using the example above, in a second-price auction, Advertiser A would pay $3.76, or $0.01 more than the second-highest bid.
While second-price bidding has historically been the standard, giants like Google Ad Manager have moved to the first-price model in recent years.
For buyers to remain competitive in a world of first-price auctions, bid shading has emerged as an automated method for bidders to better determine bid amounts. Bid shading algorithms analyze historical bid data, looking at factors like site, ad size, exchange, and competitive dynamics to inform bid recommendations. Bid shading is so nuanced that it can help predict bid prices by tactic, which allows publishers to receive the highest bid while ensuring buyers keep their bids as cost-effective as possible.
To learn more about the benefits of bid shading, check out Why Bid Shading Should Be Part of Your Programmatic Strategy.
There you have it: from waterfall auctions to the introduction of bid shading, RTB has seen many evolutions since its beginning.
Hungry for more? Check out Basis’ Programmatic Advertising Essentials Certification to gain a fundamental understanding of the programmatic landscape.
Plus, keep your eyes peeled for next month’s installment of Programmatic 101 to learn more about Private Marketplace deals, another form of real-time bidding!
Each month, Basis Technologies' Programmatic 101 series tackles a different facet of programmatic advertising—from best practices for buyers, to competitors in the space, to trends you should know.
Programmatic advertising has been around since 1994, but like a 90’s trend, it’s really blown up in the 2020’s. There’s no overstating the prevalence of programmatic: AdAge predicts that by 2025, 90 cents on the dollar of digital ad spend will be allocated to programmatic channels (up from 70 cents on the dollar currently).
In general, programmatic advertising can be defined as the automated buying and selling of digital advertising. In this post, we’ll take a look at the benefits of using programmatic technology, as well as the history that led to the automation, transparency, and accessibility we all enjoy today.
Back in 1994, AT&T signed a three-month agreement with Wired.com to showcase the internet’s first banner ad, pictured below:
By today’s best practices, this ad shouldn’t have performed well. It doesn’t contain a strong call to action and it doesn’t communicate any messaging about AT&T or even mention the brand itself. However, it delivered click-through rates (CTR) of 44%! That's pretty darn good, given that average CTR rate today ranges between 0.05-0.09%.
This one-to-one buy was the start of direct buying and while it had its advantages (relationship building, premium placements, flexibility with ad sizes and inventory) it was very manual. As the number of online websites increased, there needed to be a way for advertisers to automate buying so that marketers’ ads could serve a variety of websites. This led to the birth of ad networks.
A year after the first display ad graced the internet, the first display ad network was founded by an ad agency called WebConnect. Marketers no longer had to go from one publisher to the next, but instead could save time and money by working with brokers who could sell inventory across websites. Adoption was slow at first, but by 1998 there were a variety of competitors in the space who specialized in different types of inventory.
While this solved the problem of automation, advertisers started to realize that there was duplication across ad networks and that the intermediary cost of working with those networks was driving down efficiency.
In 1999, GoTo.com introduced pay per placement (what we know now as pay per click) and this new buying model had advertisers questioning what margins or fees they were paying to run on these networks. In 2000, when Google released AdWords, marketers started seeing the transparency that was possible and wanted greater control over their campaigns.
In 2005, the first ad exchange was born. An ad exchange is a technological platform that facilitates the buying and selling of digital media inventory, allowing billions of impressions to be bought and sold in real time via real-time bidding (RTB). What a mouthful!
This meant that marketers were able to use technology, instead of publisher contact or a 3rd party network rep, to inform their decisions about what advertising inventory they should buy in real time—aka RTB.
The evolution from ad networks to ad exchanges had a gigantic impact on the advertising industry as we know it. There were no longer fixed site lists, fixed pricing, or even fixed impression amounts. Instead, marketers were able to pay only what the demand was at the time their impression was served. In addition, publishers were able to use technology to automate selling their inventory.
Unlike direct buying, where marketers had to rely on relationships and minimum spends, ad exchanges and RTB allowed a more democratic approach to ad buying. Smaller brands and advertisers could tap into programmatic technology to stretch their media dollars further than they could before, and since everything happened in real time, there was no fear of premium inventory already being bought by the top advertisers in the industry.
So there you have it: The need for automation, transparency, and accessibility were key contributors to the development of programmatic advertising. But while the invention of programmatic technology brought more of these three elements to the advertising landscape, it also introduced more complexity. Our industry continues to strive for increased automation, transparency, and accessibility, an effort that paves the way for more innovation and better solutions for advertisers.
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Want to learn more about the fundamentals of programmatic advertising? Check out our free Digital Media Essentials course!
The cannabis landscape continues to grow (pun intended!) with more holding companies, growers, and dispensaries being created each month. While there is a lot of opportunity within this vertical, keeping up with the latest regulations (whether that be at the state/federal level or within the cannabis lumascape) can be a full-time job.
Read on for a deep dive into cannabis, CBD, and the channels that can be included in a paid cannabis marketing plan.
In order to know what channels to tap into, we first need to understand the differences between cannabis and CBD. While both come from the same plant, cannabis sativa, the key difference is that cannabis is allowed to have THC levels of 0.4% or higher, while CBD must have a THC level less than 0.3%.
This subtle difference is the reason why cannabis brands look to programmatic and site direct partners for paid media opportunities.
There are scale implications when running both age-gaited and geo-exclusive campaigns, but programmatic advertising allows brands to access multiple inventory sources, including:
It also allows brands to layer on custom behavior personas, top-performing site lists, prescriptive contextual segments, hyper-local targeting, and hand-raisers who have begun their exploratory research.
We recommend adding site direct partner(s) to a media mix if the brand needs unique data, inventory, or has found a publisher who doesn’t offer programmatic inventory.
Some examples include:
The rest of the partners (local and premium sites, high impact creators, mobile-first partners, etc.) can be bought and accessed through a programmatic private marketplace deal (PMP). This allows cannabis brands access to premium inventory without having to pay a fixed CPM.
While there are other compliant inventory sources (ad networks and data providers, for example) there is more opportunity to create custom messaging and scale with programmatic and site direct partners.
As this the industry continues to expand, we expect other channels to pivot their restrictive guidelines in order to capture the growing ad revenue from holding companies and brands alike.
Want to know more about cannabis advertising? Check out Centro’s partnership with MyRemede and our performance with Pax Premium Vaporizers.
If you’re ready to start building your cannabis media mix, connect with us to learn more.