Welcome to the third episode of The Digital Dish, where we dish on all things ad tech! This episode focuses on attribution, including an overview of what it is, what the industry challenges are, the pros and cons of four different types of methodologies, and best practices for maximizing your media strategy.
Prefer to read? Here’s the transcript:
This series topic is attribution. The digital age has brought a wealth of opportunity for businesses of all sizes. With that opportunity comes a new set of challenges, among them attribution.
Attribution, or the process of determining which channels are driving customer behavior, has always been important for businesses, and in this age, it’s more important than ever. With so many channels and so much noise, it’s more difficult to determine which channels are actually driving sales and conversions.
In this episode of the Digital Dish, we’ll look at:
What we know is that all media strategies are tied to outcomes. Whether your brand is looking to measure engagement or actual direct response, you need to know whether you are getting a return on your investment. It’s likely that as a marketer, you are running a combination of media and need to track the sources of your traffic and leads to determine what is and isn’t working.
This is where attribution comes in. While attribution isn’t always easy to understand, it is crucial. It’s hard to track a customer’s journey:
But ultimately, you do need to know what the customer journey was (from start to finish) and understand which channels played a part in the consumer’s journey to buy or engage with your product or service.
There are a LOT of attribution models out there, so today, we are going to uncover some truths and simplify them for you.
So why measure? Why is attribution so important? Well, to start, you can’t improve what you don’t measure. By tracking the success (or lack thereof) of your marketing efforts, you can determine what’s working and what’s not. And that knowledge will help you allocate your resources more effectively in the future, working towards the goal of increasing overall ROI.
The most common methodology you’ll hear of is the last-touch model of attribution. The flaw here is that last touch refers to just that – the last touch point a customer had interacted with before making a purchase. This isn’t always accurate, however, since we are all subject to so many screens, shared screens, and innumerable distractions. The single-user journey just isn’t the case anymore.
For example, if a customer sees a social media ad, hears an audio ad, and then sees a display ad that they click on and finally make a purchase, the social and audio ad would not receive credit in the last touch attribution model.
That’s why it’s important to use more than one attribution model. By using multiple models, you can get a more accurate picture of how your marketing is performing and where you need to make adjustments.
Now we’re going to review four attribution methodologies, as well as their pros and cons, so you can be informed and ultimately choose the right one for your business. So, what are they?
Let’s first start with last touch methodology, which is the most commonly used. Last touch refers to the last touch point a customer had interacted with before making a purchase and therefore giving this last touch point the entire conversion credit.
The single-user journey just isn’t the case anymore. If you are working with a long buying cycle or want to understand how the consumer goes from the initial brand awareness stage through the funnel to becoming a customer or user, this methodology isn’t for you.
The next is first touch methodology, which is the opposite of last touch. With first touch, the first time that a customer interacts with your company that is determined the most important reason they ended up purchasing with you.
Next up, we have linear attribution, which is a multi-touch model–meaning it takes into account and attributes credit to multiple touchpoints along the customer journey. In fact, this model actually takes into account every single interaction that a prospect has had with your brand prior to purchasing. This method is incredibly easy to understand as every single touchpoint is given the same amount of credit, so the equation is as follows:
100% of the overall credit / the number of touchpoints in the conversion path = the amount of credit per touchpoint
For example, if your customer has four interactions with your brand before deciding to convert, each would, therefore, have 25% of the credit.
Finally, there’s time decay attribution. This is similar to linear in that it’s a multi-touch model that gives some credit to all channels that led to the conversion. However, this is different with that amount of credit being less (or “decaying”) the further back in time the channel was interacted with. This assumes that the first channel your customer interacted with merely planted the seed, and the customer’s interest in committing to a purchase grew over time with repeated exposure to various marketing channels. Think of this as a rising level of interest and commitment from the customer over time.
Now that we can understand what attribution is and the different methodologies you can consider, the next step is to review your current strategies to see which ones are working and which ones need improvement. Here are a few tips to help you master attribution in the digital age:
Use a consistent methodology across all channels and platforms. This involves creating benchmark goals and making sure that all channels are performing according to those standards.
Set up advanced tracking capabilities, such as key conversion pixels on high-traffic landing pages or mobile app tracking for detailed insights. Having multiple data points will help you pinpoint any weaknesses in your attribution model and make timely adjustments.
Be proactive in finding ways to increase return on investment from existing channels. You don’t want to track just performance; rather, you should continually seek out ways to improve results through optimization techniques such as A/B testing or identifying new audiences with demographic data. If you are a brand or agency with a goal to understand which touchpoint or variable along the consumer path is driving conversions and in the past have been directed to a single touch model, for example (like last or first touch), start thinking outside of that assumed-consumer path and consider a methodology that would best suit your specific set of challenges.
Leveraging these best practices and taking advantage of the latest technologies, as well as our custom attribution guidance, will help you master each methodology and create a more sophisticated strategy to drive best results.
As technology advances, so too does our ability to measure and optimize attribution. Thanks to powerful data visualization tools, machine learning algorithms, and the ever-growing volume of available customer data, marketers now have access to a wealth of insights when it comes to understanding where each consumer interaction is coming from.
Detail is key here, with the right technology, we can analyze granular data about each marketing initiative – including specific channels, campaigns, and even individual creatives – to understand how each variable impacts your customer journey and overall bottom line. Our full-funnel attribution methodologies help all advertisers across all verticals make better decisions about which tactics are most effective for driving results, wherever they may lie in the marketing funnel.
Take an example that we recently worked on with a DTC e-commerce brand. They had a digital strategy spanning multiple channels, from Performance CTV to Podcasts, Social, and Display Retargeting, and it was becoming increasingly difficult to equate purchases to any one variable and, thus, hard to dedicate specific spend to any one channel.
Both the first and last touch methodologies were not a suitable solution for this client, as they were new to market, had a higher price point of goods, and a longer sales cycle. It meant that typically, a customer interacted with media multiple times across multiple channels before making a purchase. Our team at Digital Remedy assessed every impression of this campaign and provided traceable data correlating each impression to a conversion, whether that was an online purchase, homepage visit, or email sign up. This full-funnel data allowed us to flag variables that were significant in the customer journey and therefore recommend the allocation of higher spend to these channels, and ultimately drive down the CPA for this client.
Attribution is a core digital marketing function, and it’s more complex than most people realize. It’s not just about correctly identifying the channels that drove a conversion but also understanding how each channel works together to influence conversions and from there, determining the most effective allocation of dollars and resources.
Getting attribution right is essential for optimizing your return on investment. With these data sets and insights, you can make more informed decisions about where to allocate your budget, and which channels are most likely to drive down cost per acquisition.
The digital landscape is constantly evolving, and it’s crucial that, as marketers, we stay ahead of the curve and master the art of attribution. With the right tools and knowledge, you can make sure your marketing efforts are always working as hard as they can to drive conversions and growth. Think of attribution as step one. Once we get that right, we can build upon its foundation with specialized optimization, incrementality, and halo effect methodologies.
Interested in learning more? Check out our Trends & Insights report—or speak to a team member! Be sure to follow Digital Remedy on LinkedIn, Twitter, and Instagram for the latest updates.
As the direct-to-consumer (DTC) space becomes increasingly competitive, marketers are under constant pressure to understand campaign performance and prove the value of their efforts. Now accountable for driving outcomes through their campaigns rather than building general awareness among consumers, many brands are rethinking their media mix—exploring new channels that can effectively deliver on their campaign goals and maximize their budget.
Digital Remedy partnered with Dynata, the world’s largest first-party data company, to field a marketing-focused survey with the goal of gaining a deeper understanding of DTC marketers’ top priorities, current campaign practices, and media partner preferences.
Download our report, which provides valuable insights, including:
For the latest industry trends and insights, check out our blog and sign up to receive our monthly newsletter.
Whether you’re a DTC marketer just getting started in the CTV/OTT space or looking to take your current campaigns to the next level, Digital Remedy is here to help. Discover how your brand can garner greater brand impact, generate more outcomes, and turn TV into a powerful performance channel. For additional information, visit www.digitalremedy.com/ott-ctv or schedule a demo to see our award-winning platform in action.
Welcome to the second episode of The Digital Dish, where we dish on all things ad tech. This episode focuses on Incrementality, including an overview of what it is, what the industry challenges are, and what solutions advertisers and agencies can tap into when utilizing incrementality methodology.
Prefer to read? Here’s the transcript:
Incrementality addresses two major concerns, especially when you’re trying to assess a single, possible experimental media type. Native demand for products/site visits/leads etc., and all the other media, especially walled-garden media. With proper incrementality testing, we can factor both out entirely, getting right to the heart of the media type we’re analyzing to assess a true, bottom-line impact.
In its simplest form, incrementality is the percentage of total conversions that otherwise would not have happened. When we measure incremental lift: that involves what percent more likely someone who is exposed to a campaign is to convert than someone who is not exposed to a campaign.
We now live in a world where measurement is growing more and more important, and we start to bootstrap spending and really assess where and how marketing dollars are impacting our bottom line. And Incrementality answers questions like:
In this video, we’ll look at:
What we’ve seen in the industry is that traditional marketing performance metrics have been around for many years and marketers still rely on them. Traditional marketing metrics such as CPA, CTR, and VCR, while important, can leave room for additional questions requiring custom analytics to answer. With limited media budgets and advertisers now experimenting with different channels and formats, understanding the true impact of your marketing is crucial in making budgeting decisions and being able to justify scale. Most marketers would (understandably) only want to spend money on channels that drive those bottom-line results and ensure a positive brand experience.
Marketers are facing a looming cookieless future, evolving privacy regulations, fragmented customer data, and the accelerating complexities of identity management. They’re seeking an alternative approach to evaluating the effectiveness of their campaigns. In response to new privacy rules requiring mobile apps to get things like explicit user consent and access to an IDFA device. eMarketer states that 64% of marketing executives plan to invest more in other measurement solutions like incrementality and media mix modeling. When implemented correctly, incrementality can boost a brand’s revenue and help marketers prove their value.
An increasing share of the industry is looking to move away from last-touch attribution. The majority of marketers really do still depend on it because of its ease of use. While last-touch is a simple way to assign credit, it’s not the most accurate. Incrementality is really there to identify the causal event of a conversion, and it’s allowing businesses to properly allocate their budget and reduce wasted ad spend.
Based on the conversations I’ve had, it’s pretty critical for advertisers to understand how their digital marketing efforts are impacting their sales. Incrementality offers deeper insights and more accuracy when measuring metrics like attribution, lift, and return on investment. Incrementality done correctly, and in comparison to the other methodologies, it’s reinforcing the need for marketers to have the right tools in place to drive a brand’s growth and their marketing efforts forward.
The industry challenge for many different advertisers is to make the most out of their dollars. And that means maximizing their media budgets. Advertisers are always experimenting with different channels and formats, and they want to understand the true impact of their efforts. This is really crucial in making budgeting decisions, as well as justifying scale.
In today’s ad landscape, brands run across many different media channels—this can be from paid search to CTV, display, and direct mail, and customers are often exposed to several different media types—as well as several different ads within each media type—before making a purchase or whatever their conversion point might be. And so, therein lies the challenge: How do marketers know which specific ads, or which specific campaigns actually drove the conversion, when so many other campaigns and ads are running concurrently? And how do they separate an individual channel from the rest, or from their own organic or native demand? This is where incrementality comes into play.
One of the examples that I’m about to share will allow you to understand that incremental lift can be measured across numerous variables:
Our team was working with a very well-known QSR advertiser. One of the ways that we wanted to involve incrementality was through the measurement of their creative and publisher lists. We wanted to look at these two pieces and provide them with insights they would not have been able to derive on their own. When we launched incrementality with this QSR advertiser, we could get really granular and look at these different variables. We were able to see which publishers were showing the highest incremental lift as well as which creatives were driving the highest incremental lift, and then also which ones weren’t.
From there, looking at this data, we were able to make optimizations toward these findings. And not only that, but we were able to provide the client with more insights to enable them to make informed decisions around their creative strategy, and this client actually ran a handful of different creative messaging throughout the campaign. Incrementality allowed us to show them which creative messaging drove the highest incremental lift in 2022. And from those results, they’re actually currently shooting to provide us with additional videos that align with this same messaging so that we can have more variety within this creative batch, and then additionally, on the publisher side, we really saw that sports inventory was a top performer for them, so we’re keeping that in mind as we go into 2023 and really maximizing the inventory that is sports related for their 2023 campaign. That’s just one real-life example of how incrementality is allowing us to see the lift across these different variables and find those points of success and then make pushes in those specific areas for continued improvement.
When discussing incrementality, we’re talking about measuring conversions that otherwise would not have happened. The reason why this is important is because we wanna make sure that any conversions that we’re driving with our OTT or our CTV spend are actually driving conversions that are not duplicative with the marketer’s other channels.
Marketers are running search and social and maybe affiliate marketing, and those are doing a great job driving conversions. And there’s a middle of that diagram with the conversions that we will also drive. The way that we do this is we set up a test and what we want to do is we want to analyze the difference in behaviors between two groups.
The first group is an exposed group, that’s people who have seen the campaign that we’re running for a client. The second group is a control group, and these are people who are actively prohibited from seeing an advertisement from this campaign.
And what we’re doing is we’re measuring the lift of that exposed group over that control group, or we’re seeing how those ads impact the behaviors of the people who actually saw them. We do this by looking at conversion rates between the two groups. And the reason we look at conversion rates and not just the number of conversions is that the group size is different.
And the way that we actually create that holdout group is through a process called ghost bidding. What Ghost bidding is, is if you imagine we’re set to serve five ads to individuals, all the targeting that’s applied, goes, behaviors, devices, all of that targeting is applied.
And then, instead of serving one of those five ads, we actually end up holding that person who is eligible for that ad out of the campaign. In other words, four ads get served, one ad gets held out. And then we’re creating a holdout group that mimics the exposed group exactly from a behavioral-, from a targeting-, from a demographic perspective.
All of the targeting that is applied to the exposed group is also applied to the control group because those individuals are actually eligible for bits. There’s no skew this way. So we avoid any skew that would’ve happened with a random sample. And instead, we’re making sure the two groups look exactly alike.
And then the second benefit is, because we’re not actually serving the ad, we don’t have to pay for the impression. So we can run this analysis free of charge. After we, have created that holdout group, what we’re able to do is measure the conversion rate lift of the exposed group over that holdout group.
And not only can we do this at the overall level, we can do this below the aggregate, which means that we can measure which publishers, which creatives, which times of day, which audiences are over-indexing or or have a higher incremental lift, than other times of day or other audiences or, other creatives or publishers, whatever that variable set might be.
Because we’re able to get below the aggregate, we can actually apply this analysis into the way that we optimize. So as opposed to just optimizing toward a standard ROAS goal or a standard CPA goal, we’re able to actually optimize toward an incremental CPA or an incremental ROAS. Adding another touchpoint and making sure that the creatives and the publishers and the day parts and the audiences that we’re targeting and ultimately moving money toward are actually driving bottom-line results, not just an in-platform low CPA reporting result.
This is all part of an effort to unsilo OTT. We wanna make sure that whatever we’re doing for our clients is actually in the best benefit from a bottom-line perspective, not just that we’re looking good from a reporting perspective in-platform.
Interested in learning more? Check out our Intro to Incrementality series or our Trends & Insights report—or speak to a team member! Be sure to follow Digital Remedy on LinkedIn, Twitter, and Instagram for the latest updates.
Welcome to the second episode of The Digital Dish, where we dish on all things ad tech. This episode focuses on Incrementality, including an overview of what it is, what the industry challenges are, and what solutions advertisers and agencies can tap into when utilizing incrementality methodology.
Prefer to read? Here’s the transcript:
Incrementality addresses two major concerns, especially when you’re trying to assess a single, possible experimental media type. Native demand for products/site visits/leads etc., and all the other media, especially walled-garden media. With proper incrementality testing, we can factor both out entirely, getting right to the heart of the media type we’re analyzing to assess a true, bottom-line impact.
In its simplest form, incrementality is the percentage of total conversions that otherwise would not have happened. When we measure incremental lift: that involves what percent more likely someone who is exposed to a campaign is to convert than someone who is not exposed to a campaign.
We now live in a world where measurement is growing more and more important, and we start to bootstrap spending and really assess where and how marketing dollars are impacting our bottom line. And Incrementality answers questions like:
In this video, we’ll look at:
What we’ve seen in the industry is that traditional marketing performance metrics have been around for many years and marketers still rely on them. Traditional marketing metrics such as CPA, CTR, and VCR, while important, can leave room for additional questions requiring custom analytics to answer. With limited media budgets and advertisers now experimenting with different channels and formats, understanding the true impact of your marketing is crucial in making budgeting decisions and being able to justify scale. Most marketers would (understandably) only want to spend money on channels that drive those bottom-line results and ensure a positive brand experience.
Marketers are facing a looming cookieless future, evolving privacy regulations, fragmented customer data, and the accelerating complexities of identity management. They’re seeking an alternative approach to evaluating the effectiveness of their campaigns. In response to new privacy rules requiring mobile apps to get things like explicit user consent and access to an IDFA device. eMarketer states that 64% of marketing executives plan to invest more in other measurement solutions like incrementality and media mix modeling. When implemented correctly, incrementality can boost a brand’s revenue and help marketers prove their value.
An increasing share of the industry is looking to move away from last-touch attribution. The majority of marketers really do still depend on it because of its ease of use. While last-touch is a simple way to assign credit, it’s not the most accurate. Incrementality is really there to identify the causal event of a conversion, and it’s allowing businesses to properly allocate their budget and reduce wasted ad spend.
Based on the conversations I’ve had, it’s pretty critical for advertisers to understand how their digital marketing efforts are impacting their sales. Incrementality offers deeper insights and more accuracy when measuring metrics like attribution, lift, and return on investment. Incrementality done correctly, and in comparison to the other methodologies, it’s reinforcing the need for marketers to have the right tools in place to drive a brand’s growth and their marketing efforts forward.
The industry challenge for many different advertisers is to make the most out of their dollars. And that means maximizing their media budgets. Advertisers are always experimenting with different channels and formats, and they want to understand the true impact of their efforts. This is really crucial in making budgeting decisions, as well as justifying scale.
In today’s ad landscape, brands run across many different media channels—this can be from paid search to CTV, display, and direct mail, and customers are often exposed to several different media types—as well as several different ads within each media type—before making a purchase or whatever their conversion point might be. And so, therein lies the challenge: How do marketers know which specific ads, or which specific campaigns actually drove the conversion, when so many other campaigns and ads are running concurrently? And how do they separate an individual channel from the rest, or from their own organic or native demand? This is where incrementality comes into play.
One of the examples that I’m about to share will allow you to understand that incremental lift can be measured across numerous variables:
Our team was working with a very well-known QSR advertiser. One of the ways that we wanted to involve incrementality was through the measurement of their creative and publisher lists. We wanted to look at these two pieces and provide them with insights they would not have been able to derive on their own. When we launched incrementality with this QSR advertiser, we could get really granular and look at these different variables. We were able to see which publishers were showing the highest incremental lift as well as which creatives were driving the highest incremental lift, and then also which ones weren’t.
From there, looking at this data, we were able to make optimizations toward these findings. And not only that, but we were able to provide the client with more insights to enable them to make informed decisions around their creative strategy, and this client actually ran a handful of different creative messaging throughout the campaign. Incrementality allowed us to show them which creative messaging drove the highest incremental lift in 2022. And from those results, they’re actually currently shooting to provide us with additional videos that align with this same messaging so that we can have more variety within this creative batch, and then additionally, on the publisher side, we really saw that sports inventory was a top performer for them, so we’re keeping that in mind as we go into 2023 and really maximizing the inventory that is sports related for their 2023 campaign. That’s just one real-life example of how incrementality is allowing us to see the lift across these different variables and find those points of success and then make pushes in those specific areas for continued improvement.
When discussing incrementality, we’re talking about measuring conversions that otherwise would not have happened. The reason why this is important is because we wanna make sure that any conversions that we’re driving with our OTT or our CTV spend are actually driving conversions that are not duplicative with the marketer’s other channels.
Marketers are running search and social and maybe affiliate marketing, and those are doing a great job driving conversions. And there’s a middle of that diagram with the conversions that we will also drive. The way that we do this is we set up a test and what we want to do is we want to analyze the difference in behaviors between two groups.
The first group is an exposed group, that’s people who have seen the campaign that we’re running for a client. The second group is a control group, and these are people who are actively prohibited from seeing an advertisement from this campaign.
And what we’re doing is we’re measuring the lift of that exposed group over that control group, or we’re seeing how those ads impact the behaviors of the people who actually saw them. We do this by looking at conversion rates between the two groups. And the reason we look at conversion rates and not just the number of conversions is that the group size is different.
And the way that we actually create that holdout group is through a process called ghost bidding. What Ghost bidding is, is if you imagine we’re set to serve five ads to individuals, all the targeting that’s applied, goes, behaviors, devices, all of that targeting is applied.
And then, instead of serving one of those five ads, we actually end up holding that person who is eligible for that ad out of the campaign. In other words, four ads get served, one ad gets held out. And then we’re creating a holdout group that mimics the exposed group exactly from a behavioral-, from a targeting-, from a demographic perspective.
All of the targeting that is applied to the exposed group is also applied to the control group because those individuals are actually eligible for bits. There’s no skew this way. So we avoid any skew that would’ve happened with a random sample. And instead, we’re making sure the two groups look exactly alike.
And then the second benefit is, because we’re not actually serving the ad, we don’t have to pay for the impression. So we can run this analysis free of charge. After we, have created that holdout group, what we’re able to do is measure the conversion rate lift of the exposed group over that holdout group.
And not only can we do this at the overall level, we can do this below the aggregate, which means that we can measure which publishers, which creatives, which times of day, which audiences are over-indexing or or have a higher incremental lift, than other times of day or other audiences or, other creatives or publishers, whatever that variable set might be.
Because we’re able to get below the aggregate, we can actually apply this analysis into the way that we optimize. So as opposed to just optimizing toward a standard ROAS goal or a standard CPA goal, we’re able to actually optimize toward an incremental CPA or an incremental ROAS. Adding another touchpoint and making sure that the creatives and the publishers and the day parts and the audiences that we’re targeting and ultimately moving money toward are actually driving bottom-line results, not just an in-platform low CPA reporting result.
This is all part of an effort to unsilo OTT. We wanna make sure that whatever we’re doing for our clients is actually in the best benefit from a bottom-line perspective, not just that we’re looking good from a reporting perspective in-platform.
Interested in learning more? Check out our Intro to Incrementality series or our Trends & Insights report—or speak to a team member! Be sure to follow Digital Remedy on LinkedIn, Twitter, and Instagram for the latest updates.
Performance marketing has grown in popularity over the last decade, as marketing budgets have been slashed to maximize return on investment. While brand awareness is important, many marketers are focused on driving (and measuring) bottom-funnel actions, such as website visits, in-store visits, and purchases. Improvements in measurement for once-considered upper-funnel media are coming fast and furious. These improvements show that lower-funnel media can have branding impacts, and upper-funnel media can have performance impacts.
In short, more measurable real-world results and more granular reporting for marketers. Performance TV allows marketers to deliver ads to target audiences, measure campaign performance, and attribute bottom-funnel results. Two main benefits of performance TV are the ability to:
Performance TV advertising is done through connected TV (CTV) devices that help to attribute and report on those campaigns. Performance CTV provides a unique opportunity for marketers to reach highly-engaged audiences.
CTV offers the high-impact, brand storytelling power of traditional TV plus the targeting, analytics, and interactivity of digital to provide a compelling environment for audiences to engage with messaging alongside premium content.
“Linear TV and CTV are converging; however, similar to the shifting holiday season, which is promoting earlier shopping each year, that doesn’t mean it has made what to buy, where to buy, and whether or not you have the best deal clear for media buyers (and consumers), which is the case for advanced TV.”
–Matt Sotebeer, Chief Strategy Officer, Digital Remedy
For brands looking for new ways to maximize their marketing efforts, CTV is the perfect channel.
“We’re seeing brands start to gear up for Black Friday and Cyber Monday and look toward new channels to leverage. CTV is definitely top-of-mind for these advertisers as long as their investment can be backed up by performance. This makes attribution and optimization on this channel more important than ever.”
– Ben Brenner, VP of Business Development & Strategy, Digital Remedy
CTV’s ability to merge the often-separated performance and brand marketing worlds—including its inherently addressable nature—is redefining the digital ad space and giving marketers a way to take their campaign measurement to the next level.
Finding the right performance CTV partner can make all the difference in optimizing your media strategy and maximizing ROAS in this fast-growing, highly-profitable market. While many ad tech vendors offer different solutions, not all of them have the full scope of resources to make the most of advertising on this medium. Digital Remedy offers first- and third-party data integrations, direct access to premium OTT publishers, real-time optimization, and granular, transparent bottom-funnel reporting through Flip, our performance OTT stack.
Digital Remedy provides comprehensive campaign performance reporting and data-driven capabilities to help advertisers and agencies connect with target audiences at the best time.With Flip, brands gain access to a sophisticated reporting dashboard to monitor their campaigns and real-time performance insights to optimize towards the KPIs that matter most. Flip provides a new standard in tracking, transparency, and results, including:
With these valuable insights, marketers can make more effective optimizations and investment decisions—to effectively grow their business and drive measurable campaign performance by leveraging the biggest screen in the home to deliver brand messaging.
Download the full report for full insights, including myths surrounding CTV. Interested in learning more? Watch our Digital Dish episode or speak to a member of our team.
As the marketplace becomes more saturated and competitive, direct-to-consumer (DTC) marketers are under an increased amount of pressure to prove the effectiveness of their campaigns – leaving them to explore new media channels to drive results for their brands and reach new audiences.
David Zapletal, Chief Operating Officer, was featured in The Drum discussing the benefits of connected TV (CTV) as a new media channel for DTC brands to improve campaign performance. Here’s a look at what he shared:
Many of the reasons why DTCs are increasingly upping their CTV investments are straightforward—even intuitive. DTCs, from already doing so much business digitally, have access to the first-party data that enables efficient CTV targeting.
CTV is high-quality media, with low levels of fraud relative to many other digital channels and with 100% viewable inventory. They can use similar KPIs in CTV that they use in online campaigns – online conversions, site visits, and the like. And they can target very specifically, allowing them all the benefits of consumers’ engagement with the TV screen without dealing with the cost of reaching all viewers of a program via linear. DTCs are seeing the value of developing compelling creative for CTV and shifting spend to maximize their budget.
CTV allows marketers to define outcomes and choose a sophisticated attribution model – a custom attribution window allows brands to define their conversion window, methodology (for example, first- and last-touch, and more advanced methods like linear and time decay), and KPIs. From there, they can optimize towards the interactions that drive performance – and effectively measure real-world actions that were driven by the campaign.
When making a push into CTV, DTC marketers need to consider the metrics and outcomes that are important to them and understand where and how to get insights on those metrics and outcomes. CTV offers exceptionally broad insights to help meet DTC marketers’ unique goals. Impressions and video completion rates (VCR) don’t tell the whole story of a campaign. Marketers need to track user actions throughout the entire marketing funnel. CTV advanced reporting measures valuable bottom-funnel actions, including site visits, online purchases, in-store visits, app downloads, and more – not just brand lift and awareness.
CTV isn’t simply the next emerging channel for DTC marketers to explore—it’s a powerful channel that can drive performance metrics where previously “tried and true” channels aren’t cutting it and can inform cross-platform campaign optimization.
Whether you’re a DTC marketer just getting started in the CTV/OTT space or looking to take your current campaigns to the next level, Digital Remedy is here to help. Speak to a member of our team to learn more.
Check out the full article on The Drum, and be sure to follow us on LinkedIn and Twitter for the latest Digital Remedy updates.
In Part 1 of our Intro to Incrementality series, we went through the basics of incrementality analysis. We talked about our two groups, test (or exposed) and control (or holdout), and how this type of analysis measures the lift caused by a specific variable of one over the other, thereby attaining a true measure of that variable’s impact. In Part 2, we homed in on that control (or holdout) group and explained exactly how to go about creating that group through ghost bidding in a way that avoids skewing the incrementality analysis. So, we’ve covered the basics and we’ve made sure that by the time we get to our incrementality results, we can trust that there’s no skew or bias.
In this last installment, we will discuss the final, and perhaps most important, part of this process: putting this analysis to work in real time. To get to the heart of it, let’s look back on our initial basketball example:
An NBA basketball team has two players who both make 40% of their foul shots. The team wants to improve that percentage, so it decides to hire a shooting coach. It designs a test to evaluate that coach, and assigns him to only one of the two players. Both players are told to do everything else they had been doing, exactly as they had been doing it. For instance, they’re told to spend the same amount of time in the gym, keep asimilar diet, and maintain their same weight. After a year, the player who worked with the shooting coach makes 80% of his free throws the following season, while the player not assigned the shooting coach makes 50% of his free throws.
While this example has been useful in demonstrating the basics of incrementality, the test (the player assigned the coach), the control (the player not assigned the coach), and the new variable (the coach), it neglects some crucial real-world implications.
For starters, if something appears to be working, it doesn’t necessarily benefit the team to wait a whole season to evaluate exactly how well it’s working. If that coach got assigned to the control player mid-way through the season, the results might differ, but it’s quite likely the team could have boosted two players’ free throw percentage instead of one. It’s also worth taking a look at exactly what the coach is doing. Is it just the extra repetitions demanded by the coach that are causing the improvement? Is it an adjustment in form? Is it some mental component or confidence boost? Finally, and most importantly, this coach represents an investment. And the team is paying. The team has to determine if that investment is justified by the incremental improvement. If it is, it has to then determine whether should it increase that investment, and how. Our example simplifies a problem that, in basketball or marketing, is quite messy. The point of this final installment is to discuss cleaning up that mess.
1. In marketing, incrementality analysis should be ongoing and in real time. Marketers don’t have an unlimited budget or the luxury of conducting tests in a vacuum. It’s important to note that what incrementality results look like after two weeks might be different than what they look like after two months, but that doesn’t invalidate the two-week results. It’s a continual process of data collection and analysis that should inform decision making. What decision making? We’ll get there shortly.
2. Incrementality analysis is often conducted at the media type level. In our former marketing example, we discussed determining the incremental impact of adding a CTV campaign to a larger marketing mix. The reality is that the CTV campaign very likely consisted of several streaming services, maybe Sling, Hulu, and Pluto, and several creatives, maybe a:30 second creative and two :15 second creatives, across more than one audience, maybe an intent-based audience and a demographic-based audience. When we conduct this type of analysis, it’s important to get more granular than just the overall media type to unearth additional valuable insights.
3. This will come as no surprise to anyone, but paid media costs money. We cannot, and should not, treat this analysis as independent of cost.
How do we put this analysis to work in real time, granularly, and factoring in cost? We apply it to campaign optimization. Here’s another marketing example:
A brand decides to add a $1k CTV test to their marketing mix that previously consisted only of search and social media campaigns. The brand’s goal is to optimize toward the lowest cost-per-checkout (CPC) possible for its CTV campaigns. The brand has only one creative and is testing only one intent-based audience, but it doesn’t want to put all its eggs in one basket, so it decides to test three publishers, Sling, Hulu, and Pluto TV.
Most performance CTV vendors don’t report incremental conversions, so the brand observes the following checkouts and cost-per-checkout across the variables in the CTV campaign.
Any brand that sees those results would think: “well, it looks like Sling is the best, we should put more budget there and less budget in Hulu and Pluto TV”—and, in a vacuum, the brand would be absolutely correct. But media doesn’t work in silos, it works across silos, and the brand is also running search and social, plus, it’s got all this organic demand it worked so hard to build up.
The brand, knowing this, decides to add incrementality analysis as an additional data point, and it finds that Sling’s incrementality percentage is 10%, Hulu’s is 80%, and Pluto TV’s is 50%. In other words, it finds that 90% of conversions recorded from Sling would have happened despite those Sling exposures, 20% of conversions recorded from Hulu would have happened despite those Hulu exposures, and 50% of those conversions recorded from Pluto TV would have happened despite those Pluto exposures.
This is worrisome and tricky when it comes to future budget allocation. What the brand is seeing is a commonplace occurrence in the marketing world: conversions reported by platforms are duplicative because each platform the brand operates in works only with the media it runs. So the brand’s CTV vendor takes credit for its social media conversions, the brand’s search vendor takes credit for its CTV conversions, and so on.
But the brand has a secret weapon: incrementality-informed optimization. Instead of using only CPA metrics, the brand can, very simply, apply the incrementality analysis to the cost-per-checkout and the result is a new metric: cost-per-incremental-checkout (iCPC). By multiplying the number of checkouts and the incrementality percentage, the brand unearths the below results:
The takeaway? Without incrementality analysis applied to the brand’s performance numbers, it would have been optimizing its CTV campaign in a way that was actually counter to its bottom line, toward conversions that would have happened anyway. By adding this additional analysis, it can actually see it would be best served spending more on Hulu, and that the top performer from only a CPC standpoint (Sling) actually finishes well behind the top performer from an iCPC standpoint (Hulu).
As brands get smarter with their budget allocation across and within media types, incrementality analysis becomes a crucial stepping stone on the path to profitability and cross-channel ROAS.
Flip, our performance CTV platform, not only offers incrementality analysis but also allows brands the option to leverage incrementality-informed optimization strategies, ensuring that their CTV dollars are getting put to work efficiently within a cross-channel media mix. To learn more speak to a member of our team today.
A confluence of factors is driving a boom in the OTT/CTV advertising space. With more households watching more streaming content, advertisers have, of course, taken notice, increasingly shifting ad dollars toward OTT and CTV channels. In fact, CTV ad spending is expected to surge 40% by the end of this year, totaling over $14.4b, and is forecast to more than double to $29.5b by 2024.
Naturally, as viewers and ad dollars have shifted toward OTT/CTV, many advertising technology platforms have followed suit, with more providers now offering CTV programmatic buying options with various targeting and attribution capabilities. CTV’s ability to merge the often-separated performance and brand marketing worlds—including its inherently addressable nature—is redefining the digital ad space and giving marketers a way to take their campaign measurement to the next level.
Ben Brenner, VP of Business Development & Strategy, shared six questions with Street Fight to ask to help brands who are new to the performance CTV space:
First, ask yourself: does your brand fall within the bucket of brands that can benefit most from performance CTV, and is your brand prepared to get started? Brand awareness-focused advertisers will not be the best candidate for performance CTV, as it focuses on direct performance, conversions, and attribution. Marketers must decide whether performance CTV fits their brand, strategy, campaign goals, and budget. It’s quite different from buying linear TV—it’s much more granular and requires detailed audience knowledge—so it’s important to be certain you have the data and insights to make it effective.
Before you invest any money, you must first determine which consumer actions (sales, leads, site visits, etc.) matter most for your strategy. Will you consider CTV’s impact within your larger media mix or as a standalone channel? You’ll want to ask partners how transparent they are with conversion reporting. Otherwise, it’ll be difficult to see how it stacks up in return on ad spend (ROAS) and cost per action (CPA) compared to other channels.
Innovations in attribution have moved the focus away from just video completion rate and toward ROAS and CPA metrics, and even these are still just scratching the surface of CTV measurement. That’s why transparency in reporting by partners is essential. Many vendors in the CTV space are stuck on last touch but effective performance CTV measurement means going beyond last touch—you need to know all touches that drove the desired action. For example, with the right platform you can measure first touch (conversion credit assigned to the first exposure), last touch (conversion credit assigned to the last exposure), time decay (with more credit assigned to exposures approaching the point of conversion), and linear (where conversion credit is evenly distributed across all exposures). But all of these have inherent variables as well. Working with a provider that can help you sort through these variables to define attribution is critical for refining your budget strategy and future campaigns.
To determine the true business value of advertising, marketers must move past clicks and impressions toward higher-value metrics like revenue and net profit—how the marketing spend actually moves the needle. Incrementality allows you to do just that by measuring the lift in desired outcome in the campaign results above baseline native demand. And sometimes the interaction that influences the desired outcome may look more costly, but the impact delivers a higher ROAS. Regardless of whether you’re working with a multi-touch attribution provider across channels, when you’re running on CTV specifically, what works on direct-basis (without an incrementality multiplier) most likely doesn’t work with incrementality multiplier. For example, Creative A might have CPA of $15, and Creative B’s CPA is $20. But Creative A delivers only 50% incremental lift while Creative B delivers 90%. While A is cheaper, you should be optimizing for B based on effectiveness. You’ll want to choose a provider that allows you to measure incrementality to get a true picture of the impact of OTT on your overall ROAS while factoring in other media channels and ensuring native demand is accounted for.
A big part of the value in CTV advertising is that it’s fast and fluid—unlike buying linear TV where the buy is set with an IO and requires you to wait. CTV buying can be much more agile and dynamic, allowing you to optimize spending based on results as they come in. Having all this granular data is great, but what good is it if it just sits on the shelf? That’s why any platform you choose must provide real-time optimization and the ability to change audience targeting parameters on demand. Twice a week isn’t nearly enough—daily is ideal.
In the linear world, when you buy spots on broadcast and cable, you own that spot for however long the IO dictates. And some CTV providers who buy spots direct from the publisher operate the same way. Look for a performance CTV provider that offers flexible optimization that allows you to shift spending across publishers and platforms as you need to. In the performance CTV realm, marketers need to be able to move ad spend at constant, flexible rates—leveraging data-driven campaign performance insights.
CTV allows marketers to take advantage of new ad opportunities, and performance CTV, while not meant for every advertiser, offers a game-changing advantage in the modern media landscape. But taking the plunge into performance CTV can be daunting, especially without the right platform partner. By first learning the basics of how it works and then asking these questions of potential vendors, marketers can leverage sophisticated ad tech solutions to meet their needs without getting in over their heads.
Check out the full article on Street Fight and be sure to follow us on LinkedIn and Twitter for the latest Digital Remedy updates.
Targeting technology in the digital ad space has evolved dramatically to provide more relevancy and better personalization, but it’s not without flaws. Oversaturation is still a problem and automation can sometimes over-optimize for a specific, perhaps unintended, trend. Artificial intelligence (AI) should be, by design, devoid of biases and influence. But when it comes to advertising, there’s a lot of intuitive information that must be considered, especially as it relates to human behavior.
Leveraging AI, backed with human intuition, to optimize targeting and delivery provides a much more curated experience that adds value for the consumer. TJ Sullivan, SVP of Sales, was recently featured in VentureBeat, where he explained how marketers can leverage AI to deliver a better consumer experience:
Effective performance measurement requires multiplatform, real-time analysis — how ads are performing across multiple channels examined together — and real-time optimization to be effective. By using AI to analyze and optimize, marketers can eliminate repetitive, annoying, or misplaced ads.
Digital marketing has traditionally relied on first- or last-touch attribution. In reality, it’s more likely that multiple touchpoints in a specific placement strung together in a series actually drove the action—and experience infinitely different across every consumer. AI can analyze this dynamic journey, learning the specific touchpoints and cascade across multiple channels that drive efficacy and delivering that just-right experience to influence buyer behavior.
AI-based ad platforms are optimized for performance. But to a machine, high performance means getting the most ads in front of the largest, most valuable audience—which can have a negative firehose effect and blow through budget. It is important to adjust variables to manage the volume of ad delivery, including setting frequency caps that span multiple platforms, so consumers aren’t bombarded and then ghosted.
Beyond just making ads relevant to the viewer based on known interests or intent, AI can also make them relevant based on the context in which they appear. AI can tell the difference in programming segments and deliver ads appropriately to ensure the best fit.
With AI, marketers can optimize for attention metrics, which typically means getting our message out within the context of higher-quality, more compelling content—the type of content audiences are less likely to turn away from. AI helps brands to do that in real time, but again, it requires human insight to know what’s gripping and will keep people’s attention.
It’s important for marketers to reach people in a way that’s meaningful and addressable without being annoying or interrupting their experience. Despite AI technologies making a big impact to improve the ad experience, it still takes a human touch to interpret and inform the model. Human guidance is key to preventing AI from optimizing incorrectly.
Check out TJ’s full commentary on VentureBeat and be sure to follow Digital Remedy on LinkedIn and Twitter for the latest updates.
In our first Intro to Incrementality piece, we explained what incrementality analysis is and how marketers can use it to make more informed decisions about what success means for their media campaigns. We mentioned how, at the very heart of this analysis, there is a comparison between two groups:
In this part of our incrementality series, we talk specifically about that second group, diving deeper into how to create a control group and what it should be composed of for unskewed analysis.
Remember our basketball example? Here’s a quick refresher:
An NBA basketball team has two players who both make 40% of their foul shots. The team wants to improve that percentage, so it decides to hire a shooting coach. It designs a test to evaluate that coach and assigns him to only one of the two players. Both players are told to do everything else they had been doing, exactly as they had been doing it. For instance, they’re told to spend the same amount of time in the gym, keep a similar diet, and maintain the same weight.
Player A: 40% of free throws ┃ Player B: 40% of free throws
After a year, the player who worked with the shooting coach makes 80% of his free throws the following season, while the player not assigned the shooting coach makes 50% of his free throws.
Player A: 50% of free throws ┃ Player B: 80% of free throws
There are some very important concepts embedded in this example but two are chief among them. First, both players make 40% of their shots. The observed behaviors of both groups are similar, if not the same. Second, everything else going on—except the shooting coach—remains the same. No after-practice shots allowed, no extra weight room, etc. Allowing additional variables into the equation creates noise that can skew or obscure incrementality analysis.
Bringing this back to marketing, we’re left with this challenge: how can we create a control group where the observed or expected behaviors match the exposed group, and how can we limit the noise around this analysis? Prior to 2020, there were three common methods for creating control (or holdout) groups for marketing and advertising campaign analysis:
Pick two similar markets, turn media or a certain type of media on in one market, leave it off in the other, and observe behavior in each. The issue with market A/B testing is that no two markets behave exactly alike, no two markets contain populations that are comprised of the exact same demographics and interests, and the background noise can reach dangerous levels.
Using existing technology and cookie, IP, or Mobile Ad ID (MAID) lists, agencies and advertisers were able to actively hold out randomly generated subsets of people from seeing ads. However, as soon as targeting is applied to the campaign, the analysis skews heavily toward the exposed group. For example, imagine you’re selling men’s running shoes and you want to run incrementality analysis. Your campaign is targeting men, but your randomly-generated control is observing men and women equally. Your exposed group is going to perform better because half the control group can’t even buy your product.
The exposed group, with all its relevant demographic and behavioral targeting, is served ads for the brand. Meanwhile, that same demographic and behavioral targeting is applied to a different group, and instead of ads for the brand, they’re served ads for public service or completely unrelated products or activities. The two groups remain separate and distinct but mirror each other in behaviors essentially (e.g. back to the basketball example: they both shoot 40% at the line). This allows us to see a much-less-skewed, noise-filled outcome of the incrementality test running.
The main drawback of PSA holdouts is that someone has to pay for that PSA ad to be served. Impressions don’t come free, and in order to run this analysis, impressions must actually be purchased to run PSA ads on, so a substantial portion of the advertising budget is going toward actively NOT advertising! Additionally, when a PSA is served, it actually prevents competitive brands from winning that same impression and possibly swooping in on a customer, which might be good for business but is very bad for statistics—and creates a skew toward the exposed group.
In 2020, a new method for creating a holdout emerged called ghost bidding, which allows media buyers to create a holdout by setting a cadence against individual line items or ad groups. The cadence dictates how often the buying platform will send an eligible bid into the control group, instead of actually bidding on the impression. So, at a ghost bid cadence of 20%, for every 5 impressions bid on, 1 user receives a “ghost bid”, or gets placed in a holdout group where it cannot receive ads for the duration of the campaign.
Because the ghost bid isn’t an actual impression, the ad space remains open and the “ghost bid” is free. Additionally, because it’s set against the line item or ad group itself, all the targeting matches the exposed group exactly and all optimization on the exposed group follows suit on the control group. If Millennial Urban Men are converting at a higher rate and receiving more budget in the exposed group, they’re also receiving more ghost bids, making sure the composition of the control follows suit.
Ghost bidding addresses the pitfalls of market tests, random samples, and PSA’s while remaining inexpensive and relatively easy to implement. As a result, ghost bidding has become increasingly popular with sophisticated marketers and media buyers. It is Digital Remedy’s preferred method for creating holdouts for our Flip product.
In our next piece of this series, learn why optimizing incrementally is a crucial capability for brand marketers and agencies looking to drive tangible bottom-line results. Can’t wait? Check out our full insights report or speak to a member of our team today.
