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.
The CTV/OTT space presents many advantages for B2B marketers, including clear targets, ideal buyer profiles, and precise targeting strategies. However, B2B marketers are feeling increased pressure to prove the value of CTV/OTT in reaching their target audience and without the right digital attribution, it can be tough to prove success and maximize return on ad spending (ROAS).
TJ Sullivan, SVP of Sales at Digital Remedy was recently featured in MarketingProfs, where he discussed digital attribution in the OTT/CTV space. Specifically, TJ outlined five ways to succeed with CTV/OTT, improve ROAS, and boost your bottom line:
Multi-touch attribution for CTV/OTT gives you the ability to see the customer journey beyond just first- and last-touch—which is especially crucial for B2B marketers looking to understand the full customer journey.
CTV/OTT metrics let you dive into the details to understand which tactics moved the needle with the right people in the right way. Factoring in incrementality lets you see the real picture by taking all attributed conversions and filtering out ones that the campaign didn’t directly drive.
Good B2B marketers know how to recognize buy signals—those actions and behaviors that indicate a prospect is ready to buy. Being able to recognize those “telling” signals and match them to the strategy—optimizing creative, targeting, and placement to drive those signals—is a key advantage in CTV/OTT advertising when it’s done right.
The beauty of CTV is that it enables real-time optimization of ad buys in comparison with Linear, which means you are able to move your budget toward the publishers, creatives, dayparts, audiences, and geographies that are working to drive the most action to fully maximize your spend.
When you combine multi-touch attribution with real-time performance metrics, it creates a powerful platform to fully optimize targeting and content delivery.
While it may be relatively new to some marketers, multi-touch attribution must become a top priority, as it allows you to not only identify the specific touchpoints throughout the consumer journey that triggered the desired action, but also leverage valuable performance insights to optimize future campaigns. With the Flip OTT performance platform, Digital Remedy is making it accessible and simple for brands and marketers at all levels to win in the OTT/CTV space. To learn how you can leverage alternative attribution methodologies within our award-winning CTV platform, visit www.digitalremedy.com/flip.
Check out TJ’s full insights on MarketingProfs and be sure to follow Digital Remedy on LinkedIn and Twitter for the latest updates.
2021, and marketing budgets have returned to normal. This is in part due to the launch and expansion of new ad-supported streaming services. CTV ad spend pricing has increased throughout the last year, which has in reaction, increased overall spend. CTV ad revenue growth will decrease slightly, following the huge spike, however, CTV ad spend will gain share of overall digital ad spending through 2025, passing 10% by 2024. The future of CTV ad spending is bright—more than doubling in 2025 to surpass $30b.1CTV viewership increased across all generations this year compared to 2020. Gen Z and Millennial users will continue to increase through 2025. YouTube is the number one OTT platform in the U.S. when it comes to penetration among OTT video service users, with market share of 95.5%. YouTube CTV viewers grew almost 63% last year and surpassed the 50% threshold for the share of viewers who watched YouTube content on CTVs.2 In addition, YouTube was found to be the number two streaming service when comparing total hours spent watching content on CTVs in the U.S.
Ad fraud detection and measurement will be extremely important because the demand for inventory will outweigh the high-quality supply. Most CTV inventory is bought and sold through private markets, which prevents fraudsters’ ability to fake ad impressions. However, the demand and price of CTV inventory is increasing which results in two scenarios:
1. Advertisers are attracted by the lower pricing in the open market
2. Fraudsters are attracted by the high demand and premium they can charge on fake inventory
Last year, Pixalate, a firm that monitors ad fraud, estimated that more than 20% of programmatic OTT and CTV ad impressions were served as invalid traffic.3 Given the significantly complex ad environment, fueled by ever-changing technologies, it’s nearly impossible to eliminate fraud completely. However, there are certain steps advertisers can take to protect their brand and their budget—ensuring exposure to fraud is minimal and ad spend is directed where it’s intended. As CTV fraud increases in scope and sophistication, it’s critical for marketers to work with trusted partners that have the experience, knowledge, scale, and ability to identify and block new threats as they emerge. Check out our recent report to learn more.
Models for marketing attribution have been used for many decades, but as the needs of customers have changed and companies have undergone digital transformations, marketing attribution has evolved. The roots of marketing mix models (MMMs) can be traced back to the 1950s. As campaigns become more complex in today’s multichannel world, accurate campaign measurement and attribution has become a major struggle for advertisers and agencies, and, unfortunately, many lack the time and resources to do so. Not to mention, a list of long-standing measurement and attribution problems that plague marketers, including a lack of standardized metrics and definitions across traditional and digital TV and video, issues with basic data accuracy and infrastructure, a lack of organizational alignment, and the difficulty of understanding and validating attribution models.
The truth is, attribution is evolving and advertisers need to reevaluate the methodologies they are leveraging to ensure they are assigning credit accurately. While innovations in attribution have brought the focus away from video completion rate (VCR) and toward return on ad spend (ROAS) and cost per action (CPA) metrics, those goals are merely scratching the surface of CTV measurement. Many performance marketers are looking to optimize their CTV campaign performance, but don’t know where to start. The ability to track a customer journey and measure ad impact is incredibly powerful, especially for today’s agile marketers that are laser-focused on managing and optimizing their spend with flexibility in a changing environment. Moving forward, advertisers who expand their attribution methodologies will help refine their budget strategy and ensure a lasting impact on future ad campaigns.
than that from external sources. Until opting out, these are your customers, and they have chosen to share their contact information and purchasing choices with your business. Third-party data regulations are preventing tracking, companies are needing to shift their efforts. 42% of U.S. data users will increase spending on the use of first-party data. This shift was forced due to changing regulations on third-party cookies that are being collected. With consumers increasing concern about how and what data is being collected, companies are going to need to shift how they obtain data. The highest-performing companies are taking note and starting to utilize first-party data, which will not only be a new buzzword but the foundation and future of the marketing world.While hindsight might be 20/20, forward thinking will allow brands to stay competitive and be prepared for the inevitable shifts in consumer preferences, media consumption, and more. One thing is certain, working with a partner that specializes in diversified digital ad strategies and offers innovative technology can be a lifesaver in today’s dynamic environment. To discover how Digital Remedy helps advertisers and agencies of all sizes navigate the ever-changing digital ad space, visit www.digitalremedy.com.
Digital Remedy recently hosted a Tech-Talk webinar “To Last Touch & Beyond: Measuring Performance CTV” through eMarketer, explaining why marketers should move beyond last-touch attribution methodology, focusing instead on more nuanced ways to drive bottom-line results via OTT/CTV—and why working with an experienced media partner is crucial in today’s complex ad space. If you missed it, here’s a recap:
As the duopoly becomes increasingly saturated, improvements in measurement have proved that lower-funnel media can have branding impacts, and upper-funnel media can have performance impacts—and this is most evident in the CTV space. CTV’s ability to merge the often separated performance and brand marketing worlds is redefining the digital ad space.
In the modern age of marketing, the typical consumer requires an average of 56 touchpoints before making a purchase. Very rarely does someone convert after a single ad exposure. Marketers need to take every touchpoint (across devices and platforms) into consideration and assign credit accordingly. While last-touch attribution has long been the status quo of the ad space, marketers can discover more insights by expanding their attribution methodology.
While innovations in attribution have brought the focus away from VCR and toward ROAS and CPA metrics, those goals are merely scratching the surface of CTV measurement. Many marketers are looking to optimize their CTV campaign performance, but don’t know where to start. Working with the right media partner will allow you to uncover valuable—previously unattainable—campaign performance insights.
You can watch the full presentation on-demand or view the slides. Interested in learning how you can start driving bottom-line results? Schedule a custom Flip demo to see our award-winning CTV performance platform in action.
For the latest industry trends and insights—including more on how to leverage multiple attribution methodologies and incrementality—check out our blog and sign up to receive our monthly Trends newsletter delivered right to your inbox.
