What Is View-Through Attribution (VTA) And How Does It Work?
by Frankie Karrer
7 Min Read
8 Min Read
If you’re a marketer, data has undoubtedly had a profound effect on your job. In many ways, it’s simplified your life. Gone are the days of the “spray and pray” approach to advertising; now you can track what’s working and what isn’t.
But how do you sort the good data from the bad—and accurately measure how your campaign is actually performing with your key audiences?
With great amounts of data come many ways of measuring what made the difference and influenced a conversion. That’s where OTT attribution comes into play.
OTT stands for Over-The-Top TV. (“OTTTV” would’ve been a little excessive.) In this context, “over the top” refers to content delivery methods that are distinctly different from traditional methods like cable or satellite.
OTT content is streamed over the internet, which may cause some confusion. Isn’t Connected TV (CTV) also streamed over the internet? Good question!
OTT refers more to the technology and infrastructure used to deliver content to users via the internet, which is subtly different from CTV, which refers more to the content itself. You can see a more in-depth discussion about OTT and CTV here.
OTT advertising is the act of marketing to the audience that’s streaming OTT content, and OTT attribution refers to the process of determining which touchpoint(s) contributed to successfully achieving a positive outcome, or key performance indicator (KPI).
So, was it the first ad spot that caught your consumer’s attention and led them to make a purchase? Or was it the last one they saw that finally pushed them to a relationship with your brand? The answer will vary, depending on which attribution models you use.
One of the biggest issues of traditional TV attribution is the untraceability of its success.
Let’s say you bought a TV ad spot to air during Seinfeld. You (of course) want the most viewers possible to have seen your advertisement, but with traditional TV attribution, you’d have no real way of quantifying how of those Seinfeld fans who saw that ad actually converted.
In the past, there was simply no way of tracking the results—particularly, that ad’s return on ad spend (ROAS)—when you advertised the old-fashioned way. There’s little to no data or way of knowing which sales came as a consequence of your specific ad initiatives—a source of great frustration for sales and marketing departments.
OTT attribution solves that traditional TV attribution problem—its main benefit is the data advertisers can now collect about customers’ buying path.
If your viewers can watch your ad on their phone or laptop, and then click immediately on a link to buy that product, that’s a fairly direct and attributable response to that spot. That gives you a better sense of how well your campaign is designed and how well you’re using your resources. It also means you can iterate a different version of the advertisement if it’s not resonating with viewers.
As with all things data, there are many different ways of making sense of it all.
First-touch attribution gives all the credit for the achievement of a KPI to the first touchpoint in a consumer’s journey. The logic here is that the first spot that promoted awareness of your product is truly what led to its purchase.
Last-touch attribution gives 100% of the credit for the KPI to the last advertising spot the consumer saw before driving their KPI event, likely making a purchase. The logic here is that it’s the final ad someone viewed —for example, a discount or a special offer—that finally drove their decision.
View-through attribution (VTA) measures an ad’s effectiveness based on behaviors that occur during a set period after someone watches it. It allows for the simple fact that many consumers will not click on an ad immediately but will mull it over before making a decision.
VTA Advocates say it offers a more holistic and accurate sense of whether your campaign is working. For example, if a user watches an ad and then, within a set timeframe, downloads your app or performs some similar measurable action, VTA counts that as attributable to your ad—not organic (or coincidental).
Click-through attribution works similarly to view-through attribution, giving credit to a certain spot for a certain amount of time. The key difference is that it measures the act of clicking on ads rather than just viewing them.
Multi-touch attribution acknowledges the simple fact that your consumers will be seeing your ads in a multitude of ways, particularly as they switch between screens from phone to TV to computer. It’s a more sophisticated approach, in that it algorithmically measures the cumulative effect of numerous touchpoints on the consumer journey, including elements that are often discarded like paid search.
Multi-touch attribution acknowledges the old proverb that success has many fathers, but it’s also able to parse exactly which touchpoints made the biggest difference by eliminating biases.
There are various sub-models of multi-touch attribution like the Time Decay model, in which the percentage of credit gradually ramps up to the final touchpoint—giving credit to touchpoints along the way, but giving the main allocation of credit to the last one.
OTT attribution models are complex and often misunderstood. Mentioning or using an algorithm isn’t the same as understanding how it works. But this short guide should help you get a grounding in how OTT attribution models work. (For a breakdown of CTV attribution specifically, that’s here.)
Since OTT TV is connected to the internet, it allows for precise data collection, just like many popular social media platforms or websites. This ranges from the demographics of the household—so that marketers can target ads to their most likely audiences—to knowing when people tune out or tune in to different shows.
Additionally, OTT provides data at the household level. So for a family that shares a Hulu account, it’s hard to know who’s watching The Bear and who’s watching Real Housewives.
Data may be collected in first-, second-, or third-party fashion. First-party data comes directly from the household itself; second-party data may be exchanged by businesses that collaborate, such as hotel and airline companies. Third-party data is a little more fraught these days, thanks to an increasing number of laws and policies aimed at protecting privacy. But the act of streaming itself allows for specific data collection as to who’s watching your ad and when they are clicking, or not.
Once you have your data, you input it into your marketing attribution model, which is a set of rules that gives a rough picture of how your ads are influencing consumer behavior. As discussed above, these models come in many shapes and forms. If you’re only looking to increase brand awareness, you may want to measure first-touch attribution (despite its flaws). In addition to the models discussed above for OTT attribution, there are many more complex attribution models you can employ.
Your brand may have different goals at different times, leading to varied marketing metrics and KPIs. Amazon offers its own set of measurable outcomes of its advertisements, including brand reach, for which you may want to employ first-touch attribution.
Ultimately, attribution reporting has to involve some measure of return on investment (ROI) so you can determine the effectiveness of your ad spend. Cost per completed view, for example, is one of many such KPIs that can show you how much it’s costing your company per ad that your viewers finish.
There are limits to both the models and the data used in these touchpoint measurements. The downside to the view-through attribution method is it can over-credit one spot someone saw—when in fact it was an email they received, a social post they saw, or a search they executed that made the difference in the ensuing 20 minutes. Your audience can absorb a lot of data and stimulation in 20 minutes (or whatever time period you set), after all. So, for an allegedly scientific, data-driven approach, view-through attribution is a somewhat vague way of approaching OTT attribution.
While first and last-touch models are easy to set up and straightforward to understand, they are also overly simplistic and potentially misleading. Is it better to have a flawed model or no model at all?
Similarly, data can be incredibly fungible. Netflix controversially counts even two minutes of streaming a show as a “view,” a perfect reminder that data collection is only as accurate as your filters allow it to be. If you want the truth about which touchpoint is responsible for which outcome, you need truly accurate data. Good data, for example, should be accessed from a variety of devices—not just the big screen TV that your audience is half-watching while also scrolling on their phone.
Some older models, like last-click attribution, are simply inaccurate when it comes to OTT attribution, as CTV ads aren’t clickable.
It may all seem like a lot to get a handle on, but the good news is that MNTN has already mastered OTT attribution—particularly with its Verified Visits™ proprietary model.
With MNTN’s Performance TV, OTT TV becomes a true performance marketing channel. It makes it easy to upload your creative work, target your ideal audience, launch your campaign—and then use the power of data to make sure that your work is really connecting.
Moreover, MNTN ensures your attribution is accurate. MNTN’s Verified Visits™ ensures that your data is rock solid by crediting CTV only when that credit is really due. When a viewer sees a MNTN user’s CTV ad and then visits that brand’s website, the MNTN platform runs diagnostics to ensure those visits were definitely driven by that ad—and not by another channel (like emails, search, or social marketing). So you can rest assured that any ad attributions you receive from MNTN are accurate, and you know if your campaign is working as it should.
Over-the-top attribution is crucial for understanding which ads and platforms are working for you and which ones aren’t. But with OTT attribution comes a host of issues related to the reliability of data and measurement. With MNTN, you can make sure that your data and reporting are reliable—getting you the results you want.
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