Get To Know Connected TV’s Built-In Brand Safety
by Jaci Schreckengost
4 Min Read
8 Min Read
Marketers have found a new successful model for targeting customers: Connected TV (CTV). More than a third of people in the United States say they watch streaming TV more than once a day, and that number is expected to keep growing over the next five years. And the best part for marketers: CTV advertising offers the best features of both traditional TV advertising and digital advertising, allowing businesses to not just reach a captive audience of millions as they watch their favorite shows, but also target specific audiences with tailored messaging.
But calculating the return on investment for Connected TV can be tricky (just like any other digital marketing channel). That’s where CTV attribution comes in.
Connected TV attribution is a term that describes the process of connecting the dots between conversions and the CTV ad that inspired them, in part or entirely.
There are a number of models for this measurement process. One common method is view-through attribution, where businesses attribute any new website visitors to a CTV ad if they click within a certain time period following the ad.
But view-through attribution is just one way of measuring ad success—and it may not be the best model for you. To gauge whether or not a campaign was successful, accurate CTV attribution tracking is key. It helps you replicate your hits, learn from your misses, and ensure future CTV ads are as powerful as they can be.
Video advertising is powerful and can have a lasting impact. Most people (80%) remember a video ad they’ve seen in the past 30 days. But traditional TV attribution models usually only measure short-term spikes in website traffic and in-store conversions, which can’t account for potential lag time.
When customers might remember your CTV ad days, or even weeks after seeing it, wouldn’t you still want to know whether that ad drew them to your brand—even if it was after that traditional measurement period?
Traditional attribution models may also focus too heavily on key performance indicators (KPIs)—such as open rates, clicks, and impressions—instead of measuring success throughout the customer journey. If someone saw your ad and became aware of your brand for the first time, the ad was successful even if they didn’t make a purchase right away.
Connected TV attribution solves many of these issues that still plague traditional TV attribution.
For one, it’s easier to track. And as the platform continues to evolve, CTV advertisers are working on new ways to accurately track success rates. Unlike traditional television, where you’re creating ads that will appeal to the masses, CTV allows you to target users based on their content preferences and other demographic information—and appeal to who they are and what they care about.
And as CTV attribution models continue to evolve, so does the data you can use to make your ads more effective.
These are the types of Connected TV attribution models commonly used to measure ad success rates. Each one can be understood through the lens of a customer journey—how they initially meet your brand, what they see next, and how they end up ultimately converting—and in what order those things happen.
This attribution model gives all credit for a click-through or conversion to the first marketing touchpoint. It’s the “first thought, best thought” philosophy: whatever the customer sees first is what makes them decide to hit “buy.”
For example, if a customer sees your paid social media ad and then sees a Connected TV ad (which was served based on their social media activity), you’d still attribute their website visit and/or purchase to the social media ad.
As you can probably imagine, first-touch attribution isn’t always accurate, because many customers (including you, most likely!) interact with a brand multiple times before they decide to make a purchase.
Last touch attribution gives credit to the last touchpoint a customer has with your brand before they make a purchase. It’s the “he who laughs last, laughs best” philosophy: whatever the customer sees right before visiting your site is what made them decide to shop with you.
For example, say the customer from the example above decides to visit your store after seeing your brand on social media and Connected TV. They are getting ready to walk out but are persuaded to make a last-minute purchase based on a clever point-of-sale display. In this case, credit for the sale would be given to the POS display.
Again, much like first-touch attribution, last-touch attribution isn’t as accurate as it could be, since it all but ignores the rest of the campaign guiding the customer’s journey. (Did the POS display reeeally do all that work?)
As mentioned above, the view-through attribution model is the recency philosophy: it gives credit to CTV ads for spikes in website traffic and sales for a certain time period following an ad.
But this model isn’t always accurate, either, because, again, television ads often drive business to a company for far longer than the typical measurement period. Unless you’re specifically tracking conversions by asking customers how they heard about your company, this model may be undervaluing your ad’s true effectiveness.
View-through attribution is also based on estimates. Although you can make an educated guess on whether spikes in web traffic are due to a CTV ad, there is no way to accurately measure how many customers are coming to your site because of the ad. (Imagine you ran a CTV ad that was just okay, but then a podcast host with a niche but enthusiastic audience name-dropped your brand that same week—if you didn’t learn about the podcast in time, your attribution model would credit the ad for that week’s spike in conversions.)
This type of attribution model is popular with digital marketing because it’s easy to measure. It involves setting up a landing page or tracking website to determine how many people clicked on an ad.
Click-through attribution doesn’t really work well with Connected TV, because customers can’t click through to the website. They also probably won’t stop watching their show just to click on your ad. (When’s the last time you did that?)
Multi-touch attribution is more like a subclass of attribution models, but overall, it’s a more accurate way of measuring ad success, because the average customer will likely interact with your brand multiple times before making a purchase, and multi-touch takes that reality into account. Each multi-touch model measures attribution in a different way, but they all assign credit to each touch point based on how it influenced the sale.
Although this model is more accurate, it’s also time-consuming and complex, and it’s hard to determine how to accurately weigh various touch points.
Like any analytical framework, any CTV attribution model involves a few moving pieces. Marketers use the following steps to measure Connected TV ad success rates.
The first step of any CTV attribution model is to collect data on your customers’ behavior, usually after they view your CTV ad. You might measure website visits, conversions, social media subscribers, or other related activities.
Once you have your data, use actual attribution modeling to frame that data in the context of your CTV ad to see how it impacted your company. This step involves analyzing customer data and determining the most probable cause of customer actions.
For example, if you see a spike in your website visits after a CTV ad, you might assume the ad caused people to visit your site (but you still may want to take into account whatever other ads contributed to their decision).
Once you’ve analyzed your data, this is the final step: measuring KPIs associated with your CTV ad, and compiling a report (or reports) showing the ad’s overall success rate.
Current CTV attribution models require a lot of speculation, so they aren’t as accurate as they could be.
Pay-per-click advertising, social media, and other digital channels allow you to track clicks and immediate web browsing behaviors. Connected TV ads, on the other hand, are usually not clickable, which means you can’t track that cause-and-effect data directly (hence the inexact science of all of the above models). As mentioned above, traditional attribution models don’t take the long-term impact of CTV ads into account when measuring success.
As a leader in Connected TV advertising, MNTN understands the limits of CTV attribution, and we’ve developed a new attribution model to more accurately measure CTV success rates. Our model, which we call Verified Visits™, leverages our unique knowledge of behaviors in connected households to:
With our Verified Visits model, your company gets a better idea of how their Connected TV ads are working to drive web traffic. Instead of relying on incomplete data and educated guesses, our model credits CTV only when a visit is driven by your ad.
This tool gives you a more accurate idea of how your CTV ads are performing. You can assess which are more successful and which may need changes. With this kind of reporting, you can create more effective ads and target them to different audiences until you find a formula that works for you.
Request a demo today to learn more about how it works.
It’s important to track your return on investment for all marketing channels, including Connected TV. This data helps you prioritize ad spending and gives you the information you need to set marketing goals and optimize your campaigns.
Traditional CTV attribution models can help, but they don’t give you the full picture. Many of these models are based on educated guesses instead of real-time data. Many traditional models don’t take delays into account. People who view your CTV ads will likely remember them for at least a month, meaning that your ad could be converting far into the future when you’ve stopped measuring.
With Verified Visits, you can accurately measure how much traffic your CTV ads are driving to your business, giving you a better understanding of your ad’s success rate. When you can correctly attribute customer activity to a Connected TV ad, your ads are better targeted—and your campaigns are optimized to yield the best possible results.
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