Connected TV (CTV) has revolutionized the way we consume media. With the rise of streaming platforms like Netflix, Hulu, and Amazon Prime, traditional television is no longer the only option for viewers. Since 88% of US households own at least one internet-connected TV device, it’s clear that cable is out — and streaming is in.
For marketers, CTV opens up a whole new world. This shift from traditional TV to CTV means more than just a change in the medium; it also brings a drastic change in advertising opportunities.
Unlike cable TV, CTV enables marketers to target specific demographics with personalized ads. Sophisticated attribution models help marketers track customer journeys, measure campaign effectiveness, and make data-driven decisions.
CTV ads are non-skippable, guaranteeing message delivery to viewers. Best of all, these ads are delivered on the big screen, allowing marketers to create immersive pitches for potential customers in a TV-like experience.
So, what exactly are CTV attribution models and why are they important? In today’s article, we’ll tell you everything you need to know about them.
What is CTV Attribution?
CTV attribution is the process of identifying the actions a viewer takes after seeing an ad on their connected TV and attributing these actions back to the ad exposure.
It’s like the breadcrumb trail from a viewer’s interaction with an advertisement to their subsequent behavior, be it making a purchase, downloading an app, or even visiting a brick-and-mortar store.
The ability to link certain behaviors back to a particular advertisement enables marketers to understand the effectiveness of their CTV campaigns, assess return on investment (ROI), and optimize future marketing strategies. Simply put, CTV attribution is the key to unlocking the full potential of advertising in the streaming TV landscape.
How Does CTV Attribution Work?
There are three main components to the CTV attribution process: data collection, attribution modeling, and measurement/reporting.
- Data collection is the first step. You’ll collect data on consumer behavior from your website, landing pages, online store, social media channels, and other digital touchpoints. Most companies use a customer data platform for this.
- Attribution modeling determines the specific impact of all your customers’ actions leading up to a purchase. It involves analyzing and assigning credit (i.e., percentage of sales revenue) to each touchpoint in a customer’s journey, including CTV ads. This helps identify which assets had the most impact on their behavior.
- Measurement and reporting is where you’ll track and analyze specific KPIs like conversions, click-through rates, website traffic, and return on ad spend (ROAS), to measure campaign effectiveness.
CTV attribution is basically the same as other types of revenue attribution. CTV advertising is a technology-enabled process, so as long as you integrate your data with the rest of your marketing stack and customer touchpoints (most importantly, your sales touchpoints), you can track the journey from ad exposure to conversion.
Types of CTV attribution models
There are various CTV attribution models, each with its own strengths and limitations. The most common ones include:
Last-Touch Attribution
Last-touch attribution is pretty simple to understand — it’s all about giving credit to the very last interaction a viewer has with an ad before taking action. The idea behind this is that the last touchpoint is the one that had the most impact on a customer’s decision to make a purchase or take action.
Let’s say, for example, a viewer sees an ad on their connected TV, then later on, they see the same ad on their mobile device and decide to make a purchase. In this scenario, the mobile device ad gets all the credit because it was the last touch point.
The strength of this model is its simplicity and clarity, but it has a significant limitation: it doesn’t consider the impact of other touch points along the viewer’s journey. If the first CTV ad played a crucial role in sparking the viewer’s interest, its influence would be ignored in a last-touch attribution model.
In the evolving world of multi-device, multi-channel marketing, this may not provide a holistic view of a campaign’s effectiveness. To measure the success of a demand generation campaign or capitalize on immediate sales from a brand-new product (e.g., an info product) last touch attribution is the model of choice.
First-Touch Attribution
The opposite of last-touch attribution, first-touch attribution gives all credit to the first interaction a viewer has with an ad. Like last-touch, this model is straightforward and easy to understand but fails to recognize the impact of subsequent interactions.
In our previous example, if the mobile device ad was the first one seen by the viewer and they made a purchase, it gets all the credit. However, if they saw an ad on their connected TV first and then saw the mobile ad but didn’t take action until later, the connected TV ad wouldn’t receive any credit.
This model is useful for marketing teams looking to understand how effective their initial engagement with customers is, such as lead generation campaigns.
Linear Attribution
Linear attribution is a more complex model that gives equal credit to all interactions along the customer journey, regardless of when they occurred. It recognizes the importance and influence of every touch point, making it ideal for campaigns with multiple ad exposures across different channels.
Going back to our example, if the viewer saw an ad on their connected TV, mobile device, and social media, all three touchpoints would receive equal credit for the purchase. This model provides a more comprehensive view of campaign effectiveness and can help identify which channels have the most impact on customer behavior.
Custom Attribution
Custom attribution models allow marketers to create their own rules and assign specific weights to each touchpoint based on their unique needs and objectives. This is helpful for companies with specific goals or customer journeys that don’t fit into traditional attribution models.
For example, a company with a long sales cycle may want to assign more weight to touchpoints that occur closer to the time of purchase. Or, a company with multiple products may want to give different weights to touchpoints based on which product was being advertised.
Custom attribution models require more data and analysis, but they offer the most flexibility and tailored insights for a company’s specific needs.
Final Thoughts
With the ever-changing landscape of streaming TV, it’s important to stay current and adapt your attribution model as needed to ensure the most accurate tracking of customer behavior. CTV attribution is essential for any marketer looking to make the most of their advertising campaigns.
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