Connected TV, or CTV, is any TV that connects to the internet and access content beyond what is available via the normal broadcast networks. These include smart TVs and devices like game consoles and streaming boxes that bring internet content to traditional TVs.
In today’s digital age, it has become one of the most important pieces of tech in our daily lives. Almost every American has at least one smart TV in their home, and 83% of consumers in the US subscribe to at least one video-on-demand (streaming) service.
As more and more people shift their TV viewing habits from traditional cable to streaming services, businesses have shifted their focus to reaching customers on those platforms. There are a few benefits to this:
- Better targeting
- Better data
- Ability to test messaging and ad creative
- Faster time to market
- More room for creativity and innovation
However, with this shift comes the challenge of accurately measuring the performance of CTV ads. This is where CTV attribution modeling comes into play. In this article, we’re going to tell you all about CTV attribution models.
What is CTV attribution modeling?
Connected TV attribution is a method businesses use to measure the effectiveness of advertising on connected TV devices. In simpler terms, it lets them track the impact of their CTV ads on consumer behavior (most importantly, purchases from their ads).
Attribution modeling uses data from a variety of sources, such as impression data from ad servers, website visit data from analytics tools, and offline sales data. Each piece of content your customer looks at sends clickstream and impression data that can be traced back to the ad server. Each activity is assigned a specific weight in the purchase funnel, which equates to a proportion of the total sales revenue it drove.
CTV attribution modeling in practice
Let’s say you run an ecommerce business that sells dog toys. You decide to run a Hulu ad that targets dog owners. A customer sees your ad and clicks on it, which takes them to your website, where they browse through your products but don’t make a purchase.
But they do click on your social media profile and share a cute dog video of yours with their friends on TikTok. In the video, they were holding the same toy. Later that week, the same customer sees another one of your ads while watching TV and decides to buy a toy for their furry friend.
Each touchpoint clearly had an impact on the purchase decision — viewing the ad on Hulu brought attention to your product, your website showed them your products, and your social media profile got them engaged. By the time they saw your final ad, it was practically calling their name!
Types of CTV attribution models
In the example above, which one gets credit for the sale? The answer is: They all do.
The exact weight assigned to each touchpoint depends on the attribution model you choose.
Last-Touch Attribution
Last-touch attribution gives 100% of the credit to the last touchpoint. It posits that the customer’s final interaction with your business is ultimately what led them to make a purchase. This model is often used when the goal is to understand which final marketing efforts are pushing customers over the edge to make a purchase. It also works well when measuring the impact of a demand generation campaign or a go-to-market strategy.
However, it overlooks the contribution of earlier touchpoints and interactions that may have played a crucial role in nurturing the customer along their purchasing journey.
First-Touch Attribution
First-touch attribution gives 100% of the sales credit to the first touchpoint (the Hulu ad in this case). It assumes the first impression is what really matters and sets off a chain of events that leads to a purchase.
This model works well when measuring brand awareness and the effectiveness of top-of-funnel campaigns. Like last-touch attribution, though, it doesn’t accurately reflect the contribution of other touchpoints in influencing the customer’s purchase decision.
Multi-Touch Attribution
Multi-touch attribution weighs each touchpoint differently throughout the customer journey. This allows for a more comprehensive understanding of how each touchpoint influenced the customer’s decision to purchase.
There are multiple types of multi-touch attribution models, including:
- Linear — Each touchpoint receives equal credit.
- Time decay — Touchpoints closer to the time of purchase receive more credit than those further away.
- Position-weighted (U-shaped) — The first and last touchpoints receive higher weights, with 40% each, and the remaining 20% is split between the middle touchpoints.
- Score-weighted (W-shaped) — First and final touchpoints receive higher weights, with 30% each. The touchpoint that turns your customer into a lead also receives 30% attribution. The remaining 10% is split between middle interactions.
- Algorithmic — A custom-built model that uses machine learning to assign weights to different touchpoints based on your business’s specific goals and objectives.
Which CTV attribution model is best for your campaign?
Choosing the right CTV attribution model for your campaign depends on several factors. It’s crucial to consider the unique characteristics of your products or services, your business goals, and the customer journey.
Understand your customer journey.
Consider the path customers typically take to purchase your product or service. If it’s a straightforward, short journey, a last-touch model might be sufficient. However, if the journey involves multiple touchpoints and a longer decision-making process, multi-touch attribution models might be a better fit.
Determine your campaign goals.
What are you trying to achieve with your campaign? If your goal is to increase brand awareness, a first-touch model could be beneficial. If you’re trying to understand which marketing efforts are driving customers to purchase, a last-touch model is more appropriate.
Consider your product or service.
If you’re selling a high-ticket item that requires a significant investment from the customer, there will likely be multiple touchpoints involved in the decision-making process. In this case, a multi-touch model would provide a more accurate picture of the customer’s journey. Some products, like B2B SaaS, require an algorithmic or W-shaped model because they entail longer sales cycles.
Evaluate your resources.
Some attribution models, particularly multi-touch and algorithmic models, require a significant amount of data and advanced analytics capabilities. Before choosing these models, make sure you have the resources to support them.
Test and refine your model.
Finally, remember that attribution modeling isn’t a one-and-done process. It requires ongoing testing and refinement. As you gather more data and learn more about your customers’ behavior, you can adjust your model to reflect their journey and your business goals better.
Final thoughts
CTV attribution modeling is a powerful tool for understanding how different marketing efforts contribute to customer purchases. By carefully considering your business goals, customer journey, and available resources, you can choose the right model for your campaign and gain valuable insights into your customers’ behavior. Remember to regularly test and refine your model to ensure it accurately reflects the ever-evolving nature of consumer behavior.
