How to Build a Custom Marketing Attribution Model
by Frankie Karrer
6 Min Read
8 Min Read
This is why there has been more focus than ever on the TV attribution model. It’s the bridge that connects television exposure with tangible outcomes. By harnessing the power of data-driven insights, marketers gain the ability to make informed decisions and ultimately boost the overall return on investment (ROI) of their advertising efforts.
TV attribution refers to the process of understanding and, more specifically, measuring the impact of TV advertising on consumer behavior and business outcomes. It involves determining how effective TV ads are in driving specific actions, such as website visits, online purchases (conversions), or return on ad spend (ROAS).
TV attribution helps advertisers and marketers assess the ROI of their TV campaigns and make data-driven decisions to optimize their advertising strategies.
There are several types of TV attribution models. Each type is different in the way they assign credit to TV ad exposures and their impact on consumer actions or outcomes. Here are the five most common TV attribution models and what sets them apart.
The first-touch attribution model gives complete credit to the first touch point or the first click in which a consumer lands on a website and converts.
For example, a customer named Taylor clicks on an ad from a search engine results page for a smartphone, visits a blog or social media page, and signs up for a newsletter before making a purchase. The initial search engine ad receives full credit for the conversion, regardless of any subsequent interactions with other ads or websites.
The last-touch attribution model is the most common attribution model used. It’s also known as the “last click” or “last interaction” model because the entirety of its conversion credit goes to the final touch or visit a consumer took before converting.
If Taylor sees a smartphone ad on Facebook, then later sees an ad for the same smartphone on Instagram and makes a purchase, the Instagram ad receives full credit for the conversion, while the Facebook ad receives none.
Instead of giving credit to direct traffic, the last non-direct touch attribution model focuses on the last marketing channel that influenced the user’s journey, such as search ads, social media ads, email campaigns, or referrals, providing insights into the effectiveness of various marketing channels in driving conversions.
Taylor views a display ad for a smartphone brand on a popular technology website but doesn’t click on it. Later, Taylor receives an email from the same brand with a promotional offer and clicks on it, ultimately leading to the purchase. In this case, the email marketing channel receives credit as the last non-direct touch that influenced the conversion.
With the attribution models mentioned so far, one ad has been given full credit for the conversion. However, linear attribution models are different — they equally divide conversion credit across all customer interactions. This method is a multi-touch attribution model.
For example, Taylor first sees a display ad for a smartphone brand, then clicks on a search ad, and later receives an email promotion. Finally, Taylor makes a purchase after seeing a social media ad. Each touchpoint — the display ad, search ad, email, and social media ad — then receives equal credit for contributing to the conversion, acknowledging the cumulative impact of multiple marketing channels on Taylor’s decision to purchase.
The time-decay attribution model is another multi-touch model where conversion credit is assigned to each marketing touchpoint based on its position in the customer’s journey, thus touchpoints occurring closer to the conversion have a greater impact and receive a higher proportion of the credit compared to those encountered earlier in the customer’s journey.
In this scenario, Taylor is considering purchasing a new laptop. A couple of weeks ago, Taylor sees a display ad for a laptop brand without interacting with it. Then, last week, Taylor clicks on a search ad and visits the brand’s website to explore their products. Finally, Taylor comes across a social media ad for the same laptop brand and makes the purchase. In the time-decay model, the social media ad would receive more credit for the conversion since it occurred closer to the purchase event.
TV attribution models rely on several components to work. Below are a few of the more important components.
Data collection for TV attribution involves analyzing various data types to understand the influence of TV ads on consumer behavior. The methods for data collection vary depending on factors such as data availability, privacy regulations, the network provider, and the type of attribution model used.
Data collected may include viewership, ad exposure, cross-channel integration, consumer behavior, and other advanced analytics.
Attribution modeling is sometimes complicated and may require advanced knowledge of statistical techniques, machine learning algorithms, and data integration. Data collection, touchpoint mapping (such as the different touchpoints within a viewer’s journey), analysis and insight metrics, and credit assignment are all factors in attribution modeling. Further, attribution modeling means determining which model to use and what attribution rules to follow.
Attribution modeling relies on data availability and objectives, such as understanding TV ad effectiveness, audience targeting, and optimizing ad spend allocation.
The data can also help advertisers optimize their campaigns. The right platform will analyze the data and use the results to serve to ads to those most like to convert. This decision-making helps advertisers optimize their campaigns for the best results.
One of the biggest pitfalls of traditional TV attribution is that measuring consumer behavior will never be an exact science. Traditional methods often rely on surveys and focus groups, which collect self-reported data; as a result, you have to take what people say at face value, whether or not it’s entirely accurate. Plus, metrics like viewership ratings may provide a broad understanding of audience reach but fail to capture detailed insights into viewer engagement and other performance data.
With traditional attribution methods, it can also be challenging to track the effectiveness of TV ads across multiple channels, especially when consumers engage across various devices and platforms. Finally, traditional attribution methods often lack real-time data and insights. It can take significant time to collect, analyze, and interpret data from surveys, focus groups, or ratings, which hampers the ability to make timely adjustments to campaigns or capitalize on emerging opportunities.
Whether you’re a broadcaster or advertiser, there are benefits to running TV attribution models.
For broadcasters, or network providers such as NBC, ABC, and HBO, the benefits are:
Advertisers can see the following benefits:
As with any attribution, TV attribution models can make analysis complicated. For example, it can be difficult to pinpoint the exact touchpoint or channel that directly led to a conversion. Multiple touchpoints across various marketing channels may have influenced the consumer’s decision, making it challenging to attribute the conversion to a single source.
Another challenge is the difficulty in gathering specific metrics and insights for TV attribution. Metrics like ad exposure, viewer engagement, and detailed behavioral data may not always be readily available or easy to capture accurately. Limited availability of precise metrics can hinder the ability to measure and analyze the impact of TV ads effectively.
At MNTN, we’ve created our own proprietary model to help solve the problem of TV attribution. MNTN Verified Visits™ allow MNTN customers to accurately track the impact of their CTV advertising campaigns alongside their other efforts. Here’s how it works:
With Verified Visits, you can ensure you are accurately tracking the impact of your efforts without duplication. It can also be integrated into your Google Analytics account to be tracked alongside your other performance marketing channels. With Verified Visits, the problems of TV attribution in years past are just that — in the past.
TV attribution is the future of television advertising. With it, advertisers and marketers can effectively measure their campaigns, impact consumer behavior, and make informed decisions about their TV advertising strategies.
With Connected TV providers becoming more prominent than traditional television, it’s the perfect time to make the switch and embrace all that TV attribution has to offer. Through CTV platforms, users can view advertisements, visit an advertiser’s website, and convert all within one window. Interested? MNTN can help.
Discover how Performance TV delivers revenue, conversions and more through the power of Connected TV. Request a demo today to speak to an expert.