Marketing attribution models are tools that advertisers can use to tie their campaign conversions back to certain marketing channels and touchpoints. For example, a campaign might include native ads, email newsletters, and social media posts. Marketers will want to know which of those channels was responsible for driving each conversion.
Advertisers can use attribution models to help optimize their campaigns, understand audience behaviors, and improve website performance. In fact, attribution modeling strategies are especially important in today’s digital landscape. Customer journeys can be lengthy and complex, ranging across multiple touchpoints. With attribution analysis, marketers can more clearly measure the effectiveness of each touchpoint and adjust their budgets and strategies accordingly.
In this guide, we’ll outline different types of revenue attribution models so you can find the right solutions for your marketing goals and campaigns.
What Is an Attribution Model?
Attribution models allow businesses to work smarter, providing the data they need to see which channels drive conversions and performance results.
Here’s another way to understand the meaning of attribution models: It’s like the marketing version of attribution theory — a psychological term for understanding how people tie the effects of everyday events to certain causes.
It’s also important to understand marketing mix modeling (MMM) and incrementally testing. These are two marketing analysis techniques that complement attribution models and help marketers shape their strategies. MMM is a technique that marketers can use to measure the impact of various marketing activities on sales and ROI. Incrementality testing is a statistical method that marketers can use to measure the incremental impact of their marketing activities on campaign goals.
Main Types of Marketing Attribution Models
There are two main types of marketing attribution models: single-source and multi-source. Single-source attribution assumes that only one channel or touchpoint can be accounted for, while multi-source attribution attempts to understand the influence of all possible touchpoints.
Here’s a closer look at what each attribution model entails.
Single-source Attribution Models
Single-source attribution models are straightforward, attributing credit to only one marketing touchpoint in the buyer’s journey. In a single-source model, for example, a conversion might be attributed just to paid search, even if the campaign also includes ads on social media.
There are two main types of single-source attribution models: first-touch attribution and last-touch attribution.
First-touch attribution models give full credit to the customer’s first touchpoint — even if they encountered several other touchpoints before converting. So, if a customer engaged with a native ad, then an Instagram post, and then a paid search result, the conversion would be attributed to the native ad.
First-touch attribution can help you learn what new customers are engaging with and optimize brand awareness campaigns. However, they can also gloss over important steps in the customer journey after that initial interaction.
Last-touch attribution models give full credit to the customer’s final touchpoint before converting — even if they engaged with several other touchpoints beforehand. So, if a customer encounters a search ad, native ad, and then social post, the conversion would be attributed to the social post.
Last-touch attribution can help you understand what customers are engaging with at the end of the funnel and what motivates them to ultimately make a purchase. However, it doesn’t provide insight into the channels that might influence their decisions early in the customer journey.
Multi-source Attribution Models
Multi-source or multi-touch attribution models assign value to all points along the customer journey — not just one, as single-touch models do. As such, they can provide a more detailed and accurate view of which touchpoint carries more weight at each stage of the funnel. For example, a cross-platform attribution model might give 50% of the credit to paid search and another 50% to organic search for the same conversions.
There are several multi-touch attribution models, including: the linear model, time decay model, U-shaped model, and W-shaped model.
Linear attribution models distribute credit equally among all steps in the customer journey. So if a customer engages with an email newsletter, native ad, social ad, and search ad before converting, each channel receives 25% of the attribution credit.
Given this model’s simplicity, it only provides a general view of each channel’s impact on conversions. So, one or two linear models may not offer a totally accurate picture of each channel’s role in the path to conversion. But you can use several data sets to start identifying patterns and repeat channels that continue to pull their weight.
Time Decay Model
Time decay models are similar to linear models, but they give more credit to touchpoints that occur towards the end of the funnel — so, those closer to the actual conversion event.
In other words, this model recognizes the importance of brand awareness and consideration channels, but gives more weight to channels that drive decisions and purchases. That’s why time decay models can be good solutions for tracking lengthy campaigns and customer journeys.
U-shaped attribution models use a clear formula: 40% of the credit goes to the first and last touchpoints, and the remaining 20% is split between the middle steps in the path to purchase. This bell-shaped model can be an effective solution for campaigns with strong pushes towards brand awareness at the top of the funnel and conversions at the bottom of the funnel.
W-shaped models attribute 30% of the conversion credit to each of these three main touchpoints in the customer journey: the first, middle, and last. Hence, the “W” shape. The remaining 10% is distributed equally among all other touchpoints.
This is a unique model for marketers that want to weigh each channel and interaction without distributing credit equally to all, as with the linear model.
Full-path Attribution Model
The full-path attribution model is similar to the W-shaped model. But instead of largely crediting three touchpoints, it distributes 22.5% of the credit to each of these four touchpoints: the first one, the one that initiated contact, the one that initiated the conversion, and the one that finalized the conversion. The remaining 10% is evenly distributed across all other interactions on the path to purchase.
Data-driven Attribution Model
Data-driven attribution (DDA) involves using a machine learning tool to automatically pinpoint top-performing sources and identify behavior patterns. This can help advertisers save time with attribution and gain a more accurate understanding of each customer journey and channel interaction.
Data-driven attribution also involves using Markov chains. A Markov chain is essentially a model of the various steps in a customer journey. These chains are not only used to map out current paths to conversion; they can also be used to gauge the impact of each customer touchpoint and predict the probability that certain paths will lead to conversions in the future. As such, Markov chains can help marketers build more intuitive and high-performing paths to purchase.
Choosing the Right Marketing Attribution Model for Your Business
Choosing the right attribution model can take time because you need to find the solution that fits your business goals and campaigns. However, since priorities and strategies change, the right attribution model for this quarter might not be the best solution next quarter. So it’s important to understand and consider all options. Data-driven attribution (DDA), for example, is a leading model among today’s marketers.
On Taboola’s leading native advertising platform, each marketer can choose the best attribution model to analyze Taboola Ads performance campaigns, and experiment with different solutions to improve their campaign goals. Marketers can also access robust, real-time data and analytics reports to help track campaign performance across the web and improve conversion rate optimization.