What drove your customers to install that app or make that final purchase? Was it the new Instagram feed or the paid search ad? Should you be spending more on email campaigns or social media ads? The challenge marketers face today lies in distinguishing high-performing campaigns from underperforming ones in a complex world of concurrent marketing campaigns across multiple channels.      

This is where attribution models come in. Attribution models serve as the lynchpin of digital marketing and assist you in making informed marketing decisions that make sense.

As a winning marketer, you should understand the strengths and weaknesses of different attribution models and determine what works best for your marketing success! 

Let's start with the basics.

What is an attribution model?

An attribution model is used in marketing analytics to give conversion credit to touchpoints across the customer journey. Conversion generally refers to an in-app event such as an app installation or purchase, and touchpoints are all engagement points in the customer journey, such as ad clicks and views.

To gain maximum return from a limited marketing budget, you need to know which ad campaign was most effective in driving the final conversion or which channel acquired the most users that led to the most conversions. Attribution models can provide the insights you need for effective media mix planning amid challenges posed by the rising complexity of the customer journey. 

There is no one-fits-all model that conveniently feeds you with the data you need, and different attribution models have different strengths and weaknesses. Therefore, you need to know which model to leverage in a particular context for your product. Attribution models are largely categorized into rule-based models and data-driven models.

Single-touch Attribution Model

The single-touch attribution model, one of the most widely used rule-based models, gives full attribution credit to only one touchpoint, no matter how many touchpoints the customer went through until the conversion. The first-touch and the last-touch attribution models are the two variations of the single-touch attribution model.

First-touch Attribution Model

The first-touch attribution model gives full credit to the first touchpoint for the conversion made. This model is recommended when your product has a short sales cycle, and you are running only a few concurrent campaigns within a limited number of channels. Using the first-touch attribution model, you can determine the most effective marketing channel to acquire customers. Focusing your ad spend on this channel may increase your product awareness among a wider audience pool.

Using the first-attribution model means focusing on top-of-the-funnel marketing, which aims to raise brand awareness and generate new leads. As straightforward as it is, the model is easy to implement. However, the first-touch attribution model has its downsides as the model ignores all the other touchpoints beyond the first touch. As a result, the data may lead to an under-investment in retargeting or CRM campaigns and a loss of additional conversion opportunities.

In particular, this model is unsuitable for high-involvement products with a long user journey, such as cars, or services with extended intervals between use, such as travel services. The first touchpoint may lie outside the lookback window and gets lost in the data collection process, failing to measure attribution.

💡 A lookback window or an attribution window is a defined period in which touchpoints such as ad clicks and impressions are considered for attribution. The length of the window should vary depending on the product and marketing channel.

Last-touch Attribution Model

The last-touch attribution model is the most commonly used model, which gives full credit to the last touchpoint for the conversion made. This model is helpful for products that have already established brand equity and need to focus their marketing efforts on the final conversion. The model provides insights into late-stage campaigns that triggered the ultimate purchase decision and can help you invest in high-performing campaigns. In particular, the last-touch attribution model comes in handy for products with a relatively long sales cycle or when implementing the multi-touch attribution model is too complex and requires a simpler way. 

However, the last-touch attribution model ignores all other touchpoints except for the final touchpoint that led to the ultimate conversion. It does not consider the incremental impact of multiple touchpoints or the effect of brand equity and oversimplifies the complex customer journey. 

Multi-touch Attribution model

The multi-touch attribution model addresses the limitations of the single-touch attribution model. Today, the customer journey has become more complex than ever, spanning multiple devices and channels. In this ever-competing digital marketing world, the multi-touch attribution model is the key to success in optimizing your marketing mix. 

The multi-touch attribution model assigns conversion credit to different touchpoints with weights based on varying logic. The model looks into various touchpoints along the path toward conversion, including paid ad campaigns and owned media, and provides a granular and person-level view. Here is a list of multi-attribution models that Google Ads currently provides its users for performance measurement. 

Linear Model

The linear model gives equal credit to all touchpoints across the customer journey. This model is useful when the advertiser values all campaigns evenly and wants to see them on equal footing. However, suppose the customer journey is highly complicated, or a particular channel has a significant influence on the market. In that case, the linear model can not provide the real story behind the conversions. 

Time Decay Model

The time decay model gives more credit to the touchpoint closer to the final conversion. The closer to the conversion, the higher the attribution weight is given to the touchpoint. This model is useful for high-involvement products and products with long sales cycles as it focuses more on touchpoints at the bottom of the marketing funnel. However, the model could mislead to underinvestment in early-stage touchpoints that are effective in acquiring customers, resulting in the loss of conversion opportunities. 

Position-based Model

Google Ads assigns 40% of the credit to the first and last touchpoints each and the remaining 20% to the in-between touchpoints. This is called the U-shaped attribution model. This model is your pick if you value the first touchpoint for brand awareness and the last touchpoint for conversion as the two most important touchpoints within the customer journey. Unfortunately, the model is not suited for products with a relatively short sales cycle. The model is also not recommended for products with user journeys where the impact of respective marketing channels is unclear. 

Data-driven Attribution Model 

The data-driven attribution model assigns the attribution credit based on user-level data such as ad views or clicks. Using this model, you can determine which keywords, ads, and campaigns significantly influence conversions. Here are two methods that are commonly used in the data-driven attribution model:   

• The Shapley Value is a solution concept that compares the conversion outcome across random variations of touchpoint arrangements and measures a touchpoint's average marginal contribution to the conversion. It provides the conversion likelihood of particular touchpoints by comparing the conversion likelihood with or without particular ad engagements. Google Ads uses this concept for its data-driven attribution model and provides insights through Google Analytics on various types of conversions driven by Google Search (including Shopping), YouTube, and other display ads.
• The Markov Chain is a stochastic model used for marketing attribution to figure out the transition probabilities, which is the probability of moving between 'states,' which are marketing channels within the customer journey. The impact of a particular marketing channel is determined by calculating the conversion rate measured without that channel.   

The data-driven attribution model is based on real-life data and drives a more accurate view of the performance of individual marketing campaigns. For this reason, Google Ads applies the data-driven attribution model for channel attribution. This data-driven approach captures the complexities of today's customer journey and provides granular insights for effective channel planning. 

Pick the Attribution Model that Makes Sense

No one-size-fits-all marketing attribution model is available for every marketing context, and choosing one is down to individual business needs. Therefore, the first step to measuring your actual marketing performance is understanding the pros and cons of various attribution models. You may pick a model from the list above or customize a model to your needs.

You can experiment by comparing the results of different models and find the one that is well-aligned with your marketing strategy. Along the way, you may also feel the need for marketing mix modeling that provides a macro view to set a long-term, large-scale marketing plan leveraging various channels and tactics, including mobile, web, TV, and outdoor advertising.

Remember, marketing optimization starts with measuring marketing performance with the right tools. If you want to know how Airbridge can help your company with accurate performance measurement, request a demo today!