Blog
Building unwavering customer loyalty: Your blueprint for maximizing value & driving ROI
Learn more
For marketers today, understanding the customer journey to an actual sale is just as valuable as the sale itself. But it’s not an easy task. Between devices, consumer IDs, channel variation, walled garden platforms and much more, how does a marketer actually begin to understand an individuals unique journey to buying? That’s where attribution modeling comes in.
Marketing attribution models allow marketers to assign credit to different touchpoints along a customer’s path to purchase. They help marketers get a clearer picture of when and how different marketing channels contribute to conversion events.
In this guide, you’ll learn everything you need to know about marketing attribution modeling, including its structure, benefits and limitations.
Digital attribution modeling encompasses numerous methods marketers use to segment and assign conversion credits to the appropriate channels visitors take to get to the final conversion behavior.
Many advertising platforms, like Facebook and Google, provide you with several ways to evaluate the data attribution model within their platforms. For instance, Google Ads lets you know which particular keyword search added most to the particular user conversion if they visited many keywords within Google Ads. However, it's important to keep in mind that because it’s a walled garden platform, you won't know for certain how that information relates to the user journey outside of Google. The same goes for Facebook and Amazon, or any other walled garden platform you use.
The digital advertising ecosystem has a number of different ad attribution models based on the provider you’re working with and/or the channel you’ve chosen.
As a simple way to explain the how different attribution models assign value to certain marketing efforts, we’ll look at one user’s buying journey across a number of different interactions that ultimately resulted in a sale: an ad impression, ad click, email open, app impression and direct search.
Here are a few of the models you may encounter:
It is the default conversion model of Google Analytics that gives full credit to the very last traffic source which resulted in the conversion of a user.
Example: If a person wants to book a cruise and searches for “Carnival Cruises in the Carribean,” and ultimately converts, the value would be given to the direct search, as it was the last customer interaction prior to conversion.
Although it’s a pretty straightforward model and easy to integrate, it doesn’t guarantee accurate attribution data. This is because Last Interaction Attribution doesn’t take any other user engagements with the company's marketing efforts into consideration. Per the example, the search is more to simplify navigation for the users and doesn’t account for what actually caused them to choose Carnival in the first place—as that was already included in their search query. We know that the customer journey is often nuanced and sporadic, and that many different online and offline interactions often guide a customer to convert—not just the last event. However, this model could be helpful to understand your best channels that customers often convert from.
Also called first interaction attribution, this model gives credit to the first user interaction with a brand.
Example: A person may initially click on an Instagram ad to a website. The person doesn’t buy from that initial engagement, but then they start seeing digital media ads for the product they were looking at on Instagram. A few weeks later, they buy the product after researching it more. In first-click attribution, all of the credit would go to that initial Instagram ad and none of it would go to the subsequent media buys, which clearly also had an influence on the individual.
Similar to the the last interaction model, first click attribution doesn’t provide conclusive data. This is because it ignores any subsequent engagements the customer may have had with other marketing efforts after the first interaction. While this model is also extremely straightforward and easy to integrate, it completely ignores important and valuable marketing interactions a customer may have had after the first one.
This model assigns all credit to the last visit of the users that’s not from the “direct” channel.
Example: A customer is looking for a new refrigerator. They engage in a few marketing efforts and checking the brand's app before ultimately using direct search on Google to purchase the refrigerator. Last indirect click attribution would assign total value to the app impression, because the customer already knew about the brand from previous marketing efforts, guiding them to reach the website via direct search.
Using last indirect click attribution is a bit more helpful that last interaction attribution because when a company receives direct traffic, a customer is willingly searching and visiting your site—meaning they already know about your brand. But how did they know? Most likely, there were multiple different marketing events that informed the customer about your brand prior to that direct visit, so this model assigns value to the last marketing interaction prior to that direct visit. However, last indirect click attribution still gives the most value to only one event.
This model attributes credit to all traffic sources that are involved in the conversion process evenly. Unlike the other models, it offers a vast improvement in terms of reporting accuracy.
Example: A person initially sees a display ad for a new pair of shoes. After engaging in different digital marketing efforts, they eventually search for the brand and convert. Linear attribution would assign equal weight to all the digital marketing interactions the customer had prior to buying the shoes, rather than assess which channels may have been the most influential to the purchase.
While this does provide a more balanced view of what influenced a customer to convert than the previous models discussed, it does assign equal value to every marketing interaction. In reality, some channels may have been far more effective in getting a customer to convert than others.
Time decay attribution is a more advanced variation of linear attribution that gives more credit to the traffic sources that are closer (in time) to the ultimate conversion.
While time decay attribution is similar to linear attribution in that it spreads credit out to multiple marketing events, it assigns greater value to those that occur directly before the customer converts. This model, while still helpful, overall ignores more top-of-the-funnel awareness efforts.
This model gives the most credit to the first and last engagement while the rest is assigned equally to the touchpoints that occurred in between. In Google Analytics, this model gives 40% credit to the first and last interaction of the entire conversion journey, while linearly distributing the remaining 20% to the rest of the visits.
Example: A customer purchases a new bathing suit. Prior to the purchase, their first point of contact with the brand was from an ad impression and the last was a direct search to the website. Time decay attribution allots the majority of credit to those two interaction and splits the rest evenly through the middle.
While model also assigns credit to all marketing events, the most value is given to the first and last interaction. This method is ultimately helpful for discerning which channel first engages your customers and the last prior to converting, but it still does not fully consider the influence of marketing efforts between.
Regardless of the type of attribution, digital marketing professionals face the continuing challenge of being able to tie all the various touchpoints available to their customers together for a bigger picture.
Below is a table that summarizes the pros and cons of each model so that you can understand how the different models for attribution stack up:
Attribution model
Pros
Cons
Last Interaction
Maximum extension, popular, standard
The other channels are not taken into consideration
Last Indirect Click
Favors the last channel that’s not related to direct traffic
Same as above, but the moment of conversion is moved to slightly up in the chain of events
First Click
Credits the interaction that initiates the buying process (or creates the need)
Channels that register participation later are not credited
Linear
All channels are equally credited
A banner click is given the same credit as a newsletter click. This means a new click is as valuable as one already in your CRM
Time Decay
The closer to the sale, the more value
The click that initiated the buying process is not credited
U-Shaped
Both the opening and closing clicks are credited
Blindly assigning more credit to the first and last interactions can result in giving too much credit to two potentially low-value touchpoints
Attribution modeling helps marketers better understand which parts of their marketing are bringing in the most prospects, leads and opportunities into their sales funnel.
However, a valid advertising attribution model must include all channels your visitors might use to arrive at your website. Otherwise, your calculation will be inaccurate and mostly overestimated. While each model discussed above does have its own set of pros and cons for your business, often a customized solution with a combination of models provides the most accurate view of the customers journeys. Ultimately, working with a partner who can assess your unique business case will provide you with the most accurate results.
Are you seeing some gaps in how your brand measures your marketing programs? We can help.
At Epsilon, we can help you develop long-term brand strategies, spearhead your customer data integration, implement cross-device tracking and build one-of-a-kind media marketing programs.
