Click attribution is a way to determine what sources or campaigns are driving the most results for online companies. Many people like click attribution because it is trackable back to its site, email, or source, and click-through links can be programmed to include several attributes. Click attribution also allows people to see the relative performance of different messages, executions, or marketing techniques. Plus, it is a strong signal of intent or interest. Whatever content was clicked, you can assume it was compelling enough to incite action by that user.
The most common click attribution models are first-click attribution, last-click attribution, and linear attribution. There can be many variations of attribution algorithms that assign different values based on the type of transaction and channels involved. These three attribution models are common and not proprietary or algorithmic, so they are a great introduction to attribution.
First click attribution is a model that assigns 100% of the credit for a sale to the first channel that a user clicked through. Some customers convert on the very first interaction with a company, but many will have at least two interactions during their journey to purchase. The first click attribution model rewards the marketing channels or activities that are deemed introducers to the brand.
Last-click attribution is a model that assigns 100% of the credit for a sale to the last known channel that a user clicked through. This is in some ways a time-decay model: rather than giving fractional attribution to the last channel a user touched, it gives all the credit to it. Last-click attribution tends to be common among many companies regardless of their web analytics platform.
Linear attribution breaks the credit for a sale or action into equal parts pending how many touchpoints were measured in the course of the customer’s purchase journey. If the user had four marketing channel interactions that ultimately resulted in a sale, each channel would be assigned 25% credit for the sale.
Most companies will choose one attribution model to use in standard reporting, and often this is last-click attribution. Last-click attribution will favor channels or marketing activities that are lower in the funnel, meaning that the customer is ready to make a purchase rather than being in their discovery or shopping phase.
When evaluating the results of last-click attribution, companies should consider their entire marketing mix and targeting strategies. The truest measure for last-click attribution is an email or text channel. Almost immediately upon receiving these messages, customers or clients either do or don’t take action. Other channels, such as search, paid social, podcasts, etc. are likely driving one another. How your attribution rules are configured can make a difference in the end result of which channels or activities get ‘credit’ for the conversion.
Company A runs a marketing campaign that includes paid social ads, podcast ads, and online display banners. The customer hears a podcast ad and is curious, so they look up the company in a search browser and visit the website to learn more, but they do not make a purchase. After visiting the website, the customer begins receiving online display banners and paid social ads advertising the company and product. They later hear another podcast ad the following week and note that there is a promo code offered for a discount. Later that day, the customer clicks a paid social ad, shops on the site, and at check out they enter the promo code from the podcast before submitting their order.
Pending how attribution rules are configured, this order could be attributed in two ways. Either it would either be classified as Paid Social, since that was the last channel that a click occurred, or it would be classified as Podcast, since that it’s that channel that had the associated promo code. Ultimately, the order can only be classified to one channel. Which channel do you think should be deemed responsible for driving this purchase?
This example may seem complex, but in reality, this is a simple example. It does not include more complicating factors like marketplace sales or brick and mortar.
It’s for this reason that understanding attribution is both art and science. There are many algorithms available on the market and countless companies trying to crack the code to have the most accurate tracking, but none of them can solve this for every piece of information or every touchpoint a consumer has. This is why comparing first click and last-click attribution models is a good place to start. Google Analytics Attribution Models are great for this too, because it includes first click and last-click in their default suite. With this, you can easily compare sales that were measured both ways side by side across multiple channels.
As the example above shows, promotional codes are another method for improving attribution. They’re often used as a measurement and attribution tactic for social influencers, on podcasts, radio, tv, and in direct mail. A great way to add an additional attribution layer is to ask customers what caused them to purchase or how they learned about the company. By introducing this one question, you can gain a better understanding of which interaction the customer found most memorable.
Combining all of these data sources to draw insights using a marketing analytics platform will give you a good idea of how your marketing activities are performing. Ultimately, you will have a range of performance pending which data sources you have. Understanding which activities are upper funnel (introducing your brand to new potential customers) and which are lower funnel (capturing the sale from someone ready to purchase) will further help you determine what the corresponding metrics should be.
At the end of the day, there is no silver bullet to having the perfect attribution model. By collecting as much data as possible and considering the role your media mix plays in a customers' path to purchase, you can optimize your marketing spend to customer conversion based on what your optimal channel mix looks like.
Check out these tips to learn more tips on creating an effective attribution model.
Daasity has approached attribution analysis in multiple ways in our direct-to-consumer (D2C) Analytics Suite, which integrates seamlessly with Looker. The data model can use additional data beyond Google Analytics to prioritize attributes such as specific promo code usage, post-checkout survey results, or map orders to marketing channels. Using that data mapping with Looker to visualize results, users can slice and dice data by initial order marketing channel to better determine financial metric targets.
Additionally, the D2C Analytics Suite allows users to easily view results by first click, last-click, and ad platform (view + click) in one simple graph to help gauge results.
Daasity and Looker continue to find ways to make it easier for eCommerce and D2C brands to access and see the data they need to inform strategies and tactics for growth.
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