It’s rare for a user to visit a website, assemble a shopping cart, and makes an order in one session. Especially when it comes to a complex product with a long sales cycle. Customers google the product and visit the site from the search, see ads, subscribe to social networks, read partner newsletters or blogger reviews. And all this together forms the client’s attitude towards the brand. And therefore, it influences the purchase decision. In this article, newage. will tell how to evaluate the channels that lead customers to a brand.

In copyright, attribution refers to identifying the author of a work. In psychology, attribution is the attribution of human behavior to some cause. In marketing, we are interested in conversion attribution.

Attribution in marketing is the attribution of user conversions to the points of interaction with a brand. Based on the chosen attribution principle, the analyst or analytical system decides what role each individual source, channel, and campaign played in the conversion.

Touchpoints need to be attributed in order to optimize marketing activities – to strengthen channels that lead to targeted actions and remove those that do not.

There are several principles that allow us to evaluate the role of individual marketing activities in conversion. Which one to follow depends on the specifics of the company, niche and product. Let’s consider several attribution models in marketing.

First interaction attribution assigns all the value to the channel that first brought the user to the site.

This model is useful when entering a market and launching campaigns aimed at increasing brand awareness. If your goal is to fill the top level of the sales funnel and reach as many people as possible, use first-click attribution.

It is especially important to consider delayed conversions: post-view and cross-device. For example, a user saw your ad on Youtube but didn’t click on it. And a couple of days later, they visit the site using a branded query in organic search. A user wouldn’t google a brand if they didn’t see the ad, but the standard tools won’t take into account the view and will consider organic as the most valuable source.

We use Comprehensive Analysis to track pending conversions and correctly attribute first contact with a brand.

In this model, all the credit goes to the last source of the referral. Last-click attribution is useful for businesses that rely on impulse purchases or during campaigns focused on simple conversions.

Let’s say your online magazine launches a mailing list and the target conversion of the campaign is to leave the email in the subscription field. It is a simple, impulsive action, for which the user does not need to be warmed up and courted in advance. Once they get to the site, they will either fill out the form in the pop-up or not. In this case, you can ignore past interactions and evaluate which channels led to the subscription.

This is an improved, slightly cleaner version of last click attribution. If a person has bookmarked the site and the last visit before conversion was from direct/none, this attribution model will assign value not to the “direct” source, but to the one from which the user came to the site. If the last click before conversion was from any source other than direct, then the value will be assigned to it.

Here, the conversion value is distributed among all touchpoints. This kind of ad attribution is useful for companies with a long sales cycles, where marketing accompanies the lead as it moves through the funnel.

A standard situation in the sale of primary real estate. The client has talked to the sales manager and is slowly moving towards signing documents: periodically communicating with the manager, consulting with relatives, collecting some documents, looking at competitors’ offers. This can last for months, and at any moment the client can say: “You know, I’ve changed my mind about buying an apartment.” To prevent this from happening, marketing continues to warm up the lead, sends “signs of fate” to him in the form of remarketing, invites him to subscribe to the newsletter or social networks, releases PR materials that remove objections, etc.

And when the conversion has already taken place, the client signed the documents and made the first payment. You can look back at all the activities carried out and say: “Thanks to all these actions, we kept him on the line.”

The main disadvantage of such an attribution model is the “leveling”. Both contextual advertising, after which a person left his contacts, and a meme that he simply liked on Facebook are considered equally useful here. But this approach allows you to notice and cut off activities that pass by customers and do not give any result.

The closer an interaction is to a conversion, the more valuable it is. This model is used to take into account which touch leads to a conversion, but do not want to lose sight of the previous customer journey. This can be useful when organizing promotions.

For example, a candy brand is holding a UCG content contest: take a photo with the product and win a prize. Participants have two weeks to post, but it is immediately clear that the peak of activity will be on the day of the deadline. It is logical to give the most weight to the touch that motivated the client to participate. But at the same time track how he learned about the competition and warmed up to participation.

The focus of this model on the first and last touchpoints. We find out which channel introduced the client to the brand and which channel motivated the conversion – they get 40% of the weight. And the remaining 20% ​​is distributed among intermediate stages.

Such an attribution model is useful, for example, when introducing a new product to the market. If you are building product awareness through media channels while running a conversion-focused performance campaign, use this model.

Data-driven attribution

Data-driven attribution is available in Google tools. The system analyzes account information, interaction chains, conversions, and draws a conclusion about the contribution of each channel to the conversion.

It’s not entirely clear from Google’s help how exactly the algorithm works and how it distributes the value of the conversion. But if you trust the system, you can try this function. This attribution model is available to advertisers who have had at least 3,000 clicks on their ads and 300+ conversions in the last month.

If all of the above models don’t fit your business model and don’t fit your current campaign goals, you can set up a custom attribution model. This option is for advanced marketers who understand what type of interaction is most valuable to the company. Also, custom attribution models can be useful for experimentation and hypothesis testing.

Most of the attribution models listed are available in Google tools. Here’s a reminder of where to set up and check attribution models in each of them.

To view the attribution report in Google Ads, open the “Goals” menu, and in the “Measurement” section, go to the “Attribution”.

Talking about Google Analytics 4, all the attribution models are gathered in the Advertising section. Here are the way to find these sections and examples of atribution reports, shown for the website without ads.

Testing attribution models in Google Ads can help you understand how different touchpoints influence conversions and optimize your marketing strategy. Here’s a step-by-step guide to effectively test attribution models:

1. Set Clear Objectives: Define what you aim to achieve with the attribution model test, such as identifying the most effective touchpoints or understanding the customer journey.
2. Select Attribution Models: Google Ads offers several attribution models like Last Click, First Click, Linear, Time Decay, Position-Based, and Data-Driven. Choose the models you want to compare based on your objectives.
3. Create Conversion Actions: Ensure you have the appropriate conversion actions set up in your Google Ads account. This might include purchases, sign-ups, or other key actions that reflect your goals.
4. Set Up an Experiment: Use the Google Ads Experiment tool to create a draft campaign. Apply different attribution models to the original and draft campaigns for a side-by-side comparison.
5. Run the Experiment: Launch the experiment and let it run for a sufficient period to gather significant data. The duration will depend on your traffic and conversion volume.
6. Analyze Results: Compare the performance of each attribution model based on key metrics such as conversions, cost per conversion, and return on ad spend (ROAS). Look for patterns that indicate which model best attributes value to your marketing efforts.
7. Make Data-Driven Decisions: Based on the experiment results, choose the attribution model that provides the most accurate insights into your customer journey and helps you optimize your ad spend.
8. Implement the Chosen Model: Once you’ve identified the best attribution model, apply it across your campaigns to improve the overall effectiveness of your Google Ads strategy.

By systematically testing and analyzing different attribution models, you can gain valuable insights into your advertising performance and make more informed decisions to enhance your marketing efforts.