Choose the Right Attribution Model
Direct conversions are often the result of a combination of marketing efforts across different channels. If you are struggling to figure out which touchpoints in the customer’s journey drive your conversions, you should understand the pros and cons of attribution models.
Data-Driven Attribution Model
Implementing a data-driven attribution model will help you understand the complex customer journey by determining the most critical touchpoints and assigning the appropriate credit to them. The model can analyze and evaluate extensive amounts of data due to its machine-learning algorithms. This approach helps you understand the role each channel plays in influencing conversions and allows you to optimize your marketing efforts accordingly. Therefore, it is our preferred attribution model in the agency and we apply it whenever possible with our clients, as there are some requirements that need to be met: only eligible conversion actions can be switched to a data-driven model (for the past month, they would need to have at least 3,000 ad interactions and 300 conversions).
Rules-Based Attribution Model
Rules-based attribution models rely on simple, predetermined rules for assigning credit regardless of the user’s behavior. Some examples are the following:
Last-Click (assigning credit to the last click event, without considering the touchpoints before that)
First-Click (assigning credit to the first click event)
Linear (equally distributes the credit among all click events)
Time Decay (distributes more credit to the events that happened closer to the conversion)
Position-Based (40% of the credit goes to the first and last click event, the rest are distributed across the middle touch points)
It is important to note that as of June 2023, first-click, linear, time decay, and position-based attribution models will be gradually phased out, making data-driven attribution the default one. The last-click attribution model will be still available and can be used for accounts that do not meet the data-driven model requirements.
Analyze Assisted Conversions
Assisted conversions occur when a marketing channel contributes to the overall conversion process but does not directly lead to the final conversion. By analyzing assisted conversions, you can identify the effectiveness of marketing efforts that may not result in immediate conversions but still play a crucial role in nurturing leads and moving them through the sales funnel.
This type of analysis is essential when judging the success of middle-of-funnel (MOF) or top-of-funnel (TOF) channels, whose primary goal is to build brand awareness (e.g.: YouTube, PMax, Discovery). Usually, these channels reach a “colder” audience (people who are in the early stages of the buyer’s journey—discovering and researching their needs, problems, and potential solutions). By effectively leveraging these channels, you can attract a broader audience, engage them with your brand, and gradually nurture them toward conversion. You can understand the hidden contribution of the MOF/TOF channels by analyzing several data points.
Channel-Specific Attribution Reports
Platforms like Google Ads and Meta offer their own attribution reports, which reveal the halo contribution of particular campaigns within the channel. For instance, in Google Ads, you can observe the indirect conversions brought by MOF/TOF channels, such as YouTube, on other Google Ads campaigns. With a client of ours we saw that in a given month, YouTube accounted for 2% of the overall direct conversions. However, when considering its indirect conversions, this channel contributed to 22% of all conversions. It can be inferred that if we were to pause YouTube, we might lose approximately 20% of our total direct conversions in Google.
GA4: Conversion Path Report
This report displays the journey your customers take toward conversion. Moreover, it allows you to easily see and compare various attribution models and the conversion credit they allocate. The platform presents a visual representation of the channels responsible for initiating, assisting, and ultimately converting users.
Test the Halo Effect of MOF/TOF Campaigns
The halo effect in advertising and marketing means that customers are more likely to perceive your brand positively if they associate it with something positive. By leveraging the power of the halo effect, you can create marketing campaigns that resonate with your target audience and drive results.
Test the Campaign and Its Halo Effect in a Specific Region
You can conduct a test to determine if the MOF/TOF campaign is indirectly contributing to overall results. In order to have as accurate as possible results, not impacted by seasonality, you would need to compare a test group and a control group. For your test group, you would need to pick a specific region, with high enough demand, and test the campaign there. Compare the results versus a control group – a region where the specific MOF/TOF campaign is not running.
After launching the campaign in the selected test region, monitor if it positively affects conversions from other channels (paid media, organic, direct) over time. If you observe that overall conversions in the other channels increase following the TOF campaign’s launch, while conversions in the control region exhibit a stable trend without sudden fluctuations, you can assume that seasonality isn’t responsible for the positive upward trend in the test region.
Please note that you shouldn’t run other tests or make significant changes while evaluating the TOF campaign. If you do, it will be unclear whether the positive impact is due to the test or other changes you’ve implemented.
Perform a Brand Lift Study Using the Google or Meta Platforms
Brand lift studies are part of incrementality testing in digital marketing. They are used to measure the effectiveness of advertising campaigns in increasing brand awareness, recall, consideration, and other brand-related metrics. These tests will help you understand how your campaigns influence consumer perception and behavior. Additionally, you can gain valuable insights into the effectiveness of your advertising campaigns and make data-driven decisions to improve your marketing performance. You can conduct such tests via Google or Meta platforms, by following the steps:
Identify the key campaign goals and brand metrics you want to measure
Determine the specific audience you want to target
Establish a control group and a test group
Develop survey questions that will address the specific brand metric you want to measure
A recent brand lift study that we conducted for a SaaS client of ours in Google focused on measuring the effectiveness of video ads, showing that YouTube had a direct positive influence on brand awareness and on generating search demand for branded and non-branded keywords. We observed a 9% increase in brand awareness, a 240% increase in brand searches, and a 13% increase in non-brand, product-related searches. The test ran for a month and a half, reaching 2.3mil impressions, and 800k unique users.
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