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“Where should we attribute revenue?” is a question every digital marketer has asked themselves at some point. In two different accounts, we found that referral and affiliate websites were receiving revenue credit for paid-initiated traffic. These two clients both use Analytics revenue for the account’s target ROAS goals rather than the platforms. At times, we had to pull back our paid spend when Analytics dipped under the target goal. After further analysis, I wondered if we should reconsider the attribution approach.
In one account, we noticed a 90% increase in revenue attributed to referral websites month-over-month. This caught my attention because this was a significant increase.
I investigated what sources were contributing to the increase in referral traffic. Sometimes, platforms or banks appear under referrals. Sometimes, payment processing websites appear under Referrals, such as PayPal or to Affirm. These types of sources are not responsible for driving the traffic and are common referral exclusions. This means Analytics would ignore these sessions and give the credit to the previous interaction.
If we drill down into the referral websites, we can see that many of the websites receiving revenue credit are coupon websites.
Many times, the coupons do not even work on these websites, but Analytics will still credit the purchase to these domains if the user clicks the link that directs them back to the website.
If we look at the Paid-Initiated paths that end with referrals, the coupon websites received $39,062 in revenue.
If we look specifically at Referral-initiated paths, out of the $87,729 in revenue, the coupon websites were only responsible for initiating 6 visits with $4,701 in revenue.
After a discussion with the client, they believed these coupon websites were not valuable and often did not have valid coupons. So, we made the decision to exclude these coupon websites to avoid having them interfere with our marketing objectives.
In the second account, we noticed it became increasingly difficult to hit their target ROAS goals during the summer. However, since this immediately followed the distribution of the first round of stimulus checks, we believed this attributed to the spike in revenue. They also had some larger coupon promotions on their website during the spring months.
As you can see in this month-over-month trend, ROAS dropped below 300% starting in June. The value the platforms were reporting revenue $250k higher than what Analytics was reporting.
A few months ago, Analytics had revenue bucketed as other, and it was often the last interaction in Analytics. This channel was responsible for 14% of the revenue in Analytics for 8 months.
All of the revenue under Other Advertising was being attributed to a CJ Affiliates source. This started around the same time the account began to struggle meeting its ROAS goals.
In the Multi-Channel Funnels in the Top Conversion paths, it shows that $1.4M out of the $2.5M of paid-initiated traffic was attributed to Other Advertising (CJ Affiliates). Most of this revenue would have been attributed to Paid Search if the affiliate source was not present.
Note: This report is filtered to conversion types as transactions only. It is also filtered for traffic that begins with Paid Search and ends with Other Advertising.
During one conversation with the client, we all agreed while the affiliates may be contributing to the revenue, but the question was exactly how much. If we change up the filters to show traffic that Begins with Other Advertising, we can see this channel is only responsible for driving traffic that resulted in 281 transactions and $47,268 in revenue.
So, in this case, while we could say the affiliate program is assisting in the searchers making the purchase, it does not appear to be the primary channel driving searchers to the website. So, Analytics attributing 100% of the revenue in MCF is greatly devaluing Paid Search traffic’s role.
Another important consideration is how Analytics is reporting conversions and revenue. By default, Analytics is set up to give the last non-direct visit 100% of the conversions or revenue credit. One issue with this model is the user journey is complex; assigning all the credit to the “last touchpoint” may undervalue other sources.
In the paid-initiated traffic, we can see they visit the coupon or affiliate websites right before making a purchase, and then the revenue is attributed to the coupon websites. Sometimes we see the searcher visit multiple coupon websites in the same session. Also, we can see that some revenue was attributed to the Affirm payment option for when searchers prefer to make payments over time.
For example, if a person clicks to your website from a Paid ad, then returns as a coupon ‘referral’ or ‘other advertising’ traffic to convert, Analytics will report 1 transaction for Referral or Other Advertising. The Multi-Channel Funnels report will show 1 conversion with the path paid search > referral. Paid search will get 1 assisted conversion.
In the Last Interaction attribution model, the last touchpoint—in this case, the Referral channel—would receive 100% of the credit for the sale.
In the following scenarios, the final touchpoint will get 100% of the credit in Analytics’ Last Click model unless it is Direct and then it gives credit to the previous source.
In the scenarios above, the last click may be the reason you purchased, but it is not the reason you were interested in the first place. It may be worth considering upgrading your attribution model to something that gives more credit to other touchpoints along the journey. This type of attribution makes it difficult to assign credit where credit is due.
Let’s say we updated it to a Position-Based model. The attribution credit would look something like this with 40% attributed to the first and last touchpoints and 20% divided to anything in between.
In Google Ads, most of us have moved away from the Last Click attribution model. This article From Last Click to Position-Based: An Attribution Test does a great job of discussing how changing the model impacted Google Ads campaigns. If your account has enough clicks and conversions, then the Data Driven Model will be an available option.
You can use the Model Comparison Tool in Analytics or it is called the Model Comparison Explorer in Analytics 360. They can be found under Conversions > Multi-Channel Funnels > Model Comparison Tool.
In the above scenario, the channels that would benefit the most are Paid Search, Organic Search, and Social Network. This data shows us that these paths may begin the journey more often and the Last Click model is not giving them the credit they may be entitled to receive.
You can also use the Attribution Beta in Analytics to explore the difference in the models without changing the settings.
In this case, we see a large portion of the revenue is being attributed to coupon websites. These websites dominate the search results when you look for coupons for many brands. Oftentimes these coupons do not work, but searchers will try to get a promotion. You can see some ads are offering discounts for Macy’s here.
One option might be to switch to another attribution model in Analytics. If the Data-Driven Attribution model is available this might be the best option. Your account would need to meet specific criteria for this option to be available. Another option would be to switch to Position-Based for conversions that involve multiple touchpoints.
Another option might be to create a special coupon page for your website that is not easily found on your website. Then you can set up a Brand Coupon ad group and target these discount terms to bring searchers back to your website with a valid coupon. While some people may continue checking out without a coupon, others may choose to abandon their cart.
It may be time to really think about how we are attribution revenue in Analytics. The searcher’s journey can often be complex. Is the Last Click approach attributing too much revenue to sources that are less valuable? Are these referral sources devaluing your marketing efforts? Even if you decide you are not ready to rethink the attribution model in Analytics, it would be worth the time to deep dive into the list of referral sources getting credit for revenue. Maybe some of these referral sources could be excluded to give you a better vantage point of what is contributing the most.
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