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You may have seen the Google blog from July 22, 2024, which mentions that Chrome will not be eliminating third-party cookies after all. Instead, Google is working on a new solution that hasn’t been revealed yet.
So, why are we still talking about a world without cookies, and what does this mean for you as an advertiser?
Regardless of Google's decision not to eliminate cookies on the Chrome browser, the reality is that data tracking and analysis methods have significantly evolved since 2020 with iOS 14.5 updates and increased data privacy.
Many of the important identifiers that used to track user activity across different browsers and devices have already disappeared, especially on Apple devices and other major browsers.
This creates ever-evolving challenges for marketers and e-commerce businesses advertising on platforms like Meta. The lack of accurate ad performance tracking and attribution leads to wasted ad spend when scaling paid marketing budgets.
In an attempt to create awareness around the changes that began in 2020 with iOS 14.5 privacy updates that led to a lack of usable consumer data, we’ve put together this 3-part blog series in order to share insights on -
Let’s dive into the first part of this 3-part blog series and learn about the intricacies of modern consumer journeys in an online shopping environment.
Before we start talking about the 6 key consumer journeys and how they affect your marketing efforts, we quickly want to shed light on what consumer journeys really are.
Consumer journeys are the series of experiences a person has when interacting with a brand. It usually starts outside the brand’s own platforms (often through a paid ad that a user might engage with on their social media platform) and then continues on the brand’s site, app, or other online spaces if the person decides to engage further.
For brands with multiple touchpoints, a consumer’s journey might also include visiting physical stores, calling customer service, or interacting with their sales teams.
We will describe each of these consumer journeys and also highlight the data you as a brand can collect across these touchpoints, which can in turn be shared with ad platforms and/or your 3rd-party attribution solution to power accurate reporting so that you can make informed decisions about your marketing strategies.
Note: For illustration purposes we will consider consumer journey with the “Now In Jade” Facebook ad for one of our partner brands Ten Thousand (sports apparel).
This is a pretty straightforward scenario — let’s imagine there is an informed ‘in-market’ buyer who has done their research already and is ready to buy.
They see an ad for Ten Thousand on their Facebook feed, click on the ad, the web link opens an in-app browser of the Ten Thousand website, and they complete the purchase in the same session.
In this scenario, both Facebook and the brand will rightfully attribute that purchase to the ‘Now In Jade’ ad the user last saw on Facebook.
If all purchasers followed a similar linear consumer journey, then we can expect to see Facebook click reporting and the brand’s internal reporting (or third-party attribution solution) to be a lot closer, if not identical.
Let’s now imagine the user sees the ad on their Facebook feed and clicks on it, but they aren’t ready to purchase yet.
Maybe they needed to do their own research, read online reviews, and talk to their friends and family for feedback before pulling the trigger and making a purchase.
Let’s say, It took them a couple of hours to do their research, and they are now finally ready to make the purchase.
At this point they know the brand well, so they open up Safari (a different browser) on their iPhone and go straight to tenthousand.cc and complete their purchase in that session.
In this scenario, a 3rd-party attribution tool will report 2 different session cookies (Client IDs) for the same user. One on the Facebook in-app browser, for which there will also be a Facebook click ID on that 1st client ID considering the user visited the merchant’s website from a click on the Facebook ad.
However, on the Safari browser, the purchase will have a brand new client ID considering the user finished their purchase on Safari even though they first engaged with the product by clicking the ad on their Facebook feed which opened into Facebook’s native browser.
The third-party attribution tool in this scenario won’t have any information indicating that these two separate events came from the same user. It won’t be able to link these two client IDs together because it didn’t have any personally identifiable information (PII) of the user in the first session.
These consumer journeys are all too common. In these cases, the third-party attribution tool will assign the purchase to an organic session instead of giving credit to the ad shown to the user on Facebook, as it couldn’t tie the purchase to a paid ad or a CRM touchpoint.
This is another common scenario — where the user discovers the brand and product on Facebook while browsing on their iOS or Android phone or tablet, decides that they need additional time to complete their research and make up their mind about the purchase.
After having done additional research, they make the purchase on a different device altogether. Let’s say they use a browser on a MacBook Pro or a Windows desktop, which might be shared with their family members, they prefer to complete financial transactions on larger devices such as their desktops.
In this scenario, when they open a new Chrome browser on their desktop, the 3rd party attribution tool will have no additional information.
Specifically, it won’t have any personally identifiable information (PII) from the Facebook in-app browsing session to stitch this desktop purchase to the initial Facebook click ID (fbclid) from the previous session that made the user consider making a purchase in the first place.
And the 3rd-party attribution tool will treat this as an organic purchase, similar to what we observed in scenario 2.
Let’s consider another scenario similar to scenarios 2 and 3, where the user, after discovering the brand and product through an ad on Facebook while scrolling on their device, needs some additional time to make up their mind.
Once they are ready to purchase, they open their iPhone and search Google for 'Ten Thousand shorts’.
This is a branded search because the term includes the brand name.
Here they will come across Ten Thousand’s sponsored listings that appears before organic ones just like the search results you can see on the 3rd screen in the image above.
These sponsored listings are from Ten Thousand itself, ensuring they don't lose high-intent search traffic to competitors.
The user in this scenario then clicks on the sponsored link, visits the Ten Thousand website, and completes the purchase in the same session.
In this example, the user session that happens on Safari will be seen coming from Google search ads (with that exact branded search term) and an associated Google Click ID (gclid).
Here, the 3rd-party attribution tools will give 100% credit for the purchase to Google search ads. They won't be able to stitch and link the Facebook ad clicks to this Google Ads experience, and Facebook will lose out on purchase attribution again.
To share insights on how to tackle this attribution issue we will discuss a little later how to best allocate credit in this scenario to multiple channels that influenced the purchase (e.g., Facebook and Google Ads).
In this scenario, let’s consider another consumer journey. After the user clicks on the ad, they see a pop-up for an email signup to unlock an additional 10% off their first order.
The user clicks on the offer, which takes them out of the Facebook in-app browser and opens the email lead magnet screen in a new Safari browser. They complete the email signup but abandon the journey there.
A little while later, the user receives an email from Ten Thousand with the 10% coupon code. They open the email on a desktop and complete the purchase on the desktop's Chrome browser.
Here, 3rd-party attribution tools may be able to link the second and third session cookies with the same email address (the signup email matching the order email).
However, they won’t be able to tie the first session cookie with Facebook click ID (fbclid), therefore, will not be able to tie this purchase event to the original click that happened on Facebook.
This scenario is applicable for the brands that sell direct as well as through affiliates and super aggregator sites.
Here, the user first sees the ad directly from the brand and clicks on it.
A few hours later performs a branded search ‘Ten Thousand Shorts’ on Google and sees Google Shop ads at the top of the screen.
They see several ads all from different merchants, including the Ten Thousand’s ad directly.
They see the first ad in the carousel is from a super aggregator site, Box Basics, which offers a cheaper price for the same product (with the exact desired size and color available) compared to buying directly from the brand. Therefore, they complete the purchase on Box Basics' website.
Here, if the purchase was made on a super aggregator or affiliate site that shares purchase data with the brand (if the brand is going to fulfill that order), then the brand will receive the order details and the brand will be able to share the purchase details with Meta as an affiliate order.
However, 3rd-party attribution tools won’t be able to stitch that order to the Meta ad the user clicked on their Facebook feed.
Additionally, the super aggregator will record this purchase as originating from their Google paid search ad with a non-branded keyword and will record the Google Click ID (gclid)associated with the session.
Once again, we see that due credit and purchase attribution were not given to Facebook, the original marketing source that made the purchase possible for Ten Thousand.
These consumer journeys, both simple and complex, clearly illustrate how data collection challenges can lead to misattribution. This means they won't know which marketing channels and/or campaigns are helping them acquire customers and increase sales.
With inaccurate data, marketers at e-commerce businesses and omni-channel retailers can't make informed decisions when allocating marketing budgets to campaigns, creative, strategies, and channels. This can lead to wasted marketing spend when scaling ad campaigns.
Marketers at e-commerce businesses and brands are now encountering complex challenges in ad attribution and performance measurement due the disappearance of crucial cookies that were utilized for tracking user activity and how they interact with a brand, their ads and products across various browsers and devices.
This problem will only increase with evolving data privacy regulations and browser security measures being implemented by the likes of Google and Apple.
Hence, understanding modern consumer journeys and online shopping behavior in a cookieless world is essential for accurately measuring your ad performance.
Additionally, with increased comprehension of the complexities of consumer journeys across different touchpoints and marketing channels, as a marketer you can adapt your attribution methods and leverage advanced solutions to navigate the changing landscape and challenges of data tracking.
We hope these insights into consumer journeys will help you avoid over-attribution and accurately allocate credit to the campaigns, strategies, and channels that influence conversions, ensuring you gain a more precise understanding of your spend’s effectiveness.
Keep an eye out for the second part in our blog series, where we’ll take a deep dive into best practices for proper measurement of campaigns, creatives, strategies, and even data tools like CAPIs on Meta (and similar ad platforms) using A/B tests, so that purchase credit is accurately allocated, especially when trying to track the types of consumer journeys we’ve just covered.
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