July 19, 2021
7 eCommerce Lookalike Audiences That Are Worth Testing is a guest post by Elad Levy, Head of Growth at Fixel – a tool specializing in building powerful audience segments.
Lookalike audiences are simple and powerful. Take an audience group that has performed well for you and then use platforms like Facebook or Google to find users that exhibit similar behavior patterns. It’s a great way to prospect new audiences and scale a successful product company beyond its local environment. It is not, however, a one size fits all solution and more often than not, tactics deployed effectively in one scenario may be ineffective in another operating under different assumptions.
Though we’re referencing Facebook a lot, this list of tactics is relevant for any ad platform with a “lookalike” functionality. For example, Google’s similar audiences and Taboola, Outbrains variation on lookalikes to name a few.
While many guides that discuss the nuts and bolts of how to set up a particular lookalike audience for a particular campaign type, few address the tactics behind each maneuver. Below is a birds-eye view of lookalike audiences we’ve seen marketers use and the conditions in which they have been successful.
For a company that has a backlog of purchases and customers, lifetime value is possibly the best metric to use when creating a lookalike audience. This tactic involves taking the creme de la creme of your customers, the top percentile in terms of lifetime value and use them as a lookalike audience seed, creating an audience that is likely to generate very positive results.
Facebook now offers this kind of custom audience natively (see resources below). And for building something similar on Google Ads you’ll want to import a csv of you customer data filtered by the top 20% LTV customers.
A very common “best practice” for businesses. When deployed site-wide, this tactic is amazing for businesses that have a solid sales volume and feature a few products that cater to a specific user journey. For example, a band selling merchandise. In situations where ‘conflicting products are sold under the same roof, it’s important to separate the audiences by product category at least so that each audience shares a specific purchase intent. For example, a pharmacy store that sells both diapers and bodybuilding supplements might find that these product categories are best segmented into separate lookalike audiences, one for each category and presented with ads for products that present a natural continuation of the user journey.
Another option for companies with an extensive backlog is focusing on customers who checked out with particularly large carts and using them as a seed for lookalikes. This creates a lookalike that’s likely to repeat this binge shopping pattern. This tactic is especially effective in seasonal / holiday context.
In remarketing, a visitor that’s added a product to cart is indicating both clear purchase intent and a sense of immediacy, making add to Cart audiences some of the highest quality audiences to work with. In Lookalikes, these audiences are an acceptable alternative to audiences based on previously completed purchases, for businesses operating on a smaller scale, or trying to develop a new product.
Using visitors that viewed a page as a lookalike seed sounds like a great idea, but in reality, takes skill to pull off well. The challenge is rooted in the fact that people may visit the page by accident, click on the wrong link, or otherwise arrive at the page without any purchase intent. Your media budget will be entirely wasted on these visitors and it’s important to find ways to filter them out of your campaigns.
Though it is notoriously difficult to measure, engagement is a great indicator for purchase intent. Basic engagement metrics like time on site and scroll depth are fairly simple to set up with GTM, with Facebook going so far as to offer metrics like top percentage of time on site within their ads platform, allowing marketers to build campaigns based on site-specific engagement patterns. By creating a threshold which filters out unengaged audiences, a metric like Page views can become a viable audience.
In some cases, engagement is the only metric available, for example, a content-oriented page like a blog or magazine. For these websites, understanding engagement is key to effectively taking advantage of lookalike audiences and remarketing in general.
*Pro Tip: To build a lookalike audience based on website engagement metrics, you need to identify what level of engagement is ideal for flagging intent. Use Google Analytics and create a filtered segment for converters. Then inspect that average time-on-site, number of pages viewed, and scroll-depth events to better understand engagement benchmarks of converters… and use them as the basis for you new lookalike audience.
Although you could set up event tracking manually through Google Tag Manager, you can also automate and improve your engagement based lookalike audiences by leveraging plug-and-play data-science.
Google offers conversion optimization to websites that 500+ monthly eCommerce transactions and 10k+ page views. A steep curve. Websites that don’t have that kind of volume, receive optimization based on statistical data from similar websites.
Facebook allows you to target a percentile of visitors that stayed the most on your website. For example “Visitors by Time spent – Top 25% – Time on Site”, and other combinations. Being a facebook solution it’s only available within the Facebook ad platform.
Fixel uses machine-learning that looks acros 60+ data points to create a unique model for each website and deliver powerful engagement audiences scored from high to low intent – syncing directly into most ad platforms.
One question worth addressing is the question of similarity percentage. The optimal lookalike similarity percentage is often debated. The chief tradeoff being between the audience scale and compatibility. In simpler terms, you choose between a smaller audience that’s expected to deliver great performance or a larger audience that’s expected to deliver “ok” performance.
Fortunately, we don’t have to rely on gut feeling on this. AdEspresso ran an experiment on the subject and we’re happy to share their conclusions.
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