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How to Scale with Lookalike Audiences For Facebook Ads & More

Etgar Shpivak, CEO, Fixel.ai

June  06, 2019

Lookalike audiences. Today’s guest post comes to us from Etgar Shpivak, the CEO of Fixel.ai, who explains the ins and outs of how to scale this valuable and popular type of audience targeting.

Lookalike Audiences are one of the most powerful tools in the modern marketer’s arsenal. Often used to expand existing audiences to new locations and products, their true potential is broader and can help scale virtually anything.

lookalike audiences

First introduced by Facebook in 2013, Lookalike audiences are now an industry staple, available on many ad platforms including Facebook, Google Ads, Outbrain and Quora to name a few. The unifying premise is that given a group of users, the ad platform’s algorithm can assemble a second group that resembles the original one. Facebook even added a degree of similarity ranging from 1% difference to 20% difference sample size.

Let’s break it down and dive a little deeper into what lookalike audiences are and what you need to know to consistently build effective lookalikes.

The Seed

All lookalikes start with a seed. This is the ‘first group’ that we mentioned earlier and it’s the basis of the audience. Much like its plant-based analogy, not all seeds are created equal and picking the right one is a bit of an art form, but we’ll get to that shortly.

Seed Size

Generally speaking the ideal size for a lookalike audience is around two thousand users. Contrary to intuition a bigger seed does not mean a better audience, in fact, the opposite is true. If your lookalike seed is made up of tens of thousands of users, the ad platform will have a difficult time finding users that match with everyone in the group, resulting in a less accurate lookalike audience.

Lookalike Audience Similarity VS Reach

Lookalike audiences take the top percentile of visitors that share the most common traits as the audience’s seed. Choosing the top 1% will result in a smaller but more homogeneous audience. Choosing a larger percentile, e.g. 5% or 10%, will result in a larger audience that is less homogeneous. It’s a classic balancing act of scale vs effectiveness.

Principles for Building a Lookalike Audiences Seed

There are many ‘best practices’ for building lookalike seeds. These are often effective for a specific use case and less so for another. In reality, there isn’t a single clear, cookie-cutter solution that works for everyone, rather it’s important to match the lookalike seed to the available data, metrics and business goals. To that end, we can offer a few guiding principles.

lookalike audiences

Relying on Metrics

There are many metrics that can be used as the basis for a lookalike seed. Here are a few practical examples:

CRM Data

CRM Data refers to information that’s typically stored in a CRM and has a broader historical context. These metrics tend to be more strategic and long term.

For example:

  • Highest Lifetime Value – Customers who accounted for the highest total value over a ‘Lifetime’ period which may have included several or many deals
  • Highest Cart Value – Customers whose individual deals or purchases were of the highest value

Conversion Data

Conversion Data refers to the ‘Hard’ actions taken by a visitor, actions that signal intent and are fairly simple to measure.

For example:

  • Form Submissions – Visitors that submitted a contact form or requested a quote
  • Add to Cart – Visitors that added a specific product to cart
  • Purchase – Visitors that bought a product or completed a deal becoming customers

Engagement Data

Engagement Data refers to ‘Soft’ actions taken by a user that are often more complex to measure and understand intent.

For example:

  • Page Viewers – Visitors that viewed a specific page which signals a desirable intent, for example, a visitor to a travel agency looking into the weather and exchange rates of a proposed destination.
  • Engagement Level – Visitors that performed an action that signals their engagement level. These can range from specific micro conversions like scroll depth, time on site to generated metrics like Facebooks’ top percentile lists or Fixel’s AI generated engagement scoring. For example, a visitor who engaged with a product page. An audience based on these visitors would be similar to an ‘Add to Cart’ audience but larger.

Substitution for Lookalike Audiences

It’s important to note that many of these metrics are simpler or lower grade versions of each other. For example, a Past Purchase Data can be substituted by Add to Cart Audiences which in turn can be substituted by Product Viewers who were highly engaged.

Metric substitution allows you to create consistent lookalike seeds, which is very important.

lookalike audiences

Keep Seeds Consistent With User Intent

One of the key factors to keep in mind when building a lookalike seed is consistency. The basic rule being, the more consistent the lookalike seed is, the easier it is for Facebook’s algorithm to find users that match it.

To achieve consistency, it’s a better idea to use data from a specific category, even if the quality of the actions is not as good. For example, using add to cart data from a specific category should be preferred to overall purchase data across all categories.  

To explain the reasoning, consider a convenience store that caters to many different consumer types. Using purchase data without a category context would result in a seed that has both mothers who purchased diapers, men who purchased razors and aftershave next to athletes who purchased muscle balm. The lookalike seed in this example would be very ineffective because the sample groups do not share the same purchase intent.

Look Beyond Metrics

To keep with the principle of consistency it is important to look beyond raw metrics and into the user journey, understanding the unifying motivations and context that drove the specific behavior you’d like to replicate in the lookalike audience.

To explain this principle, consider Valentine’s Day for the same CVS styled store. A great lookalike seed could be built based last years purchase data in the period leading up to and immediately after the holiday. The logic being that purchases made in the perfume department at this time frame are gifts and the same type of people would be interested in buying them again come next holiday season.

lookalike audiences

Examples of Lookalike Audience Seeds

Value-Based Lookalikes Seeds

  • Relevant Metrics – Lifetime Value, Highest Cart Value, Past Purchases, Add to Cart
  • Similarity – 1%-2% prefered

Value-based lookalikes are the most commonly used lookalike seeds. When done correctly they deliver some of the most effective lookalike audiences available.

It is, however, important to remember that consistency is key and it’s better to have a consistent audience seed of users who Added a Product to Cart over an inconsistent audience based on lifetime value or purchases.

Seasonal and Recurring Seeds

  • Relevant Metrics – Highest Cart Value, Past Purchases, Add to Cart in a specific time frame
  • Similarity – 1% preferred

The holiday season provides massive sales and growth opportunities for eCommerce businesses. However, with media prices reaching an all-time high, it’s important to focus on the right audiences.

An effective tactic is to create 1% lookalikes based on newly acquired customers from the past years season, these audiences will represent people that found your products or services useful within the context of the holiday season.

This tactic also works for any kind of recurring event or enterprise. From conferences to live events or seasonal services.

Engagement Based Lookalikes

  • Relevant Metrics – Add to Cart, Content, Micro conversions, Fixel’s engagement scoring
  • Similarity – 1% preferred

For a newly formed agency offering several services or an eCommerce business offering several distinct products, waiting until each product or service reaches enough sales or deals to make for a solid lookalike seed might be impractical or simply take too long.

Many in this case sacrifice granularity and use the general ‘Purchases’ metric as a lookalike seed. This is a common mistake. It’s better to use engagement metrics to keep your lookalike seeds consistent.

Ultimately, engagement-based lookalikes allow for faster and more granular scaling.

Ad Scaling with Lookalikes

  • Relevant Metrics – Ad conversions such as Clicks
  • Similarity – 1% preferred

When launching a new product it’s often difficult to know who to target your product at. An interesting solution involves targeting a broad audience and then using click conversions as a lookalike seed, trying to find a unifying factor in the audience.

lookalike audiences

Summary

Despite the obvious advantages lookalike audiences have, they are not all-powerful and even Ad Platform algorithms have limitations. If there’s one takeaway that sticks with you from this article, let it be the importance of seed consistency and granularity.

Ultimately, building a great lookalike audience requires the balanced use of metrics coupled with the ability to see beyond them into the motivations of your users, while making sure your lookalike seeds are consistent and granular.

This approach will help you use this powerful feature in new and specific ways that are relevant to you, effectively growing and scaling anything.

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Cookie Name TDID
Cookie Expiry 1 year
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Provider adsrvr.org
Purpose This cooke registers a unique ID for to recognise returning visitors. The ID is used for targeted advertising.
Host(s) .adsrvr.org
Cookie Name TDCPM
Cookie Expiry 1 year
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Provider Google
Purpose Cookie from Double Click (Google) which helps to build a profile of your interests and show relevant ads for remarketing purposes.
Host(s) .google.com
Cookie Name NID
Cookie Expiry 6 months 2 days
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Provider Bing
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Host(s) .bing.com
Cookie Name MUID,MUIDB
Cookie Expiry 1 year 3 weeks 4 days 1 second
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Provider Double Click (Google)
Purpose Cookie from Double Click (Google) which helps us to analyze and optimize our advertising campaigns.
Host(s) doubleclick.net
Cookie Name IDE
Cookie Expiry 1 year 3 weeks 4 days
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Provider Facebook Ireland Limited
Purpose Cookie by Facebook used for website analytics, ad targeting, and ad measurement.
Privacy Policy https://www.facebook.com/policies/cookies
Cookie Name _fbp,act,c_user,datr,fr,tr,m_pixel_ration,pl,presence,sb,spin,wd,xs
Cookie Expiry Session / 1 Year
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Provider Trendemon
Purpose Track surfing habits and provides Banners and tailored content
Privacy Policy https://trendemon.com/privacy.html?v=2018
Host(s) .kenshoo.com
Cookie Name trd_vid_l, trd_vid_2003
Cookie Expiry 1 year 22 seconds
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Provider Trendemon
Purpose Track surfing habits and provides Banners and tailored content
Privacy Policy https://trendemon.com/privacy.html?v=2018
Host(s) .kenshoo.com
Cookie Name trd_vuid_l
Cookie Expiry 1 year 22 seconds
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Provider Trendemon
Purpose Track surfing habits and provides Banners and tailored content
Privacy Policy https://trendemon.com/privacy.html?v=2018
Host(s) .kenshoo.com
Cookie Name trd_sid
Cookie Expiry 30 minutes
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Provider Trendemon
Purpose Track surfing habits and provides Banners and tailored content
Privacy Policy https://trendemon.com/privacy.html?v=2018
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Cookie Name trd_pws
Cookie Expiry 30 minutes
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Cookie Name trd_pw
Cookie Expiry 1 year
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Purpose Track surfing habits and provides Banners and tailored content
Privacy Policy https://trendemon.com/privacy.html?v=2018
Host(s) .kenshoo.com
Cookie Name trd_ma_cookie
Cookie Expiry 12 hours
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Provider Trendemon
Purpose Track surfing habits and provides Banners and tailored content
Privacy Policy https://trendemon.com/privacy.html?v=2018
Host(s) trackingapi.trendemon.com
Cookie Name trd_gvid, trd_gavid_2003
Cookie Expiry 7979 years 6 months 2 weeks 1 day 22 hours 48 minutes 21 seconds
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Provider Trendemon
Purpose Track surfing habits and provides Banners and tailored content
Privacy Policy https://trendemon.com/privacy.html?v=2018
Host(s) .kenshoo.com
Cookie Name trd_first_visit
Cookie Expiry 1 year
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Provider Trendemon
Purpose Track surfing habits and provides Banners and tailored content
Privacy Policy https://trendemon.com/privacy.html?v=2018
Host(s) .kenshoo.com
Cookie Name trd_exitintentclient
Cookie Expiry 1 year
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Provider Trendemon
Purpose Track surfing habits and provides Banners and tailored content
Privacy Policy https://trendemon.com/privacy.html?v=2018
Host(s) .kenshoo.com
Cookie Name trd_cid
Cookie Expiry 1 year 25 seconds
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Host(s) kenshoo.com
Cookie Name s-9da4
Cookie Expiry 15 minutes 25 seconds
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Host(s) kenshoo.com
Cookie Name kenshoo_tapad_id_sync
Cookie Expiry 1 day
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Host(s) kenshoo.com
Cookie Name kenshoo_id_match
Cookie Expiry 1 week
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Host(s) .xg4ken.com
Cookie Name kenshoo_id
Cookie Expiry 1 year
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Host(s) kenshoo.com
Cookie Name d-a8e6
Cookie Expiry 1 year 25 seconds
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Provider google.com
Cookie Name pagead/1p-user-list
Cookie Expiry Session
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Provider google.com
Purpose Used by Google AdWords to re-engage visitors that are likely to convert to customers based on the visitor's online behaviour across websites.
Host(s) google.com
Cookie Name ads/ga-audiences
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Host(s) .pardot.com, kenshoo.com, www2.kenshoo.com
Cookie Name visitor_id#-hash
Cookie Expiry 10 years
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Provider Trendemon
Host(s) trackingapi.trendemon.com
Cookie Name api/events/pageview
Cookie Expiry Session
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Provider Trendemon
Host(s) trackingapi.trendemon.com
Cookie Name api/events/uplift
Cookie Expiry Session
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Provider Trendemon
Privacy Policy https://trendemon.com/privacy.html?v=2018
Host(s) kenshoo.com
Cookie Name trd_ctahistory
Cookie Expiry Persistent
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Provider Google Tag Manager
Host(s) kenshoo.com
Cookie Name _uetsid_exp
Cookie Expiry Persistent
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Host(s) kenshoo.com
Cookie Name _uetvid_exp
Cookie Expiry Persistent
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Provider Owner of this website
Purpose Enables the website to make variations of their landing-page. This is used to minimize bounce-rates, which means that fewer users leave the page immediately.
Host(s) kenshoo.com
Cookie Name ub-emb-id
Cookie Expiry Persistent
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Name
Provider adsrvr.org Pixel
Purpose Presents the user with relevant content and advertisement. The service is provided by third-party advertisement hubs, which facilitate real-time bidding for advertisers.
Host(s) adsrvr.org
Cookie Name track/cmf/generic
Cookie Expiry Session
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Name
Provider Twitter
Purpose This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.
Host(s) twitter.com
Cookie Name personalization_id
Cookie Expiry 730 days
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Provider lionbridge.com
Purpose This cookie is associated with TrenDemon, a content marketing attribution tool.
Host(s) kenshoo.com
Cookie Name trd_utmcampaign, trd_utmsource
Cookie Expiry 1 year

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