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7 Ways To Segment Your Audience For Successful Retargeting



7 Ways To Segment Your Audience For Successful Retargeting

If marketing is the art of persuasion, then retargeting is that art at its finest.

A user that has expressed interest in our brand, products, or services can be considered a warm lead. Therefore, you can expect that – with the right approach – our chances to convert are greater than when marketing a cold lead.

However, no matter how warm our lead might be, the strategic approach is key to closing the deal.

This is where it’s essential that you use all available information about the users and how he/she has interacted with your brand.

Why We Segment Audiences For Retargeting

Information such as demographic, which channel was the source of the lead, whether the interaction was on-site or off-site, and the level of interaction are just a few examples of the data that you can use to segment your audience.

This enables you to cluster users into different lists in order to maximize your chances to convert.

The above is also critical in order to be able to choose the most appropriate time and location for when to re-engage, and for the right messaging.

Think about it – marketing leverages psychological triggers to get people to take the actions you want them to take.

Remember some time ago when Google used to talk about micro-moments?

Retargeting means personalization that makes a connection in those micro-moments.

Understanding our users’ needs and motivations helps us to successfully use all of the above signals and give our retargeting campaigns the best chances to succeed with more personalized ads and experiences.

Let’s have a look at some easy-to-implement, practical examples of how you can segment our audiences into successful retargeting lists.

First, What Not To Do

To start, you must begin with the most obvious and avoid common mistakes that will sabotage your best efforts.

Too often, advertisers create a one-size-fits-all retargeting strategy that doesn’t acknowledge any of the information they have about the users and how they have interacted with the brand. They use the same generic messaging for all.

They might even land them all onto the homepage!

The most obvious place to start is segmenting our audience based on where and how they have interacted with our assets.

If that is on-site, you can create different lists based on the web pages they have visited and how far into the conversion path they went.

Those using Google Analytics with EEC (Enhanced Ecommerce) will find that the platform does the heavy lifting for them straight out of the box.

Different lists are automatically created to split users that have visited a product page from those that have gone a step further and added to the cart, or those who dropped at the checkout.

Here, the retargeting strategy should address any possible barrier for which users have dropped out and consider the appropriate messaging/possible incentive(s) required to get the user to convert.

Now that we’ve covered the basics, let’s have a look at something a little bit more creative, exciting, and sophisticated!

1. Don’t Think Channels; Think Users, Instead

Advertisers tend to think too much in terms of channels and in that way, they compartmentalize their strategy.

The reality is that things are much simpler. This is even more so in the case of retargeting, as you shouldn’t think about channels but focus on your users instead.

If you can overcome that default channel-based mindset, you start opening up to endless possibilities.

For example, you can run retargeting campaigns across multiple channels.

It is quite normal when setting things up to have Facebook prospecting and retargeting campaigns.

But why limit it to that?

It’s easy and quick to create lists of website users based on the source of the traffic.

In Google Analytics, for example, you can do that by selecting Traffic Sources and then Source, Medium, and Campaign as required.

Screenshot from Google Analytics, January 2022

In my example, you have created a list of users that have visited your website after clicking on a Facebook ad, advertising a Valentine’s Day promo.

What this means is that you can not only retarget those users within the Facebook network (Facebook, Instagram, Messenger, etc), but you are also able to amplify our reach and re-engage with those users across Google Display Network, YouTube, and more properties.

In a similar way, you could retarget users that have clicked on an email or have been referred by an affiliate site.

2. Flirting With Our Competitors’ Users

Now, this could be a bit controversial.

You’ll often see advertisers going to the extent of setting up campaigns that target their competitors.

If you are okay with bidding on your competitors, why stop there?

It’s not often that they follow up and continue engaging with those users that have clicked on their ads.

Most of the time, competitor campaigns are judged by impression share or direct conversions.

But if you’ve started flirting with your competitor’s audience and they have shown interest, you should really make the effort to continue engaging with them.

Additionally, you can use RLSA (Remarketing Lists for Search Ads) in Google to target users that have been on your website but are now searching for your competitors. Try to stop them before it’s too late!

3. Using Sequential Messaging And Storytelling For Engagement

We often think of ad campaigns as a one-dimensional interaction.

Our target audience shows interest in our ads by clicking on them or engaging with them, and marketers consider the job done.

But what about developing a series of ads that are all linked to one another?

For example, you could have the first ad setting up the story.

A number of ads follow, either in a linear way (i.e. ad 2 follows ad 1, and it is then followed by ad 3, ad 4, etc) or with a few alternative follow-ups that keep the story open and engaging.

Although this would require some creative effort to set up the ads in a storytelling sequence, from an audience perspective it’s actually quite simple.

Segments can be created to feed on each other with the trigger being whether the user has clicked, seen, or engaged with the previous ads.

4. Broaden Your Strategy By Targeting Life Events

Use business knowledge and data to create new segments to target audiences based on life events.

While these are generally readily available for prospecting campaigns, you can create your own audience segments for your retargeting ads.

For example, removalists, storage, and utility companies are likely to want to target people that are actively looking to buy a property, since they could also be interested in their services.

Creating a new audience with the targeting criteria as per below will help reach out and engage with website visitors that are on the move.

demographics displayed on Google AnalyticsScreenshot from Google Analytics, January 2022

Why is this important?

Because knowing the why – the reason why someone is interested in our products or services – allows us to greatly refine our messaging strategy and personalize the user experience.

Continuing with our example, and assuming you run a storage company, you could retarget your in-market audience with a message like this:

setting up the story in adsImage created by author, January 2022

5. Contextual Retargeting

Continuing from the idea of retargeting users based on the moment they are in, something similar you can do is to create audiences based on social and demographic profiling.

For example, you could segment avid TikTok or Instagram users and retarget them based on the context they are in.

A higher education provider such as a University or College could create ads and campaigns that are triggered when their users are in a specific location or attending an event of public interest – when they are in the proximity of a campus or attending an Open Day, for example.

Here, the profiling and segmentation of our audience is key to the success of the ads as you must understand our target users and their expected behavior.

6. Retargeting Users That Have Run A Site Search But Not Transacted

An often underutilized resource, site search can be turned into a powerful way to gather valuable information about our website visitors, especially those that haven’t converted.

Going back to Google Analytics, you could create a new audience by selecting the following criteria to segment our audience.

First, you need to specify the conditions which will define our filter, so after going into Audience Builder you choose Conditions, and select Site Search Status equals to Visits With Site Search.

After that, you can add an additional condition and select AND Days Since Last Session is equal or less than 2, if you want to focus on retargeting warm leads.

For the last condition, you also add the AND operator and select Transactions (per user) are equal to 0.

Now you can save the filter and create the audience.

For a practical example, imagine being a florist in the business of selling online fresh flowers delivered locally and nationally.

It is sometimes impractical to have a website that can cover every possible flower type with a dedicated page, or at times availability could be scarce and the stock quickly sells out.

So it is common for users to use the site search.

In this case, you could retarget our new audience with display ads as soon as stock is back on sale, or offer an alternative arrangement.

7. Retargeting Our Most Valuable Audience Segments Through (Buying) Personas

The concept of personas has been around since the beginning of marketing.

But we often think about them as a complicated piece of work that requires a lot of time and effort to put together.

In reality, anyone with access to website analytics is likely to be able to at least create a simplified version of personas.

For example, in Google Analytics, it’s easy to identify the gender, age, location of our most valuable customers.

But not only that – you can see what device they use, the model and OS, when they are most likely to be active on our site, and much more – including what they are (broadly) interested in and even what they are looking to buy (in-market).

With that information, you can create audiences based on the same exact traits and specifically retarget them after they have visited our site.

The advantage is that you can create ads and campaigns that specifically talk to them and in the way that is most likely to resonate with them.

See How to Use Website Traffic Analysis for Persona Development to learn more.

Final Thoughts

For many years now, we’ve been told personalization is key in all things marketing.

With increasing channels, competition, and the difficult markets we may now find ourselves operating in, it is certainly important.

Retargeting is often be overlooked and underutilized but as we’ve discussed, it doesn’t have to be a complex undertaking.

You know your customers and no doubt have the information you need.

Investing a bit of time and using the points above, you can convert more of those warm leads with smarter segmentation for your retargeting campaigns.

Not only will you add incremental value but you will also engage more personally and successfully with your customers, creating better experiences with your brand.

And that’s a win.

More resources: 

Featured Image: mentalmind/Shutterstock

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Google Documents Leaked & SEOs Are Making Some Wild Assumptions



Google Documents Leaked & SEOs Are Making Some Wild Assumptions

You’ve probably heard about the recent Google documents leak. It’s on every major site and all over social media.

Where did the docs come from?

My understanding is that a bot called yoshi-code-bot leaked docs related to the Content API Warehouse on Github on March 13th, 2024. It may have appeared earlier in some other repos, but this is the one that was first discovered.

They were discovered by an anonymous ex-Googler who shared the info with Erfan Azimi who shared it with Rand Fishkin who shared it with Mike King. The docs were removed on May 7th.

I appreciate all involved for sharing their findings with the community.

Google’s response

There was some debate if the documents were real or not, but they mention a lot of internal systems and link to internal documentation and it definitely appears to be real.

A Google spokesperson released the following statement to Search Engine Land:

We would caution against making inaccurate assumptions about Search based on out-of-context, outdated, or incomplete information. We’ve shared extensive information about how Search works and the types of factors that our systems weigh, while also working to protect the integrity of our results from manipulation.

SEOs interpret things based on their own experiences and bias

Many SEOs are saying that the ranking factors leaked. I haven’t seen any code or weights, just what appear to be descriptions and storage info. Unless one of the descriptions says the item is used for ranking, I think it’s dangerous for SEOs that all of these are used in ranking.

Having some features or information stored does not mean they’re used in ranking. For our search engine,, we have all kinds of things stored that might be used for crawling, indexing, ranking, personalization, testing, or feedback. We even have things stored that we aren’t doing things with yet.

What is more likely is that SEOs are making assumptions that favor their own opinions and biases.

It’s the same for me. I may not have full context or knowledge and may have inherent biases that influence my interpretation, but I try to be as fair as I can be. If I’m wrong, it means that I will learn something new and that’s a good thing! SEOs can, and do, interpret things differently.

Gael Breton said it well:

I’ve been around long enough to see many SEO myths created over the years and I can point you to who started many of them and what they misunderstood. We’ll likely see a lot of new myths from this leak that we’ll be dealing with for the next decade or longer.

Let’s look at a few things that in my opinion are being misinterpreted or where conclusions are being drawn where they shouldn’t be.


As much as I want to be able to say Google has a Site Authority score that they use for ranking that’s like DR, that part specifically is about compressed quality metrics and talks about quality.

I believe DR is more an effect that happens as you have a lot of pages with strong PageRank, not that it’s necessarily something Google uses. Lots of pages with higher PageRank that internally link to each other means you’re more likely to create stronger pages.

  • Do I believe that PageRank could be part of what Google calls quality? Yes.
  • Do I think that’s all of it? No.
  • Could Site Authority be something similar to DR? Maybe. It fits in the bigger picture.
  • Can I prove that or even that it’s used in rankings? No, not from this.

From some of the Google testimony to the US Department of Justice, we found out that quality is often measured with an Information Satisfaction (IS) score from the raters. This isn’t directly used in rankings, but is used for feedback, testing, and fine-tuning models.

We know the quality raters have the concept of E-E-A-T, but again that’s not exactly what Google uses. They use signals that align to E-E-A-T.

Some of the E-E-A-T signals that Google has mentioned are:

  • PageRank
  • Mentions on authoritative sites
  • Site queries. This could be “site: E-E-A-T” or searches like “ahrefs E-E-A-T”

So could some kind of PageRank scores extrapolated to the domain level and called Site Authority be used by Google and be part of what makes up the quality signals? I’d say it’s plausible, but this leak doesn’t prove it.

I can recall 3 patents from Google I’ve seen about quality scores. One of them aligns with the signals above for site queries.

I should point out that just because something is patented, doesn’t mean it is used. The patent around site queries was written in part by Navneet Panda. Want to guess who the Panda algorithm that related to quality was named after? I’d say there’s a good chance this is being used.

The others were around n-gram usage and seemed to be to calculate a quality score for a new website and another mentioned time on site.


I think this has been misinterpreted as well. The document has a field called hostAge and refers to a sandbox, but it specifically says it’s used “to sandbox fresh spam in serving time.”

To me, that doesn’t confirm the existence of a sandbox in the way that SEOs see it where new sites can’t rank. To me, it reads like a spam protection measure.


Are clicks used in rankings? Well, yes, and no.

We know Google uses clicks for things like personalization, timely events, testing, feedback, etc. We know they have models upon models trained on the click data including navBoost. But is that directly accessing the click data and being used in rankings? Nothing I saw confirms that.

The problem is SEOs are interpreting this as CTR is a ranking factor. Navboost is made to predict which pages and features will be clicked. It’s also used to cut down on the number of returned results which we learned from the DOJ trial.

As far as I know, there is nothing to confirm that it takes into account the click data of individual pages to re-order the results or that if you get more people to click on your individual results, that your rankings would go up.

That should be easy enough to prove if it was the case. It’s been tried many times. I tried it years ago using the Tor network. My friend Russ Jones (may he rest in peace) tried using residential proxies.

I’ve never seen a successful version of this and people have been buying and trading clicks on various sites for years. I’m not trying to discourage you or anything. Test it yourself, and if it works, publish the study.

Rand Fishkin’s tests for searching and clicking a result at conferences years ago showed that Google used click data for trending events, and they would boost whatever result was being clicked. After the experiments, the results went right back to normal. It’s not the same as using them for the normal rankings.


We know Google matches authors with entities in the knowledge graph and that they use them in Google news.

There seems to be a decent amount of author info in these documents, but nothing about them confirms that they’re used in rankings as some SEOs are speculating.

Was Google lying to us?

What I do disagree with whole-heartedly is SEOs being angry with the Google Search Advocates and calling them liars. They’re nice people who are just doing their job.

If they told us something wrong, it’s likely because they don’t know, they were misinformed, or they’ve been instructed to obfuscate something to prevent abuse. They don’t deserve the hate that the SEO community is giving them right now. We’re lucky that they share information with us at all.

If you think something they said is wrong, go and run a test to prove it. Or if there’s a test you want me to run, let me know. Just being mentioned in the docs is not proof that a thing is used in rankings.

Final Thoughts

While I may agree or I may disagree with the interpretations of other SEOs, I respect all who are willing to share their analysis. It’s not easy to put yourself or your thoughts out there for public scrutiny.

I also want to reiterate that unless these fields specifically say they are used in rankings, that the information could just as easily be used for something else. We definitely don’t need any posts about Google’s 14,000 ranking factors.

If you want my thoughts on a particular thing, message me on X or LinkedIn.

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Do Higher Content Scores Mean Higher Google Rankings? Our Data Says It’s Unlikely.



Do Higher Content Scores Mean Higher Google Rankings? Our Data Says It's Unlikely.

I studied the correlation between rankings and content scores from four popular content optimization tools: Clearscope, Surfer, MarketMuse, and Frase. The result? Weak correlations all around.

This suggests (correlation does not necessarily imply causation!) that obsessing over your content score is unlikely to lead to significantly higher Google rankings.

Does that mean content optimization scores are pointless?

No. You just need to know how best to use them and understand their flaws.

Most tools’ content scores are based on keywords. If top-ranking pages mention keywords your page doesn’t, your score will be low. If it does, your score will be high.

While this has its obvious flaws (having more keyword mentions doesn’t always mean better topic coverage), content scores can at least give some indication of how comprehensively you’re covering the topic. This is something Google is looking for.

Google says that comprehensively covering the topic is a sign of quality contentGoogle says that comprehensively covering the topic is a sign of quality content

If your page’s score is significantly lower than the scores of competing pages, you’re probably missing important subtopics that searchers care about. Filling these “content gaps” might help improve your rankings.

However, there’s nuance to this. If competing pages score in the 80-85 range while your page scores 79, it likely isn’t worth worrying about. But if it’s 95 vs. 20 then yeah, you should probably try to cover the topic better.

Key takeaway

Don’t obsess over content scores. Use them as a barometer for topic coverage. If your score is significantly lower than competitors, you’re probably missing important subtopics and might rank higher by filling those “content gaps.”

There are at least two downsides you should be aware of when it comes to content scores.

They’re easy to cheat

Content scores tend to be largely based on how many times you use the recommended set of keywords. In some tools, you can literally copy-paste the entire list, draft nothing else, and get an almost perfect score.

Scoring 98 on MarketMuse after shoehorning all the suggested keywords without any semblance of a draftScoring 98 on MarketMuse after shoehorning all the suggested keywords without any semblance of a draft

This is something we aim to solve with our upcoming content optimization tool: Content Master.

I can’t reveal too much about this yet, but it has a big USP compared to most existing content optimization tools: its content score is based on topic coverage—not just keywords.

For example, it tells us that our SEO strategy template should better cover subtopics like keyword research, on-page SEO, and measuring and tracking SEO success.

Preview of our upcoming Content Master toolPreview of our upcoming Content Master tool

But, unlike other content optimization tools, lazily copying and pasting related keywords into the document won’t necessarily increase our content score. It’s smart enough to understand that keyword coverage and topic coverage are different things.


This tool is still in production so the final release may look a little different.

They encourage copycat content

Content scores tell you how well you’re covering the topic based on what’s already out there. If you cover all important keywords and subtopics from the top-ranking pages and create the ultimate copycat content, you’ll score full marks.

This is a problem because quality content should bring something new to the table, not just rehash existing information. Google literally says this in their helpful content guidelines.

Google says quality content goes beyond obvious information. It needs to bring something new to the tableGoogle says quality content goes beyond obvious information. It needs to bring something new to the table

In fact, Google even filed a patent some years back to identify ‘information gain’: a measurement of the new information provided by a given article, over and above the information present in other articles on the same topic.

You can’t rely on content optimization tools or scores to create something unique. Making something that stands out from the rest of the search results will require experience, experimentation, or effort—something only humans can have/do.

Enrich common knowledge with new information and experiences in your contentEnrich common knowledge with new information and experiences in your content

Big thanks to my colleagues Si Quan and Calvinn who did the heavy lifting for this study. Nerd notes below. 😉

  • For the study, we selected 20 random keywords and pulled the top 20 ranking pages.
  • We pulled the SERPs before the March 2024 update was rolled out.
  • Some of the tools had issues pulling the top 20 pages, which we suspect was due to SERP features.
  • Clearscope didn’t give numerical scores; they opted for grades. We used ChatGPT to convert those grades into numbers.
  • Despite their increasing prominence in the SERPs, most of the tools had trouble analyzing Reddit, Quora, and YouTube. They typically gave a zero or no score for these results. If they gave no scores, we excluded them from the analysis.
  • The reason why we calculated both Spearman and Kendall correlations (and took the average) is because according to Calvinn (our Data Scientist), Spearman correlations are more sensitive and therefore more prone to being swayed by small sample size and outliers. On the other hand, the Kendall rank correlation coefficient only takes order into account. So, it is more robust for small sample sizes and less sensitive to outliers.

Final thoughts

Improving your content score is unlikely to hurt Google rankings. After all, although the correlation between scores and rankings is weak, it’s still positive. Just don’t obsess and spend hours trying to get a perfect score; scoring in the same ballpark as top-ranking pages is enough.

You also need to be aware of their downsides, most notably that they can’t help you craft unique content. That requires human creativity and effort.

Any questions or comments? Ping me on X or LinkedIn.

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Unlocking Brand Growth: Strategies for B2B and E-commerce Marketers



Unlocking Brand Growth: Strategies for B2B and E-commerce Marketers

In today’s fast-paced digital landscape, scaling a brand effectively requires more than just an innovative product or service. For B2B and e-commerce marketers, understanding the intricacies of growth strategies across different stages of business development is crucial.  

A recent analysis of 71 brands offers valuable insights into the optimal strategies for startups, scaleups, mature brands, and majority offline businesses. Here’s what we learned. 

Startup Stage: Building the Foundation 

Key Strategy: Startups focus on impressions-driven channels like Paid Social to establish their audience base. This approach is essential for gaining visibility and creating a strong initial footprint in the market. 

Case Study: Pooch & Mutt exemplified this strategy by leveraging Paid Social to achieve significant year-on-year revenue gains while also improving acquisition costs. This foundational step is crucial for setting the stage for future growth and stability. 

Scaleup Stage: Accelerating Conversion 

Key Strategy: For scaleups, having already established an audience, the focus shifts to conversion activities. Increasing spend in impressions-led media helps continue generating demand while maintaining a balance with acquisition costs. 

Case Study: The Essence Vault successfully applied this approach, scaling their Meta presence while minimizing cost increases. This stage emphasizes the importance of efficient spending to maximize conversion rates and sustain growth momentum. 

Mature Stage: Expanding Horizons 

Key Strategy: Mature brands invest in higher funnel activities to avoid market saturation and explore international expansion opportunities. This strategic pivot ensures sustained growth and market diversification. 

Case Study: Represent scaled their efforts on TikTok, enhancing growth and improving Meta efficiency. By expanding their presence in the US, they exemplified how mature brands can navigate saturation and seek new markets for continued success. 

Majority Offline Brands: Embracing Digital Channels 

Key Strategy: Majority offline brands primarily invest in click-based channels like Performance Max. However, the analysis reveals significant opportunities in Paid Social, suggesting a balanced approach for optimal results. 

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