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Are Brand Keywords Valuable For Every Audience? [Case Study]

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Are Brand Keywords Valuable For Every Audience? [Case Study]

Hello, my fellow search industry friends.

If you’ve followed me, you know I am a staunch supporter of using branded search keywords (and those New York Jets).

So this column may throw you for a little bit of a loop. We’re talking about using ads to target brand searches – but only from those who don’t know the brand.

Hear me out.

I also thought the concept was illogical and went against industry best practices.

But there is a way to make it work and make it purposeful.

Interestingly, two separate clients in very different verticals approached me with a similar scenario:

  • Client B: “How do we keep a brand presence, but not advertise to consumers who are already going to come to us?”
  • Client N: “We want to see how first-time buyers on our brand name interact with us versus repeat buyers. But we want it as clean and clear as possible. How do we separate prior buyers on the brand name from people searching our name who haven’t bought before?”

Both essentially equate to in-market, brand-aware audiences, driven to look for us by a different form of media.

After I reminded them about the 1+1=3 SEO+SEM incrementality insights, I sat and tried to figure out how these Google Ads would work, what they would look like, and how to determine success.

Two Different Game Plans for Audience Lists

Since the requests were similar but different, we had to come up with a game plan to execute them. This would, in turn, influence the design.

To my surprise, this was actually simpler than expected.

Both clients are heavy users of Google Analytics (something I highly advocate for), making this design fairly effective.

First, I needed audience lists.

We created a list of all site visitors for Client B and made the range 365 days on it.

We used this list for exclusionary purposes, and the Google Similar Audience (i.e., Look a Like) generated would be for observation.

Image from Google Analytics, March 2022

Client N was slightly different. They had window shoppers but never buyers on their website, so we needed a somewhat less aggressive stance.

We created a list of anyone who made a minimum of one purchase on the website and used GA’s max range of 540 days.

We used this list as exclusionary.

This allowed prior visitors to come in still and get deals, providing they hadn’t purchased yet.

540 Day Remarketing List from Google Analytics based on prior purchasesImage from Google Analytics, March 2022

Next, we had to wait.

We informed both clients the initial kick-off would take four to six weeks to allow the audience segments to build some history and be any degree of effectiveness.

We also supplemented these lists with CRM list uploads to bolster the accuracy where possible.

Eventually, we saw site visitors go from targeted to blocked or diverted, based on their behavior with the site.

Pro tip: Have a drawn-out timeline for deployment of anything that involves any first-party data audience lists. This allows the lists to grow and become more useful as time progresses.

How Do We Structure These Campaigns?

This isn’t rocket science (unlike trying to fold a fitted bed sheet).

In fact, there are different ways to do it. But I am for the most concrete separation of it, so I like to splinter things at the campaign level.

Client B is the easier structure – import audiences from GA into Google Ads.

Go into the campaigns and exclude the 365-day all visitor audience.

Boom, set to roll.

If you want to get fancy pants on this, add in the similar audience lists and in-market audiences you’ve vetted as observational.

Want to feel like an industry all-star? Add in a bid modifier.

But yeah, Client B is set.

Client N is slightly more complicated; they want to be visible on brand searches but to separate repeat buyers from non-buyers.

Monitor to track new customer growth, measure some branding efforts, and decide if you need incentives for non-buyers who know our name.

Note that there is a similar setup for non-brand, which already incentivizes new shoppers.

For Client N, we duplicated our brand keyword campaigns.

One for an exclusive audience target of the remarketing list of purchases in the past 540 days (plus a CRM list of confirmed purchases) will be called Repeat.

The other campaign excluded that remarketing list (and the CRM list); this campaign is called NTF (the internal naming acronym is all you need to know about why).

Over time, the 540-day list grows, so the non-buyer deals continue to go to those who have not purchased before.

In Client N, the two campaigns mirror each other in terms of bid keywords, bid strategy (not the most recommended approach), and landing page.

The primary difference between them (besides the audiences who see it) is that the creatives are slightly different.

Are Brand Keywords Valuable For Every Audience? [Case Study]Image from Google Analytics, March 2022

How Do We Determine Campaign Success?

To be honest, “success” in this scenario is a relative term.

Like starting my own small chicken farm in my back yard… in a city. There isn’t a clear but/defined line, but more of an observation of the audience.

For Client B, the concept of success was along the lines of capturing strong market share in our hyper-targeted geography of those who know our brand name but haven’t visited the site before.

Additionally, if we can get our conversions cost-effectively vs. that of all audiences, it would be a success.

Client N was different.

There was no true measurement of success.

There was just an observation of the value of a new customer vs. a repeat and seeing how they perform.

So like I said before, “success” here is relative.

In my eyes, for the record, my small flock chicken farm is successful.

Pro tip: Do not put your face near a chicken; they will bite you. They also do not enjoy wearing costumes. Both of those statements are directly correlated.

The Results:

With Client B, it was quite interesting.

We said, “If you’ve been here before, we aren’t paying for you again!”

We measured the data from all audiences to just new visitors seven weeks before the change vs. seven weeks after the change (three-week differential between the two time frames to deal with holidays).

7 week over 7 week look at performance for Client BImage from Google Analytics, March 2022

Not surprisingly, CPC went up a bit, and CTR went down a bit; these are not end of the world differences.

Our conversion rate took a hit of 19%, and our cost per click (CPC) skyrocketed by 50%.

But when you look at the relative numbers, they aren’t as terrifying.

Given the low CPC of brand terms, our aggregate CPA goal was under $10, which we weren’t concerned about in this scenario.

What is important to note is, if market share is our primary success metric (translated over here as Impression Share), then we were “successful.”

Yes, our traffic dipped a bit (about 44%), but we captured more of the brand-aware, never visited our site audience.

Client N remains different.

Once again, we wanted to understand the difference in behavior between the audiences and make it as clean and clear-cut as possible.

How Client N's First Time vs Repeat Shopper behavior performedImage from Google Analytics, March 2022

The data was clear-cut and insightful.

We finally understood what non-search marketing was contributing to brand search demand.

It also showed us that repeat buys have a 7% higher average order value (AOV) and convert again at a conversion rate (CVR) of 220%+ higher than a first-time buyer on a brand term.

This also indicated a need to incentivize first-time buyers on brand terms, because of such a high chance of converting again on brand terms, and for more.

Essentially a repeat brand buyer, at a minimum (because our data is only eight months old), is worth 206% more in sales than a single buyer.

So, What Does This All Mean?

More or less, what you already suspected.

Being present on brand terms remains incredibly important.

But if you ever had to stray away from brand – at the very least, to save a few dollars – do not leave your first-time engagers.

They will get you into your CRM and help you show the value of non-search higher funnel marketing.

Also, chickens do bite.

More resources: 


Featured Image: FOTOSPLASH/Shutterstock




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Google To Upgrade All Retailers To New Merchant Center By September

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Google To Upgrade All Retailers To New Merchant Center By September

Google has announced plans to transition all retailers to its updated Merchant Center platform by September.

This move will affect e-commerce businesses globally and comes ahead of the holiday shopping season.

The Merchant Center is a tool for online retailers to manage how their products appear across Google’s shopping services.

Key Changes & Features

The new Merchant Center includes several significant updates.

Product Studio

An AI-powered tool for content creation. Google reports that 80% of current users view it as improving efficiency.

This feature allows retailers to generate tailored product assets, animate still images, and modify existing product images to match brand aesthetics.

It also simplifies tasks like background removal and image resolution enhancement.

Centralized Analytics

A new tab consolidating various business insights, including pricing data and competitive analysis tools.

Retailers can access pricing recommendations, competitive visibility reports, and retail-specific search trends, enabling them to make data-driven decisions and capitalize on popular product categories.

Redesigned Navigation

Google claims the new interface is more intuitive and cites increased setup success rates for new merchants.

The platform now offers simplified website verification processes and can pre-populate product information during setup.

Initial User Response

According to Google, early adopters have shown increased engagement with the platform.

The company reports a 25% increase in omnichannel merchants adding product offers in the new system. However, these figures have yet to be independently verified.

Jeff Harrell, Google’s Senior Director of Merchant Shopping, states in an announcement:

“We’ve seen a significant increase in retention and engagement among existing online merchants who have moved to the new Merchant Center.”

Potential Challenges and Support

While Google emphasizes the upgrade’s benefits, some retailers, particularly those comfortable with the current version, may face challenges adapting to the new system.

The upgrade’s mandatory nature could raise concerns among users who prefer the existing interface or have integrated workflows based on the current system.

To address these concerns, Google has stated that it will provide resources and support to help with the transition. This includes tutorial videos, detailed documentation, and access to customer support teams for troubleshooting.

Industry Context

This update comes as e-commerce platforms evolve, with major players like Amazon and Shopify enhancing their seller tools. Google’s move is part of broader efforts to maintain competitiveness in the e-commerce services sector.

The upgrade could impact consumers by improving product listings and providing more accurate information across Google’s shopping services.

For the e-commerce industry as a whole, it signals a continued push towards AI-driven tools and data-centric decision-making.

Transition Timeline

Google states that retailers will be automatically upgraded by September if they still need to transition.

The company advises users to familiarize themselves with the new features before the busy holiday shopping period.


Featured Image: BestForBest/Shutterstock

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Meta AI Introduces AI-Generated Photos to All Platforms

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Meta AI Adds AI-Generated Images to Social and Messaging Platforms and Expands Availability to More Languages and Countries

Meta just released multiple updates to Meta AI which brings advanced image generation and editing capabilities directly to Facebook, Instagram and WhatsApp feeds, plus availability in more countries and languages.

New Meta AI Creative Tools

Meta AI is bringing AI generated and AI Edited photography that can be generated at the moment a user is making a post or sending a message with a new tool called Imagine Me.

Imagine Me is a prompt that can be used to transform an uploaded image that can be shared. This new feature is first rolling out as a beta in the United States.

Meta explains:

“Imagine yourself creates images based on a photo of you and a prompt like ‘Imagine me surfing’ or ‘Imagine me on a beach vacation’ using our new state-of-the-art personalization model. Simply type “Imagine me” in your Meta AI chat to get started, and then you can add a prompt like “Imagine me as royalty” or “Imagine me in a surrealist painting.” From there, you can share the images with friends and family, giving you the perfect response or funny sidebar to entertain your group chat.”

New Editing Features

Meta products like Facebook, Messenger, WhatsApp and Instagram now have advanced editing capabilities that allow users to add or remove objects from images, to change them in virtually any manner, such as their example of turning a cat in an image into a dog. A new Edit With AI button is forthcoming in a month that will unlock even more AI editing power.

Adding AI generated images to Facebook, Instagram, Messenger and WhatsApp within feed, posts, stories, comments and messages is rolling out this week in English and coming later to other languages.

Screenshot of a Facebook user adding an AI generated image into their post

Meta AI In More Countries And Languages

Meta AI is now available in seven additional countries, bringing the total countries to to 22. It is also available in seven more languages.

List of Seven Additional Countries:

  1. Argentina
  2. Cameroon
  3. Chile
  4. Colombia
  5. Ecuador
  6. Mexico
  7. Peru

Meta AI is now also available in the following seven additional languages:

  1. French
  2. German
  3. Hindi
  4. Hindi-Romanized Script
  5. Italian
  6. Portuguese
  7. Spanish

Advanced Math And Coding

Meta AI is making their most advanced model, Llama 405B, available for users to take advantage of its advanced reasoning abilities that can answer complex answers and excells at math and coding.

Meta AI writes:

“You can get help on your math homework with step-by-step explanations and feedback, write code faster with debugging support and optimization suggestions, and master complex technical and scientific concepts with expert instruction.”

Read the official announcement:

Meta AI Is Now Multilingual, More Creative and Smarter

Featured Image by Shutterstock/QubixStudio

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System Builders – How AI Changes The Work Of SEO

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Kevin Indig's Growth Memo for SEJ

AI is terraforming tech. The content and SEO ecosystem is undergoing a massive structural change.

Human-written content gains value faster for LLM training than for end consumers as the pure profit licensing deals between LLM developers and publishers show.

Publishers struggle to survive from digital subscriptions but get millions that go straight to their bottom line for providing training data.

Content platforms, social networks, SaaS companies and consumer apps coat their products with AI. A few examples:

  • Spotify DJ (AI-generated playlist).
  • AI Overview (AI answers in Google Search).
  • Instagram AI personas (celebrity AI chatbots).
  • Ebay’s magical listing (turn a photo into a listing).
  • Redfin Redesign (try interior designs on real house pictures).
Image Credit: Kevin Indig

The quality of machine-generated content (MGC) challenges human-generated content (HGC). I ran an experiment with my Twitter and LinkedIn followers: I asked them to choose which of two articles was written by a human and which by a machine – and they had to explain their answer.

Only a handful of people figured out that AI wrote both pieces. I intentionally framed the question in a leading way to see if people would challenge the setting or believe that one piece was written by a human if told so.

  • Not an isolated experiment: A survey of 1,900 Americans found that 63.5% of people can’t distinguish between AI content and human content.1
  • People seek help: Google search demand for [ai checker] has reached 100,000 in May 2024 (Glimpse).
  • Dark side: scammers use MGC to make money, as 77% of AI scam victims lost money.2
Search demand for AI checkerImage Credit: Kevin Indig

The quality level of LLMs pushes SEO work towards automating workflows and learning with AI, while writers will take content from good to great instead of zero to one.

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

How AI Changes The Work Of SEOImage Credit: Lyna ™

System Builders

Clients, podcasters and panel hosts often ask me what skills SEOs need to build for the AI future. For a long time, my answer was to learn, stay open-minded and gain as much practical experience with AI as possible.

Now, my answer is SEOs should learn how to build AI agents and workflows that automate tasks. AI changes the way search works but also the way SEOs work.

AI + No-code Allows SEOs To Automate Workflows

A few examples:

1/ Cannibalization

  • Old world: SEOs download search console data and create pivot tables to spot keyword cannibalization.
  • New world: SEOs build an AI workflow that sends alters, identifies true keyword cannibalization, makes content suggestions to fix the problem, and monitors the improvement.

2/ Site Crawling

  • Old world: SEOs crawl websites to find inefficiencies in internal linking, status code errors, duplicate content, etc.
  • New world: SEOs build an AI agent that regularly crawls the site and automatically suggests new internal links that are shipped after human approval, fixes broken canonical tags and excludes soft 404 errors in the robots.txt.

3/ Content Creation

  • Old world: SEOs do keyword research and write content briefs. Writers create the content.
  • New world: SEOs automate keyword research with AI and create hundreds of relevant articles as a foundation for writers to build on.

All of this is already possible today with AI workflow tools like AirOps or Apify, which chain agents and LLMs together to scrape, analyze, transform data or create content.

Moving forward, we’ll spend much more time building automated systems instead of wasting time on point analyses and catalogs of recommendations. The SEO work will be defining logic, setting rules, prompting and coding.

building automated systems Building workflows with AirOps (Image Credit: Kevin Indig)

You Can Learn (Almost) Anything With AI

I never made the time to really learn Python or R, but with the help of Chat GPT and Gemini in Colab, I can write any script with natural language prompts.

When the script doesn’t work, I can paste a screenshot into Chat GPT and describe the issue to get a solution. AI helps with Regex, Google Sheets/Excel, R, Python, etc. Nothing is off-limits.

Being able to write scripts can solve problems like data analysis, a/b testing and using APIs. As an SEO, I’m no longer dependent on engineers, data scientists or writers to perform certain tasks. I can act faster and on my own account.

I’m not the only one to figure this out. People are learning to code, write and many other skills with AI. We can learn to build AI workflows by asking AI to teach us.

Search demand for coding with AI is explodingImage Credit: Kevin Indig
Search demand for write with AI is explodingImage Credit: Kevin Indig
Search demand for learn with AI is explodingImage Credit: Kevin Indig

When you can learn almost anything, the only limit is time.

The Work Of Writers Changes

Against common belief, writers won’t be crossed out of this equation but will play the critical role of editing, directing and curating.

In any automated process, humans QA the output. Think of car assembling lines. Even though AI content leaps in quality, spot checks reduce the risk of errors. Caught issues, such as wrong facts, weird phrasing or off-brand wording, will be critical feedback to fine-tune models to improve their output.

Instead of leg work like writing drafts, writers will bring AI content from good to great. In the concept of information gain, writers will spend most of their time making a piece outstanding.

The rising quality work spans from blog content to programmatic content, where writers will add curated content when searches have a desire for human experience, such as in travel.

A mini guide to Los AngelesTripadvisor’s attraction pages feature human-curated sections. (Image Credit: Kevin Indig)

Unfair Advantage

As often with new technology, a few first-mover people and companies get exponential value until the rest catch up. My worry is that a few fast-moving companies will grab massive land with AI.

And yet, this jump in progress will allow newcomers to challenge incumbents and get a fair chance to compete on the field.

AI might be a bigger game changer for SEOs than for Google. The raw power of AI might help us overcome challenges from AI Overviews and machine learning-driven algorithm updates.

But the biggest win might be that SEOs can finally make something instead of delivering recommendations. The whole value contribution of SEOs changes because my output can drive results faster.

Survey: ChatGPT and AI Content – Can people tell the difference?

Artificial Intelligence Voice Scams on the Rise with 1 in 4 Adults Impacted


Featured Image: Paulo Bobita/Search Engine Journal

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