SEO
Four tips for SEM teams to adjust to a privacy-focused future
30-second summary:
- Within the digital marketing space, the conversation around privacy and cookie changes has focused heavily on programmatic and paid social
- But how will third-party cookie deprecation and new privacy regulations impact paid search?
- Here is what search marketers can expect and how to prepare
In the digital marketing world, targeting, measurement, and optimization have foundationally relied on the ability to accurately track user behaviors and performance across the web. However, as we all know, platforms like Google and Apple have introduced privacy-focused initiatives over the past few years that complicate targeting and measurement for advertisers.
When discussing the impacts of these changes, much of the conversation has focused on programmatic and paid social, which are undoubtedly the digital channels feeling the greatest impact. What has not been discussed in great detail is the impact on search marketing. How should advertisers adapt their paid search strategies to adjust to these new realities?
Before digging into action items, let’s recap the newest updates and how they’ll impact paid search campaigns.
Chrome’s privacy updates will have a greater impact than iOS
There are two key privacy changes top-of-mind for search marketers in 2021. App Tracking Transparency (ATT), introduced through Apple’s iOS 14.5 update, requires a user to opt-in before a company can track their data across other apps or websites. Fortunately, the impact of this update on search programs for most advertisers is limited. Advertisers may see fluctuations in universal app campaign (UAC) volume, and search properties with a larger app-based audience (for example, YouTube) will experience some degradation in measurement and targeting. By and large, though, the ATT update is more of an issue for programmatic advertisers than search marketers.
Google Chrome’s third-party cookie deprecation, coming in 2023, will have a larger impact on paid search. From a targeting perspective, remarketing lists for search ads (RLSA) will become less effective without data on users’ behaviors across non-Google properties. As of Q3 2020, RLSA accounted for 20 percent of Google search ad clicks for Merkle advertisers – so this is a significant segment of traffic. There will also be new measurement challenges, especially for companies relying on proprietary reporting tech.
While iOS 14.5 is already a reality for advertisers, there is more than a year left to prepare for Google’s third-party cookie deprecation. There are several steps search marketers can take now to optimize performance within a more privacy-focused environment.
1. Lean into first-party data audience solutions to target
Effective audience segmentation and targeting will continue to be critical in search moving forward. Google offers several in-platform audience options, such as in-market and affinity audiences, that don’t rely on third-party data and can be leveraged by advertisers indefinitely.
However, there’s a greater opportunity for organizations to differentiate themselves by crafting a strong audience strategy using their own first-party data with Customer Match. Many advertisers already use Customer Match to some degree, but the data may not be refreshed regularly, or it may not be segmented in detail. The transition away from third-party cookies is the perfect impetus for fine-tuning a first-party data strategy.
First, advertisers should assess the quality of their first-party data. How comprehensive is the data that’s collected? Are there a lot of duplicate records, or is there a reliable unique record for each customer? All of the slicing and dicing in the world won’t be helpful if the data you’re working with is fundamentally flawed.
Next, marketers should assess opportunities to segment their customer lists in meaningful ways – a single “email subscribers list” isn’t going to cut it anymore. Smart segmentation is always important, but it will become even more critical because it will empower Google to build more tailored similar audiences.
After establishing segments, there must be a plan to refresh those audiences frequently. Determine an appropriate cadence for updating customer match lists and determine who’s responsible for doing it. Currently, this can be done through the Google Ads API or within the Google Ads interface.
Once a foundation is in place for your audience strategy, revisit your approach quarterly to ensure that segments continue to align with attributes important to your customers and your business. This also creates a natural check-in point to confirm that lists are being updated as expected and that they’re all receiving traffic. If needed, audience bid modifiers should be adjusted to reflect current performance.
On the topic of bidding…
2. Test or transition to Smart Bidding to take advantage of Google’s proprietary signals
While we, as advertisers, will have lesser user data available to us without third-party cookies, Google will continue to have a wealth of information about its users and their behavior on Google-owned properties. Google Ads’ Smart Bidding allows advertisers to take advantage of those audience signals to reach the right person at the right bid with machine learning. That’s not to say that segmentation isn’t important with Smart Bidding – it still is. One of the many signals the bidder looks at is all of the audiences a given user belongs to, including customer match audiences.
Advertisers can and should take advantage of custom audience segmentations through Google Analytics, Looker, or Google Cloud Platform (Big Query). And they should automate the pushing of defined customer audiences to Google marketing activation to maximize business data with Google’s Smart Bidding.
Whatever your advertising goals may be, there is likely a Google Ads Smart Bidding strategy to suit your business needs. For search marketers not yet using Smart Bidding, it’d be smart to start testing in early 2022 to iron out any kinks and have a full-blown Smart Bidding approach before 2023.
3. Get comfortable with new reporting methods
We’ve talked a lot about adapting to the changes to come with targeting, but privacy updates also create challenges for reporting. There will be a measurement gap that advertisers need to solve. Fortunately, Google Ads has solutions in place to help fill holes with enhanced and modeled conversions.
Enhanced conversions improve reporting accuracy by using an advertiser’s hashed first-party data to tie a conversion event to an ad interaction. Enhanced conversions are powerful in that they make a one-to-one connection between an impression or click and a purchase. Modeled conversions, on the other hand, find their power in scalability; Google has been using them to report on cross-device conversions for several years. When used in combination, advertisers get the benefit of precision where a one-to-one connection exists, while smartly estimating conversions in areas where it does not.
As privacy regulations increasingly muddy the reporting waters, the stakes are higher to work with Google to fill the gaps. If you’re relying primarily on proprietary technology for reporting, consider using Google’s measurement system to get a more complete picture of performance. Understanding the full impact of search is critical for being able to optimize and allocate budgets effectively. Note that Google’s global site tag or tag manager is required to appropriately track conversions.
4. Monitor universal app campaigns for performance changes
Advertisers using UAC to drive app downloads via paid search should closely monitor performance for those campaigns. So far, Merkle has observed a slow downward trend in tracked installs as a result of Apple’s ATT update. To avoid the effects of ATT, some advertisers are increasing their investment in Android or shifting spend there entirely. UAC can continue to be an effective channel for marketers, but reduced visibility on iOS may require bid or budget shifts in order to hit performance goals.
Conclusion
Privacy updates are changing the way marketers approach targeting and measurement. Don’t panic – but do put a plan in place. With the right adjustments, search advertisers can effectively pivot along with the industry. More than ever, advertisers must value first-party audiences driven by search to further customer engagement, experiences, and marketing ROI. Using that first-party data, in conjunction with machine-learning-based bid strategies and modeled and enhanced reporting, will create a foundation to help future proof search campaigns for privacy updates in the years to come.
Matt Mierzejewski is SVP of Performance Marketing Lab and Search at Merkle Inc.
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SEO
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SEO
How To Use ChatGPT For Keyword Research
Anyone not using ChatGPT for keyword research is missing a trick.
You can save time and understand an entire topic in seconds instead of hours.
In this article, I outline my most effective ChatGPT prompts for keyword research and teach you how I put them together so that you, too, can take, edit, and enhance them even further.
But before we jump into the prompts, I want to emphasize that you shouldn’t replace keyword research tools or disregard traditional keyword research methods.
ChatGPT can make mistakes. It can even create new keywords if you give it the right prompt. For example, I asked it to provide me with a unique keyword for the topic “SEO” that had never been searched before.
“Interstellar Internet SEO: Optimizing content for the theoretical concept of an interstellar internet, considering the challenges of space-time and interplanetary communication delays.”
Although I want to jump into my LinkedIn profile and update my title to “Interstellar Internet SEO Consultant,” unfortunately, no one has searched that (and they probably never will)!
You must not blindly rely on the data you get back from ChatGPT.
What you can rely on ChatGPT for is the topic ideation stage of keyword research and inspiration.
ChatGPT is a large language model trained with massive amounts of data to accurately predict what word will come next in a sentence. However, it does not know how to do keyword research yet.
Instead, think of ChatGPT as having an expert on any topic armed with the information if you ask it the right question.
In this guide, that is exactly what I aim to teach you how to do – the most essential prompts you need to know when performing topical keyword research.
Best ChatGPT Keyword Research Prompts
The following ChatGPT keyword research prompts can be used on any niche, even a topic to which you are brand new.
For this demonstration, let’s use the topic of “SEO” to demonstrate these prompts.
Generating Keyword Ideas Based On A Topic
What Are The {X} Most Popular Sub-topics Related To {Topic}?
The first prompt is to give you an idea of the niche.
As shown above, ChatGPT did a great job understanding and breaking down SEO into three pillars: on-page, off-page & technical.
The key to the following prompt is to take one of the topics ChatGPT has given and query the sub-topics.
What Are The {X} Most Popular Sub-topics Related To {Sub-topic}?
For this example, let’s query, “What are the most popular sub-topics related to keyword research?”
Having done keyword research for over 10 years, I would expect it to output information related to keyword research metrics, the types of keywords, and intent.
Let’s see.
Again, right on the money.
To get the keywords you want without having ChatGPT describe each answer, use the prompt “list without description.”
Here is an example of that.
List Without Description The Top {X} Most Popular Keywords For The Topic Of {X}
You can even branch these keywords out further into their long-tail.
Example prompt:
List Without Description The Top {X} Most Popular Long-tail Keywords For The Topic “{X}”
List Without Description The Top Semantically Related Keywords And Entities For The Topic {X}
You can even ask ChatGPT what any topic’s semantically related keywords and entities are!
Tip: The Onion Method Of Prompting ChatGPT
When you are happy with a series of prompts, add them all to one prompt. For example, so far in this article, we have asked ChatGPT the following:
- What are the four most popular sub-topics related to SEO?
- What are the four most popular sub-topics related to keyword research
- List without description the top five most popular keywords for “keyword intent”?
- List without description the top five most popular long-tail keywords for the topic “keyword intent types”?
- List without description the top semantically related keywords and entities for the topic “types of keyword intent in SEO.”
Combine all five into one prompt by telling ChatGPT to perform a series of steps. Example:
“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, Step 4, and Step 5”
Example:
“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, Step 4 and Step 5. Step 1 – Generate an answer for the 3 most popular sub-topics related to {Topic}?. Step 2 – Generate 3 of the most popular sub-topics related to each answer. Step 3 – Take those answers and list without description their top 3 most popular keywords. Step 4 – For the answers given of their most popular keywords, provide 3 long-tail keywords. Step 5 – for each long-tail keyword offered in the response, a list without descriptions 3 of their top semantically related keywords and entities.”
Generating Keyword Ideas Based On A Question
Taking the steps approach from above, we can get ChatGPT to help streamline getting keyword ideas based on a question. For example, let’s ask, “What is SEO?”
“Perform the following steps in a consecutive order Step 1, Step 2, Step 3, and Step 4. Step 1 Generate 10 questions about “{Question}”?. Step 2 – Generate 5 more questions about “{Question}” that do not repeat the above. Step 3 – Generate 5 more questions about “{Question}” that do not repeat the above. Step 4 – Based on the above Steps 1,2,3 suggest a final list of questions avoiding duplicates or semantically similar questions.”
Generating Keyword Ideas Using ChatGPT Based On The Alphabet Soup Method
One of my favorite methods, manually, without even using a keyword research tool, is to generate keyword research ideas from Google autocomplete, going from A to Z.
You can also do this using ChatGPT.
Example prompt:
“give me popular keywords that includes the keyword “SEO”, and the next letter of the word starts with a”
Tip: Using the onion prompting method above, we can combine all this in one prompt.
“Give me five popular keywords that include “SEO” in the word, and the following letter starts with a. Once the answer has been done, move on to giving five more popular keywords that include “SEO” for each letter of the alphabet b to z.”
Generating Keyword Ideas Based On User Personas
When it comes to keyword research, understanding user personas is essential for understanding your target audience and keeping your keyword research focused and targeted. ChatGPT may help you get an initial understanding of customer personas.
Example prompt:
“For the topic of “{Topic}” list 10 keywords each for the different types of user personas”
You could even go a step further and ask for questions based on those topics that those specific user personas may be searching for:
As well as get the keywords to target based on those questions:
“For each question listed above for each persona, list the keywords, as well as the long-tail keywords to target, and put them in a table”
Generating Keyword Ideas Using ChatGPT Based On Searcher Intent And User Personas
Understanding the keywords your target persona may be searching is the first step to effective keyword research. The next step is to understand the search intent behind those keywords and which content format may work best.
For example, a business owner who is new to SEO or has just heard about it may be searching for “what is SEO.”
However, if they are further down the funnel and in the navigational stage, they may search for “top SEO firms.”
You can query ChatGPT to inspire you here based on any topic and your target user persona.
SEO Example:
“For the topic of “{Topic}” list 10 keywords each for the different types of searcher intent that a {Target Persona} would be searching for”
ChatGPT For Keyword Research Admin
Here is how you can best use ChatGPT for keyword research admin tasks.
Using ChatGPT As A Keyword Categorization Tool
One of the use cases for using ChatGPT is for keyword categorization.
In the past, I would have had to devise spreadsheet formulas to categorize keywords or even spend hours filtering and manually categorizing keywords.
ChatGPT can be a great companion for running a short version of this for you.
Let’s say you have done keyword research in a keyword research tool, have a list of keywords, and want to categorize them.
You could use the following prompt:
“Filter the below list of keywords into categories, target persona, searcher intent, search volume and add information to a six-column table: List of keywords – [LIST OF KEYWORDS], Keyword Search Volume [SEARCH VOLUMES] and Keyword Difficulties [KEYWORD DIFFICUTIES].”
Tip: Add keyword metrics from the keyword research tools, as using the search volumes that a ChatGPT prompt may give you will be wildly inaccurate at best.
Using ChatGPT For Keyword Clustering
Another of ChatGPT’s use cases for keyword research is to help you cluster. Many keywords have the same intent, and by grouping related keywords, you may find that one piece of content can often target multiple keywords at once.
However, be careful not to rely only on LLM data for clustering. What ChatGPT may cluster as a similar keyword, the SERP or the user may not agree with. But it is a good starting point.
The big downside of using ChatGPT for keyword clustering is actually the amount of keyword data you can cluster based on the memory limits.
So, you may find a keyword clustering tool or script that is better for large keyword clustering tasks. But for small amounts of keywords, ChatGPT is actually quite good.
A great use small keyword clustering use case using ChatGPT is for grouping People Also Ask (PAA) questions.
Use the following prompt to group keywords based on their semantic relationships. For example:
“Organize the following keywords into groups based on their semantic relationships, and give a short name to each group: [LIST OF PAA], create a two-column table where each keyword sits on its own row.
Using Chat GPT For Keyword Expansion By Patterns
One of my favorite methods of doing keyword research is pattern spotting.
Most seed keywords have a variable that can expand your target keywords.
Here are a few examples of patterns:
1. Question Patterns
(who, what, where, why, how, are, can, do, does, will)
“Generate [X] keywords for the topic “[Topic]” that contain any or all of the following “who, what, where, why, how, are, can, do, does, will”
2. Comparison Patterns
Example:
“Generate 50 keywords for the topic “{Topic}” that contain any or all of the following “for, vs, alternative, best, top, review”
3. Brand Patterns
Another one of my favorite modifiers is a keyword by brand.
We are probably all familiar with the most popular SEO brands; however, if you aren’t, you could ask your AI friend to do the heavy lifting.
Example prompt:
“For the top {Topic} brands what are the top “vs” keywords”
4. Search Intent Patterns
One of the most common search intent patterns is “best.”
When someone is searching for a “best {topic}” keyword, they are generally searching for a comprehensive list or guide that highlights the top options, products, or services within that specific topic, along with their features, benefits, and potential drawbacks, to make an informed decision.
Example:
“For the topic of “[Topic]” what are the 20 top keywords that include “best”
Again, this guide to keyword research using ChatGPT has emphasized the ease of generating keyword research ideas by utilizing ChatGPT throughout the process.
Keyword Research Using ChatGPT Vs. Keyword Research Tools
Free Vs. Paid Keyword Research Tools
Like keyword research tools, ChatGPT has free and paid options.
However, one of the most significant drawbacks of using ChatGPT for keyword research alone is the absence of SEO metrics to help you make smarter decisions.
To improve accuracy, you could take the results it gives you and verify them with your classic keyword research tool – or vice versa, as shown above, uploading accurate data into the tool and then prompting.
However, you must consider how long it takes to type and fine-tune your prompt to get your desired data versus using the filters within popular keyword research tools.
For example, if we use a popular keyword research tool using filters, you could have all of the “best” queries with all of their SEO metrics:
And unlike ChatGPT, generally, there is no token limit; you can extract several hundred, if not thousands, of keywords at a time.
As I have mentioned multiple times throughout this piece, you cannot blindly trust the data or SEO metrics it may attempt to provide you with.
The key is to validate the keyword research with a keyword research tool.
ChatGPT For International SEO Keyword Research
ChatGPT can be a terrific multilingual keyword research assistant.
For example, if you wanted to research keywords in a foreign language such as French. You could ask ChatGPT to translate your English keywords;
- The key is to take the data above and paste it into a popular keyword research tool to verify.
- As you can see below, many of the keyword translations for the English keywords do not have any search volume for direct translations in French.
But don’t worry, there is a workaround: If you have access to a competitor keyword research tool, you can see what webpage is ranking for that query – and then identify the top keyword for that page based on the ChatGPT translated keywords that do have search volume.
-
Or, if you don’t have access to a paid keyword research tool, you could always take the top-performing result, extract the page copy, and then ask ChatGPT what the primary keyword for the page is.
Key Takeaway
ChatGPT can be an expert on any topic and an invaluable keyword research tool. However, it is another tool to add to your toolbox when doing keyword research; it does not replace traditional keyword research tools.
As shown throughout this tutorial, from making up keywords at the beginning to inaccuracies around data and translations, ChatGPT can make mistakes when used for keyword research.
You cannot blindly trust the data you get back from ChatGPT.
However, it can offer a shortcut to understanding any topic for which you need to do keyword research and, as a result, save you countless hours.
But the key is how you prompt.
The prompts I shared with you above will help you understand a topic in minutes instead of hours and allow you to better seed keywords using keyword research tools.
It can even replace mundane keyword clustering tasks that you used to do with formulas in spreadsheets or generate ideas based on keywords you give it.
Paired with traditional keyword research tools, ChatGPT for keyword research can be a powerful tool in your arsenal.
More resources:
Featured Image: Tatiana Shepeleva/Shutterstock
SEO
OpenAI Expected to Integrate Real-Time Data In ChatGPT
Sam Altman, CEO of OpenAI, dispelled rumors that a new search engine would be announced on Monday, May 13. Recent deals have raised the expectation that OpenAI will announce the integration of real-time content from English, Spanish, and French publications into ChatGPT, complete with links to the original sources.
OpenAI Search Is Not Happening
Many competing search engines have tried and failed to challenge Google as the leading search engine. A new wave of hybrid generative AI search engines is currently trying to knock Google from the top spot with arguably very little success.
Sam Altman is on record saying that creating a search engine to compete against Google is not a viable approach. He suggested that technological disruption was the way to replace Google by changing the search paradigm altogether. The speculation that Altman is going to announce a me-too search engine on Monday never made sense given his recent history of dismissing the concept as a non-starter.
So perhaps it’s not a surprise that he recently ended the speculation by explicitly saying that he will not be announcing a search engine on Monday.
He tweeted:
“not gpt-5, not a search engine, but we’ve been hard at work on some new stuff we think people will love! feels like magic to me.”
“New Stuff” May Be Iterative Improvement
It’s quite likely that what’s going to be announced is iterative which means it improves ChatGPT but not replaces it. This fits into how Altman recently expressed his approach with ChatGPT.
He remarked:
“And it does kind of suck to ship a product that you’re embarrassed about, but it’s much better than the alternative. And in this case in particular, where I think we really owe it to society to deploy iteratively.
There could totally be things in the future that would change where we think iterative deployment isn’t such a good strategy, but it does feel like the current best approach that we have and I think we’ve gained a lot from from doing this and… hopefully the larger world has gained something too.”
Improving ChatGPT iteratively is Sam Altman’s preference and recent clues point to what those changes may be.
Recent Deals Contain Clues
OpenAI has been making deals with news media and User Generated Content publishers since December 2023. Mainstream media has reported these deals as being about licensing content for training large language models. But they overlooked a a key detail that we reported on last month which is that these deals give OpenAI access to real-time information that they stated will be used to give attribution to that real-time data in the form of links.
That means that ChatGPT users will gain the ability to access real-time news and to use that information creatively within ChatGPT.
Dotdash Meredith Deal
Dotdash Meredith (DDM) is the publisher of big brand publications such as Better Homes & Gardens, FOOD & WINE, InStyle, Investopedia, and People magazine. The deal that was announced goes way beyond using the content as training data. The deal is explicitly about surfacing the Dotdash Meredith content itself in ChatGPT.
The announcement stated:
“As part of the agreement, OpenAI will display content and links attributed to DDM in relevant ChatGPT responses. …This deal is a testament to the great work OpenAI is doing on both fronts to partner with creators and publishers and ensure a healthy Internet for the future.
Over 200 million Americans each month trust our content to help them make decisions, solve problems, find inspiration, and live fuller lives. This partnership delivers the best, most relevant content right to the heart of ChatGPT.”
A statement from OpenAI gives credibility to the speculation that OpenAI intends to directly show licensed third-party content as part of ChatGPT answers.
OpenAI explained:
“We’re thrilled to partner with Dotdash Meredith to bring its trusted brands to ChatGPT and to explore new approaches in advancing the publishing and marketing industries.”
Something that DDM also gets out of this deal is that OpenAI will enhance DDM’s in-house ad targeting in order show more tightly focused contextual advertising.
Le Monde And Prisa Media Deals
In March 2024 OpenAI announced a deal with two global media companies, Le Monde and Prisa Media. Le Monde is a French news publication and Prisa Media is a Spanish language multimedia company. The interesting aspects of these two deals is that it gives OpenAI access to real-time data in French and Spanish.
Prisa Media is a global Spanish language media company based in Madrid, Spain that is comprised of magazines, newspapers, podcasts, radio stations, and television networks. It’s reach extends from Spain to America. American media companies include publications in the United States, Argentina, Bolivia, Chile, Colombia, Costa Rica, Ecuador, Mexico, and Panama. That is a massive amount of real-time information in addition to a massive audience of millions.
OpenAI explicitly announced that the purpose of this deal was to bring this content directly to ChatGPT users.
The announcement explained:
“We are continually making improvements to ChatGPT and are supporting the essential role of the news industry in delivering real-time, authoritative information to users. …Our partnerships will enable ChatGPT users to engage with Le Monde and Prisa Media’s high-quality content on recent events in ChatGPT, and their content will also contribute to the training of our models.”
That deal is not just about training data. It’s about bringing current events data to ChatGPT users.
The announcement elaborated in more detail:
“…our goal is to enable ChatGPT users around the world to connect with the news in new ways that are interactive and insightful.”
As noted in our April 30th article that revealed that OpenAI will show links in ChatGPT, OpenAI intends to show third party content with links to that content.
OpenAI commented on the purpose of the Le Monde and Prisa Media partnership:
“Over the coming months, ChatGPT users will be able to interact with relevant news content from these publishers through select summaries with attribution and enhanced links to the original articles, giving users the ability to access additional information or related articles from their news sites.”
There are additional deals with other groups like The Financial Times which also stress that this deal will result in a new ChatGPT feature that will allow users to interact with real-time news and current events .
OpenAI’s Monday May 13 Announcement
There are many clues that the announcement on Monday will be that ChatGPT users will gain the ability to interact with content about current events. This fits into the terms of recent deals with news media organizations. There may be other features announced as well but this part is something that there are many clues pointing to.
Watch Altman’s interview at Stanford University
Featured Image by Shutterstock/photosince
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