Earlier this month, we announced platform properties for Search Console,
allowing you to track how your social and video posts on Instagram, TikTok, X, and YouTube perform on Google Search,
Discover, and Google News. Today, platform properties are globally available to everyone.
Read more at Read More
Incrementality and attribution are two approaches to measuring marketing performance that are frequently discussed as though they are competing lenses viewing the same data. But they’re actually designed to answer very different questions, using different forms of evidence.
Attribution asks which observed marketing touchpoints should receive credit for a conversion. Incrementality asks whether the marketing activity caused additional conversions that wouldn’t have occurred without it.
Attribution is the favored child of marketing analytics teams everywhere, circa 2015. Marketers discovered that some conversion paths contained multiple touchpoints across the digital landscape, like this:
That raised questions about which channel should get what “credit”:
That’s where attribution modeling came in. Attribution modeling provided frameworks for deciding how that credit should be distributed. Some models assigned the entire conversion to a single touchpoint. Others divided it among multiple interactions.
So if the final value of the conversion is $100, an attribution model tells marketers that display can take credit for $30, email for $30, and the remaining $40 is split between paid social and organic.
Then, when you’re evaluating the success of your channels, you have a more nuanced framework for distributing revenue credit. And when you’re deciding on what channels get what budget for the next fiscal year, you have a way to compare and contrast.
Example: How a $100 conversion might be distributed across four marketing touchpoints.
| Attribution model | Display | Paid Social | Organic Search | How credit is assigned | |
| First-touch | $100 | $0 | $0 | $0 | All credit goes to the first observed interaction. |
| Last-touch | $0 | $0 | $0 | $100 | All credit goes to the final observed interaction before purchase. |
| Linear | $25 | $25 | $25 | $25 | Credit is divided equally among every observed touchpoint. |
| Position- based |
$40 | $10 | $10 | $40 | The first and last interactions receive the most credit, while the middle interactions split the remainder. |
| Time-decay | $10 | $20 | $30 | $40 | Touchpoints receive progressively more credit as they occur closer to the conversion. |
| Data-driven | $30 | $20 | $20 | $30 | Credit is distributed according to each touchpoint’s estimated contribution to the conversion. |
Note: These are simplified examples. Position-based models can use different weighting rules, time-decay allocations depend on timing, and actual data-driven models vary.
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
Incrementality began to gain renewed interest from marketers around 2020.
Rather than dole out credit for a sale to different touchpoints and channels based on a mathematical equation, incrementality relies on carefully guardrailed tests of real, live sales data that attempt to prove the “true” impact of a marketing activity rather than its correlation. Incrementality tries to answer the question:
The answer to that question is what marketers call lift. And through tightly controlled tests leaning on the scientific method, marketers were able to isolate the difference in sales between a group exposed to the marketing activity and an equivalent group that wasn’t exposed to marketing materials.
Incrementality is best explained through an example.
Let’s say you want to discover the lift of a given marketing campaign. So you divide your audience into two groups: a control group of folks who won’t be exposed to the campaign and an exposed group that does see the campaign.
You run your campaign for 30 days, then look at the results. While the exposed group completed 1,000 purchases, the control group completed 800 purchases. The incremental lift of the campaign would be 200 purchases.
An attribution model could associate many or all 1,000 purchases with the campaign. It would allocate the value across the platforms and touchpoints involved according to the model you choose.
Incrementality, on the other hand, would conclude that only those 200 additional purchases were actually caused by the campaign.
Dig deeper: Why attribution and impact are no longer the same thing in PPC
Where marketers go wrong is when they go all in on either framework. The two concepts can play nicely together (provided you’re using the right one to answer the right question). If you’re looking to optimize your campaigns or deep dive into the user journey of your customer, attribution is going to be your best friend, helping you evaluate platforms and touchpoints by giving you a shared success metric with which to compare them.
If, on the other hand, you’re defending your budget from a proposed cut, incrementality is going to be your strongest source of evidence regarding which channels actually create additional business with their budget, rather than capturing business that would have happened anyway.
| Attribution | Incrementality | |
| Primary question | Which observed marketing touchpoints should receive credit for a conversion? | How many additional conversions occurred because of the marketing activity? |
| Best use case | Ongoing campaign optimization, understanding customer journeys, and allocating credit across measurable channels. | Validating whether an investment creates additional business value and informing higher-level budget decisions. |
| Main blind spot | Correlation is not causation: a touchpoint may receive credit for a conversion it did not actually create. | Tests can be expensive, slow, or difficult to design, and results may not explain which individual touchpoints influenced the customer. |
| Most likely stakeholder | Channel managers, performance marketers, platform teams, and marketing analytics teams. | Marketing leadership, finance, data science, growth strategy, and budget owners. |
If there’s one question that has haunted me throughout my career, it’s this one: “Why don’t these numbers match?”
Most often, it’s asked when a channel platform’s reported revenue or conversions differ from the numbers found in the client’s CRM, web analytics, or other source of truth. They almost never line up perfectly.
What can be tough to explain succinctly to a client is this: The fact that they differ doesn’t necessarily mean either is incorrect. Each system applies its own logic based on the interactions it can observe, which conversions should qualify for credit, and how long after an interaction credit can still be attributed. But neither is “wrong.”
An advertising platform may correctly observe and report that a customer viewed or clicked on an ad before purchasing. But evidence that an ad was seen before a purchase isn’t necessarily proof that the ad caused it, nor is it proof that the ad didn’t cause it.
This becomes particularly important with automated campaigns, especially as platforms continue to push these automated solutions on marketers. Automated systems are designed to maximize performance based on the conversion signals defined inside the platform. They’re simply not designed to maximize performance based on your carefully calculated incremental lift test results.
As a result, automated campaigns target audiences, placements, and queries already associated with users likely to convert, such as existing customers, branded searchers, and remarketing audiences. Those conversions may be entirely valid according to the platform’s attribution model, while creating less additional revenue than the campaign report implies.
In other words, automated campaigns can increase the number of conversions credited to a given campaign without actually causing an equal increase in total sales.
Dig deeper: Your ROAS looks great — but is it actually driving growth?
It’s true, platforms are increasingly offering lift studies and other incrementality-focused tools. But it’s a mistake to assume that incremental value is automatically incorporated into automated campaign optimization.
After all, “data without insights is meaningless, and insights without action are pointless.” In other words, a lift study will only affect performance if and when someone applies its findings to the campaign’s objectives, inputs, or budget decisions.
A platform like Google Ads may provide a controlled lift experiment, but unless the advertiser applies the test findings — or selects a campaign setting explicitly designed to optimize for incrementality — the measurement system and the delivery system are still going to be working toward two different definitions of success.
Some platforms are starting to address this. Meta, for example, now offers a very promising incremental attribution model intended to optimize delivery toward conversions it predicts were directly caused by advertising. For now, though, that’s a specific optimization choice that, again, requires action by the advertiser and isn’t an inherent feature of every automated campaign.
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
In sum, attribution and incrementality aren’t competing methods for finding one definitive metric. They’re different tools designed to answer different questions. And like most tools, they perform best when they’re doing the job they’re designed for. You wouldn’t try to use your Allen wrench as a hammer, would you?
Attribution helps us marketers understand which touchpoints contributed to a conversion and provides a shared basis for comparing channels. Incrementality helps businesses understand whether their marketing investment generated additional conversions that wouldn’t have occurred otherwise.
The best marketers need both. While attribution provides the ongoing signals needed to optimize campaigns and understand customer journeys, incrementality helps validate whether those optimizations are creating new business value or simply capturing demand that already existed.
As automated campaigns take greater control over targeting, placements, bidding, and budget allocation, understanding both sides will only become more important. A system can become exceptionally efficient at maximizing attributed conversions without becoming equally effective at producing incremental growth.
So the next time a platform report contradicts your CRM, don’t assume either number is wrong — ask which question each number was designed to answer.
Dig deeper: The end of easy PPC attribution — and what to do next
Read more at Read More
One of AI Max’s capabilities is creating assets for you. This can take the strain off the PPC team by reducing the need to customize ads for every single ad group.
We wanted to quantify how well the text customization feature worked for various companies, so we ran tests with three different companies to understand how much we should or shouldn’t be using text customization.
As we’re a software company that helps companies manage their PPC accounts, not the agency doing the work, we worked with the companies to guide them on how to set up and analyze the tests.
We’ll start by examining the process we used to help the companies set up the tests so you can run this experiment yourself, and then we’ll examine the test results.
Before you run this test, you need to understand the features involved.
First, you need to turn on AI Max and the text customization feature.
Since this is AI working in the background for you, it can tailor the assets in every single ad group to the keywords in that group. While this sounds nice, the assets can sometimes be used to run promotions or advertise products and services you don’t offer.
To help guide the system, you should apply messaging restrictions when using auto-created assets.
Messaging restrictions can help guide the system on what the ads should and shouldn’t say. In addition, you can give it rules around your brand guidelines.
The overall process for creating good messaging restrictions is fairly simple and only takes an hour or two:
Dig deeper: Is your account ready for Google AI Max? A pre-test checklist
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
We wanted to see how these assets performed across different types of companies and different levels of campaign optimization.
Therefore, we first chose three business types:
In each account, we only wanted campaigns that met a specific set of criteria:
Most companies have some campaigns that are their best performers, where the team spends a lot of time optimizing the campaigns. Then you have your campaigns that are long-tail, do well in aggregate, but receive less attention.
To understand how well text customization was going to perform, we used two campaigns that were highly watched and two that were somewhat neglected from each account.
We also wanted to see how the new assets performed, so we chose campaigns that didn’t rely heavily on pinning, which excluded many of the highest-spending campaigns, since most large and enterprise companies extensively use pinning in their top campaigns.
Finally, we wanted to test only the assets, not URL expansion, so none of the campaigns used the final URL expansion feature.
As you run these tests, you’ll want to monitor the auto-created assets and remove any that don’t align with your brand messaging or offers.
When looking for AI-generated assets, it’s essential that you change the default filters to include the ad, as this filter isn’t chosen by default.
The companies in the test monitored these assets as they were created and removed them before they received many impressions. Ignoring the B2B results (more on that later), approximately 19% of the auto-created assets were removed.
This ecommerce company sells over 100,000 SKUs, so it has a lot of products, and many users are accustomed to visiting the website and searching again if the landing page doesn’t have the specific product they’re looking for.
At first glance, it appeared that both AI Max and text customization were incredibly successful.
However, after further analysis, we found that AI Max was poaching impressions, clicks, and conversions from other campaigns and that the overall revenue for the account declined.
The company added many search terms as keywords to help Google prioritize the correct ad group and campaign. Then it added more negative keywords and audience lists to slow cannibalization and reran the tests.
We observed that AI text customization wasn’t as effective as human management of assets for highly optimized campaigns. However, it was good at assisting the long-tail campaign.
Dig deeper: Why your brand campaign may not be ready for AI Max
When creating RSA assets for B2B companies, one of the most important considerations is how to properly prequalify your audience through your asset usage. You want your ads to be unattractive to B2C searchers and appeal to B2B searchers.
This company had previously used pinning quite extensively across its assets to ensure this qualification. However, it wanted to see how well Google could optimize its accounts, so it removed its pins during this test.
The nuance of prequalifying a B2B audience is one that text customization clearly doesn’t understand. The B2B accounts saw their CTRs skyrocket. However, the conversion rates declined significantly since the ads were attracting many B2C searchers.
The messaging restrictions included language to ensure the assets prequalified users as part of a B2B audience. While some of the assets met this criterion, the overall ads that were shown to users didn’t properly appeal to B2B buyers.
The other tests ran for over a month. However, after three weeks, the results were so poor that this company stopped the tests and went back to pinning its ads and removing the auto-created assets. Within a week, its results returned to their pretest levels.
Our last test was with a B2C lead generation company that localizes its ads through geographic ad copy or geographic insertion.
Its optimized campaigns had tailored ad copy for the keywords in every single ad group. Its long-tail campaign had a few headline assets in each ad group, tailored to the keywords, but most assets were reused across ad groups.
Seeing formulaic ad copy in lower-priority campaigns is quite common, and this is where we were hoping to see AI auto-created assets perform well, since there was a lot of opportunity for better ads.
AI Max auto-created assets didn’t disappoint in these low-priority campaigns. While these assets didn’t outperform the assets humans had spent a lot of time testing in their top campaigns, AI performed quite well for the long-tail campaign.
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
AI is a fantastic tool at your disposal. For ads where you’re spending a lot of time thinking through your messaging, it can be good at helping you generate ideas, but human-created assets still outperform AI-generated assets.
If you need specific types of assets, such as prequalifying users for B2B audiences, specific offers, or short-term promotions, then you should take control of the assets yourself and not turn them over to AI.
However, auto-created assets shine when you just don’t have enough time to fully optimize your creatives. Using AI to create your assets, assuming you have good messaging restrictions and regularly review these assets, can help your overall performance.
We’re still a long way from AI being a turn-on-and-forget setting. It needs babysitting and oversight. However, having AI do the heavy lifting in areas where you don’t have the time to fully optimize, and then spending your time reviewing and tweaking the outcomes, is the best use of AI in ad creation.
Read more at Read More
For every dollar you spend on SEO, how much do you get in return?
Impressions, clicks, rankings, and query growth can show the results of SEO activity. But they don’t tell executives what they need to understand: how much it costs to acquire a customer (CAC), how that cost changes as SEO efforts continue, and whether overall acquisition efficiency is improving.
The challenge is that SEO rarely operates within the clean boundaries that a channel-level CAC calculation implies.
SEO creates entry points across the customer journey and influences other acquisition channels along the way. Its value, then, isn’t only in the customers directly attributed to organic search. It’s also in how SEO can make the broader acquisition system more efficient.
A user may first find a company through a nonbrand search, return through paid search, compare alternatives through content found in ChatGPT, sign up for a newsletter on the website, read through guides for a week, and then finally convert through the owned channel.
The final conversion might be attributed to email. Paid search may receive some credit for the return visit. The original organic discovery may disappear from the standard report entirely.
But SEO still influenced the acquisition and may have reduced its total cost.
CAC can be measured by individual channel or across channels as blended CAC. CAC expectations vary considerably by channel:
Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions.
Paid search may have the cleanest attribution. Users search for solutions to their problems, product terms, categories, or other related queries. You pay for the click, and a percentage of those clicks convert.
From there, you get spend / customers acquired = paid search CAC.
It’s also usually close to the transaction, which makes it easier to credit regardless of other factors, such as whether paid social may have warmed the audience first.
A user who clicks a search ad may already know the brand through social campaigns, podcast appearances, how-to guides, recommendations, or competitor research. The demand is high-intent, and paid search captures its final expression.
Paid social’s impact on CAC is often indirect because its primary strengths are creating awareness, warming audiences, building retargeting or feeder pools, and connecting users with problems they may want to solve before they’re ready to buy.
It’s unlikely that your users are scrolling on Instagram thinking, “I would like to spend some money right now.”
But seeing a product address their problem during their free time may make the brand more familiar when they later search for a solution at work, improving blended CAC efficiency.
If you looked only at paid social as spend / customers acquired = paid social CAC, you’d probably cut the budget. But then, if you look at what it does to paid or branded search CAC through holdout tests, you’d potentially restart the social budget after seeing the overall acquisition engine decline.
It’s a form of incrementality. Experiments compare exposed and unexposed groups to estimate how much additional activity a marketing investment produces instead of simply assigning credit to the last recorded touchpoint.
Lifecycle channels like email work differently. If you own an audience through email capture and you look at converting them into paid users or continuous purchasers, you can think of email CAC along the lines of cost of email program / converted customer value.
But then you still have to run paid to capture the emails in the first place, or you need a strong SEO presence to do so. The apparent efficiency is highly dependent on other channels.
SEO touches all of these channels, and all of these channels can influence SEO in return. For example, a paid social campaign could generate 100 brand mentions that benefit your overall organic visibility.
Together, they create a connected acquisition system.
Dig deeper: SEO and PPC alignment starts with your org chart
It sounds like the solution is a better attribution model, and then SEOs can speak about CAC more effectively and get more budget. But there are still limitations.
Last-click attribution would just give credit to the final measurable source. First-click would just give credit to the initial source. Linear or position-based models distribute credit, and data-driven attribution would observe data to estimate what contributed most.
Data-driven attribution may improve reporting, but it remains a model rather than a complete record of the customer journey.
Because how do you evaluate interactions that aren’t observed, identified, or connected to a user’s journey? There are deleted cookies, consent restrictions, cross-device behavior, long sales cycles depending on your niche, offline conversions, and that list of limitations could go on for a while.
So, regardless of your attribution model, it doesn’t always provide a complete record of causality and shouldn’t be treated as such. This further reinforces that acquisition is a system, not an isolated channel.
As the search ecosystem changes, even more of SEO’s influence is becoming difficult to observe.
Dig deeper: Why first-touch analytics matters more than ever for SEO
Measuring CAC for SEO as an isolated channel is becoming increasingly difficult.
SparkToro’s analysis of Similarweb clickstream data found that 68.01% of U.S. Google searches ended without a click during the first four months of 2026. In 2024, the figure was 60.45%, representing an increase of roughly 7.6 percentage points in two years.
Users can still see a company in an AI Overview, read a search snippet, or engage in other behaviors, but fewer and fewer are measured through impression → click → conversion.
SEO still influences these interactions, but its impact may appear smaller in a dashboard.
There’s also significant overlap between SEO efforts and AI visibility, depending on which agency or existential hill you’re standing on.
SEO’s biggest advantage is that its financial returns can compound. A paid campaign stops sending traffic to the website when the budget stops, but a strong organic presence can continue creating entry points long after the initial investment.
If you build topical authority across a category with meaningful demand, the cost to maintain that visibility, including the costs of keeping up with competitors, is often lower than continuously buying the same demand through paid search.
That could include technical improvements, content production, digital PR, product pages, and ongoing optimization.
During the first few months, the program may appear inefficient from a CAC perspective because the investment occurs before returns materialize. Then, as visibility grows, that same work starts to increase customer volume while spend stabilizes at maintenance levels, and CAC decreases.
That’s one way SEO leans out blended CAC. It does this by:
When considering the impact, it can be true that SEO is among the more efficient levers for reducing a business’s blended CAC.
Dig deeper: 3 ways to build a more complete SEO ROI model
See where your brand appears, where it doesn’t, and exactly how to win more visibility across search, AI, local, social, and every channel that matters.
The attribution model can point to various channels as the highest-performing, but the organic infrastructure may be contributing to that performance.
That’s where SEOs should point when reframing the conversation.
Instead of answering how much you get in return for every dollar spent on SEO, consider how much more money you’ll have to spend on other channels for every dollar not spent on SEO.
If SEO is doing its job, it’s part of a cohesive system, and its role is to increase volume while reducing blended costs.
SEO teams should still report channel CAC when the data allows, but executives should evaluate it alongside influenced pipeline, replacement costs, and changes in blended acquisition efficiency.
Read more at Read More
Creator content increasingly informs AI answers.
After all, a large language model (LLM) can’t have an opinion. It can’t decide whether your product is the best moisturizer or the water softener that’s the best value.
So when someone asks a subjective question, an LLM borrows a point of view from wherever humans have already shared one. Reviews, community threads, editorial content, retailer pages, and creator content guide these answers.
Various teams own all of those sources. This makes showing up in AI answers less of a content creation issue and more of a coordination problem.
In Tinuiti’s Q1 2026 AI Citation Trends Report (Disclosure: I’m the senior director of AI SEO innovation at Tinuiti), we found that roughly 82% of AI citations pointed to earned media, not a brand’s own site.
Creators are a fast-rising component of that mix. If you take a look at YouTube’s search engine results page (SERP) presence, inclusion in Google AI Overviews grew from 3.6 million to 36.2 million keywords year over year. That’s likely partially due to the increase in AI Overviews.
Yet YouTube visibility across the entire SERP has been evolving for a long time. Video has now become a format AI uses when it needs to explain or validate something.
Dig deeper: AI search engines cite Reddit, YouTube and LinkedIn most: Study
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
It’s tempting to invest in creators across industries, but the data doesn’t necessarily support that approach. In our Q2 2026 AI Citation Trends Report, social platforms drove about 13% of AI citations on apparel prompts — but only 3% on over-the-counter health.
Social citation trends move quickly. For example, Perplexity’s share of citations from social media fell from 31% to 13% in a single quarter as it pulled back from Reddit.
The social platforms that matter in each category continue to evolve, too. This requires deeper research into where LLMs source conversations. That’s exactly why a single team watching one social channel can’t see the whole picture.
Dig deeper: Why AI visibility starts before search and ends with citations
It would be easy to file all of this under AI search. But I’d argue it’s a pattern Google has been building toward as consumer behavior has moved to social.
In 2025, the search engine began automatically adding social media links to Google Business Profiles at scale. This update began surfacing brands’ recent posts right on their profiles.
Google has also started to display a short videos block on SERPs and within the search bar. This block includes vertical clips under five minutes, pulled from sources like Facebook, YouTube, and TikTok.
Based on Semrush organic research data for the past year (U.S.), YouTube’s estimated organic traffic roughly doubled. It’s now Google’s single largest organic domain. Meanwhile, organic traffic climbed by about 60% for both Facebook and Instagram.
Google keeps building surfaces to ingest, display, and now measure social conversations. If we use trends to help inform predictions, I think it’s safe to say there’s high value in social conversations and content, in the spirit of search everywhere optimization strategies.
The content that wins YouTube citations isn’t the obvious kind. Long-form video accounts for 94% of AI citation, with 40.83% of cited videos having fewer than 1,000 views, OtterlyAI’s YouTube Citation Study 2026 found.
This indicates that popularity doesn’t predict citation value. Instead, structure does.
Mid-size niche creators tend to make well-structured content. These videos typically have clear titles, clean formatting, and a specific focus.
The sentiment that these videos express also affects AI search. In one client program, when mid-size creators published genuine review content, their language began appearing in citations for branded prompts and in our sentiment theme occurrence rates. A YouTube review and a blog each reached roughly 0.23% citation share on a target term, and it’s kept climbing.
AI search leans on creator content because it’s authentic and not branded. As a result, flooding search with citation bait breaks the very thing that makes it useful. The teams doing this well scale creator content when there’s a real idea or a brand story worth reinforcing.
Dig deeper: How AI-driven shopping discovery changes product page optimization
Most organizations run influencer and SEO on separate budgets, chasing distinct goals with reach and engagement over here and rankings over there. Instead, they should point teams at the same target and encourage them to exchange insights.
The influencer team gets to see its real role in search, while the SEO team brings citation data and measurement. Micro-influencers and repeated sentiment themes can matter as much as any single high-reach post. Shared reporting can show which creator content is influencing search and help both teams make better decisions.
Our tooling is catching up. In July, Google introduced platform properties in Search Console. This feature lets you track how your Instagram, TikTok, X, and YouTube content performs in Google Search, including the queries that send people to your posts. These insights can make creator choices evidence-based instead of instinctive.
Dig deeper: Why 2026 is the year the SEO silo breaks and cross-channel execution starts
Creator content is only one part of what AI engines encounter when forming a picture of your brand. They also draw from content, PR, commerce, social, affiliate, video, and paid.
When those sources reinforce the same narrative, AI models get a clearer picture. Creators are a smart place to start because they’re a growing source of opinions and experiences AI engines draw from.
The opportunity goes beyond any one channel. It’s about giving AI engines a consistent story about your brand wherever they look.
Read more at Read More
Most AI website builders promise you can create a website in minutes, but what does that timeline actually include? While AI can generate a professional-looking website surprisingly quickly, you’ll still need time to choose a domain, review the generated content, make a few edits, and publish your site. In this guide, we’ll break down the entire journey and share a realistic estimate.
Every website follows a similar journey before it goes live. Whether you’re creating a portfolio, a business website, or an online store, you’ll need to plan your website, register a domain, set up hosting, create pages, add content, and test everything before publishing.
The time required for each stage depends on your website’s complexity, the resources you already have, and how prepared you are before you begin. The table below provides a realistic estimate of how long each stage typically takes.
| Stage | What it involves | Typical time |
| Define your website’s purpose | Decide what your website should achieve, identify your target audience, and outline the pages and features you’ll need. The clearer your goals are, the easier the rest of the process becomes. | 30 minutes–1 day |
| Research your market (Recommended) |
Review similar websites to understand industry trends, gather inspiration, and identify opportunities to differentiate your website. | 1-3 days |
| Choose a domain name | Brainstorm a memorable domain name, check its availability, and register the one that best represents your brand. | 30 minutes–1 day |
| Set up web hosting | Choose a hosting provider, create your hosting account, and configure the essentials needed to make your website accessible online.
Tip: Bluehost Web Hosting includes reliable hosting, a free SSL certificate, domain management, and website management tools in one place, helping you get online without juggling multiple services. |
30 minutes–1 day |
| Create your website | Build your website’s structure by creating pages, organizing navigation, choosing layouts, and adding the functionality your visitors will use. | 1-7 days |
| Add your content | Write and review your page copy, upload images and videos, add contact information, and make sure every page accurately represents your brand. | 1-7 days |
| Review and test your website | Test your website on different devices, verify links and forms, check loading speed, review mobile responsiveness, and fix any issues before launch. | 2-8 hours |
| Publish your website | Connect your domain, perform final checks, and make your website publicly available for visitors. | 30 minutes–2 hours |
The timeline above reflects the steps involved in building a typical website. However, certain features and functionality can add additional time depending on your requirements. For instance, if you’re building an eCommerce website or a multilingual website, the following will be added to your timeline:
| Requirement | Additional time |
| Setting up an online store | 1-5 days |
| Adding products and categories | Several hours to multiple days |
| Configuring product variants (size, color, etc.) | A couple of hours to 2 days |
| Setting up payment gateways | 1-2 days |
| Configuring shipping and tax settings | 1-3 days |
| Configuring shipping and tax settings | 2-8 hours |
| Connecting third-party tools (CRM, email marketing, analytics, booking systems, etc.) | A couple of hours to 2 days |
| Creating multilingual pages | Several days to weeks |
Keep in mind that the estimates above focus solely on the website creation process. Some projects may require additional planning before you even begin building. For example, businesses entering a competitive market often spend extra time researching competitors, refining their brand positioning, defining their target audience, or planning their content strategy. While these activities can extend your overall timeline, they often result in a more focused website that better serves your visitors and supports your business goals.
Traditionally, creating a website involves much more than writing content. Before you can even start customizing your site, you have to make a series of decisions that shape its overall structure and design.
Building a website from scratch typically involves tasks such as:
None of these tasks is particularly difficult on its own, but together they can easily add hours to the website creation process.
AI website builders shorten this setup phase by turning it into a guided onboarding experience. Instead of making every decision manually, you answer a few questions about your business, and the AI generates a website structure, page layouts, navigation, design elements, and draft content for you.
Rather than starting with a blank canvas, you start with a complete first draft that’s ready for review.
To see how this works in practice, I used the Bluehost AI Website Builder while building a website with AI from scratch.
The process was straightforward:
Once onboarding was complete, the AI produced a complete first draft in about 3 minutes. The generated website already includes:
It’s still WordPress underneath
One feature that stood out was that the generated website wasn’t locked inside a simplified editor. Because Bluehost’s AI Website Builder is built on WordPress, I could immediately access the complete WordPress dashboard to:
In other words, the AI accelerated the initial website setup without limiting what I could do afterward.
Although AI significantly reduces the time required to create the first version of a website, there are still several areas where you’ll likely invest additional time.
AI-generated copy provides a strong starting point, but it should reflect your products, services, brand personality, and unique value proposition. Reviewing and personalizing the content is one of the most important steps before publishing.
The first draft may include placeholders or stock images. Replacing these with your own photography, product images, or branded visuals helps create a more authentic and trustworthy website.
Depending on your business, you may want to create additional pages beyond the initial draft, such as an About page, service pages, FAQs, testimonials, pricing information, or a blog. AI accelerates the initial launch, but your website can continue to grow over time.
Publishing your website doesn’t mean your work is finished. Optimizing page titles, meta descriptions, internal links, headings, and on-page content, as well as creating valuable blog posts, is what helps people discover your website through search engines over the long term.
While AI can dramatically reduce the time it takes to create a website, the biggest improvements usually come after the first draft. Personalizing your content, using original visuals, expanding your website with valuable pages, and continuously improving your SEO are what transform an AI-generated starting point into a website that genuinely represents your brand and stands out from the competition.
If you’re wondering whether AI website builders really save time, the answer is yes, but perhaps not in the way most marketing claims suggest.
The website generation itself is remarkably quick. In my experience, the Bluehost AI Website Builder produced a complete first draft in around three minutes after the onboarding process. However, creating an account, verifying your email, answering the AI’s questions, reviewing the generated website, and making your own edits naturally add to the overall timeline.
For most people starting from scratch, it’s reasonable to expect a basic website to be online within 30 to 90 minutes, depending on how prepared you are. If you already have your domain, branding, and content ready, you could realistically publish in under 15 minutes. On the other hand, if you’re building a more polished website with custom copy, additional pages, and SEO improvements, you should expect to spend a few hours or even a full day refining it.
The biggest impact of AI isn’t that it builds a perfect website in minutes. It’s that it compresses what used to be the most time-consuming part of website creation, setting up the structure, layouts, navigation, and first draft into a guided process that takes only a few minutes. Instead of spending hours building a website from an empty canvas, you can dedicate your time to improving the content, design, and user experience, which are ultimately the elements that make a website successful.
So, are AI-generated websites any good?
For most individuals and small businesses, yes, they’re a strong starting point. AI website builders can generate a professional-looking first draft in minutes, but you’ll still get the best results by personalizing the content, visuals, and SEO to reflect your brand and goals.
Once your website is live, keeping it useful, discoverable, and up to date still requires ongoing effort. AI website builders can help you create and publish a website much faster than traditional methods, but launching your website is only the beginning.
Here are a few areas to focus on after launch.
Whether you’re running a business website or an online store, regularly publishing helpful content keeps your website fresh and gives people more reasons to visit. If you’re not sure where to start, check out our guide on writing an SEO-friendly blog post.
Your website should evolve alongside your business. As your products, services, pricing, or contact information change, make sure your website reflects those updates. You can also expand your website over time by adding new landing pages, service pages, FAQs, or case studies as your business grows.
If you are using Yoast SEO, creating FAQ pages becomes very easy. The plugin’s FAQ content block converts your questions into structured data, making them easier for search engines and AI crawlers to digest.
Yoast SEO Premium has a smart content analysis that helps you take your content to the next level!
Publishing your website is only the first step. To help people find your website, you’ll need to continue optimizing both your content and your website’s technical foundation.
That includes creating helpful content around the topics your audience is searching for, improving internal linking, and regularly reviewing your pages to keep them accurate and relevant. It’s also worth checking that search engines can easily crawl and index your website, that your XML sitemap is up to date, that your pages load quickly, and that there are no broken links or indexing issues affecting your visibility.
If you’re using WordPress, our definitive guide to WordPress SEO covers both on-page and technical SEO best practices to help you build a strong foundation for long-term growth.
Understanding how people discover and interact with your website helps you make better decisions.
Start by connecting your website to Google Search Console to monitor search performance, identify indexing issues, and see which queries drive traffic to your site. Pair it with Google Analytics to better understand user behavior and measure the impact of your SEO efforts over time.
Helpful resources:
As AI-powered search experiences continue to evolve, it’s becoming increasingly important to understand how your brand is represented beyond traditional search results.
If you’re using Yoast SEO AI+, Yoast AI Brand Insights helps you understand how AI platforms perceive your brand and identify opportunities to strengthen your online presence as AI search continues to grow.
AI website builders don’t eliminate every step of website creation, but they dramatically shorten the time it takes to go from an idea to a working website. Instead of spending hours manually setting up the foundation, you start with a complete first draft that you can immediately personalize.
Here’s how the experience typically compares.
| Traditional website creation | Building a website with AI |
| Days or weeks to create the initial website | A working first draft in minutes |
| Browse and compare templates manually | AI recommends a design based on your requirements |
| Build page layouts section by section | AI generates complete page layouts |
| Create navigation menus manually | Navigation is generated automatically |
| Write placeholder copy from scratch | AI creates an initial content draft |
| Start with a blank page | Start with a complete website structure |
| Spend time assembling the website | Spend time refining and personalizing it |
The biggest difference isn’t that AI removes the need to edit your website; it changes where your time is spent. Rather than investing hours creating the foundation, you can focus on improving the parts that have the greatest impact on your visitors, such as your messaging, branding, content, and SEO.
Creating a website no longer has to be a weeks-long project. AI website builders have transformed what was once the most time-consuming part of the process, setting up layouts, pages, navigation, and initial content, into something that can be completed in minutes.
That doesn’t mean AI builds a perfect website on its own. A successful website still depends on thoughtful content, strong branding, regular updates, and ongoing SEO. What AI does provide is a significant head start, allowing you to skip the blank page and spend your time refining a website instead of building one from scratch.
If you’ve been putting off creating a website because it felt too technical or time-consuming, AI has made getting started easier than ever. The sooner you launch, the sooner you can begin growing your online presence.
The post How long does it really take to build a website with AI? appeared first on Yoast.
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If you use ChatGPT for your marketing (content ideas, outlines, copy, strategy), you’ve probably run into the same wall: the AI doesn’t know what’s actually ranking. It doesn’t know how competitive a keyword is. It doesn’t know whether 500 monthly searches is high or low for your industry. It doesn’t know if your competitors are already dominating a topic.
You could paste data in manually. But that means switching to Ubersuggest, pulling a report, copying the numbers, and going back. Every time. For every question.
That gap is now closed.
Ubersuggest is now an official app inside ChatGPT. When you use it, ChatGPT calls Ubersuggest in real time to fetch live SEO data (keyword volumes, difficulty scores, related keywords, SERP snapshots) and weaves the results directly into the conversation. You ask. ChatGPT fetches. You get an answer built on actual data.
The Ubersuggest ChatGPT app is a native integration built using OpenAI’s App platform and Model Context Protocol (MCP). Not a browser extension. Not a workaround. A first-party connection that lets ChatGPT call Ubersuggest’s APIs directly during your conversation.
The first capability is live keyword research. When you ask ChatGPT a question that requires keyword data, the app fetches it in real time from Ubersuggest’s database of over 100 million keywords. This includes:
The Ubersuggest ChatGPT app is launching in phases. Keyword research is live now. Coming soon:
ChatGPT is excellent at reasoning, pattern recognition, and language. But it has a fundamental limitation when it comes to SEO data: it’s working from training data, not live search data.
When you ask ChatGPT what keywords to target, it’s generating plausible answers based on patterns in its training corpus. It’s not querying an actual keyword database. It doesn’t know current search volumes. It doesn’t know what’s ranking right now.
The Ubersuggest ChatGPT app solves this by making ChatGPT’s SEO answers data-grounded. ChatGPT still does the reasoning, strategy, and language. Ubersuggest provides the live facts it’s reasoning from. The combination is more useful than either alone.
Without the Ubersuggest app: You ask for long-tail keywords for your home cleaning service. ChatGPT gives you a list of logical-sounding phrases. No volume, no difficulty, no way to know if any have real traffic worth pursuing.
With the Ubersuggest app: ChatGPT queries Ubersuggest and presents real data in context: “‘house cleaning service Austin’ has 880 monthly searches with difficulty 32, achievable for a newer site. ‘One-time cleaning Austin’ has 480 searches with difficulty 19, a lower-competition entry point. Here’s the full breakdown…”
Same question. Completely different answer.
Semrush launched their ChatGPT integration in December 2025, available to Business, Pro, Plus, Guru, and Enterprise plan subscribers, at prices that put it out of reach for most solo operators and small teams.
The Ubersuggest ChatGPT app takes a different approach: available to any Ubersuggest user, on any plan, including free. You don’t need an enterprise license. You don’t need to upgrade. If you have a ChatGPT account and an Ubersuggest account, you’re set.
Setup takes under a minute:
Yes. Connect it with a free Ubersuggest account and a free ChatGPT account. Data depth may vary by Ubersuggest plan tier.
The app works on web, mobile, and desktop ChatGPT, anywhere you access ChatGPT with your account.
Yes. The connection uses OAuth 2.0. No passwords are shared. Ubersuggest only receives the queries your session generates. No account data or personal information is exposed beyond what’s needed to answer your query.
The team is rolling out additional capabilities in phases. Check app.neilpatel.com/en/chatgpt-app for the latest.
Looking to take advantage of this functionality? You can add Ubersuggest to ChatGPT in under a minute on any plan, completely free to start. Follow the link here to add Ubersuggest to ChatGPT,
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AI search and AI Link Attribution is changing how people find information, and how publishers reach them. Google’s AI Overviews and AI Mode now deliver direct answers inside the search results page, which has raised a real question: if users never need to click, what happens to your traffic?
Google’s latest update is a direct response to that concern. The company announced five improvements to how AI Mode and AI Overviews credit sources, making citations more visible and giving users more paths to explore beyond the AI-generated answer.
Here’s what changed and what it means for your SEO strategy.
Google’s latest update goes beyond adding more links. The focus is on making AI link attribution more useful.
Previously, one of the biggest criticisms of AI Overviews was that source attribution often felt secondary. Users could receive an answer without clearly understanding where the information came from or why a particular website had been cited.
The latest changes make attribution much more visible throughout the AI experience.
Each update is worth a closer look.
One of the most noticeable changes is the placement of citations directly alongside the content they support. Instead of grouping sources at the end of an AI-generated response, Google now places links next to the relevant text, making it easier for users to identify where specific information originated.
On desktop, users can now hover over many citations to preview information about the linked webpage before clicking. These previews provide additional context about the source, helping users decide whether they want to explore the website further.
Google is also giving news publishers greater visibility by displaying clearer publisher branding and, in some cases, subscription information alongside AI-generated responses. Providing more context about where reporting originated helps users identify and access the original source.
Google is also improving how it references social content.
AI Mode now provides richer attribution for social media content by displaying creator names or profile handles alongside links, giving users enough context to recognize who created the content before they click.
The final update may have the biggest long-term impact.
Rather than ending with a single AI-generated answer, Google now suggests additional questions and related topics users may want to explore. These prompts push users to keep researching instead of treating the AI response as the final word.
Since AI Overviews launched, one concern has dominated conversations among publishers and SEO professionals: will AI answers reduce website traffic? It’s a reasonable one. If users receive complete answers directly in search results, fewer people may feel the need to visit the websites that originally produced the information.
Google has consistently argued that AI search should help users discover high-quality content, not replace it. These latest attribution updates reinforce that message.
Citations now sit closer to the relevant text, and hover previews add context before a click. Publisher identities are easier to spot too. Together, these changes make it easier and more appealing for users to continue their journey beyond Google’s interface.
These updates are a positive step. They show Google listening to publisher feedback and working to balance the user experience with the health of the open web as generative search matures.
Whether these improvements lead to higher click-through rates remains to be seen, though they demonstrate that Google recognizes the importance of supporting the broader web ecosystem as AI search evolves.
Google’s attribution updates don’t change the fundamentals of SEO, but they do reinforce what Google values as AI-powered search continues to evolve. Rather than introducing a new ranking system, these changes make it easier for users to discover and engage with the sources behind AI-generated answers. For marketers, that means authoritative, user-focused content is still the clearest path to visibility.
As AI Overviews and AI Mode become a larger part of the search experience, earning a citation within an AI-generated response becomes another opportunity to reach potential visitors. Google’s latest updates make those citations more prominent, giving brands another way to stand out beyond traditional organic rankings.
AI-generated answers rely on trustworthy sources. That raises the value of original research and firsthand expertise. Content that demonstrates real knowledge and offers something users can’t find elsewhere is more likely to earn both traditional rankings and AI citations.
Google’s new attribution features are designed to help users better understand where information comes from before they click. Richer source information and hover previews create a more transparent search experience that could encourage users to visit the original content instead of stopping at the AI-generated summary.
Google’s latest attribution updates don’t require a completely new SEO strategy. Instead, they reinforce what has always contributed to search visibility: content that’s original, built on real expertise, and useful to the people reading it. As AI search continues to evolve, these qualities remain the foundation of search visibility.
Google’s AI systems need reliable information to reference. Original research, proprietary data, or genuine firsthand experience gives your content value that AI-generated answers can’t easily replicate.
Experience, Expertise, Authoritativeness, and Trustworthiness (EEAT) remain key indicators of content quality. Clear author credentials and reputable sourcing help establish credibility, and regularly updated content reinforces it further for both users and Google’s AI systems.
Well-organized content is easier to understand and easier to cite. Use descriptive headings, answer questions directly, and support key points with statistics or concise explanations that can stand on their own.
As Google continues refining AI Overviews and AI Mode, keep a close eye on your analytics. Monitoring changes in impressions, click-through rates, and referral traffic can help you understand how new AI features affect your organic visibility and where your strategy may need to adapt.
One of the biggest concerns surrounding AI search has been that users might get all the information they need without ever leaving Google’s results page.
These attribution updates suggest Google is moving in a different direction.
By making citations more visible and giving users richer context around sources, Google is positioning AI search as a gateway to high-quality content rather than a replacement for it.
For marketers and publishers, that’s an encouraging signal. While AI search will continue to evolve, success will still depend on creating original, trustworthy content that’s valuable enough for users and Google’s AI systems to reference.
Google introduced five enhancements to AI Overviews and AI Mode that improve how sources are displayed, covering inline citations, hover previews, publisher attribution, social media context, and suggested follow-up topics. Together, they’re designed to make it easier for users to find and visit the content behind AI-generated answers.
The updates appear to address concerns from publishers and content creators about declining referral traffic from AI search experiences. By making sources more visible and informative, Google aims to create a better balance between providing instant answers and driving users to the original content.
Focus on publishing original, authoritative content that demonstrates expertise and provides unique value. Strengthening your EEAT signals, organizing information clearly, and supporting claims with credible sources can improve your chances of being referenced by Google’s AI systems.
No. Traditional SEO remains essential, but AI search introduces another layer of visibility. Rather than replacing organic rankings, AI Overviews create additional opportunities for authoritative content to reach users through AI-generated responses.
Google’s latest AI link attribution updates may seem like small interface improvements, but they represent a meaningful shift in how AI-powered search connects users with the broader web.
By making citations more prominent and adding richer context around publishers and creators, Google is acknowledging that high-quality content remains the foundation of its search experience, even in the era of generative AI.
For marketers, the takeaway is clear. The best way to earn visibility in AI search isn’t to chase every new feature. It’s to continue producing original, authoritative content that users and Google’s AI systems can trust.
As these experiences continue to evolve, keep a close eye on your analytics and be prepared to adapt your strategy. The fundamentals of SEO haven’t changed, but the ways users discover your content certainly have.
If you want to stay ahead of Google’s latest search developments, explore our resources on AI SEO and content strategy, or reach out to the NP Digital team to learn how emerging search experiences can fit into your long-term marketing strategy.
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AI can be an extremely useful tool for content creation, but guest posting is one area where it can pay off to pull back.
That’s because AI adoption is flooding editor inboxes with low-quality pitches and generic posts.
Lilach Bullock, an accomplished content marketer and guest-posting expert, says she sifts through 30 to 60 guest-post pitches a week. And data from BuzzStream suggests that 43 percent of posts are most likely AI-generated, which makes perfect sense given that Bullock says she approves maybe two of the 30 to 60 weekly posts she gets.
So, how do you write a pitch and a post that gets attention and makes the grade in a sea of monotone AI content?
This guide to guest blogging covers tactics that work right now. You’ll learn how to identify publications worth your time and structure posts that build topical authority and leads. I’ll also cover what’s changed in the AI era and the moves that still earn a yes.
A guest post (or guest posting) is an article you write and publish on someone else’s website, usually within your industry or an adjacent one. Strategically, it’s a way to put your expertise (and your link) in front of an audience you don’t already own.
People guest post for three main reasons:
I made a YouTube video on building an effective guest-posting strategy that gives your brand these benefits and more, which you can watch below. Or you can keep reading this guide to guest posting.
The previous section covered the three reasons people guest post: backlinks, referral traffic, and thought leadership. Before you start prospecting, pick one as your primary goal, as each one points to a different kind of target site.
If your goal is referral traffic or thought leadership, you want publications with active readers and steady engagement signals (comments and shares). A site with respectable domain authority (DA) but no real audience won’t move the needle.
If you’re building backlinks, you want sites with strong root domain authority. You can check this for free using tools like Ubersuggest.
Backlinks as a goal can be a bit tricky. Google’s link spam policies explicitly classify “links with optimized anchor text in articles, guest posts, or press releases distributed on other sites” as a violation. The same policy now flags AI-generated content that doesn’t add value under scaled content abuse.
What does that mean for you?
Pitch fewer publications, and make every post earn its placement.
With your goals set, it’s time to find guest-post opportunities. First and foremost, look for sites relevant to your niche or industry.
The blogs you target should fit the following criteria:
If you’re selling plant seeds, for example, you’d find gardening blogs with engaged audiences of gardeners.
Let’s go a little deeper on finding the right kind of guest-post opportunities.
Google is a great place to start your search for guest-posting opportunities. Try any of the following queries to find blogs that accept guest posts. Replace “keyword” with a term from your industry:
These searches should lead you to a blog’s guest post guidelines page, guest post submission page, or actual guest posts by other writers.
Know of prolific guest bloggers in your industry? If you read enough blogs in your industry (which you should), you’ll likely have a sense for the names that repeatedly pop up in guest blogs.
To fill out your list, search the name of a prolific guest blogger, plus the phrase “guest post by.” This will reveal the publications they’ve contributed to, and those sites are likely a good fit for your guest posts, too.
If you happen to know any of these writers personally, even better. A warm introduction to an editor goes a long way.
If you (or your online marketing agency) have ever pulled up a backlink analysis of a competitor while working on your SEO campaign, chances are one or more of your competitors have backlinks from guest posts they have done.
With access to Ubersuggest, you can look at a detailed backlink profile of your competitors and spot any of the blog posts they have written.
Bloggers and guest posters tend to share their latest contributions on social media, which makes platforms like X and LinkedIn useful prospecting tools.
Try searching “your keyword + ‘guest post’” on X or LinkedIn to find recent guest posts in your industry. Follow the links to see which blogs are publishing guest content.
Not every site that accepts guest posts is worth your time. Before you pitch, run each prospect through these four criteria.
One more thing to note is that quality matters more now than it used to. Google’s March 2024 core update folded the helpful content system into its core ranking signals and set a goal to reduce low-quality, unoriginal content in search results by 40 percent. Google has actually surpassed that goal, reducing low-quality search results by 45 percent through these measures.
This guide to guest posting finds us in an environment where editors get flooded with AI-generated pitches and can spot automated outreach instantly.
Relationship-first outreach is the new differentiator.
Pitching well starts before you open an email. Spend time on your target blog, keeping three questions in mind as you research:
Next, audit how guest posts perform on the site. Do they receive the same level of engagement as the owner’s posts? Who are the typical guest contributors, and do you fit the mold?
Before pitching, make yourself a familiar face. Spend a week or two leaving thoughtful comments on recent posts and sharing them on social, tagging the owner. Watch for natural openings to pitch as you go, such as the blog mentioning your business or asking for contributors.
Read the submission guidelines closely, too. Do they want pitches or full drafts? A specific format? An author account?
Now it’s time to personalize the email. Find the owner’s name on the About page or in social bios and use it. Don’t use generic greetings like “Dear Webmaster” or “Hi.”
Introduce yourself with a one-line credibility statement, and link to two or three of your strongest published pieces. Ideally, they’ll be ones with visible engagement that showcase the strength of your blogging formula.
Structure is as big of a deal as personalization. Open with one short paragraph referencing something specific from a recent post on the site. Pitch two or three topic ideas drawn from formats that have performed well there. Close with links to two or three of your best published pieces.
Editors recognize AI-generated outreach on sight. A short, human email that proves you’ve read the site beats a polished AI-generated one every time.
A personalized pitch might look something like this:
[Editor first name], your piece on [recent post] made me rethink [specific takeaway]. I write about [your beat] and could pitch [topic 1], [topic 2], or [topic 3]. Recent work: [link], [link]. Worth a look?
The email template below gives you an idea of what this could look like in action:
Source: https://www.saleshandy.com/blog/guest-posting-email-template/
Landing the pitch is half the work. Now it’s time to back up your outreach. Here’s how to write a blog post that gets you invited back:
Want to see what these rules look like in practice? The post below on ProBlogger nails all of them.
The author covers blogging as a business in great depth, clearly writing for ProBlogger’s audience rather than resorting to self-promotion. The entry matches ProBlogger’s format and closes with a simple CTA inviting people to engage in the comments section (and, boy, did they).
No guide to guest blogging is complete without covering your guest post bio. This is usually the only place to include self-promotion links to your website, blog, product, service, or book. What you write depends on your guest-blogging goals:
Keep the bio itself short. Include a bit about your background and experience to demonstrate why you’re qualified to write a guest post. Include your current job or projects that may be of interest to readers.
Google’s guidance on guest posting is scattered across several policy pages, which makes it easy to miss the bigger picture. Here are the relevant policies in one place:
The simplest rule is to write things people will actually want to read. High-quality, helpful content placed where it belongs and transparency around commercial relationships help keep you Google-compliant from the start.
If traffic is the goal, you need to measure what’s coming back. In Google Analytics 4 (GA4), the cleanest place to start is the Traffic acquisition report. You can find it by going to the Reports menu, then selecting Traffic acquisition.
Source: https://measureschool.com/ga4-referral-traffic/
Next, add the Session source as your primary dimension and filter for each host domain you’ve guest posted on. You’ll see sessions and key events from every referral source side by side.
For deeper analysis, build a custom segment in Explorations. Pull in every guest post domain at once and review them across whatever metrics matter most: engaged sessions, conversion rate, or revenue if you’ve configured ecommerce.
Don’t stop at page views. Set up key events (GA4’s replacement for goals) to track what guest post visitors do once they land. A guest post that drives 200 visitors and zero key events is a different story from one that drives 50 visitors and five paying customers.
Guest blogging means writing and publishing an article on someone else’s website, usually within your industry. It’s a tactic used to build authority and reach new audiences while earning backlinks from sites your target customers already read.
Start by picking a goal: backlinks, traffic, or thought leadership. Find sites that align with that goal and vet them using the criteria in this guide. Then send a personalized pitch that references the site’s recent content. If you land the placement, write a draft that matches the site’s house style.
Start with Google searches like your niche + “write for us” or your-niche + “guest post guidelines.” You can also use tools like Ubersuggest or Ahrefs to look at your competitors’ backlink profiles and find sites that accept guest contributions.
Read the site’s submission guidelines first. Most blogs want either a short pitch or a full draft submitted through email or a contributor form. Have your bio and relevant links ready to include.
Focus on fewer, higher-quality sites. Build the relationship before you pitch. Write for the host’s audience as opposed to your sales funnel. Then track results in GA4, so you know which placements are most worth repeating.
This guide to guest blogging contains very different information than it would have just five years ago. A lot has changed.
Volume pitching doesn’t work anymore. Editors filter out AI-generated outreach on sight, and Google’s systems flag content produced at scale. A single well-placed contribution on the right site now does more for your authority than 50 placements on weak ones.
Relationship building is your best bet for success. That means vetting sites carefully and engaging with their content before you pitch. Punctuate those efforts by writing drafts that earn their place on the page.
While sound guest-blogging strategies are important, guest posting is just one channel in a bigger picture. It can be powerful when done properly, but it works best alongside a strong content marketing and link-building foundation.
Use this ultimate guide to guest posting with the resources on my YouTube channel and the rest of the NP blog. There’s enough information here to get you going, but my team is more than happy to help you build a plan if you ever get lost along the way.
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Google has released three new AI models today Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber and one of those models is rolling out now for Google Search. 3.5 Flash-Lite is Google’s “fastest, most cost-effective 3.5-class model, delivering 350 output tokens per second according to the Artificial Analysis Index, also significantly outperforming prior Flash-Lite generations in agentic workflows,” the company announced.
Google said 3.5 Flash-Lite is rolling out now for Google Search. Where in Google Search? Google did mention in the blog post that it is used for agentic search. But it might be used for Google AI Overviews and Google AI Mode as well.
Agentic Search. Google announced at Google I/O back in May about Google Search’s information agents and improved agentic experiences. “We’re entering the era of Search agents, where you can easily create, customize and manage multiple Al agents for your many tasks, right in Search,” Liz Reid, the head of Google Search said a couple of months back.
Rollout. 3.5 Flash-Lite is rolling out for everyone in the Gemini app and also within Google Search, the company announced.
Why we care. Google will continue to improve its AI models; the latest improvements are on its lower-end models but also faster models. Those faster models are often uses for Google Search and you soon may see those changes in Google AI Overviews, AI Mode and the agentic experiences within Google Search.
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