AI web analytics is upping the game on how website data is transformed into actionable insights — it’s making analysis easier, more accessible, and even fun.
When I made my first website in 2017, web analytics wasn’t at the forefront of my mind. I chose a CMS software based on pricing and ease of use, and it was only later that I realized the importance of knowing who’s interacting with your site and how so that you can align your site with users’ needs.
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And yet, even with PhD-level training in statistics and data analysis, I find drawing insights from website data to be a huge undertaking — which is why AI web analytics is such a game changer. You don’t need a big, in-house data team to extract relevant data, decide which analyses to run, or understand how to relate the results to real-world outcomes (i.e., your business goals).
With AI becoming the core of successful marketing campaigns, I decided to test out a batch of AI web analytics tools to see what each one had to offer and how easy it was to get started. Here, I’ll walk you through my process, as well as offer a curated list of seven tools to consider.
Table of Contents
- Web Analytics & AI in 2025
- Why use AI for web analytics?
- How AI-Powered Web Analytics Works
- How to Integrate AI with Web Analytics
- 7 AI Web Analytics Tools
Web Analytics & AI in 2025
Web analytics, at base, is about understanding the behavior of visitors to a website. The point is to draw actionable insights from this behavior in order to optimize the site and increase traffic, engagement, conversions, or sales.
This involves tracking variables like number of visitors to a site, average session time, bounce rate, and most viewed pages, and then analyzing the relationships between how users interact with your site and the decisions they ultimately make (like, to abandon or to buy).
AI analytics changes and expands the field of web analytics by using artificial intelligence (AI) technologies, particularly machine learning, to process large amounts of data without manual analysis.
In the past two years, since the commercialization of generative AI and the emergence of AI agents, AI has reshaped the landscape by drafting marketing content, resolving customer service issues, and now, driving data analysis. AI has made it faster and easier to calculate results, spot patterns, and derive insights — as well as work with unstructured data, like text, images, and video.
At the same time, all the wrinkles haven’t been ironed out. While working with unstructured data has been simplified, it still requires human-intensive approaches to get the data into shape. And trust is an underlying issue, as automatic analytics methods mean we can’t always see if something goes wrong during data collection, making transparency a concern.
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Why use AI for web analytics?
AI web analytics can help solve some of the major problems faced by businesses when drawing insights from data. Here are the top three.
Problem 1: Too Much Data
A major challenge — counterintuitive as it may sound — is that we just have too much data to deal with. Being able to sort out what’s important and what’s irrelevant to your goals presents a significant problem. And according to a 2023 Oracle study, it’s left 70% of business leaders feeling overwhelmed when it comes to decision-making.
While dashboards can tell you what’s happening on your site, they don’t tell you why. This means that while decision-makers can monitor dashboards, they often rely on data teams to dig deeper into causality.
AI can step in to remedy the overwhelm (for both teams) by differentiating which metrics are important and analyzing millions of hypotheses in real time. This means faster insights that can lead to quicker decisions.
Problem 2: Manual Manipulation
Managing and manipulating data is incredibly time-consuming — and this is required before analysis can even begin. Collecting data from various sources, cleaning it, and categorizing it is slow and repetitive work — which means it’s actually a perfect task to hand off to AI.
Data collection can be automated to pull from social media feeds, as well as structured databases, while also standardizing the data to prepare for analysis. It can also identify trends and find relationships across multiple datasets, which might not stand out otherwise. This leaves your data team with more time to work on pulling out the actionable insights.
Problem 3: Specialized Knowledge
Even with tons of data at hand, knowing how to draw correct and meaningful conclusions from it requires specialized training. In fact, a 2024 Marketing Week survey revealed that “lack of data and analytics is the biggest skills gap in their teams for the second year running.”
Further – and this is a challenge I have personally faced — data analysts must present results to distinct audiences (like business leaders or stakeholders). This requires a translation process where the language of data is turned into the language of business in order to affect decisions.
AI web analytics limits these challenges by democratizing data through interactive interfaces. With generative AI, any user can query an analytics platform by asking questions (no coding knowledge required), and they’ll get back direct answers without ever sifting through data. This means automatic insights whenever you need them — and in your own language.
How AI-Powered Web Analytics Works
AI-powered web analytics uses a host of techniques to achieve its results. Here are the main components that bring it all together.
Automated Insights
Machine learning algorithms autonomously identify patterns and anomalies — as well as key performance indicators — to generate insights and also detect fraud. Not only is it more efficient than doing the work manually, it can free up your team to work on higher-level strategy.
Prediction
Machine learning models use historical data and market trends to predict how users will act in the future. This is vital for strategizing future campaigns, as well as anticipating customer needs.
Visualization
AI tools create interactive dashboards that can be used by anyone, even with no data analytics background, to make data easy to understand and aid in decision-making. Data visualization is automated, pulling from the underlying data in seconds, and can be customized for distinct teams within your organization.
Natural Language Processing (NLP)
Processing qualitative data has long been a limitation of analytics, but NLP makes it possible to examine texts, like social media posts, user reviews, and surveys, to get an understanding of brand perception and sentiment. In addition, it allows direct interaction with web analytics platforms through “copilots” that respond to natural language questions.
Real-time insights
User behavior and market shifts can be analyzed in real time, as AI monitors and then updates dashboards dynamically. This can improve decision-making times and cut down on missed opportunities.
Personalization
Individual behaviors and preferences can be targeted to design personalized website experiences, meaning you no longer have a static site. This can increase user engagement and aims to impact conversion rates.
How to Integrate AI with Web Analytics
There’s no unique process for integrating AI with web analytics. Any AI-powered platform you choose to work with for analyzing web data will have AI tools embedded, which you can then take advantage of.
Getting started with each platform will be distinct — from how you import or connect your data, to which parts of the analysis are backed by AI. In addition, enterprise-level analytics tools offer a suite of features, and just getting set up will require time. However, that’s not the case for all AI web analytics tools.
To show you how simple it can be to get started, I’m going to walk you through HubSpot’s Content Hub setup process and turn on one AI web analytics feature — which you can start using within minutes of creating a site for the first time.
I’ll go into the details about Content Hub and what it offers in the section below. But for now, let’s assume you don’t have an account (or even a website) and you want to start from scratch.
Sound like a chore? Nope. It’s easy — and even kind of fun.
1. Go to Content Hub and create a free account.
You’ll need to add an email and password, and then answer a few questions about your industry and profession, and you’re in. No credit card info is needed to sign up for the free plan, which makes it even simpler.
2. Create a website in just a few clicks.
In the sidebar, hover over the “Content” icon and then select “Website Pages” from the list that appears. On the new screen, click “Get started with AI.”
While this isn’t an analytics tool, HubSpot uses generative AI to help you format, fill, and create content. At the prompt, fill in one line to describe your business (let’s say, “I analyze web data”), and the AI generator will create a homepage with text and photos to promote your business.
You’ll have to verify the content before you hit publish, of course, but I had a basic site built and ready for editing in under five minutes.
3. Turn on AI web analytics tools.
So, how do you get from there to analysis? Since this is a new site, with no user data yet, I’ll turn on an AI option that tests the performance of the site design, known as adaptive A/B testing.
In the sidebar, click “Test” and two options will appear: “Run A/B test” and “Run adaptive test.”
At this point, you’ll see that you need Content Hub Professional to unlock various features, including A/B testing. But fear not — there’s a free 14-day trial to test out the Professional level, so you can have total confidence in the AI options before you upgrade.
4. Run an adaptive test.
Click on the adaptive test and hit “Next.” From here, you’ll be able to select up to five site design variations to test. If you’re not sure what to try, HubSpot suggests options like layout, media, copy, or call-to-action buttons.
On my new homepage, I decided to test how the site performs with photos versus without them. To change this is simple. When you click on any contents, a sidebar appears with options for editing. In this case, I’m changing the media type from “Image” to “None.”
I do this for all three photos, and now Homepage (B) is running an adaptive test against my default homepage (A) to see how images affect interactions with my site — and it will automatically optimize for the winning design.
If you’re not exactly sure how the adaptive test works, don’t worry. I’m going to talk more about it below.
The important point here is that when you’re using an AI-powered tool, integrating AI with web analytics doesn’t require any extra steps or know-how.
In fact, it can make analytics much easier.
7 AI Web Analytics Tools
When I set out to test tools, I had a few goals in mind. With many options on the market — but the technologies still new — I wanted to try out known platforms that were already highly used in order to highlight the AI tools within them. I also focused on a mix of levels, from those for enterprise to those that can scale in either direction.
So, whether you’re already using one of these platforms but haven’t yet explored the AI tools, or you’re new to web analytics and want to know where to begin, this list will let you dip your toes in the AI waters and explore features that, at least to me, almost seem like magic.
*All pricing listed in USD
1. Content Hub by HubSpot
When you think of Content Hub, you might think of its site design capabilities. But it’s also an AI-powered marketing software built for marketers to create and manage content.
More than a scalable CMS, it offers advanced analytics to evaluate the impact of your marketing efforts across channels. With generative AI to create personalized content, and central-location content management for efficiency, it tracks performance, gives SEO recommendations, and captures leads, all backed by Breeze.
Content Hub has a lot of cool features — for example, the Content Remix tool, which calls on AI to repurpose your web pages into blog posts and your blogs into social copy, without any rewrites on your part. It’s hard not to get sidetracked, but I want to dig down into the data analytics tools and talk about the three features I like best.
SEO Recommendations
This tool crawls your site’s pages and tests for performance on SEO best practices, and then offers tailored recommendations. This is the place to start if you want to optimize your content for search engines and measure the impact of SEO efforts over time. To get started, just scan your site’s URL inside Content Hub.
Content Embed
This is a smart content feature that personalizes content for each visitor to your site. It can tailor things like product recommendations, lead forms, and offers, making your content dynamic depending on who’s viewing it. The best part is that you can customize your content in HubSpot and then integrate it into your WordPress site.
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Adaptive Testing
I talked about this feature above, but for me it’s a standout that needs further discussion. In regular A/B testing, you test two versions of your website against each other to see which performs better, and then you choose a winner.
With adaptive testing, you can select up to five variations at once, and each time, the site will choose the outperformer for you, and then run the next test. This means that it continues to iterate, optimizing performance over time, without you having to manually decide or redesign.
(If you’re a data geek like me you can read the statistical reasoning behind this from HubSpot’s machine learning team — which is super fascinating!)
Pricing
HubSpot offers “free and premium plans that grow with you,” but to check out all the features I just described, you’re going to want the Professional level pricing package.
What I like: Content Hub crafts a full content experience, taking you from AI data analytics to AI content generation with a seamlessly smooth approach. Plus, it integrates with all the HubSpot tools you’re already using.
To understand all of Content Hub’s features, I found this walkthrough tutorial helpful:
2. Hotjar by Contentsquare
Hotjar and Contentsquare are working together to offer integrated tools, so I’m putting the two under one header. While you can get full customer insights in one place here, one of the big AI features is the Copilot, which lets you ask questions, and then walks you through users’ behavior on your site to understand their experiences.
It offers an easy setup and little to no knowledge of analysis required. Once you create an account, you pull data from your website by adding the URL and installing a tag (a little block of code) on your site, and then get started right away.
While there are fewer tools than some of the other platforms have, it’s the only one I tried that’s specifically focused on understanding the “why” behind user behavior.
Frustration Scores
This feature lets you see where users experienced the most friction on your site. For example, if everyone leaves after using the search bar, this tool will direct you to replays of search bar interactions, so you can investigate if there’s a problem with the search bar.
Surveys
The AI survey assistant writes survey questions to get to the heart of users’ pain points (or where the site succeeds). Super easy to use, you can click “create new survey,” write in a survey topic (e.g., “find pain points in checkout flow”), and then click “generate questions” — and in seconds, you’ve got a whole survey to put on your site and get feedback.
Sentiment Analysis
This tool analyzes the survey responses you receive and categorizes them into positive, negative, and neutral. If you’re looking for pain points, you can go to the negative comments and see what users have to say.
Pricing
Pricing is not so straightforward. There is a free plan, which includes the AI survey generator, but sentiment analysis is offered in the next level up (the Growth plan). Both of these levels are housed within the Voice of Customer package (which is only one of three packages offered). The good news is that you can build your own package to mix and match products and plans.
What I like: With a focus on qualitative data, you can try to capture not only what is happening on your site, but why. And with the survey tool, it's easy to ask users directly where the trouble is, or how you can improve their experience.
3. Semrush
With a specific focus on keyword research, Semrush uses AI-powered insights to improve site rankings.
This is different from the other web analytics tools on this list because it’s hyper-focused on SEO, content, and competitor landscape. It analyzes your site’s keywords against your domain, and then suggests keywords and predicts how your site will perform.
Keyword Overview
This is a predictive tool that analyzes content quality and relevance to check your site’s topical authority and potential SERP positions.
Keyword Magic Tool
In order to streamline decision-making, this tool helps you spot keywords that are within your reach, and tells you how hard it might be to rank for specific keywords.
Pricing
There’s a free account, which lets you run ten keyword searches per day using the Keyword Magic Tool or the Keyword Overview (returning a max of ten results each). AI-powered personalized data requires a paid plan, but you can try them with a free 7-day trial.
What I like: This site has an easy setup, requiring only a keyword and your website URL to begin using it. You don’t need any specialized training, and it has a tracking tool to monitor your position on Google’s results page.
4. Google Analytics
GA4, the newest generation of Google Analytics, aims to understand the full customer journey, from first visit to purchase, using machine learning models to analyze data and predict actions, like purchases or churn.
One of the AI features is the ability to “converse with your data” (in Google’s words) through the search bar to find metrics or insights you’re looking for. Answers are then presented as data visualizations or reports.
The major selling points here are the predictive analytics features, but note that these are for sites that meet a certain threshold (you must have at least 1,000 returning users over a seven-day period).
Audience Building and Segmentation
As opposed to segmentation where audiences are built based on user properties, with machine learning, audiences can be built based on predicted user behavior. This forecasting capability enhances segmentation to improve marketing strategies and create targeted campaigns.
Pricing
There are only two pricing options: Free and Talk to Sales (which is for enterprise). The enterprise edition, known as Google Analytics 360, can run upwards of $50,000/year.
What I like: If you have a Google account and a website, sign up is easy and you can start pulling data right away. It offers a full suite of tools and it integrates with all of Google’s other products like Ads and Play. Plus, the small business version is free.
5. Tableau by Salesforce
This is an all-around data analysis tool that allows you to follow metrics in a “newsfeed” fashion and see how they’re trending. It uses drag-and-drop features to go from data to visuals in seconds, and then allows you to drill down into the data by asking natural language questions.
There’s also an Einstein Discovery tool that enables predictive modeling without needing to write code. And a big selling point for me, or anyone worried about data protection and ethics, is that your data are not stored in Tableau and are also not used to train large language models (LLMs).
Tableau Pulse
Tableau Pulse provides personalized summaries of insights (“data digests”) in plain language for the metrics you follow, prioritizing the KPIs you select and, over time, learns what insights you care about most.
Interestingly, while AI is used to generate the language for the summaries, AI isn’t involved in identifying insights. The point is to simplify insight consumption for business users whose primary job is not data analysis.
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Tableau Agent
Aimed to speed up decision-making, Tableau Agent is an AI assistant that allows you to type questions into the sidebar, as well as put calculations in words and get a response. For example, when asked, “which channel had high media spend?” the agent responds with graphs of top contributors, paid search, display retargeting, and online video to visualize the answer.
Pricing
Tableau has three pricing levels within three levels of users: Tableau, Enterprise, and Tableau+. Tableau Pulse is included in all levels, so long as you have Tableau Cloud.
What I like: It focuses on translating insights into easy-to-understand language for business users and marketers who are not data analysts. And with Slack integration, you can receive your insights wherever you are, not just when you’re in front of your dashboard.
6. Microsoft’s Power BI
Microsoft’s business intelligence (BI) offering is built for organizations working with extensive data who want to easily integrate with an existing Microsoft infrastructure, developing and sharing across Power Platform, Dynamics 365, and Teams.
Its main AI tool, Copilot, relies on NLP to create an end-to-end experience from analysis to presentation. And while this packs a ton of features, one caveat is that you need an organizational email (business or school) to sign up. In addition, you’ll need two-factor authentication associated with that email.
Copilot
Ask questions or describe the visuals or insights you want, and Copilot generates reports and visuals in response. It also summarizes data in easily digestible text narratives to share meaningful insights with various audiences.
For example, when presented with the instruction, “Help me build a report summarizing the profile of customers who visited in the last twelve months,” Copilot analyzes the data and creates an interactive report, which updates every time the data are refreshed.
And for analysts, you can describe what you want to calculate or model and the code is automatically generated. For me, this feels like nothing short of magic.
Pricing
While there are four levels to choose from, advanced AI features start at the premium level.
What I like: It integrates with Microsoft products you already have on your computer, but also scales up to integrate with big data. It’s good for extensive workloads and teams that have data analysts, but also allows anyone to get started with understanding insights through Copilot.
7. Alteryx
Alteryx provides unified end-to-end analytics, combining data from anywhere (cloud, local, and social media), and makes data connecting and cleaning easy with drag-and-drop features that work with various file types.
Their AI Copilot “turns words into workflows” by offering step-by-step guidance and tailored support in response to questions. For example, “Can you help me combine datasets?” results in instructions on how to join datasets.
Playbooks
When I asked their AI-generated demo for sample data to improve conversion rates, Playbooks came back with use cases to analyze customer behavior and optimize marketing channels, showcasing visuals of customer segmentation and purchasing trends.
Missions
This AI reports tool identifies key metrics, drivers, and root causes. In response to my request to improve conversion rates, it showed me social media as the main driver and then dug into why by showing campaigns and regions that contributed to the effect.
Magic Documents
After using the above tools, Magic Documents transforms output into visuals like slide decks and summaries aimed at specific audiences, such as sales or executive teams.
Pricing
The prices for AI features aren’t transparent. Two pricing levels exist, but you should contact sales for products such as Intelligence Suite, Machine Learning, and Auto Insights.
What I like: For an enterprise-level product, this is very user-friendly. It notably differentiates itself from competitors like Microsoft and Google by offering understandable and accessible user info on their website, and even their demos are AI to meet your on-demand needs.
Data analytics have literally never been so easy. And AI web analytics has taken data democratization up a notch with interfaces that hand off the hard work to copilots and machine learning.
This means that even without a background in statistics or programming, individuals and teams alike can ask questions, share insights, and generally dig into the data — which, for a data lover like me, seems like a step in the right direction.
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