The Top 15 Social Media Monitoring Tools 2026

As generative AI reshapes how people research, learn, and make decisions, a new discipline is emerging: Generative Engine Optimization (GEO). Just as SEO helped brands get discovered on Google, GEO ensures they appear accurately and positively within AI-powered platforms like ChatGPT, Gemini, and Copilot. With millions of consumers now turning to generative engines instead of search engines, GEO is fast becoming essential for marketing and PR professionals looking to influence how their brand shows up in this new discovery layer, where conversations, not clicks, determine visibility.

What is generative engine optimization (GEO)?

Why should marketing & insights teams care about GEO?

How does GEO work? Key mechanisms & signals

How can you get started with GEO? Test your GEO readiness

Which media sources to prioritize for GEO

What's the difference between GEO and SEO?

8 essentials of generative engine optimization

Increase your GEO readiness with Meltwater

Generative Engine Optimization (GEO) is the practice of ensuring that AI platforms such as ChatGPT, Google Gemini, Llama, etc., provide accurate, positive, and up-to-date information about your brand when users ask questions. These platforms are referred to as Generative Engines, Answer Engines, or Large Language Models (LLMs). They are increasingly used by consumers to research purchases and make decisions.

GEO is a way of making sure that AI platforms give people the right information about a company’s brand and products. 

For example, if a person asks ChatGPT: “What is a good family car for camping trips?” then the auto manufacturers would want ChatGPT to suggest their SUV models, and list the benefits. To make sure that happens, the auto brands need to use GEO.

GEO ensures your brand shows up correctly within these AI-generated responses.

The practice of GEO involves several steps, but it is mostly focused on creating, structuring, and publishing content in the best way to influence the responses given by AI Generative Engines. 

In this article we’ll go into the detail of how GEO works, why it’s important, and how you can use it to make sure your brand is featured positively in AI platforms.

Sometimes, generative engine optimization (GEO) is referred to as answer engine optimization (AEO). Learn more about the differences and similarities of GEO and AEO, and take a look at the best AEO tools.

For a long time marketers have been able to rely on the fact that consumers almost exclusively use Google to research buying decisions. So they have been able to use Search Engine Optimization (SEO) to ensure their brand’s website appears in the organic search results, or use paid search advertising to buy ads on relevant search results. 

This is a highly effective way of targeting the right audience at the right time. But now things are changing.

LLMs aren't just another channel. They sit on top of this ecosystem. They synthesize everything into a single conversation.

Allie Konchar, Partner & Chief Client Officer, Omniscient

Changing user behavior: AI + conversational search

Increasingly, consumers are using tools like ChatGPT to research their buying decisions. Data from Meltwater’s 2026 Global Digital Report shows that ChatGPT.com is now the fifth most visited website on the web.

While search engines remain popular, many search queries now produce an AI generated summary that often provide enough information to divert users away from clicking through to websites, because their question has already been answered. McKinsey reports that around half of all Google search queries already produce AI-generated summaries, which can lead to zero-click searches, and that number keeps rising. 

With conventional web search, consumers are performing research manually, trying to find trustworthy sources and sifting through information to reach the answers they seek. But with Generative Engines, the AI is performing the research automatically, using its existing base knowledge alongside supplemental information pulled from more recent online sources. A search engine shows users where to find answers, while a Generative Engine just gives them the answer. 

For marketers, the key takeaway is that few top-of-funnel consumers are visiting your website, because during the awareness phase their information needs are being met by Generative Engines. This makes it more difficult to move them down into the consideration phase. 

TIP: For more data around growing AI usage and online consumer behavior, download our 2026 Global Digital Report, featuring nearly 700 pages statistics to help inform your GEO strategies next year.

You already know that everybody is talking about AI, and all of your competitors are having the same discussions as you about what it means for their marketing strategy. Those who move decisively to implement GEO, ensuring their brands are represented clearly and positively in Generative Engines, will capture the largest share of those consumers who start their buying journey with an AI query.

If you’re not optimizing for generative engines, you just might never reach your audience at all.

Kelly McKenna, Regional Director, Account Management at Meltwater

Those who ignore GEO will lose ground, as growing numbers of consumers turn from search engines to Generative Engines, the size of the audiences that can be reached through conventional search marketing will decrease. 

How do generative engines (LLMs) surface content?

It’s important to understand that while Generative Engines are based on similar technology, they all work a little differently. Broadly speaking they all use two different sources of knowledge to provide an answer to a query: 

<table class="table-primary alternate-color">
  <caption>How Generative Engines (LLMs) Surface Content</caption>
  <thead>
    <tr>
      <th>Generative Engine</th>
      <th>Primary Retrieval Method</th>
      <th>Sources Most Often Cited</th>
      <th>Sources Less Likely to Be Cited</th>
      <th>Notes on Behavior</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>ChatGPT</td>
      <td>Bing search + internal training data</td>
      <td>Reuters, AP News, Wikipedia, Encyclopedic sources</td>
      <td>Social media platforms (X/Twitter, Instagram)</td>
      <td>Prefers authoritative, neutral, structured sources with high trust signals.</td>
    </tr>
    <tr>
      <td>Google Gemini</td>
      <td>Google Search + YouTube + Knowledge Graph</td>
      <td>Reddit, YouTube, Google-indexed blogs</td>
      <td>X/Twitter</td>
      <td>Strongly favors user-generated content, community threads, and Google-owned properties.</td>
    </tr>
    <tr>
      <td>Perplexity</td>
      <td>Real-time web crawling + citation-first retrieval</td>
      <td>News publishers, reputable blogs, research papers</td>
      <td>Low-trust UGC</td>
      <td>Most transparent engine — always cites sources and updates in real time.</td>
    </tr>
    <tr>
      <td>Claude</td>
      <td>Internal training data + selected retrieval</td>
      <td>Academic sources, reputable news, long-form articles</td>
      <td>Short-form social media</td>
      <td>Prefers long-form text and well-structured explanatory content.</td>
    </tr>
  </tbody>
</table>

Training data: All LLMs begin by being trained on a huge volume of existing content and data — we’re talking about dozens of terabytes (a terabyte is 1,000Gb). This training data comes from a wide variety of sources (including your website), and gives the LLM its base knowledge about the world. The LLM does not have any knowledge of anything that happened after its initial training. 

Live Web Search: To overcome the knowledge cut-off problem, the training data is supplemented with fresh information gathered from the internet. If the Generative Engine thinks that a user’s query would benefit from more up to date information than it already has, it fetches new content from trusted online sources and works that into its response. Google uses its own search engine to find fresh information, while ChatGPT uses various sources, including Microsoft Bing.

It’s also important to note that AI does not quote any of this content verbatim in its responses. Much like a human, it uses the information it has learned to inform its own answer, and that can include information from various sources, including the press, competitors, consumer reviews. So don’t expect it to always be on-message!

Another key point is that unlike a search engine, an LLM does not have an index of content which it searches to find answers. It would be impossible to search ChatGPT for all mentions of your brand — that’s simply not how it works. 

An LLM is more similar to a human brain, in that it has a lot of knowledge stored in patterns and relationships between words and concepts. It is extremely difficult, verging on impossible, to examine and understand the detail of an LLM’s learned knowledge because of the sheer scale and complexity; this is a challenge referred to as Black Box AI. 

But we do understand how Generative Engines work at a high level, and we can analyze the kind of responses they produce for relevant queries, which gives us insight into how we should optimize our content for increased visibility.

Tip: Learn more about the citation layer.

What content features improve GEO-visibility?

Bear in mind that when you’re optimizing content for Generative Engines, the goal is to write copy that is easier for AI to digest and incorporate into its responses.

Clarity: Avoid overly elaborate language. Do not use corporate-jargon. Keep it simple, professional, and to the point. Leave no room for ambiguity. 

Authority: Wherever possible back up your statements with data from reputable sources, quotes from experts or industry leaders, and reference-able proof points. 

Structure: Organize your pages well with clear titles, headings, and subheadings. Use heading tags (H1, H2, etc) correctly to specify the hierarchy of your content. 

Recency: AI platforms look for more recent information from trusted sources to supplement their base knowledge, so in both owned and earned media, aim to publish fresh content recently.

In many respects GEO shares principles with good SEO practice. If you write consistently high quality content which addresses the needs of your target audience, and structure it correctly on well designed, standards compliant web pages, you will succeed.

Reading tip: Learn how a website rebuild drove a 73% citation increase.

Test your GEO readiness: a 2-minute self-assessment.

First you need to understand your starting point. Meltwater’s GenAI Lens is a tool that gives brands insight into how they are discussed by all major Generative Engines, including ChatGPT, Claude, Gemini, Perplexity, Grok, and Deepseek. 

With GenAI Lens you’ll be able to understand how your brand is already featured in LLMs, what source websites are used in those responses, and whether outdated or inaccurate information is being used.

Reading tip: Your AI Visibility Score Is a Mirror, Not a Scoreboard

We know that LLMs pull content from the web, which they use to inform their responses. So whatever is said about your brand online will significantly affect how Generative Engines talk about your company. 

Start with your own website, this is the first place LLMs will look for information about your organization. But also focus on trusted, high authority third party sites like the mainstream news and specialist media within your industry, because these are the kinds of sources that LLMs will use. GenAI Lens can show you which websites the leading LLMS use to source information about your brand. 

Once you have a comprehensive audit of how your brand is currently portrayed online, you can perform a gap analysis against your desired messaging, and build a plan for how to reach that objective.

Tip: AI brand monitoring, AI media monitoring, and LLM monitoring will help you understand your online footprint. Learn how to track your LLM visibility and how to track LLM brand mentions.

An important difference between GEO and SEO is understanding how people search differently when using Generative Engines. Users interact conversationally with AI, and are likely to ask more complex questions in natural language, compared to simple keywords or short queries in a search engine. So it’s useful to get an idea of what kind of questions people will ask about your brand or industry, to help plan your GEO content.

Tip: Learn more about AI visibility for retail brands.

Unlike search engines, at present the leading Generative Engines do not share data on popular user queries, so it’s not easy to find out exactly what people are asking about your business. But you can look at popular keyword searches associated with your brand, and brainstorm the most likely questions people will ask Generative Engines. 

You can also use a social listening platform, like Meltwater Explore+ to analyze social media conversations around your brand and industry, to gain insight into common questions. Once you have mapped out all of the most likely questions people might ask, you can start creating content that answers those questions.

Tip: Learn how to connect social listening with generative engine optimization. 

Conventional SEO was built around search queries but GEO requires thinking in concepts, context, and authority — you need to focus on the intent of the query, rather than simply trying to match keywords. 

LLMs prioritize sources that provide comprehensive, well-structured, and contextually rich content. Focus your content strategy on:

Educational depth: Long-form resources that answer questions end-to-end. Publish explainers, FAQs, and data-backed insights that answer “how” and “why.”

Credibility signals: Citations, quotes, and data from reputable sources (analyst studies, thought leadership, or trusted media).

Rich media: Generative Engines pull information from videos and images as well as text content, so make use of varied content formats. 

Consistency across channels: LLMs learn from media, social, and owned content together; ensure your brand narratives are aligned across them.

The key thing is to ensure that your content is genuinely helpful in answering those questions you mapped out. If your content is too focused on sales messages, it’s less likely to be used by the LLMs.

TIP: Discover innovative AI-generated content examples to enhance your digital strategy.

You can optimize the content published on your website and owned channels, but LLMs also source information from trusted third party websites. That means digital PR has a key role to play in ensuring your brand is featured in high authority earned media, including news features, expert comment from your spokespeople, product reviews, and any other kind of brand story traditionally generated through a media relations campaign.

Tip: Learn more about the importance of earned media for GEO and about PR Pitch GEO optimization.

The tricky part is that unlike advertising, which can tell exciting and attention grabbing stories, PR professionals are limited to working only with the facts. But while you cannot always control the outcome of media outreach activity, ensuring that PR and GEO are aligned will help increase the volume of third party sources that feature the right messaging.

Tip: Learn how GEO is reshaping PR strategies.

Meltwater’s GenAI Lens gives you the ability to track your GEO performance over time, so you can understand whether your strategy is working well and spot areas where you need to improve. The tool provides visualizations that show brand sentiment, emotion, key phrases, people, products and things mentioned, alongside citations.

Armed with this information you can identify gaps in your GEO strategy where you might need to create more or better content. You can also discover which third party sources have the greatest impact on your GEO performance, and use that information to optimize PR outreach to focus on the most important media. 

Like SEO, this is a process of constant iteration and improvement. GEO should not be seen as a one-off project that will generate positive results in perpetuity, but rather an ongoing activity that needs to be integrated with the broader marketing mix.

Tip: Learn How to Measure PR Performance Across LLMs and Generative Search, understand how to report on LLM visibility, and take a look at executive report for AI search.

When an LLM processes a user query, it can often detect that the response might require more recent information than it has access to in its original training data. In that situation it will look online for more up to date content that will help it to form a more accurate answer, but where exactly do Generative Engines look for this information?

At Meltwater we used GenAI Lens to research which content sources the leading AI platforms were most likely to reference in their responses, and there were distinct differences between them all:

Open AI ChatGPT: Heavily favors text-based news and newswires (Reuters, AP, PR Newswire) and reference sites (Wikipedia). Much less likely to cite social media and UGC. 

Google Gemini: Leans towards high quality UGC from sites like Reddit and YouTube, as well as other online sources within the Google ecosystem. Does not index X/Twitter content. 

Google Search AI Overviews: Highly focused on UGC from Reddit and YouTube, as well as Facebook, Yahoo and LinkedIn. 

xAI Grok: Strongly skewed towards conversational UGC from X and Reddit. Less likely to cite traditional reference sources like Wikipedia, YouTube or academic sites. 

Unlike SEO, where most practitioners were happy to optimize only for Google, in GEO we have to consider the nuances of several different platforms; at the very least ChatGPT and Google AI Overviews. In short, it’s good practice to ensure you have up to date content propagated across a wide range of platforms where LLMs might look for supplemental information.  

GEO helps your brand to appear in AI platforms — but how does it work? This guide has everything you need to know to get started with GEO.

What is Generative Engine Optimization (GEO) & How to Do It

Customers are constantly leaving clues as to their preferences: in purchase history, website behavior, customer service interactions, reviews, social conversations, and market research. The challenge is working out how those signals connect—and what they reveal about changing needs, emerging demand, and consumer expectations.

What counts as consumer intelligence and how does it differ from consumer insights, market research, or social listening? This guide covers which data sources deserve attention, and tips for turning a noisy, crowded stream of information into better customer journey decisions.

Why does consumer intelligence matter, and what are the benefits?

How is consumer intelligence different from consumer insights?

What are some consumer intelligence examples?

How do you collect consumer intelligence?

What to look for in a consumer intelligence platform

Consumer intelligence comes from collecting and analyzing customer data from internal systems and external sources. It brings together information about what customers do, what they say, what they need, and how their behavior changes over time.

You might also hear it described as customer intelligence or customer intelligence analytics. The data may come from purchases, customer service interactions, website behavior, social conversations, online reviews, focus groups, surveys, and third-party market research.

Consumer intelligence gives teams a more complete basis for decisions pertaining to customer experience, marketing, communications, product development, and business strategy. Instead of relying on a single source or a snapshot in time, teams can compare multiple signals and identify patterns that would otherwise remain hidden.

Consumer intelligence connects customer behavior with the context needed to interpret it, helping organizations make more informed decisions about how to communicate, where to direct budget, and which customer problems to prioritize.

When used effectively, consumer intelligence can help teams:

Identify where customers lose interest or encounter friction during the customer journey.

Create segments based on behavior, interests, needs, or purchasing patterns.

Develop messaging and incentives that are more relevant to specific audiences.

Detect changing demand before it becomes visible in sales results alone.

Identify customers at risk of churn and prioritize retention efforts.

Give different departments reliable evidence for decisions about products, campaigns, and customer experience.

The value comes from connecting these uses rather than investigating each one in isolation. Flagging a change in website behavior, for example, becomes more useful when a team can compare it with customer feedback, purchase activity, or shifting public conversation.

Consumer intelligence is the structured customer data and analysis that organizations collect over time. Consumer insights are the conclusions and recommendations that emerge when people interpret that information for business decisions.

Consumer insights explain what customer behavior means and why it matters. They connect patterns in the data to questions such as: why demand is changing, what customers value, or where an experience is falling short.

For example, consumer intelligence might show that customers who purchase a particular product return more often. Insights explore why that pattern exists and what the brand should do in response, such as adjusting an offer to further capture their attention and businesses.

Where do consumer insights and consumer intelligence overlap?

Consumer intelligence provides the evidence that supports consumer insights, while consumer insights give that evidence more solid and practical direction. Both often draw from the same sources like customer records, social listening, market research, website analytics, and online reviews.

Consumer intelligence differs from traditional market research, which often answers a defined question through a point-in-time survey, panel, focus group, or study. Consumer intelligence is usually more continuous and can include behavioral, transactional, and unsolicited signals alongside formal research. The two approaches work best together: research can explain a question in depth, while ongoing intelligence shows how attitudes and behavior change afterward.

Consumer intelligence can support many decisions across the customer lifecycle. Common applications include personalization, segmentation, forecasting, and risk detection.

Organizations can use purchase history, browsing behavior, service interactions, and expressed interests to identify where customers engage or drop off. These patterns can inform improvements to the customer journey, from initial discovery through purchase and retention.

Consumer intelligence supports segments based on products purchased, buying patterns, interests, or responses to previous offers. Teams can use these groups to make campaigns more relevant and reach audiences with shared needs. See Meltwater’s guides to customer segmentation and audience segmentation.

Understanding customer preferences gives marketers a stronger basis for deciding which offers are likely to be relevant. Depending on the audience, that might mean a discount, free shipping, early access, a loyalty benefit, or a product recommendation tied to a known interest.

Tracking changes in purchase frequency, product interest, and public conversation can help organizations anticipate shifts in demand. The earlier a team identifies a meaningful change, the more time it has to adjust inventory, messaging, staffing, or campaign plans.

Banks and financial service providers, for example, can use patterns in customer data to identify possible fraud, unauthorized purchases, identity theft, or bad debt. Detecting unusual activity protects the organization while also helping it respond to customers before a problem becomes more difficult to resolve.

The most comprehensive consumer intelligence comes from combining internal and external sources rather than relying on one dataset. Common categories include: internal systems, social media, market research, and website analytics.

CRM, ERP, sales, customer service, and call-center systems contain information about purchases, interactions, account history, and customer value. Accessing this data often requires coordination across sales, marketing, customer service, IT, and research teams.

Social media, forums, and online reviews show what customers say in their own words, including complaints, preferences, questions, and reactions to competitors. Social listening adds structure to this conversation by identifying themes, sentiment, and changes in volume or attention.

Surveys, focus groups, panels, case studies, and third-party research provide detail about demographics, attitudes, motivations, and broader market conditions. This information can add explanation and methodological depth to behavioral and conversational data.

Website analytics show how people interact with digital experiences. Measures such as page views, session duration, exit rates, and conversion paths can reveal where visitors engage, hesitate, or leave.


<table class="table-primary alternate-color">
  <thead>
    <tr>
      <th>Data source</th>
      <th>Common use cases</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Internal systems</td>
      <td>Churn risk, purchase history, customer value, service patterns</td>
    </tr>
    <tr>
      <td>Social media and reviews</td>
      <td>Sentiment, reputation, emerging needs, competitor perceptions</td>
    </tr>
    <tr>
      <td>Market research</td>
      <td>Demographics, attitudes, motivations, market shifts</td>
    </tr>
    <tr>
      <td>Website analytics</td>
      <td>Engagement, conversion paths, drop-off, content performance</td>
    </tr>
  </tbody>
</table>

Using consumer intelligence involves categorizing the data, analyzing what it shows, and sharing the findings with people who can act on them.

First, organize information by source, audience, customer stage, topic, or another structure that reflects the question being answered. Next, analyze the data for patterns, changes, and exceptions. Be careful not to treat every data point as equally meaningful. Finally, share the findings through reporting, and briefings with relevant stakeholders, offering recommendations for follow up actions.

Consumer data needs context before it can support a sound conclusion. Research teams should understand the market, audience, product, and business decision behind the project so they can recognize relevant signals and avoid misreading industry-specific language or behavior.

2) Know what question you’re trying to answer

Begin with a defined business question rather than collecting data in the hope that something interesting appears. “What is happening?” is too general a starting point. Stronger questions ask what changed, why it changed, who it affects, and what the organization should do next.

More data does not automatically produce a better answer. Choose sources based on the question, the audience, the market, and the decision that needs to be made. Research expertise is valuable here because it helps teams distinguish useful evidence from irrelevant volume.

Raw data becomes difficult to interpret when it contains false positives, inconsistent categories, or unrelated conversations. A search for “Hamilton,” for example, might return posts about the Broadway musical and the Formula One driver. AI-powered classification can organize large datasets more efficiently, but the taxonomy still needs to reflect the research question.

A pattern is not an insight until it’s interpreted in context. Teams also need to clearly communicate the findings to the responsible parties in marketing, customer experience, product, sales, or operations. Reporting should explain what changed, why it matters to the business, and what decision the evidence supports.

A strong consumer intelligence platform should connect the sources your organization relies on, structure the data, explain what’s changing, and make the findings usable.

Data coverage: Can the platform bring together the external sources relevant to your audience, market, and use case?

Unified data: Can teams investigate a question without switching between disconnected systems or manually reconciling exports?

AI-powered analysis: Can it classify data, identify themes, surface anomalies, and reduce the manual work required to find patterns?

Forecasting and speed: Can it show whether a rising signal is likely to grow, fade, or require immediate attention?

Executive-ready reporting: Can researchers turn complex findings into credible reports that decision-makers can understand and use?

Human expertise and reliability: Does the provider combine AI-powered capabilities with research expertise, dependable data, and ongoing support?

Meltwater consumer intelligence brings global media, social, and AI signals together in one platform. Its consumer intelligence capabilities are designed to help teams move from fragmented data to actionable intelligence, while its human expertise adds context to AI-powered analysis.

Everyone else gives you data. Meltwater gives you intelligence you can act on. Book a demo of the Meltwater platform to discuss your consumer intelligence priorities, or explore Meltwater’s product tours.

Consumer intelligence is the byproduct of collecting and analyzing customer data from sources such as purchases, CRM systems, social media, market research, online reviews, and website analytics. It helps organizations understand customer behavior, preferences, perceptions, and changing needs so they can make more informed business decisions.

What is the difference between consumer intelligence and consumer insights?

Consumer intelligence is the structured data and analysis collected from customer interactions and other sources. Consumer insights are the conclusions drawn from that evidence. Intelligence might reveal a behavior pattern; an insight explains why the pattern matters and what the organization should do next. Learn more about consumer insights.

How is consumer intelligence different from market research?

Market research often answers a defined question through a survey, panel, focus group, or other point-in-time study. Consumer intelligence is typically more continuous and can combine behavioral, transactional, conversational, and research data. The two approaches are complementary: market research adds depth, while consumer intelligence tracks change over time.

How is consumer intelligence different from social listening?

Social listening analyzes conversations to identify themes, sentiment, trends, and audience reactions. Consumer intelligence uses social listening as one input alongside internal systems, market research, website analytics, and other sources. It adds structure and context so teams can answer broader business questions than how many people mentioned a topic.

Can consumer intelligence tools integrate with our CRM or other first-party data?

Many consumer intelligence platforms offer APIs, connectors, or data workflows that support integration with internal systems. Available endpoints, supported data types, and implementation requirements vary by platform. Confirm how the platform you are evaluating can blend external intelligence with CRM, ERP, website, or other first-party data.

What should I look for in a consumer intelligence platform?

Look for reliable coverage across the sources your teams use, unified data, AI-powered analysis, forecasting capabilities, executive-ready reporting, and access to human research expertise. The platform should reduce manual reconciliation and help decision-makers understand what is happening, what it means, and what action to take.

How do you collect consumer intelligence data?

Organizations collect consumer intelligence from four main source categories: internal systems, social media and reviews, market research, and website analytics. Internal systems show customer history and value; social sources reveal public conversation; research adds depth; and website analytics show digital behavior and drop-off.

What are some real examples of consumer intelligence in action?

Consumer intelligence can personalize customer journeys, create behavior-based segments, identify relevant incentives, forecast demand, and detect suspicious activity. For example, a retailer might combine purchase history, website behavior, and public conversation to identify rising interest in a product before adjusting inventory or campaign messaging.

Discover how consumer intelligence connects customer behavior, market signals, and business decisions.

Consumer Intelligence: Definition & Examples

It’s been 15 years since we published the first of our Global Digital Reports, but data shows that digital continues to gain momentum as we head into 2027.

And the good news is that our newest report has everything you need to make sense of all the latest trends.

Your complete guide to digital in in 2027

The Digital 2027 Global Overview Report – published in partnership between We Are Social and Meltwater – delivers comprehensive insights into the adoption and use of connected tech around the world.

Weighing in at more than 900 pages, this year’s report contains detailed statistics for:

Global internet adoption, access, and usage trends

AI adoption and use, including AI use cases and LLM platform trends

The evolution of online search and its role in the purchase journey

Social media behaviours and platform preferences

Ecommerce activity, including social commerce data

Digital entertainment, covering online TV and video, digital audio, and video games

Consumption of news content via digital channels

Online marketing trends, including ad spend and engagement

Top stories in our flagship Digital 2027 report include:

Important changes in the use of search engines

And that’s not all: you’ll find a treasure trove of data, trends, and insights in the complete report, and we’ll also explore some fascinating findings in this extensive analysis article, so read on below.

Before we begin, I’d like to extend my heartfelt thanks to our wonderful research partners, who provide much of the world-leading data you’ll find in this report:

All of this analysis has been written by Simon Kemp at Manochi, without the use of generative AI [why does this matter?].

Simon has been producing the Global Digital Reports series for 15 years, so he’s intimately familiar with the global “state of digital”, and how people’s online behaviours are evolving.

If you’d like to follow Simon’s regular analyses, he’d be delighted if you connect with him on LinkedIn.

As you explore this latest report – and especially as you compare trends over time – please be aware of the following:

International sanctions mean that GWI is no longer able to field its survey in Russia. As a result, we’ve removed data for Russia from the historical GWI figures that we’ve included in this report, to deliver more representative trends over time. That means all values on the GWI timeline charts you’ll find in this particular report already reflect these changes, but please refer to individual chart footnotes to understand how changes might affect this year’s data.

Similarweb recently introduced a new methodology for its App Intelligence dataset, which has resulted in some important changes to the figures the platform reports. Fortunately, the company has been able to apply these changes to historical data too, so – once again – the timeline trends you’ll find in this latest report already reflect these changes. However, readers should avoid comparing this new App Intelligence data with figures published in previous Global Digital Reports.

The United Nations made significant changes to its approach to classifying “urbanisation” since our Digital 2026 Global Overview Report, resulting in some big changes in reported values for urbanisation and urban population.

Please also refer to our comprehensive notes on data to understand other changes to data and sources in the Global Digital Reports series.

You’ll find our complete Digital 2027 Global Overview Report in the embed below (click here if that’s not working), but read on past that to find our in-depth analysis of what this year’s numbers actually mean for you and your work.

Let’s start with a look at the latest global headlines:

The United Nations’ World Populations Prospects data shows that there are 8.32 billion people living on Earth today. That figure is 69 million higher than the total we reported this time last year, resulting in an annual growth rate of 0.8 percent. The UN’s data also illustrates humanity’s continued move towards urban societies, with 80.6 percent of the world’s population – more than 4 in 5 people – now living in towns and cities [note that the UN made fundamental changes to the way it classifies urban and rural populations in November 2025].

GSMA Intelligence’s latest research indicates that there are 5.86 billion unique mobile users around the world in October 2026, which equates to 70.4 percent of the global population. This unique subscriber figure has increased by 98 million over the past 12 months, equating to annual growth of 1.7 percent. Meanwhile, separate data from Ericsson shows that the 7.68 billion smartphones in use today account for roughly 89.6 percent of all active mobile phone handsets, and 85.8 percent of total cellular mobile connections [note that some people may use more than one smartphone].

Manochi’s analysis shows that the total number of people using the internet had reached 6.15 billion at the start of October 2026, which puts global internet penetration at 73.9 percent. Reported internet user figures have increased by 54 million (+0.9 percent) over the past year, but our analysis indicates that delays in reporting mean that the actual growth figure over the past 12 months is probably considerably higher than these numbers suggest. Despite continued improvements in internet access, however, close to 2.17 billion people around the world remained offline at the time of report production.

Manochi’s research also reveals that global social media user identities have now reached 5.81 billion, with that figure equivalent to 69.9 percent of the global population. This global user identities figure increased by 2.7 percent in the 12 months to October 2026, thanks to the addition of 151 million new user identities [note that “user identities” may not represent unique human individuals.]

According to Manochi’s latest analysis, there are now 3.24 billion active users of generative AI platforms. This figure equates to 38.9 percent of the total population, but – like our social media user identities figure – it’s important to stress that this GenAI user figure may not represent unique human individuals. Caveats aside though, active GenAI users increased by more than 1.2 billion between October 2025 and October 2026, resulting in an impressive annual growth rate of 57.2 percent.

But beyond overall adoption trends, how have people’s online behaviours and preferences been evolving?

Manochi’s latest analysis reveals that more than 1 in 3 people around the world now uses generative AI (GenAI) each month.

Our research indicates that 3.24 billion users will access at least one of the top LLM platforms in October 2026, equating to a worldwide adoption rate of 38.9 percent [learn how we calculate these numbers in this article].

It’s important to stress that – while we strive to deduplicate figures across different platforms – the base user figures reported by the platforms themselves may include some degree of duplication, because the same individual may use multiple accounts on the same platform (e.g. one for personal use, and one for work).

As a result, we recommend treating this GenAI user figure in much the same way as our social media “user identities” figure.

But having said that, research conducted by GWI indicates that ChatGPT already had more than 1.1 billion monthly active users amongst adult internet users in Q2 2026, and once we factor age and geography, this finding correlates closely with the ChatGPT figures that we use in our calculations.

Manochi’s analysis also indicates that monthly active GenAI users have increased by more than 57 percent over the past 12 months, thanks to the addition of 1.2 billion new users.

For perspective, figures reported by OpenAI indicate that ChatGPT alone has added roughly half a billion weekly active users over the past year, which our analysis suggests translates to an increase of roughly 700 million monthly active users.

And for added perspective, Similarweb’s App Intelligence data shows that active users of ChatGPT’s mobile apps have more than tripled over the past 2 years.

Meanwhile, CNNIC’s research shows that GenAI users in China have increased by at least 150 million over the past 12 months.

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