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Generative AI is everywhere now. You see it in content, campaigns, customer experiences, and even internal workflows. It promises speed, scale, and efficiency, but there’s a harder question sitting underneath all that progress; Do your audiences actually trust it?

Trust in the Age of AI: What Consumers Really Think About AI Generated Content

Today, PR teams aren’t just tracking media coverage and share of voice. They’re also measuring how their brand shows up in AI-generated answers, search summaries, and large language models (LLMs). This new LLM visibility layer is quickly becoming a critical part of PR performance.

15 Must-Have PR KPIs and How to Track Them

See how one platform can power every decision across PR, communications, and marketing.

The Meltwater Blog

Blog Topics: Generative Engine Optimization, Media Intelligence, Media Relations, Social Listening, Consumer Intelligence, Influencer Marketing

Find out how you can leverage Facebook to boost your brand's online presence and gain a competitive edge. Explore the Meltwater category page today!

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Generative Engine Optimization (GEO) is transforming how brands show up in AI-powered search. Explore insights, strategies, and best practices to help your content rank and resonate across conversational engines like ChatGPT, Gemini, and Copilot.

Explore insights and strategies for Generative Engine Optimization (GEO) to help your brand stay visible and discoverable in AI-powered search.

Generative Engine Optimization (GEO) Blog Collection | Meltwater

Every day billions of conversations take place on social media: tweets, posts, comments, blogs, reviews, podcasts, and much more.
Learn how to use social listening to make sense of all of that data.

On our social listening category page, you find all our blogs covering topics around social listening and social media monitoring. Read more.

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Business Reputation Management: What does it take to add reputation management to your marketing strategy? More importantly, how do you keep your reputation sparkly and shiny? Here’s your all-access guide to online reputation management for the long term.

On our reputation management category page, you find all our blogs covering topics around business reputation management. Read more.

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From your brand's logo to your website, your social media presence to your customer support—these elements combined are what constitutes a brand in the minds of customers. But, there are thousands of other businesses that have a logo, website, social media presence, and website. So, how can you position your business on the market and make it stand out from the competition?
The key to success is branding & brand management!

On our brand management category page, you find all our blogs covering topics around brand management. Build your strategy and reach your goals!

Brand Management Blogs | Category

Competitive intelligence and the act of collecting competitive data can be seen as growth drivers for a company. Our guide will help you understand the concept of competitive intelligence (CI) by answering questions such as: What is the definition of competitive intelligence? How is competitive intelligence useful to organizations? What is the goal of competitive intelligence? And more.

On our competitive intelligence category page, you find all our blogs covering topics around competitive intelligence. Learn how to stay ahead.

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Let’s dive into the customer journey framework and how to optimize the process by creating a customer journey map and using metrics that matter when analyzing the customer journey.

On our customer journey category page, you find all our blogs covering topics around mapping and understanding the customer journey.

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What Is Data Analytics? [Beginner’s Guide 2023]
How can I grow my business? 
How do customers interact with my newly launched product?
How do I increase the productivity of my employees?
How do I know if my marketing efforts are working?
We have just two words for you...Data analytics!

On our data analytics category page, you find all our blogs covering topics around AI and data analytics. Technical and still easy to understand.

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Public relations and media relations can add impact to any marketing strategy. It’s an opportunity for brands to grow their reach beyond traditional sales messages. PR helps to shape the image you want people to have of your brand.
So, what is PR and how do you use it? This guide shows you everything you need to know to build a strong public relations foundation.

On our public relations (PR) category page, you find all our blogs covering public relations and communications topics. Step up your PR game.

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We all know exactly what the roles of sales or accounting are in a company, right? We might even have a slight idea of what the IT guys are doing.
But there is another business discipline, that plays a crucial role in organizations, even more so during the pandemic – internal communications!
But what exactly do we mean by that, what purpose does it serve and what steps can you take to ensure you and your employees will communicate effectively?

On our internal communications category page, you find all our blogs about internal comms topics. Learn about the intranet, internal newsletters, and more.

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As the internet has taken root in more areas of our lives — everything from entertainment to shopping to paying bills and more — traditional marketing is becoming less impactful. As any marketer knows, you need to focus on where your audience is if you want to reach them. Since the average person spends about seven hours a day online, it’s no surprise that content has maintained its hold on marketers.

Here's a closer look at what content marketing is and why it matters more than ever in our digital-first economy.

On our content marketing category page, you find blogs about content marketing topics. Learn about strategies, KPIs, tips, and more. Make your content king!

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The rise of the influencer marketing space – today a $10 billion industry, growing 50% per year – is one of the clearest examples of how social media is transforming traditional marketing. These days having a business website, presence on social, PR coverage, and your own content isn’t enough to compete.
We'll dive into the intricacies of influencer marketing to help ensure that you're equipped with the insights needed to be successful in this space.

On our influencer marketing category page, you find blogs about influencer marketing topics. Build your strategy, find influencers, and measure results.

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Have you just launched a new product or service that you want to get the word out about? Maybe you want to share with consumers why it is awesome and how it is so much better than the one your competitors offer.

Well, then, there is one thing you can't do without...Marketing!

On our marketing category page, you find blogs about marketing topics. Learn about all the different angles and types of marketing and improve your results.

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In this guide, we want to give you a peek into the definition of social media management, the daily schedule of a social media manager, social media platforms to promote your business, and the do’s & don’ts of social media management.
Are you ready to begin the journey of becoming a social media superstar?

On our social media management category page, you find blogs about social media management topics. Build and manage your online community.

Social Media Management Blogs | Category

Building a following and using social media platforms to reach a wider audience requires a well-planned social media marketing strategy. In this guide, we’ll be looking at why social media marketing is so essential and how you can make it work for your business.

On our social media marketing category page, you find blogs about social media marketing topics. Learn how to get the highest paid social ROI!

Social Media Marketing Blogs | Category

In the fast-evolving realm of digital marketing and public relations, utilizing the right tools is crucial for success. Our interdisciplinary category showcases all the pertinent tools for digital marketing & PR. These tools help in enhancing your online presence and effectively reaching target groups, whether through reputation management, influencer marketing, or social media management.

On our digital marketing and PR tools category page, you find all our blogs covering topics around mapping and understanding the customer journey.

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Public relations measurement is changing faster than at any point in the last two decades. Media coverage still matters, but it no longer tells the full story of how your brand is perceived, discovered, or trusted. Today, large language models (LLMs) and generative search engines increasingly decide what people see first when they ask questions about your company, your category, or your competitors. If you want to understand PR performance in this new reality, you need to measure how AI systems describe you, not just how journalists do.

This article looks beyond the need for PR measurement to evolve, to the metrics that define success in AI-driven environments, and the practical tools and methods teams can use today. It covers benchmarks, optimization strategies, executive reporting guidance, and future trends, before closing with how Meltwater helps automate and scale this work.

The new metrics for measuring PR success in LLMs

Tools and methods to measure PR success in LLM channels

How to improve PR performance across LLMs

Examples: Good vs. poor LLM PR visibility

Reporting PR success in an LLM-driven world

How Meltwater can help you automate your PR LLM measurement

PR measurement has a new gatekeeper: AI systems now summarize, recommend, and define brands before journalists or customers ever visit a website. Measuring PR performance means measuring how AI understands you.

Generative engines now behave like media outlets, but without bylines, deadlines, or editorial desks. When someone asks an AI assistant who leads a market, what tools to trust, or which company fits a specific need, the answer feels authoritative even when no source is visible. 

That answer often shapes perception more strongly than a single article or press release ever could.

Traditional PR metrics struggle in this environment because they measure exposure, not interpretation. An LLM does not care how many outlets mentioned you last quarter, it cares whether it has enough clear, consistent, and authoritative signals to confidently describe your brand. If those signals are weak or fragmented, the AI often fills gaps with assumptions, outdated information, or competitor narratives.

AI is also becoming the consumer’s first contact point with brands. Instead of searching, scanning results, and reading multiple pages, users increasingly ask a single question and accept the summary. That summary shapes reputation, trust, and intent in seconds. When LLMs summarize your company inaccurately or exclude you entirely, your PR performance suffers even if your media coverage looks strong on paper.

The shift isn’t theoretical, as Meltwater’s Digital 2026 Global Overview Report shows that more than one billion people now use generative AI tools each month, accelerating the move toward AI-mediated discovery and zero-click answers.

This is why PR measurement must evolve. You are no longer just tracking what was published,  you are tracking what AI understands, remembers, and repeats about your brand.

The 8 metrics that define PR success in LLMs: Visibility - Accuracy - Narrative Share - Win Rate - Sentiment - Source Credibility - Entity Clarity - Earned Media Influence

LLM brand visibility measures how often your brand appears across major AI models when users ask category-relevant questions. This metric shows whether your PR efforts have translated into AI-level awareness. If your brand rarely appears, it signals that your authority signals are too weak, too inconsistent, or too narrow to register.

Visibility alone does not equal success, but without it, nothing else matters. If AI systems do not surface your brand, they cannot recommend you, describe you, or compare you accurately.

AI summary accuracy evaluates whether LLMs describe your company correctly, including your core offering, positioning, geographic scope, and differentiators. Inaccurate summaries often come from outdated coverage, inconsistent naming, or missing entity data.

From a PR perspective, accuracy is a reputation issue because an AI that misstates what you do or confuses you with another brand erodes trust before a human ever engages with you directly.

Narrative share of voice looks at which themes and attributes AI systems associate with your brand compared to competitors. This is about meaning, not volume. If AI repeatedly frames your competitor as innovative and you as generic, that narrative will influence buying decisions even if you receive similar media coverage.

This metric helps you see whether your messaging strategy is landing at a semantic level, not just a distribution level.

Win rate measures how often an LLM selects your brand as the recommended option when users ask for solutions, tools, or providers. This is one of the clearest indicators of PR influence in AI environments because it reflects perceived authority and relevance.

A low win rate suggests that competitors have stronger earned media signals, clearer positioning, or more consistent coverage in sources AI trusts.

Sentiment analysis inside AI outputs shows whether your brand is framed positively, neutrally, or critically, but unlike social sentiment, this reflects synthesized judgment rather than raw opinion. A neutral or cautious tone may still signal risk if competitors are framed more confidently.

Tracking sentiment over time helps you understand whether PR initiatives are strengthening trust or leaving unresolved perception gaps.

6. Source traceability and credibility signals

This metric evaluates whether AI systems cite or clearly rely on authoritative sources when mentioning your brand. Strong PR performance increases the likelihood that AI draws from reputable media, analyst coverage, and high-quality owned content.

When AI answers lack traceable sources or rely on weak references, it often correlates with inconsistent or low-authority PR signals.

Knowledge graph presence measures whether AI systems recognize your brand as a distinct, well-defined entity with accurate attributes. Poor entity recognition leads to confusion, omission, or incorrect associations.

This metric sits at the intersection of PR, SEO, and data hygiene. Strong earned media helps reinforce entity clarity, but only when naming, descriptions, and facts stay consistent.

Brands with strong media coverage still disappear from AI answers due to inconsistent naming, vague positioning, or outdated source data.

This measures how often AI-generated overviews reflect or summarize stories that originated in earned media. It connects traditional PR outcomes to AI visibility and shows whether coverage is influencing generative answers.

If earned media never surfaces in AI outputs, your coverage may lack the authority or structure that AI systems prioritize.

A practical LLM PR measurement workflow: 1) Define real-world prompts. 2) Capture AI outputs across models. 3) Score visibility, accuracy, and framing. 4) Benchmark against competitors. 5) Track change over time.

Measuring PR success across LLMs requires a combination of automated monitoring, structured human review, and disciplined documentation. No single method is sufficient on its own. You should treat AI visibility as a repeatable measurement workflow, not an occasional experiment. 

The goal is to observe how AI systems respond to real-world questions over time, understand why those responses appear, and connect changes back to PR activity.

Meltwater's GenAI Lens gives PR pros insight into how LLMs see your brand, so you can improve your AI visibility. 

LLM scraping tools capture brand mentions directly from AI-generated responses at scale. In practice, teams begin by defining a stable set of prompts that reflect how customers, journalists, analysts, or buyers would realistically ask questions about their category. These prompts should include brand discovery questions, comparison questions, recommendation requests, and category-defining queries.

Once prompts are defined, scraping tools automatically run them across multiple LLMs on a recurring cadence, typically weekly or monthly. The outputs are stored as structured data, allowing teams to track whether the brand appears, how frequently it appears, and in what context. Over time, this creates a visibility baseline that shows whether PR efforts are increasing or decreasing AI-level awareness. 

When visibility drops or competitors begin appearing more often, teams can trace those shifts back to changes in earned media volume, authority, or messaging clarity.

AI visibility dashboards turn raw LLM outputs into usable PR metrics. These dashboards aggregate data from multiple models and prompts into a single view, showing trends in brand presence, sentiment, and competitive positioning. 

In day-to-day use, teams review dashboards alongside traditional media reports to understand whether coverage is translating into AI recognition.

To use dashboards effectively, teams align dashboard metrics to PR goals rather than treating them as vanity data. For example, a campaign designed to establish category leadership should result in increased inclusion in “best solution” or “top provider” prompts. Dashboards make it possible to see whether that shift is actually happening, and whether it holds across different AI systems rather than appearing in just one.

Automation alone isn’t enough. Dashboards show what AI says. Human review explains why it says it, and what to fix.

Manual prompt testing provides qualitative depth that automation alone cannot deliver, by using a controlled testing framework where prompts are written, logged, and rerun at consistent intervals. 

Each response is reviewed for accuracy, tone, framing, and omissions. Reviewers document not just whether the brand appears, but how it is described and what assumptions the AI makes.

In practice, this work is scheduled as a recurring review, often monthly, and assigned to specific owners to avoid ad hoc testing. Responses are compared against prior snapshots to identify narrative drift, emerging misconceptions, or sudden competitive displacement. 

Manual testing is especially useful for executive reporting, crisis preparedness, and validating whether AI summaries align with brand strategy.

GEO audit tools evaluate whether a brand’s content and earned media are structured in ways that generative engines can reliably interpret. These audits look at clarity, consistency, and factual density across press releases, thought leadership, corporate pages, and high-authority coverage.

Teams typically run GEO audits quarterly or around major announcements. The output highlights gaps where AI systems may struggle to extract clear meaning, such as vague positioning statements, inconsistent terminology, or missing contextual details. PR teams then use these findings to adjust messaging, improve future releases, and collaborate with SEO or content teams to strengthen AI readability without changing editorial intent.

Entity validation ensures that AI systems recognize the brand as a distinct, accurate entity rather than a loose collection of mentions. In practice, teams maintain a checklist of core entity attributes such as official brand name, product names, executive names, headquarters location, and category descriptors.

Teams periodically test whether these attributes appear correctly in AI responses. When errors surface, they trace the issue back to inconsistent usage in press materials, outdated coverage, or conflicting references across sources. Maintaining entity hygiene becomes an ongoing PR responsibility, similar to brand guidelines, but focused on how machines interpret identity rather than how humans perceive design.

AI answer citation monitoring tracks which sources LLMs rely on when generating responses about the brand. Teams review whether AI outputs reference authoritative outlets, analyst reports, or trusted publications, or whether they rely on low-quality or unclear sources.

In practice, citation monitoring is used to evaluate earned media quality rather than quantity. When high-quality coverage begins to appear in AI answers, it signals strong authority transfer. When citations disappear or shift toward weaker sources, it often indicates that PR coverage is not reinforcing credibility in ways AI systems prioritize. Teams use these insights to refine media targeting, emphasizing outlets that influence AI narratives as well as human audiences.

Strong performance in AI environments shows up as consistent brand inclusion across relevant prompts, high accuracy in summaries, and stable positive sentiment. Benchmarking against competitors matters more than hitting an abstract target because AI visibility is relative.

A brand that appears in most category prompts but loses recommendation slots to a competitor still has a positioning problem. Similarly, high citation volume from low-quality sources signals weaker authority than fewer citations from trusted outlets.

Benchmarks should reflect your category maturity, competitive density, and strategic goals, not generic industry averages.

Strengthen entity clarity in press releases

Clear, consistent entity descriptions help LLMs accurately identify who you are, what you do, and how you should be categorized. Each press release should explicitly restate core facts such as your company name, primary offering, industry, and role in the market, rather than assuming the AI model has prior context. Repetition of these fundamentals across releases reinforces entity understanding and reduces the risk of AI summaries that are vague, incorrect, or conflated with competitors.

AI models summarize with confidence when content is concrete, specific, and information-dense. PR content should prioritize verifiable facts, clear explanations, and explicit context over abstract vision statements or loosely framed thought leadership. When releases and articles answer who, what, why, and how in plain language, LLMs are far more likely to surface your brand accurately and prominently in generative search results.

LLMs rely on pattern recognition, so inconsistent brand names, abbreviations, or product references weaken AI confidence and fragment authority signals. PR teams should use the exact same company name, product names, and descriptors across press releases, coverage, and owned content. Consistency helps AI systems consolidate signals correctly, improving brand recognition, accuracy, and visibility in generative search answers.

Use GEO frameworks for every announcement

Generative engine optimization emphasizes clear structure, explicit context, and authoritative signals that AI systems can reliably interpret. Applying GEO principles to press releases means clearly stating what changed, why it matters, and how it fits within the broader category, using straightforward language and consistent framing. This approach improves AI comprehension and summarization without altering the editorial voice or strategic intent of the announcement.

Coverage in trusted, high-authority outlets remains one of the strongest signals AI systems use to assess brand credibility. When reputable publications consistently describe your company accurately, LLMs are more likely to reference, summarize, and recommend your brand with confidence. PR teams should prioritize quality and relevance of coverage, not just volume, to strengthen AI trust and long-term generative search visibility.

Structured data helps AI systems accurately identify entities, attributes, and relationships across your content ecosystem. When press releases, company pages, and supporting content use structured formats to reinforce key facts, LLMs can more easily connect those details across sources. This strengthens entity recognition, reduces ambiguity, and improves consistency in how generative engines summarize and represent your brand.

Distribute releases across trustworthy publishers

Distribution quality matters more than reach when it comes to AI interpretation and generative search visibility. LLMs heavily weight signals from trusted, authoritative publishers when synthesizing answers, often ignoring low-credibility or duplicate sources. Prioritizing respected outlets increases the likelihood that earned coverage is reflected accurately in AI summaries and recommendations.

Tip: Learn more about AI press release optimization

Imagine an LLM summarizing your brand incorrectly. The model might misstate your founding date, mislabel your product category, or confuse you with a competitor. This happens when your public data lacks clarity or when authoritative sources contradict one another. You fix this by reinforcing your core facts across owned and earned content, validating entities, and correcting outdated information.

To understand PR performance in a world of LLM-driven search, you need to measure how AI systems describe you, not just how journalists do.

How to Measure PR Performance Across LLMs and Generative Search

Lattes hold a special place in today’s “little treat” culture, still going strong decades into becoming a mainstream offering around the world. With that kind of longevity, it’s no wonder that these caffeinated concoctions are constantly moving through waves of flavor trends. From the matcha craze to the ube emergence, marketers can mine valuable lessons from this dynamic corner of the food and beverage sector. 

We used Explore+, our enterprise-level media, social, and consumer intelligence solution, to do just that, diving into three leading flavors: matcha, ube, and pistachio. What we found is that while one flavor undoubtedly dominates, all three offer interesting insight into the contours of trend adoption stages.

Note: This analysis examines data from Explore+ sourced from traditional, digital, and social media sources (including Pinterest, Instagram, and X), from December 16, 2025, to January 16, 2026. Percent change figures compare data from December 16, 2025, to January 16, 2026, to that from November 16, 2025, to December 16, 2025.  

Matcha vs. Ube vs. Pistachio: Which Latte Flavor Is Most Popular?

Our social listening analysis found that matcha is by far the most talked about latte flavor, compared to ube and pistachio.

<table style="width:100%; border-collapse:collapse; font-family:Arial, sans-serif;">
  <thead>
    <tr>
      <th style="background:#28bbbb; color:#fff; padding:12px; text-align:left;">Flavor</th>

      <th style="background:#28bbbb; color:#fff; padding:12px; text-align:center;">Mentions (12/16/25 to 1/16/26)</th>
      <th style="background:#28bbbb; color:#fff; padding:12px; text-align:center;">Mentions (11/16/25 to 12/15/25)</th>
      <th style="background:#28bbbb; color:#fff; padding:12px; text-align:center;">Mentions % Change</th>

      <th style="background:#28bbbb; color:#fff; padding:12px; text-align:center;">Engagement (12/16/25 to 1/16/26)</th>
      <th style="background:#28bbbb; color:#fff; padding:12px; text-align:center;">Engagement (11/16/25 to 12/15/25)</th>
      <th style="background:#28bbbb; color:#fff; padding:12px; text-align:center;">Engagement % Change</th>
      <th style="background:#28bbbb; color:#fff; padding:12px; text-align:center;">Engagement / Post</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style="padding:10px; font-weight:bold; color:#000000;">Matcha</td>

      <td style="padding:10px; text-align:center;">50,046</td>
      <td style="padding:10px; text-align:center;">46,144</td>
      <td style="padding:10px; text-align:center; color:#28bbbb; font-weight:bold;">▲ 8.5%</td>

      <td style="padding:10px; text-align:center;">16,079,842</td>
      <td style="padding:10px; text-align:center;">15,446,939</td>
      <td style="padding:10px; text-align:center; color:#28bbbb; font-weight:bold;">▲ 4%</td>
      <td style="padding:10px; text-align:center;">321</td>
    </tr>

    <tr style="background:#f4fafa;">
      <td style="padding:10px; font-weight:bold; color:#000000;">Pistachio</td>

      <td style="padding:10px; text-align:center;">4,662</td>
      <td style="padding:10px; text-align:center;">3,527</td>
      <td style="padding:10px; text-align:center; color:#28bbbb; font-weight:bold;">▲ 32%</td>

      <td style="padding:10px; text-align:center;">389,200</td>
      <td style="padding:10px; text-align:center;">449,947</td>
      <td style="padding:10px; text-align:center; color:#b627a1; font-weight:bold;">▼ 13%</td>
      <td style="padding:10px; text-align:center;">83.5</td>
    </tr>

    <tr>
      <td style="padding:10px; font-weight:bold; color:#000000;">Ube</td>

      <td style="padding:10px; text-align:center;">1,341</td>
      <td style="padding:10px; text-align:center;">1,112</td>
      <td style="padding:10px; text-align:center; color:#28bbbb; font-weight:bold;">▲ 21%</td>

      <td style="padding:10px; text-align:center;">171,231</td>
      <td style="padding:10px; text-align:center;">83,494</td>
      <td style="padding:10px; text-align:center; color:#28bbbb; font-weight:bold;">▲ 105%</td>
      <td style="padding:10px; text-align:center;">127.7</td>
    </tr>
  </tbody>
</table>

From December 16, 2025, to January 16, 2026, there were about 50,000 mentions of matcha lattes across traditional, digital, and social media channels. This represents an 8.5% increase in matcha mentions from the six-months prior (period-over-period, or PoP), with engagement actions also growing by 4% in that time. 

Pistachio and ube generated significantly fewer mentions than matcha, but showed significantly higher growth at 32% and 21% respectively. We’ll dive further into these evolutions below, but by conversation volume, matcha is the clear winner, demonstrating the level of popularity we would expect from an established trend.

Flavor Trend Adoption Stages Explained: Matcha, Ube, and Pistachio

To understand how these flavors reached such different levels of popularity, we analyzed them through the lens of trend adoption stages. By looking beyond raw conversation volume and factoring in growth rates, engagement patterns, and where discussions are happening, clearer distinctions emerge between saturated, re-emerging, and emerging trends.

Matcha is a centuries old Japanese tea, but it first hit the global coffeeshop mainstream in the 2010s. Today, it’s a leading force in the flavored lattes conversation. During the time period analyzed, matcha had about 11x more mentions than pistachio and 37x more mentions than ube. It also dominated in terms of engagement actions with about 41x more than pistachio and 94x more than ube.

Averaging at 321 engagements per mention, the digital buzz and enthusiasm around matcha is robust, signaling its status as an established trend with longevity among consumers, rather than a fleeting fad.

Platform dynamics further underscore this maturity, with matcha mentions flourishing on multiple social channels. For example, an impressive 24% of matcha mentions and 80% of engagement occurred on Pinterest, fueling users’ inspiration for recipes and showing off at-home creations. Meanwhile, Instagram accounted for 14% of mentions and about 9% of engagement, highlighting matcha’s role in lifestyle content.

Pinterest users have liked this matcha latte recipe video (presented as a Starbucks dupe) over 5,800 times. 

No longer a novelty, matcha has officially reached the saturation trend stage, with widespread familiarity among consumers and high engagement across visual-first platforms. For brands and marketers across sectors working with trends at this stage, differentiation, rather than awareness-building, is key to remaining relevant in the cultural conversation.

Like matcha, pistachio lattes also became popular in the 2010s, debuting as a limited-edition Starbucks offering in 2019 and joining its winter menu a few years later. However, in 2025, it enjoyed a new boost of virality, helped along by the rise of Dubai chocolate. 

Though they generated less than a tenth of the conversation matcha did in the time period analyzed, pistachio lattes showed the highest growth with a 32% PoP increase in mentions. At the same time, that growth was also accompanied by the biggest decline in engagement (-13% PoP), resulting in the lowest engagement per post (83.5). 

This disconnect between volume and engagement can be explained by looking at where that buzz occurred. About 23% of pistachio latte mentions came from online news sources covering it as a viral trend or announcing the launches of related products. 

In particular, news and broadcast mentions of the flavor spiked on January 6, 2026, with the release of Starbucks’s 2026 winter menu featuring pistachio lattes, cold brew, and cortado drinks. Overall, a whopping 73.4% of pistachio mentions came from news and broadcast sources, highlighting that the discussion was driven primarily by editorial coverage rather than organic consumer engagement.

This Instagram Reel from Starbucks generated about 35,700 engagement actions in just over two weeks. 

Together, pistachio’s lower, but growing, discussion volume and lagging engagement signal its status as both a seasonal trend as well as re-emerging one. For marketers and brands working with trends in a similar stage, driving excitement is a matter of careful timing within a relatively narrow window of consumer interest. 

Ube has a rich history and great importance in Filipino culture, but in recent years it has become a trendy flavor across a range of sweet treats including lattes. Last year, Starbucks even included an Ube Espresso Martini and Iced Ube Coconut Latte on limited-edition menus at select Reserve Roasteries, suggesting a potential wider release to come.

As a relatively newer trend, ube lattes generated the lowest conversation volume compared to matcha and pistachio in the time period analyzed, but the strongest engagement growth at +105% PoP. 

With 127.7 engagements per post, ube outperformed pistachio despite its smaller audience, suggesting higher novelty and consumer interest. Mentions were concentrated on Instagram (about 24%) and online news (about 21%), where coverage often placed ube within broader food and wellness trend narratives.

A significant contributor to this engagement surge was a paid Instagram post on January 13 foodie influencers @topjaw. The Reel spotlighting London-based, Filipino bakery Panadera — along with its ube lattes — generated about 44,000 engagement actions in just three days. According to Meltwater’s AI-powered influencer marketing solution, that single post generated over $650,000 in Estimated Media Value (EMV) for Panadera and partnering brand Square UK.

Overall, ube latte’s limited conversation but surging engagement point to its novelty. Unlike matcha or pistachio, consumers are still discovering this trend. Related content still has the ability to surprise and intrigue viewers, prompting their own digital and IRL journeys of discovery. For marketers and brands working with niche and emerging trends like this one, the playing field is wide open for creative activations that spark interest and establish brand associations.

Final Takeaways: How to Analyze a Trend’s Adoption Stage 

Consider our latte flavor analysis as a jumping off point for your own trend analyses. When using social listening and consumer intelligence solutions like Explore+, follow these simple steps to determine which phase a trend is currently in.

Step 1: Look at conversation and engagement evolutions 

Our latte flavor analysis underscored how mentions volume isn’t the end all be all of understanding trend lifecycles. Instead, seeing how those metrics are changing over time can help you predict where trends are headed.

Step 2: Analyze platform distribution and concentration

Editorial coverage can accelerate trends, but most often it’s the consumers themselves who spark them. Breaking down discussions by their sources lets you see what the driving forces behind trends are. Those concentrated on social platforms like Pinterest and Instagram have the most promising momentum behind them.

Knowing who is driving visibility and engagement for a trend — along with how they are doing it — gives you valuable insight into where new opportunities may lie. For niche and emerging trends especially, influencers can help your brand enter the current cultural conversation in a meaningful way. Use consumer intelligence and influencer marketing solutions in tandem to make the most of every activation.

Frequently Asked Questions About Trend Adoption Stages

Trend adoption stages describe how a product, behavior, or idea moves from early discovery to mainstream use and eventual saturation. According to a Meltwater Explore+ analysis, these stages can be identified by examining changes in conversation volume, engagement growth, and where discussions are taking place across media and social platforms.

How can brands identify which stage a trend is in?

Brands can determine a trend’s adoption stage by analyzing how quickly mentions are growing, whether engagement is increasing or declining, and which platforms are driving visibility. For example, Meltwater’s Explore+ analysis of latte trends combined media, social, and consumer intelligence data to reveal whether momentum was coming from organic consumer interest, influencer activity, or editorial coverage.

Why isn’t conversation volume enough to understand trend maturity?

High conversation volume alone does not indicate whether a trend is emerging, re-emerging, or saturated. For example, Meltwater’s Explore+ analysis of latte trends showed that engagement per post, growth rate, and platform concentration provided clearer signals of trend novelty, longevity, or decline than raw mentions alone.

What distinguishes an emerging trend from a re-emerging one?

As Meltwater’s January 2026 Explore+ analysis of latte flavor trends showed, emerging trends can show low overall volume but high engagement growth driven by discovery and influencer-led content. A re-emerging trend, by contrast, can show renewed visibility tied to seasonal moments or product launches. Tracking visibility drivers and audience response is key to making the distinction.

How do platform dynamics affect trend adoption?

Meltwater Explore+ analysis of latte flavor trends found that mentions volume and engagement concentrated on different platforms signal different stages of adoption. Visual and inspiration-driven platforms like Instagram and Pinterest can indicate organic consumer momentum, while spikes in news and broadcast coverage can reflect short-term visibility.

How can marketers use trend adoption insights to guide strategy?

Understanding a trend’s adoption stage helps marketers decide whether to focus on awareness, differentiation, or timing. Meltwater’s Explore+ enables brands to uncover insights allowing them to align messaging, partnerships, and launches with how consumers are currently engaging with a trend rather than relying on assumptions or surface-level popularity.

Trend Adoption Stages Explained: Matcha, Ube, and Pistachio Lattes

It’s Monday morning, and leadership wants to know: Did our influencer marketing strategy work?

But you can’t offer a clear answer because your data lives across countless social media platforms, including Instagram screenshots, TikTok exports, YouTube Studio graphs, and late-night spreadsheets. You can see views, but you can’t see return on investment (ROI). You are guessing instead of deciding.

Influencer measurement tools fix this fragmentation by making data comparable. Now you can weigh engagement metrics against real outcomes to spot top influencers over content creators who only look good in screenshots.

Use this guide to understand influencer measurement tools, learn how to benchmark creators, and identify which analytics tools actually prove impact at scale.

How to Choose the Right Influencer Measurement Tools

What Metrics Matter Most in Influencer Measurement? (And How They Show Up in Meltwater)

How Does Meltwater’s Influencer Measurement Tool Track Campaign Performance?

How to Build an Influencer Measurement Framework for Your Team with Meltwater

How to Get Started with Meltwater for Influencer Measurement

Influencer measurement tools track influencer marketing campaigns across social media, monitoring reach (how many people saw the posts), user interactions (e.g., clicks or engagement), and whether influencer posts drove website visits, sign-ups, or purchases. 

Measuring influencer marketing is fragmented by default.

A typical campaign may include one creator posting Instagram Reels, another posting on TikTok, and a third running a YouTube integration. Since each platform reports performance in its own dashboard, teams often resort to manual reporting using screenshots and spreadsheets.

Modern influencer measurement tools like Meltwater centralize campaign data, enabling teams to measure influencer marketing as a unified program rather than a series of disconnected posts across platforms.

Meltwater influencer measurement dashboard displaying campaign-level metrics and platform-by-platform performance

Why influencer measurement is critical at scale

Manually reviewing posts and metrics works for teams managing a small number of influencers. But as influencer programs scale across regions and social media platforms, manual tracking struggles to keep up with fragmented data. 

Without clear measurement, influencer marketing leads to systemic problems: 

Budget waste: Funding creators who look good in-platform but fail to drive return on investment

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