As the 2026 spring break season approached in the Northern Hemisphere, new waves of consumers used LLMs to make their travel plans. There are dozens of mainstream online travel agencies (OTAs) and accommodation aggregators to choose from. 

However, LLMs recommended just a small handful in response to prompts about finding the best hotel deals.

Booking.com led with 55% visibility, closely followed by Expedia at 54% visibility, according to our GenAI analysis.

Both popular aggregator sites achieved high visibility across target prompts, though important nuances emerged between consumer asks focused on luxury, solo travel, and safety. Overall, our exploration revealed how consumers experience AI-driven travel discovery — including how LLM platforms elevate certain brands, shape perceived value, and reinforce narratives about credibility within the European hospitality market.

Methodology: The findings below refer to GenAI Lens data evaluating responses from eight LLMs — ChatGPT, Claude, Deepseek, Gemini, Google AI Overviews, Grok, Llama, and Perplexity — over the three-week period from February 18 to March 11, 2026. 
Our analysis centered on four structured prompts reflecting common travel-planning scenarios in relation to spring 2026 trips in Europe:

What are the best hotel deals in Europe in spring 2026?

What are the best hotel deals for luxury hotels in Europe in spring 2026?

I’m planning a solo trip to Europe in spring 2026. What are the best-value hotel deals right now, ideally in well-located, mid-range or boutique hotels?

I’m a woman traveling solo in Europe in spring 2026. What are the best hotel deals in safe, well-located areas with good reviews?

LLMs Recommended Booking.com and Expedia Most Across Travel Prompts and Models

Both Booking.com (55% visibility) and Expedia (54% visibility) dominated AI-generated OTA recommendations for Europe in spring 2026. Together, they appear in nearly half of all AI responses, consistently achieving high prominence placement across analyzed prompts and models. Tripadvisor came in third place, though at a significant distance from the first two with 32% visibility.  

However, as we’re seeing consistently in these analyses, AI visibility varies widely across models:

Meta’s Llama most frequently recommended Booking.com.

xAI’s Grok tied Booking.com and Expedia for top visibility.

Perplexity, by contrast, surfaced Tripadvisor most often.

Booking.com is the first aggregator website mentioned in this prompt response from Meta Llama analyzed within GenAI Lens. 

Overall, Booking.com slightly outperformed Expedia in LLM visibility thanks to its prevalence across our prompts exploring varying travel scenarios. Models like Google AI Overviews highlighted early booking discounts (15%+), Genius loyalty benefits, and flexible cancellation policies tied to premium properties. For other OTAs and aggregators, emphasizing loyalty building features like these could aid in boosting LLM visibility.

Tripadvisor Gained LLM Visibility in Prompts About Luxury Hotels and Women’s Solo Trips

As seen in the table below highlighting brand, keyword, sentiment, source, and model variations across the personas we analyzed, responses vary meaningfully across prompts. For example, LLMs (and by extension the OTA and aggregator websites they pull from) mentioned Paris, Italy, and Rome most in response to our luxury prompt. Those locations were replaced by Amsterdam and Lisbon for both our solo trip prompts. Meanwhile, when it comes to primary source types, editorial luxury influenced our luxury prompt responses most, while review sites were the top influences of our solo trip prompts. For brands looking to impact the LLM-driven narratives about them, monitoring and optimizing for target associations is key to differentiating.

The most striking of these variations: Tripadvisor was not the most visible OTA overall, but it achieved the second-highest visibility (47%) in both luxury hotel prompts and women’s solo travel prompts. For luxury travelers, LLMs highlighted filter tools, price alerts, and value rankings for spa resorts, positioning it as a decision-support platform. For women traveling solo, best-rated and boutique filters along with peer reviews were the qualities that caught AI attention.

So while Tripadvisor lacked the broad dominance of Expedia and Booking.com, it did achieve prominence in specific travel scenarios. It seems that the site’s combination of search features and UGC helped it cover wider ground among consumers with more specified needs.

As LLMs increasingly steer how travelers discover and evaluate booking platforms, AI visibility is reshaping the competitive landscape for OTAs and other accommodation booking platforms. In short: AI optimization is the new travel marketing battleground. Based on our analysis above, strategies for boosting AI visibility include:

Optimize on owned websites for prominence placement: Booking.com’s dominance correlates with early positioning and specialized webpages that LLMs pulled from. Use structured FAQ pages and schema markup targeting highly specific prompts like “Best hotel deals Europe Spring 2026.”

Emphasize unique UX and capabilities. To avoid being seen as just another price-comparison platform, expand owned content to highlight flexibility (free cancellation, payment options), loyalty benefits, curated collections, and premium or differentiated stay experiences. Based on the most-mentioned locations, there may also be opportunities to target high-intent travelers interested in more off-the-beaten-path destinations.

Track model-specific performance: Booking.com visibility ranges from 30% to 89% by model. AI optimization must be model-aware.

Strengthen review integration: UGC may have helped Tripadvisor stand apart in more specified travel scenarios. Hosting verified user reviews on owned websites helps build trust and brings authentic, personal, AI-visibility boosting content home.

FAQ: Online Travel Agency and Accommodation Booking Platform AI Visibility Performance

Does OTA visibility vary across AI models?

Yes. Meltwater’s GenAI Lens found significant model variance in OTA visibility.

Booking.com ranged from 30% visibility in ChatGPT to 89% in Meta Llama.

Expedia ranged from 20% in ChatGPT to 88% in Grok.

Tripadvisor was strongly amplified in Perplexity (54%) and Grok (86%) but weak in Claude and ChatGPT.

This confirms that OTA exposure in AI results is architecture-dependent and not uniform across platforms.

How does prompt framing affect AI hotel recommendations?

Meltwater’s GenAI Lens analysis shows that prompt intent shifts how AI frames hotel recommendations, even when dominant brands remain consistent.

Editorial luxury sources are frequently cited

Booking.com (69%) and Expedia (60%) reinforce dominance

Review and safety-focused blogs dominate citations

Which European cities received the most AI amplification in prompts about spring 2026 hotel deals?

Meltwater’s GenAI Lens found that destination visibility varies by travel context.

Paris shows the strongest cross-model consistency overall, while Lisbon holds the highest sentiment score (63%). AI appears to associate Paris with luxury, and Amsterdam and Lisbon with safety and functional solo travel.

What does sentiment analysis show about OTA positioning?

Meltwater’s GenAI Lens analysis of spring 2026 European hotel deals found that OTA sentiment is stable but emotionally flat.

Sentiment range: 55–64% across major brands

AI platforms frame OTAs as reliable, transactional utilities rather than emotionally differentiated or premium brands.

Why does Meltwater’s GenAI Lens analysis matter for travel brands?

Meltwater’s GenAI Lens analysis of spring 2026 European hotel deals demonstrates that AI systems have the power to shape early-stage travel discovery before consumers reach traditional search engines or booking sites.

OTA visibility is highly concentrated on LLMs. 

Model differences are significant and consequential. 

Prompt framing reveals brand visibility ranking variances. 

LLMs actively shape consumer associations with travel decisions.

As consumers increasingly use generative AI to plan travel, optimizing for LLM visibility is becoming as critical as SEO and paid media in the European hospitality market.

How Booking.com and Expedia Dominate AI Travel Recommendations

The start of the Year of the Horse was a critical moment for luxury brands. 2026’s Lunar New Year (LNY) coincides with promising outlooks for the Chinese luxury market. In the sea of campaigns tied to the event, it takes community-minded strategies to grab audience attention.

To learn more, we used Explore+ to examine Lunar New Year-related mentions of 10 major luxury fashion houses — Dior, Tod’s, Prada, Bvlgari, Bottega Veneta, Miu Miu, Celine, Valentino, D&G, and Fendi — across social, online media, and broadcast sources in English and Chinese (Simplified and Traditional). 

From January 1 through March 3, Lunar New Year campaigns from these brands generated 3.4 million mentions and 24 million engagement actions. For context, the Winter Olympics generated 3.84 million mentions, placing LNY luxury campaigns on a comparable conversational scale to one of the world’s largest global sporting events.

The data shows that while celebrity star power drove campaign visibility for multiple brands, participatory social moments helped Prada stand out from the herd.

Celebrities and Milan Fashion Week Drove Visibility

Celebrity brand ambassadors anchored luxury LNY campaigns on Sina Weibo (96% of mentions) and Douyin (50% of engagement), drawing the eyes (and clicks) of devoted fans. Though global luxury brands have gotten more sophisticated in designing their LNY collections and campaigns, big names and familiar faces overshadowed creative details and concepts.

For example, Dior dominated conversation volume, generating 70% share of voice (SOV). About 29% of those mentions centered on its brand ambassador, Chinese actor Deng Wei.

Meanwhile, 38% of Valentino’s total engagement related to its LNY campaign came from a single Sina Weibo post from actor and singer Tan Jianci, also known as JC-T. The Valentino Beauty ambassador’s post promoting the limited-edition Lucky Blue makeup collection clearly resonated with his fanbase. It proved to be a more active point of audience engagement with the brand over other campaign elements like media features and in-person events. 

This Sina Weibo post from Tan Jianci generated over a third of Lunar New Year-related engagement for Valentino.  

Finally, Tod’s global brand ambassador Xiao Zhan drove 96% of both mentions and engagement for the Italian luxury brand. In particular, the actor and singer’s prominent seating at the Tod’s runway show for Milan Fashion Week created a surge of cross-market attention, effectively linking the brand’s Asia-focused Year of the Horse campaign to one of European fashion’s biggest stages.

Overall, Milan Fashion Week drove 86% of mentions and 60% of engagement for Tod’s, which wasn’t the only brand to seize the crossover opportunity in Milan Fashion Week’s LNY overlap. Many brands’ posts documenting their activations in Italy referenced LNY limited editions or incorporated the #马年 (Year of the Horse) hashtag. As a result, Milan became a key talking point in LNY luxury campaign conversations.

For Prada, Milan accounted for 67% of total LNY campaign mentions and 49.5% of related engagement. This was largely driven by Su Xinhao and Zuo Hang, members of the Chinese boy band T.O.P. 登陸少年組合, walking in the brand’s official runway show. 

One Hashtag Helped Prada Generate Outsized Engagement

While numerous brands used high-production creative assets to spark LNY conversation, a hashtag and custom Douyin filter helped Prada distinguish itself online. Despite only having 8% SOV, the brand captured 51% of the engagement. This was largely thanks to the #马年一起Prada啪哒 hashtag (roughly meaning “Prada! Together in the Year of the Horse") which generated over 90% of the brand’s likes, comments, shares, and reposts.

This Douyin post featuring Ma Long using Prada’s fire horse prism filter generated over 713,000 engagement actions from January 28 to March 3.

Highly engaged Douyin posts from figures like table tennis champion Ma Long and boy band TOP登陸少年組合 using a custom Prada Year of the Horse filter encouraged UGC. By creating a moment that fans could actively participate in, the brand realigned its campaign with the holiday’s foundations in community and togetherness. 

The combination of star power with audience involvement paid off big in terms of advertising value equivalent, or AVE, the estimated monetary value of earned media exposure). During LNY celebrations from February 17 to March 3, Prada generated $8.4 million in AVE despite only having 8% SOV. By comparison, Tod’s, which had 50% SOV, generated about half of Prada’s AVE at $4.3 million.

The takeaway here is that conversation volume is only one aspect of determining a campaign’s success. Brands need to consider engagement efficiency, i.e. how effectively a brand converts SOV into share of engagement, to truly understand whether or not campaign content is resonating with target audiences. 

For luxury brands planning future campaigns around cultural celebrations, three lessons stand out from LNY 2026:

Celebrity alignment can make or break a campaign. One highly resonant brand ambassador can outperform broad chatter.

Use calendar overlaps as multiplier effects. Coinciding cultural events don’t have to compete against each other. Use them as opportunities to target your audience’s overlapping interests, extending your campaigns’ reach and relevance in the process. 

Engagement efficiency beats SOV. Prada’s campaign proves that converting attention into interaction is what ultimately drives media value. Simple tools like hashtags and custom filters can go a long way in encouraging audience engagement.

In the Year of the Horse and beyond, luxury brands that can turn cultural moments into shared experiences will be the ones that stand out. Pairing the right faces with the right formats, all backed with data-driven insights, will be key to transforming seasonal impact into success in the long term. 

Frequently Asked Questions: Luxury Brand Performance During Lunar New Year 2026

What was the overall scale of luxury brand conversation during Lunar New Year 2026?

According to Meltwater, luxury brand campaigns for the Year of the Horse generated 3.4 million mentions and 24 million engagements between January 1 and March 3, 2026. To put this in perspective, this volume is comparable to the Winter Olympics, which generated 3.84 million mentions, illustrating the massive cultural and commercial scale of the holiday for the fashion industry.

Which social media platforms were most effective for luxury brands during Lunar New Year 2026?

Meltwater’s analysis reveals a clear split between visibility and interaction: Sina Weibo dominated conversation volume with 96% of mentions, while Douyin drove 50% of total engagement. This indicates that while Weibo is essential for broad reach, Douyin is the primary platform for active consumer participation.

How did celebrity ambassadors influence Lunar New Year 2026 campaign results?

Meltwater identifies celebrity talent as the primary amplification lever for these campaigns. For example, Xiao Zhan accounted for 96% of both mentions and engagement for Tod’s, while a single post from Tan Jianci drove 38% of Valentino’s total engagement. Similarly, Dior’s partnership with Deng Wei accounted for approximately 29% of its total mentions.

How did Milan Fashion Week impact Lunar New Year 2026 marketing?

Meltwater analysis highlights that Milan Fashion Week acted as a global visibility multiplier because it overlapped with the core holiday celebration window. For Tod’s, Milan-related activity drove 86% of its mentions and 60% of its engagement, while Prada leveraged the event to showcase next-generation idols like Su Xinhao and Zuo Hang, accounting for 67% of its LNY-related mentions.

What role did User-Generated Content (UGC) play in Prada’s Lunar New Year 2026 success?

Meltwater analysis credits Prada’s outsized engagement to its focus on community participation rather than just "hero" content. The #马年一起Prada啪哒 hashtag alone accounted for over 90% of the brand’s total engagement, while the “Red Horse Welcomes Spring” interactive filters encouraged users to transform from passive viewers into active content creators.

Which brand generated the highest media value during Lunar New Year 2026 celebrations?

Meltwater found that Prada generated the highest Advertising Value Equivalent (AVE) at $8.4 million, nearly double the $4.3 million generated by Tod’s. The analysis suggests this was due to Prada's high engagement efficiency and its strategic investment in Gen Z-driven cultural capital.

How Luxury Brands Leveraged Celebrities and UGC for Lunar New Year 2026 Success

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

United States

Explore real-time trends, data-backed stories, demos, and analysis in one place.

The Round of 16 is complete, all three host nations are out, and the quarter-finals are being shaped by Norway’s Brazil upset, England’s Azteca classic, Messi’s comeback and Ronaldo’s farewell.

FIFA World Cup 2026 Quarter-Final Analysis: Brazil Out, Messi Leads & Hosts Exit

Explore real-time trends, data-backed stories, demos, and analysis in one place. Learn more.

Meltwater Intelligence Hub

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

Intelligence Hub archive

How Booking.com and Expedia Dominate AI Travel Recommendations

How Luxury Brands Leveraged Celebrities and UGC for Lunar New Year 2026 Success

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