Brand Sentiment Analysis

Your sentiment analysis doesn't understand emojis. Or that "sick" means amazing for streetwear brands. Or that "brutal" is praise for fitness studios. Or that 💀 means "I'm obsessed" to Gen Z. Legacy tools built for press releases are killing your UGC insights. We analyzed 1M+ posts and discovered something critical: Generic sentiment analysis fails on modern social content 73% of the time. Why? Because context changes everything. Archive's new sentiment analysis learns YOUR brand's language: • Processes video transcripts, not just captions • Understands visual context from images • Interprets emojis like your audience does • Knows your products and brand personality Real example: [solidcore]'s community says their workouts are "torture." Traditional tools: 🚨 NEGATIVE SENTIMENT CRISIS Archive: ✅ Highly positive engagement The technical breakthrough? We feed each brand's context directly to our AI. It knows your products, understands your audience, speaks your language. Results so far: → 96% accuracy on brand-specific sentiment → Processing happens in seconds, not hours → Scales to hundreds of thousands of posts This isn't incremental improvement. It's what sentiment analysis should have been from day one. Rolling out this month across our custom plan customers. Want to see your brand's real sentiment? Let's talk → #AIMarketing #BrandIntelligence #MarketingTech #SocialMediaMarketing #Innovation

Brand conversations, especially in earlier-stage B2B organizations, get stuck when we treat “brand” as a fuzzy idea instead of a measurable driver of pipeline. We covered brand and demand, working together, in last week's CMO Coffee Talk sessions. And we asked each attendee to share how they are measuring brand strength and impact today. Out of hundreds of responses, here's what stood out. What CMOs are primarily measuring: 🔥 Awareness (aided/unaided) and branded search volume as leading indicators 🔥 Consideration/shortlist rates and first-page SEO/AEO visibility 🔥 Perception/sentiment, PR reach, review-site ratings and analyst recognition 🔥 Supplementary signals: NPS/CSAT, eNPS, and % of TAM reached/engaged How leaders frame “brand” internally: Many avoid the word altogether and talk about awareness, reputation and future pipeline/early demand indicators. This focuses more on the "job to be done" and helps connect the dots to revenue. Programs most tied to measurable lift: ➕ Consistent winners were content/PR & thought leadership, Share of Search/SEO/GEO programs, events & sponsorships/keynotes, and customer advocacy initiatives. ➕ Several leaders emphasized brand-exposed cohort analysis over last-click attribution to show lift in conversion, win rate, and sales-cycle time. A few practical brand KPIs CMOs are pivoting to this quarter and into 2026: 🧮 Market indicators: Share of Search; branded search & direct visits; aided/unaided awareness; consideration/shortlist 🧮 Trust & authority: sentiment/attributes; analyst placement; review-site ratings; NPS/CSAT; eNPS 🧮 Pipeline linkage: cohort-based lifts for brand-exposed audiences (opportunity creation, win rate, cycle time) TL:DR: If you’re fighting for "brand" budget, lead with the market indicators and tie them to cohort-level pipeline outcomes (including top of funnel interest/awareness indicators). This creates a straight line from “brand work” to business impact without pretending every dollar should show up in last-touch.

I built a Clay workflow to monitor brand mentions across social media and turn them into GTM signals my team can act on. Most teams either ignore social chatter, drown in it or react when their investor sends them something they saw. None of these works. They lack a strategy and a system to enable social listening. My Clay workbook listens across Reddit, LinkedIn, and Twitter/X, analyzes sentiment, summarizes the context, and drops a clean signal straight into Slack. Here’s how the workflow works: – Pull brand mentions from Reddit, LinkedIn company mentions, and Twitter/X keywords – Visit the source URL to extract the actual post text – Analyze sentiment and assign a score so you know if it is positive, neutral, or risky – Generate a short summary instead of dumping raw text – Send everything into a dedicated Slack channel in near real time What I love most about this is how many use cases this unlocked. If sentiment is positive, you get instant feedback on what messaging resonates. If sentiment is negative, you catch brand risk early before it spreads. If buyers are talking about a problem you solve, you spot pipeline signals hiding in public conversations. And because this lives in Clay, you control everything: Keywords, sources, frequency, models and event costs. This replaces expensive social listening tools and gives GTM teams something better... a living feedback loop tied to action. If you want the full walkthrough and Clay template, it's in this week's Stack & Scale episode. Or comment "Clay workflow" AND connect with me (I've run out of InMail already), and I'll send the resources directly. Happy (social) listening! 

So we just added sentiment analysis to our GEO / AI visibility tool, and wow... Last week I was demoing our new feature to a potential customer. They run a B2B SaaS company, crushing it with traditional SEO. I pulled up their brand in our tool and showed them their AI sentiment breakdown: ✅ ChatGPT loves their "enterprise security features" (95% positive) ❌ But also mentions their "steep learning curve for small teams" (85% negative) "Wait, where is ChatGPT getting this from?" he asked. I clicked into the negative sentiment data. we showed him the exact AI responses, the specific sources being cited, even the competitor comparisons fueling these negative mentions. Turns out, a Reddit thread from 3 months ago was being cited across multiple AI platforms. Someone complained about onboarding complexity, and now AI was parroting that sentiment to thousands of potential customers. Their Google rankings were great. But AI search? Completely different story. Here's what we learned building this: → Each AI platform has its own "opinion" of your brand → The sources they trust vary wildly → You can trace almost every negative mention back to its origin → Most founders have zero visibility into this The scary part? AI is answering 40% more queries than last year. Your customers are getting these AI opinions before they even hit your website. We shipped this feature because honestly, we needed it ourselves. Tracking sentiment across ChatGPT, Claude, and Google AI Overviews was impossible manually. Now you can see exactly which themes are hurting you, on which platforms, and what sources are feeding those narratives. Sometimes the best products come from scratching your own itch.

The inbox is a firehose of unstructured data. You get hundreds of replies to your campaigns: "I love this!” "Where's my order?" "This broke immediately." Brands route these to a customer service ticket system and never look at the aggregate patterns. That's a goldmine sitting untouched. The tactic: Reply sentiment clustering via AI. Once a month, export all email replies into a CSV. Strip out names and order numbers. Paste the raw text into Claude or ChatGPT with this prompt: "Here are 300 replies to our marketing emails from the last month. Group them into 5-7 distinct categories based on the underlying emotion or unspoken need. Give each category a name that captures the real sentiment. Then tell me the one category that represents the biggest missed revenue opportunity."* Sample output you might get: 1. "The Ghost" (28%) - "I bought this 3 weeks ago and forgot I owned it" 2. "The Defender" (15%) - "Stop emailing me, I already bought it, I don't need another one" 3. "The Confused" (22%) - "I want to buy this but I don't understand which size/variant" 4. "The Evangelist" (8%) - "I told my mom about this and she wants one" 5. "The Broken" (12%) - "This arrived damaged and I'm annoyed but not annoyed enough to call support" The actionable insight: Look at #1 "The Ghost". 28% of your engaged audience forgot they own your product. That's a usage problem. AI just told you exactly what content to create by listening to what people are already telling you for free.

I have seen a lot of CMOs struggle to use qualitative insights along with hard numbers on their dashboards. It’s not easy, but I think it is essential for a complete picture. Here’s how to make it work: ---------------------------------- → Start with surveys and focus groups These give you deep insights into what customers feel about your brand. For example, a major electronics brand we worked with uncovered key emotional drivers like 'Trust' and 'Reliability' through focus groups. → Use social listening and sentiment analysis Monitor real-time public perception. A fashion retailer we worked with linked positive sentiment spikes on social media to increases in website traffic and sales. → Integrate qualitative and quantitative data Combine your survey results and sentiment data with sales metrics using unified measurement. This helps you see how customer emotions translate into real business outcomes. → Foster continuous feedback Keep feedback loops open with regular surveys and community interactions. We used this approach for an online education platform which resulted in higher course completion rates and better engagement. Bottom line? By combining qualitative insights with hard data, you gain a more accurate understanding of your brand's performance. ---------------------------------- How are you connecting the dots between customer emotions and real numbers? Let’s discuss.

I audited a multi-billion dollar retailer's AI visibility last month and the biggest reputational risk I found wasn't on their website. It was on Trustpilot. When you ask ChatGPT or Google AI Overviews "is [brand] good?" the first sentence of the answer often pulls directly from Trustpilot, Yelp, or BBB. Your review platforms quietly crossed the line from CX problem to SEO problem and most brands haven't caught up. This retailer's Trustpilot score was a 1.6 out of 5. That number isn't just tanking their conversion rate. It's now the first thing a potential customer reads in their AI-generated brand summary. Marketing has no visibility into it. CX owns the responses (or doesn't). SEO isn't tracking it. Here's what's wild: most "bad" review platforms have heavy selection bias toward dissatisfied customers. This retailer's in-store satisfaction is probably solid. But AI doesn't see in-store NPS. It sees Trustpilot. Here's 3 things every SEO and brand team should do this month: Search "is [your brand] good" in ChatGPT, Perplexity, and Google AI Overview. Read every word that comes back and don't skim. Then, audit your sentiment across Trustpilot, Yelp, Google, BBB, and SmartCustomer... whatever is publicly indexed is fair game for AI to quote. Next, build a structured review response programPublic, polite, solution oriented replies materially lift the visible average. AI reads the replies too. The brands winning AI visibility in 2026 aren't the ones with the best products... they're the ones whose customers can't stop saying so in the places AI looks. What's the first sentence AI says about your brand? Worth checking before someone else does.

If I were redoing the diligence process, I’d start with TikTok, not the data room. Not because middle-market brands are going viral (they’re not). But because real-world signals live online now, and they’re wildly underused in diligence. When we’re looking at $10M–$100M revenue consumer and services businesses, here’s where the real risk and opportunity hide: Google reviews: Customers post photos, rants, and essays. Goldmine. Reddit: Search “[brand name] + scam” or “[brand name] + experience.” You’ll learn more than any NPS score. Glassdoor: The fastest way to smell a toxic culture or a broken ops function. YouTube: Look for DIY reviews, how-tos, and “here’s why I switched” videos. Facebook community groups: Where reputation lives for local services. BBB and Trustpilot: Yeah, they’re noisy—but persistent patterns scream truth. Job boards: Can tell you if a sales team is churning or if hiring’s been frozen for months. Google Trends: Is interest in the brand growing? Flatlining? Declining? None of this shows up in a CIM. But it does show up post-close when CAC spikes and CSAT tanks. And here’s the bonus: AI is about to make all of this stupidly easy. It’ll summarize thousands of reviews, scrape forums, cluster sentiment, flag red flags, in minutes. You’ll be able to run a real-time “brand health check” before your intern has even downloaded the data room ZIP file. And yet, most firms are still stuck paying $250K for quality of earnings and zero for digital signals. We’re not buying spreadsheets. We’re buying belief, trust, and momentum.......and those show up in feeds, not footnotes. #ClaymorePartners #PrivateEquity #DiligenceDifferently #MiddleMarket #DigitalSignals #AIInDiligence

Surveys are great for growth optimization. But what about the 95% who never fill them out? They're leaving reviews everywhere - Reddit, Amazon, Trustpilot. This prompt finds them ALL and shows you exactly what's blocking growth. Your best research is already written 👀 -------- Prompt: "I want you to conduct a comprehensive review mining analysis for [BRAND NAME] [BRAND URL/PRODUCT]. Please follow these steps: 1. INITIAL RESEARCH: - Use web search, Reddit search, Amazon reviews, and any available review platforms - Search for: "[brand] reviews", "[brand] complaints", "[brand] customer service", "[brand] Reddit" - Look for recent reviews (last 6-12 months) and overall patterns - Find both positive and negative feedback - Get actual customer quotes and specific examples - 2. CREATE A REVIEW MINING SUMMARY with these sections: ## What People LOVE About [Brand]: - List main positive themes with specific customer quotes - Include citations for all claims - Rank by frequency of mention - Note specific benefits users report - ## What People DON'T Like: - List main complaints with specific examples and quotes - Focus on: customer service issues, subscription problems, product quality, pricing concerns, transparency issues - Include severity and frequency of complaints - Note any business practice concerns - ## Mixed Reviews On: - Features with divided opinions and why - ## Overall Sentiment: - Star ratings across platforms - General reception summary - Key takeaways - 3. ENHANCE WITH CUSTOMER PERSONAS: - ## Customer Personas & Their Experiences Create 5-6 distinct personas based on the reviews, including: ### [Persona Name] (Age range) Quote Examples: [Real quotes representing this persona] What They LOVE: [Specific benefits valued by this persona] What They HATE: [Specific pain points for this persona] Include sections for: - Most Satisfied Customer Types - Most Dissatisfied Customer Types - Common Threads Across All Personas - IMPORTANT REQUIREMENTS: - Use exact customer quotes whenever possible - Cite all sources - Look for red flags: subscription issues, hidden fees, poor customer service, lack of transparency - Note positive patterns: specific benefits, value propositions, success stories - Include dates/recency of reviews when relevant - Provide platform sources (Reddit, Amazon, Trustpilot, etc.) - Bold key insights - Use bullet points for easy scanning - The goal is to provide a complete picture of customer sentiment that would help someone make an informed decision about this brand, understanding both what works well and what problems they might encounter."

Do you know what your audience think about your sport sponsorship? (Ever heard of sentiment analysis?) As a brand investing in motorsport, you can track how your audience react to your sponsorship. Not just in numbers, but in sentiment. What is sentiment analysis? It’s a method that uses AI and natural language processing to analyse public perception. Whether the sentiment on a brand/event/sponsorship is positive, neutral, or negative. In sponsorships, this is a game-changer. → It helps brands understand if their investment is truly engaging fans or if adjustments are needed. *** Take the recent ELEMIS x Aston Martin F1 sponsorship. When ELEMIS announced its sponsorship of Aston Martin F1, social media buzz reflected strong positive sentiment. Words like love, amazing, and ELEMIS stood out on IG. And terms like wow, duo, and dream team reinforced excitement around the collaboration. This is a word cloud: a visual tool in sentiment analysis where the size of each word reflects how often it appears. It helps brands quickly see which keywords dominate the conversation and determine whether audience sentiment is positive or negative. Why does this matter for brands? 1. To know exactly how fans feel about your sponsorship. 2. To spot negative trends before they escalate. 3. To create content that truly connects. 4. To measure ROI beyond visibility. *** Data-driven sponsorships + expertise win. Those who know how to strategise, listen, learn, and adapt turn partnerships into real business growth. P.S. Ever heard about sentiment analysis and word cloud?