Utilizing User-Generated Content In Ecommerce

If you're a UX researcher working with open-ended surveys, interviews, or usability session notes, you probably know the challenge: qualitative data is rich - but messy. Traditional coding is time-consuming, sentiment tools feel shallow, and it's easy to miss the deeper patterns hiding in user feedback. These days, we're seeing new ways to scale thematic analysis without losing nuance. These aren’t just tweaks to old methods - they offer genuinely better ways to understand what users are saying and feeling. Emotion-based sentiment analysis moves past generic “positive” or “negative” tags. It surfaces real emotional signals (like frustration, confusion, delight, or relief) that help explain user behaviors such as feature abandonment or repeated errors. Theme co-occurrence heatmaps go beyond listing top issues and show how problems cluster together, helping you trace root causes and map out entire UX pain chains. Topic modeling, especially using LDA, automatically identifies recurring themes without needing predefined categories - perfect for processing hundreds of open-ended survey responses fast. And MDS (multidimensional scaling) lets you visualize how similar or different users are in how they think or speak, making it easy to spot shared mindsets, outliers, or cohort patterns. These methods are a game-changer. They don’t replace deep research, they make it faster, clearer, and more actionable. I’ve been building these into my own workflow using R, and they’ve made a big difference in how I approach qualitative data. If you're working in UX research or service design and want to level up your analysis, these are worth trying.

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?

Tactic 2 for influencing stakeholders from Jules Walter: Frame your message from their POV (not yours) It’s more effective to speak their language and demonstrate how your proposal will help them reach their goals, not yours. Stakeholders are focused on their own problems and are more receptive to proposals that address what’s already top of mind for them. A few years ago, when I was leading Monetization at Slack, we began to encounter diminishing returns in our product iterations, and we needed to take a bigger swing to re-ignite revenue growth. To do that, I spearheaded a controversial project to experiment with a new approach to free-to-paid conversion. The CEO, Stewart Butterfield, had strong reservations about the project. I knew from his previous statements that he didn’t want the company to be thinking about ways to extract value from users, but rather ways to create value for them. We had scheduled a review with the CEO and a few of his VPs to discuss the proposal. Since he was intensely user-driven, I framed the entire proposal around the benefits it would have for users (the CEO’s POV) rather than emphasizing the revenue impact of the project (our team’s goal). I started the meeting by anchoring the proposal on user-centric insights that we shared in a deck: - “About 10% of purchases of Slack’s paid version happen from users in their first day on Slack.” - “Paid users find more value and retain better. Yet we make it hard for people to discover that Slack has a paid version that’s more helpful.” - “How do we help new teams experience the full version of Slack from the start?” Once we framed the issue with this user-centric lens, the CEO was more open to our proposal and let us try a couple of experiments in this new direction. This user-centric framing also got the cross-functional team more excited and set an aspirational North Star with clear guardrails, which then enabled various teammates to contribute productively to the project. After we tested two iterations of our monetization experiment, we landed on a version that resulted in a significant increase in revenue for Slack (a 20% increase in teams paying for Slack) and we used what we learned to shift Slack’s monetization strategy into a new, more successful direction. Full set of tactics here: https://lnkd.in/gezP2EDw

Building a Reddit Business Insights Scraper: Turning Conversations into Opportunities 🚀 I’m thrilled to share a project where I used python for first time without writing a single line of code: Reddit Business Insights Scraper! Planned to build it after watching Greg Isenberg video about reddit scraper using Gummy Search and getting inspired by Harshit Tyagi's reddit scraper. This tool leverages Reddit’s vast, unfiltered discussions to uncover business ideas, customer pain points, and market sentiments, empowering entrepreneurs, marketers, and innovators with actionable insights. All built from scratch with Python, PRAW, TextBlob, Streamlit, and more. Let me take you through what I built, how I built it, and how its sentiment analysis feature unlocks real-world value, while showcasing the skills and passion I poured into this project. What I Built: A Tool to Unlock Reddit’s Insights Reddit is a goldmine of user-generated content, where millions share ideas, challenges, and trends daily. My Reddit Business Insights Scraper taps into this resource to help users like entrepreneurs or marketers gain a competitive edge. Here’s what it does: Searches and Scrapes: Users input a search query (e.g., “saas ideas”), specify the number of subreddits and posts to scrape, and the tool fetches relevant posts and comments from Reddit. Analyzes Sentiment: it uses advanced sentiment analysis to gauge the emotional tone of discussions. Presents Data: Results are displayed in a structured data table, with options to download reports in TXT and CSV formats for offline analysis. What makes this tool unique is its focus on sentiment analysis and accessibility. It’s not just about scraping data, it’s about understanding how people feel, making insights actionable and intuitive. The Power of Sentiment Analysis: The sentiment analysis feature is what sets this scraper apart. Here’s why it’s crucial and how it helps. Sentiment analysis reveals the emotional context of discussions, whether users are excited, frustrated, or neutral. How It Helps:   Average Sentiment by Subreddit: A quick gauge of community mood (e.g., 0.3 for positive).  Sentiment vs. Popularity: Shows if positive posts drive engagement, guiding marketing strategies.  Sentiment Distribution: Reveals the diversity of opinions (e.g., 60% positive, 20% negative), ensuring a balanced view. Real-World Applications:    Entrepreneurs: Searching “side hustle” might show a positive sentiment (0.25) with high engagement on “freelance gigs,” inspiring a new platform. The CSV report helps pitch to investors.  Marketers: Analyzing “marketing strategies” could reveal a 50% positive sentiment distribution for email campaigns, informing campaign focus.  Product Developers: A negative sentiment (e.g., -0.4) in “customer support” discussions highlights a market gap for better tools. I plan to add visualizations (e.g., sentiment pie charts), topic modeling, and cloud storage integration to enhance the tool further.

I used to think content strategy started with brainstorming. Pick a trending topic. Match it to your ICP. Hope it hits. But then I started listening to real customer conversations. Not just reviews. Comments. Reddit threads. Support chats. Places where your audience says exactly what they feel. That changed everything. Turns out, the highest-performing content doesn’t come from ideation It comes from extraction. Here’s how I now build insight-driven content with Brand24 + ChatGPT: Step 1: Listen & Collect Use Brand24 to create a project around your brand or niche. - Add broad keywords - Track platforms like Reddit, YouTube, App Store, TikTok - Add filters like product terms or emotional keywords You’ll capture raw, emotional, unfiltered audience feedback Step 2: Analyze & Segment Go to the Mentions tab inside Brand24. - Filter by sentiment, date spikes, or source - Identify reach surges and mention volume - Read top mentions and spot recurring themes Group feedback into buckets: product talk, emotional cues, value perceptions Step 3: Extract Content Opportunities Build a Google Doc or Insight Bank. - Copy-paste the most valuable quotes from Brand24 - Categorize them by themes - Optionally export a Brand24 PDF report This doc becomes the raw material for your content engine Step 4: Create Content That Resonates Drop the insights doc into ChatGPT. - Turn real feedback into content copy across formats - Generate posts, landing pages, emails, or blog content You're not creating from scratch, you're remixing real voice Step 5: Distribute, Measure, Loop Back Return to Brand24 to track new mentions. - Monitor sentiment and engagement - Identify feedback loops or new pain points - Keep refining based on what people say next The process gets smarter with every cycle Why this matters now more than ever: Here’s the new truth from Semrush’s latest AI Search study: Quora is the #1 most cited domain in Google AI Overviews. Reddit, Inc. comes second. That means the most referenced content in AI answers isn’t from top-ranking websites it’s from where real conversations happen. So if you’re not building content from real customer voice, you’re missing the most powerful signal source in modern search. In 2025, your edge isn’t speed. It’s signal clarity. Don’t guess what they want. Extract what they already said. Try it once and you’ll never create in isolation again. Using: Brand24 + ChatGPT Grab a 14-day free trial of Brand24: https://lnkd.in/eYAdJRWd ♻️ Repost if your content strategy needs a reality check. Follow me, Madhav Mistry, for more marketing workflows! #brand24partner

I see nobody doing this with AI voice agents. So I did. This is unlocking a whole new layer of intelligence on your AI voice calls. What is it? Sentiment Anlalysis. Why does this matter? Because most businesses are sitting on a goldmine of voice data... but they’re not extracting the emotional signals that drive real outcomes. Here’s where sentiment analysis actually adds value: ✅ Customer Experience Monitoring Spot unhappy customers early. Trigger an automatic follow-up if a call turns negative. ✅ Agent Performance Tracking See how sentiment shifts across reps, scripts, or time. Is your team actually creating positive experiences? ✅ Trend Recognition Negative sentiment = higher churn? Now you've got predictive insights. ✅ Training & QA Flag poor sentiment calls for review. Let AI highlight the moments that caused friction. But it's not always worth your time... Sentiment analysis is useless if: → You're not acting on the data. → Your calls are too short or robotic. → You don’t have enough volume to find patterns. → Your domain needs custom sentiment tuning (sarcasm, mixed languages, etc.). Want to make it actually useful? Here’s how: → Link sentiment to outcomes like conversions or renewals. → Create real-time alerts or dashboards for your team. → Fine-tune the model on your transcripts, not generic ones. → Combine it with other signals like talk-time, interruptions, and keywords. The emotional layer of your calls is where the real insight lives. Curious how this works in practice? I’m happy to show what I built today. Drop a “curious” below or shoot me a message.

𝗣𝗼𝘄𝗲𝗿 𝗕𝗜'𝘀 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: As a Data Analyst, I often face challenges that require creative solutions. One recent challenge involved understanding what customers were saying about our products and services. To tackle this, I turned to Power BI's sentiment analysis feature.📈 𝑺𝒆𝒏𝒕𝒊𝒎𝒆𝒏𝒕 𝒂𝒏𝒂𝒍𝒚𝒔𝒊𝒔 𝒊𝒔 𝒂 𝒕𝒐𝒐𝒍 𝒕𝒉𝒂𝒕 𝒉𝒆𝒍𝒑𝒔 𝒖𝒔 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒕𝒉𝒆 𝒇𝒆𝒆𝒍𝒊𝒏𝒈𝒔 𝒂𝒏𝒅 𝒐𝒑𝒊𝒏𝒊𝒐𝒏𝒔 𝒆𝒙𝒑𝒓𝒆𝒔𝒔𝒆𝒅 𝒊𝒏 𝒕𝒆𝒙𝒕 𝒅𝒂𝒕𝒂, 𝒍𝒊𝒌𝒆 𝒄𝒖𝒔𝒕𝒐𝒎𝒆𝒓 𝒓𝒆𝒗𝒊𝒆𝒘𝒔 𝒐𝒓 𝒔𝒐𝒄𝒊𝒂𝒍 𝒎𝒆𝒅𝒊𝒂 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔.🧑🦱 For example, I could see which features customers loved and which ones they found not good. This information was incredibly useful for our product team because it helped them prioritize improvements that would make the biggest impact on customer satisfaction and loyalty.✅ The beauty of Power BI is that it makes it easy to analyze data from different sources and turn it into insights that drive action. 🙌 #dataanalysis #powerbideveloper #powerbi #powerbidashboard #powerbidesktop #dataanalytics #businessanalytics #businessanalyst

Know Your User - or Risk a ₹100 Crore Mistake for a ₹10,000 Ad. Earlier this week in Rajkot (my hometown), Zomato put up a catchy campaign: “Dilli ka Butter Chicken, Kashmir ki Wazwan Biryani - Dono ka khaas, ab Rajkot ke paas.” Clever line. Eye-catching hoardings. High-impact placement. There was just one problem: Rajkot is a predominantly vegetarian city. Not just in practice, but in cultural identity. So much so that even top hotels avoid serving non-veg to protect footfall. The response? By 12:00 PM, all hoardings were taken down across the city - just 30 minutes after authorities noticed. As a product leader and ex-founder, this struck me hard. Too often in our world of products, growth hacks, and campaigns - We build for the persona, not the person. - We optimize for reach, not resonance. - We chase clever, not context. But real growth doesn’t come from shouting louder. It comes from listening deeper. Zomato’s campaign may have worked in Delhi. It could have worked on Instagram. But it misfired in Rajkot, because it forgot who it was really speaking to. Lesson: Understanding your user isn’t a research task. It’s a cultural one. Whether you’re building a tech product or designing a campaign, start with empathetic curiosity. The ROI is higher than any billboard. Curious - what’s the most surprising thing your users ever taught you? #UserEmpathy #Growth #CustomerObsession #IndiaMarkets #ProductMarketFit Deepinder Goyal

How to use ChatGPT + SEO + review websites for deeper customer insights 👇 Want to understand your customers better but struggling to get enough data points? Combining ChatGPT, SEO (I happened to use Screaming Frog), and review websites can uncover customer insights that you might miss otherwise. That’s because on review websites customers speak candidly about your product, share real use cases, and highlight both wins and frustrations without the filter of a formal interview. Here's what I did to get quick insights from a review website, using an SEO tool and chatGPT prompts: ☑️ Used Screaming Frog to crawl review platforms and extract all customer comments for the last year. ☑️ Exported the data to a spreadsheet (bonus: review platforms often include user location/country and date of comment which you can use to further narrow your research) ☑️ Fed the spreadsheet to ChatGPT to analyze comment sentiment, features mentioned, pain points, and use cases. Pro tip: ChatGPT can automatically analyze your spreadsheet and add new columns with the insights you need. Just give it clear instructions about what to look for. Try a prompt like:  "Please add a new column to the spreadsheet indicating whether column X's text expresses a positive or negative sentiment. Positive sentiment includes phrases like ' ' (provide examples), while negative sentiment includes phrases like ' ' (provide examples). Use these guidelines to categorize the text accordingly." Results: I uncovered patterns from customers in previously under-researched regions, which informed our messaging strategy. The best part? This approach scales way better than trying to schedule dozens of interviews. Has anyone else experimented with chatGPT for customer research? What tools have you found helpful? #PMM #ProductMarketing #CustomerInsights #SEO #SEOskills #ChatGPT

As a CPG brand marketer, you've likely encountered the challenge of creating ads that truly resonate with your audience.  But the secret to creating powerful CPG ads lies in one thing: Shifting focus from your product to your consumers.  It might seem counterintuitive. But the most effective performance ads are those that prioritize people over product features. Why does this approach work? It's simple: People connect with stories and emotions, not sales pitches.  In today's saturated advertising landscape, consumers are yearning for authenticity. They're tired of being bombarded with product information. What they really want is to feel understood. So, how can you apply this people-first approach to your advertising strategy?  Start by centering your ads on the customer's story.  Instead of leading with your product's features, show how it fits seamlessly into your target audience's lives.  Demonstrate how it solves their problems or enhances their daily experiences, even in small ways. One particularly effective tactic is to leverage user-generated content or testimonials in your ads.  There's immense power in showcasing real people using and loving your product.  It's relatable, credible, and often more impactful than any polished corporate message you could craft. Great ads don't just sell products. They build relationships.  So view your product through the lens of your consumers' lives and experiences.  It's about telling their story, not just yours.