Online Reviews in Sales

I’ve never seen a DTC brand do Meta ads this way (Scaled to $40m in 18 months too): John Hefter bought Angry Orange (Hear the whole story on Episode 134 of the Operators Podcast) He saw 100% organic reviews saying "This saved my marriage." But he didn't stop there like most brands, he put a framework and structure into place to tap into EXACTLY what made the product powerful. Then he used that to drive creative strategy. Here's the framework: 1. Step 1: Export Your 3-Star and 4-Star Reviews 2. Not 5-stars (too positive). Not 1-stars (complainers). 3. 3-4 stars tell you what's good AND what's missing. 4. Pull last 500 reviews. Use Helium 10 or any scraper. Step 2: Score Each Review Create this formula: Review Score = (Emotional Language × 2) + (Repeat Purchase × 5) + (Competitive Comparison × 3) + (Use Case Expansion × 2) Look for: → Emotional: "I can't believe this worked" (score 1-10) → Repeat Purchase: "Buying my 3rd bottle" (Yes = 5 points) → Competitive: "Tried 6 products before this" (Yes = 3 points) → Use Case: "Bought for X, now use for Y and Z" (Yes = 2 points) Sort by highest score. Top 20% = your creative goldmine. Step 3: Turn Reviews Into Ad Hooks Framework 1: "I was about to spend $3,000 replacing carpet. Then I found this orange bottle." Framework 2: "I bought this for one room. Now I use it in 6 places." Framework 3: "This product saved my marriage. (And my dog.)" Framework 4: "I don't believe in miracle products. But this proved me wrong." Use exact customer language. Not your voice. THEIR voice. Step 4: Test Week 1-2: Create 20-30 ads from top reviews Week 3-4: Test at $20/day each Week 5-6: Scale top 5 to $100/day Target metrics: → Hook Rate: >40% → CTR: >2% → CPA: 20% better than current Why This Works: - Your customers already told you what to say. You just have to amplify it. - Most brands spend $10K on agencies to "find their voice." - Your customers gave you the voice for free. Do This Today: 1. Export 500 reviews 2. Score top 20% 3. Extract 5 hooks 4. Create 10-15 ads Your reviews are sitting there right now telling you what ads to make. Go read them.

🚀 User reviews are the compass for how well our product truly performs. But getting those reviews? Yep… usually a painful process. Either you dig through your own database, or you integrate multiple sources just to collect scattered insights. And if you want to monitor competitor products too? Even more painful. Right? 😅 So I built a smart automated workflow to solve this once and for all. I’m using Google Sheets as a central URL database, making it super easy for other teams to add or update product URLs without touching n8n. Then comes the fun part: Using Decodo, the workflow scrapes the reviews and structures them cleanly. This one is breakthrough brooooo. After that, AI sentiment analysis kicks in, giving me high-level insights and summaries in seconds. ✨ What this solves: - No more manual digging through reviews - Zero engineering overhead for data updates - Shared access for cross-team collaboration - Fast understanding of customer sentiment I’ve published this workflow so you can try it too. If you’ve already used it, I’d love to hear: How did this automation improve your productivity? https://lnkd.in/g2nhkiV9 Let’s make review monitoring smarter, not harder. 💡

Stop putting shiny 5-star blurbs in your ads. They’re killing resonance. Here’s the real secret most brands ignore: the reviews that convert are not the ones that praise you perfectly. They are the ones that argue with you. Why the messy reviews win in paid acquisition: 1️⃣ Contain built-in objections ↳ 3 and 4-star reviews surface doubts in the customer’s own words. Use those words to pre-handle objections in creative and watch CTR + quality increase. 2️⃣ Reveal true hooks, not marketing speak ↳ Customers describe outcomes in ways you would never write. Copy those exact phrases into headlines and captions for instant resonance. 3️⃣ Power sequence-based journeys ↳ Run a skeptical review as your first touch, an explainer as your second, and a 5-star as the close. The algorithm learns a believable path, not a single “hero” ad. 4️⃣ Scale micro-personas fast ↳ Tag reviews by theme - convenience, durability, results - then build 5-7 quick variants per theme and map to audiences. Small library. Big variety. How to implement in 72 hours: • Export reviews and cluster by theme • Pull 6-8 verbatim lines per theme for hooks • Build 5–7 creatives per theme (skeptic → product → proof) • Feed into dynamic creative + run in controlled batches Controversial but true: polished praise sells to people who already trust you. The noisy, imperfect reviews sell to the rest. Found this useful? Like, follow, and repost ♻️ so others can too! ps. struggling with creative bottlenecks? We can help.

We built a review scraping engine that generated $180K in pipeline for a client in 3 months. It creates a sales task the moment a prospect's customers start complaining online. Runs monthly, fully automated, and the rep doesn't lift a finger until there's a real signal. Here's how it works: 1️⃣ Scrape review sites at scale Apify pulls new reviews every month from Trustpilot, G2, and Capterra for every company in the client's HubSpot. We only scrape companies that match the ICP, so it's not a firehose of noise. 2️⃣ Qualify reviews with AI in Clay Clay receives the raw scraped data and AI reads every review, flagging the ones that match the client's value prop. For a client selling workforce management software, we filtered for reviews mentioning scheduling headaches, missed shifts, and manual rostering. 3️⃣ Summarize and package the intel Clay creates a 2-3 sentence summary of each flagged review with a direct link back to the source. The rep gets a brief they can actually reference in outreach, not a wall of text they'll never read. 4️⃣ Push a custom event to HubSpot Every qualified review creates a custom event on the company record. If the company is Tier 1, it auto-creates a task for the rep who owns that account, complete with relevant contacts, phone numbers from BetterContact, and emails from Findymail The rep opens their task list and sees: "[Company] just got 3 negative reviews on Trustpilot about scheduling issues and missed shifts. Here's the summary. Here are the people to call." If its either Tier 2 or Tier 3 it gets routed into respective automations via HeyReach or Instantly.ai. -- This works for any product that solves a problem people complain about publicly. Onboarding software, uptime monitoring, billing, data security. If customers are frustrated somewhere online, that frustration becomes your pipeline trigger. I highly recommend exploring Apify as a signal source. If routed and enriched properly through Clay it can become both the cheapest and most powerful signal platform for your sales and marketing team. ♻️ Repost if you found this useful and follow Jan Brochwicz for more GTM content.

AI search has changed how ecommerce visibility works. Rankings and keywords still matter, but they no longer determine whether you appear in AI answers. LLMs pull from many sources and validate information across channels, so brands now need a practical strategy to influence what these systems surface. Here is the actionable version of the playbook: 1. Strengthen the brand mention layer These mentions usually come from Reddit, Inc., media coverage, YouTube reviews, and user sentiment. To increase volume and quality: • Seed conversations on Reddit via customer outreach and product education. • Pitch journalists with data, not product claims. • Send reviewers standardized product kits with accurate specs. • Increase review velocity on Amazon, Walmart, and your own site. Goal: create widespread awareness that models can pick up reliably. 2. Win citations by becoming a source of truth Citations influence how the model describes your brand. To increase them: • Publish structured, factual resources. Examples include comparison charts, ingredient or materials breakdowns, and step by step usage guides. • Make your product pages machine readable with flawless schema markup, identical naming, and consistent specs. • Update all your public product data quarterly. Goal: give LLMs clean, verifiable information they can quote confidently. 3. Influence product recommendations This is the highest value layer. To increase recommendation frequency: • Get included in publisher listicles and buying guides through affiliate programs or product samples. • Make sure your product appears in retailer categories with high review counts and strong Q and A sections. • Encourage customers to mention specific attributes in their reviews so AI models learn your positioning. • Publish expert testing results wherever possible. Goal: fit the queries that drive buying decisions, not just general awareness. 4. Build consensus across independent sources Models reward brands whose reputation looks the same everywhere. To create this: • Audit every major source in your category twice a year. Look at Amazon sentiment, YouTube reviews, TikTok results, Reddit threads, niche forums, and publisher guides. • Identify mismatched attributes, outdated specs, or conflicting feature claims. Fix them one by one. • Monitor competitor positioning across these same channels to understand why they appear more often in AI answers. Goal: eliminate contradictions so LLMs treat your brand as the consistent default. 5. Fix consistency across every product feed LLMs cross check your data across Amazon, Shopify, Walmart, Google Merchant, and any structured source. To avoid exclusion: • Standardize SKU names and attribute formats. • Keep pricing aligned within a narrow range. • Use the same dimensions, specs, and materials everywhere. • Remove outdated product copies from old marketplaces. Goal: reduce confusion so AI systems trust your data. (Find Step 6 and 7 in the comment section)

What if your worst review became your best marketing? Years ago, the New Bedford Whaling Museum received a one-star review: “Worst aquarium ever.” There was just one problem... It isn’t an aquarium. Instead of getting defensive, the museum leaned into the critique. It put the review on T-shirts, sweatshirts, and tote bags. The merchandise sold out online. The story went viral. In Pink Goldfish 2.0, my coauthor David Rendall and I call this Exposing. It's the E in the FLAWSOME framework. The idea is to be transparent about flaws while everyone else hides their weaknesses. For example, when the Utah ski resort Snowbird turned one-star reviews that its mountain was “too advanced” and "too much powder" into an entire advertising campaign. Or when Amber Share created the book "Subpar Parks," pairing beautiful illustrations of national parks with their ridiculous one-star reviews. My favorite was for Hawai'i Volcanoes National Park, "Didn't even get to touch the lava." The lesson: Don’t hide from criticism. Reframe it. Sometimes your worst review can become your best marketing. What critique could your brand expose, own, and turn into a strength? #PinkGoldfish #Differentiation #CustomerExperience

Here’s one of my favourite exercises to 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗹𝗮𝗻𝗱𝗶𝗻𝗴 𝗽𝗮𝗴𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗿𝗮𝘁𝗲. Put yourself in the user’s shoes. Someone sees your ad. They click. They land on your page. Will they buy immediately? Most won’t. What will they do instead? They’ll open Google. They’ll type: → “Brand name reviews” → “Is brand name genuine?” → “Brand name complaints” → “Brand name Trustpilot” Every marketer should replicate this exact behaviour. Search for your own brand like a real buyer. See what shows up. That’s where half your conversions are dying. When users search, they often see: → Business listings with reviews (good + bad) → Trustpilot → Blog posts → Random forums → Old PR articles → Or… nothing at all Now the real work begins. 𝗢𝗻𝗰𝗲 𝘆𝗼𝘂 𝘀𝗲𝗲 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝘀𝗲𝗲, 𝗳𝗶𝘅 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻: → Respond to negative reviews, show you’re listening → Increase total review volume → Clean up outdated info → Publish genuine PR that helps build trust → Create your own “Brand Reviews” page → Create a separate “Testimonials” page → Add a blog: “Things to know before buying X” → Make sure these pages actually rank for your brand terms Higher ticket size? Even more important. Lead gen folks… this is your entire game. People need more information before they commit. And don’t stop at Google. Do the same exercise on YouTube. Search for: → Your brand → Your category → Your product type If there are videos reviewing you good or bad those influence the purchase. If there are no videos, that is a gap in your funnel. Create content that naturally sits in evaluation: comparisons, honest reviews, explainer videos. 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁, 𝗮𝗻𝘆𝘄𝗵𝗲𝗿𝗲 𝘂𝘀𝗲𝗿𝘀 𝗴𝗼 𝘁𝗼 𝘀𝗲𝗲𝗸 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝘂𝘆𝗶𝗻𝗴, 𝘆𝗼𝘂𝗿 𝗯𝗿𝗮𝗻𝗱 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝘀𝗵𝗼𝘄 𝘂𝗽 𝘄𝗶𝘁𝗵 𝗰𝗹𝗮𝗿𝗶𝘁𝘆, 𝘁𝗿𝘂𝘁𝗵, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗼𝗳. No pushy selling. Just helping them make a confident decision. PS: What do you think? Does it help? #PerformanceMarketing #CRO #LandingPage

To improve ecommerce product performance: don’t ignore customer reviews. Most brands do next-to-nothing with this valuable feedback. Yet, they are a goldmine because: 1. Customer reviews are generally more honest than surveys. 2. Which means the information in these reviews can effectively inform improvements for headlines, testimonials, content, or even sales pitches. At Enavi we utilize this information through our Human-Obsessed approach, based on the following set of questions: Identifying Pain Points 1 - What issues were customers trying to solve with the product? 2 - Is there a common thread that led users to shop for the products? Recognizing Recurring Features: 3 - Which aspects of the product are repeatedly mentioned, positively or negatively? 4 - How does that compare to what we “thought” was important for users? Noticing Benefits: 5 - Are there any benefits in the customer reviews that we didn’t consider previously? Identifying Outcomes: 6 - Which specific outcomes have customers highlighted? Acknowledging Concerns: 7 - Were there any hesitations before the purchase? Use Cases: 8 - What frequent uses or applications of the product are mentioned? 9 - Do the use cases align with what is mentioned in the product description and key messages? 10 - Could these reviews be harnessed for testimonials? By following this 10-step process, we've effectively enhanced product-specific conversion rates and overall performance. Why does it work so well? Because review mining with a Human-Obsessed focus isn’t just about making adjustments. It’s about building better products and growing your business. Where data ends, human insight begins.

Reviews used to be about buyer research. Now they're training data. I've been building review programs for years. My playbook hasn't changed much. But the stakes have. I just pulled LLM citation data for the program we've built at Reachdesk. Here's what I found: → 15,335 total citations in 60 days (+43% vs. prior period) → Compare pages nearly tied with review pages as top citation source → Showing up for high-intent queries like "best account-based platforms for pipeline acceleration" That's ChatGPT, Perplexity, and others pulling review content into answers. Your reviews aren't just influencing buyers anymore. They're influencing what AI tells buyers before they ever hit your site. Here's what most teams miss: Review programs don't build themselves. And most companies treat them like a once a year ask or more like once a quarter IYKYK. I built this one to run continuously. Here's the operator breakdown: 1) Worked with the CPO to build custom reports in Zoho. About 15 different filters across customer data. Product usage patterns. Contract timing. Feature adoption. Support ticket history. The goal was to find customers who had real experience to share, not just the ones who were "happy." 2) Built gifting triggers tied to review completion. Not bribery. A thank you after they submit. The trigger fires automatically once the review is verified. This was integrated directly with Amazon. 3) Created messaging that made the ask easy. Clear CTA. Direct link. 2 minutes max. No friction. Because TBH no one likes filling out long forms. 4) We have set up a landing page to capture submissions. That triggered a HubSpot workflow for follow-up, attribution, and tracking which segments converted best. 60 days later: 106 new reviews. 4.5 average rating. 1,036 total. Based on this program, I'm seeing a correlation: a 10% increase in review volume led to roughly a 2-3% lift in LLM citations. I've built programs like this before AI made it easier. Manual outreach. Spreadsheet tracking. Begging CS to help. Now? Workflows handle the targeting, the triggers, and the follow-up. I just set the criteria and let it run. More reviews = more content on review sites = more surface area for LLMs to pull from. This isn't a vanity play anymore. It's a visibility strategy. If you want help building a review program like this, drop a comment or DM me. PS: This isn't sponsored so don't @ me. I just enjoy sharing cool stuff I'm working on these days.

I asked three different data leaders the same question over the past week: how fresh does your data actually need to be? I got three completely different answers. The first, at a healthtech company, needs sub-minute. Their operational database and their analytics layer feed the same workflows, and any gap between the two shows up as an inconsistency a clinician might see. The second runs monitoring on physical equipment. 5 minutes is the absolute ceiling, and ideally it is under one. The third runs internal analytics for a lean team. He told me 15 to 20 minutes is perfectly fine, and that probably would work for some of their agentic use cases. Most latency requirements are never traced. Someone writes "real-time" into an evaluation doc because it sounds rigorous, the vendor prices against it, and nobody asks what breaks at minute 6. The honest question sits downstream: what does the data feed, and what does it cost when it goes stale. For years, "real-time" could mean fifteen minutes and nobody got hurt. The slack was big enough that the imprecision never cost you - one dashboard, one report, one batch job, fifteen minutes covered all of it. You could write "real-time", mean almost anything, and be right. Agents close that gap. When an agent is acting on your data instead of a human, 70 seconds and 6 minutes are two different products. One catches the stale record before it acts. The other acts on it, and now you're cleaning up a decision instead of refreshing a chart. That's the shift we're seeing across the board So the rigor that never mattered suddenly does. Not "is it real-time" but what specifically breaks at 70 seconds, and what breaks at minute 6. That answer is a spec. "Real-time" is just a feeling that sounded rigorous in a doc. If you own a data platform: has anyone actually traced your strictest latency requirement lately - or is it still a word someone wrote a long time ago?