TripleDart

𝗪𝗲 𝘁𝗲𝘀𝘁𝗲𝗱 𝟮𝟬𝟬𝟬+ 𝗟𝗟𝗠 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝗖𝗵𝗮𝘁𝗚𝗣𝗧, 𝗣𝗲𝗿𝗽𝗹𝗲𝘅𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗚𝗲𝗺𝗶𝗻𝗶. Google’s #1 got cited less than 18% of the time 👇🏻 After digging into the data across ChatGPT, Perplexity, and Gemini, here’s how LLMs actually pick who to cite: → 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: LLMs scan for relevant sources, not just top-ranked ones → 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: They shortlist 5–20 sources for each answer → 𝗥𝗲𝗿𝗮𝗻𝗸𝗶𝗻𝗴: Recency, authority, relevance, and quality decide which ones move up → 𝗦𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘀: The model reads the best sources to write its answer → 𝗖𝗶𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗠𝗲𝗻𝘁𝗶𝗼𝗻𝘀: The model links to the most useful source with a URL, excerpt or just a brand mention You can win or lose at every step. Most brands lose at Stage 1 because their content does not match how people ask questions. Others lose at Stage 5 because LLMs cannot find a clean, citable paragraph. 𝗔𝗳𝘁𝗲𝗿 𝗿𝗲𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝗵𝘂𝗻𝗱𝗿𝗲𝗱𝘀 𝗼𝗳 𝗰𝗶𝘁𝗲𝗱 𝗽𝗮𝗴𝗲𝘀, 𝗜 𝗺𝗮𝗽𝗽𝗲𝗱 𝟲 𝘁𝗵𝗶𝗻𝗴𝘀 𝘁𝗵𝗮𝘁 𝗺𝗼𝘃𝗲 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱𝗹𝗲: ▸ 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗠𝗮𝘁𝗰𝗵: Write answer sections (200–400 words) that use the exact words people type into LLMs ▸ 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Put the answer up front, use clear headers, keep one idea per paragraph ▸ 𝗙𝗿𝗲𝘀𝗵𝗻𝗲𝘀𝘀: Add datePublished and dateModified in schema, show clear update dates in your titles ▸ 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘁𝘆: Get cited by .edu, .gov, and industry publications-create research others want to reference ▸ 𝗧𝗵𝗶𝗿𝗱-𝗣𝗮𝗿𝘁𝘆 𝗠𝗲𝗻𝘁𝗶𝗼𝗻𝘀: Get featured in “[Category] tools” lists, track where competitors show up ▸ 𝗖𝗶𝘁𝗮𝘁𝗶𝗼𝗻-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗙𝗼𝗿𝗺𝗮𝘁: Use standalone paragraphs, break walls of text, add sourced data The gap between teams doing this and those who are not is growing every week. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝗺𝘆 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸: → Run 20 key queries and check your citation share vs. competitors → Test prompt variations: “what is X”, “how to X”, “best X”, “X vs Y” → Find the top 20 cited domains in your space → Make sure 80%+ of your best pages are updated in the last 90 days Target: 30%+ citation share in your category. 𝗙𝘂𝗹𝗹 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 👇🏻 PS: Data from Slate, our AI search tracking platform. We run this playbook for 300+ B2B SaaS brands at TripleDart → https://lnkd.in/d3KcQ-en

𝗚𝗼𝗼𝗴𝗹𝗲 𝗷𝘂𝘀𝘁 𝗲𝗿𝗮𝘀𝗲𝗱 𝟰𝟬%+ 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗳𝗼𝗿 𝗦𝗮𝗮𝗦 𝗯𝗹𝗼𝗴𝘀 𝘁𝗵𝗮𝘁 𝘀𝗽𝗮𝗺𝗺𝗲𝗱 “𝗯𝗲𝘀𝘁 𝗫” 𝗹𝗶𝘀𝘁𝗶𝗰𝗹𝗲𝘀. Here’s what happened: 𝗦𝗮𝗮𝗦 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗴𝗼𝘁 𝗵𝗼𝗼𝗸𝗲𝗱 𝗼𝗻 𝗮 𝘀𝗵𝗼𝗿𝘁𝗰𝘂𝘁: → Publish “best” listicles on their own blog → Rank themselves at #1 → Collaborate with others for mutual promotion For a while, it worked. These listicles dominated organic search and AI results. Brands saw huge visibility gains-until Google flipped the switch. The numbers are brutal: - One $8B SaaS brand lost 49% visibility in two weeks - Another dropped 43% (85% of traffic gone from a single folder) - Multiple well-known sites lost 34–42% overnight The common thread: Blogs packed with self-promotional listicles. Some had hundreds. Some just a handful. All saw sharp declines after Google’s update. Why did this playbook fail? → “Best” in the title without real evaluation undermines trust → No evidence of testing or transparent research → Articles written for algorithms, not people Google’s review system is now punishing these tactics. AI search engines (ChatGPT, Gemini) are following suit. If your visibility depends on ranking yourself, the risk is clear. This is the “gray area” of SEO: Not illegal, but misleading. Short-term wins, long-term losses. The shift is here. Authentic, helpful content wins. Transparent methods matter. User trust is non-negotiable. PS: We help B2B SaaS brands recover and grow visibility across Google and AI search at TripleDart. If this is hitting your traffic too → https://lnkd.in/d3AbH2HW

𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗹𝗲𝗮𝗱 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗻𝘂𝗺𝗯𝗲𝗿 𝗶𝗻 𝗕𝟮𝗕 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴. That's also why your account based program can't prove it worked. 𝗧𝗵𝗲 𝘁𝗿𝗮𝗽 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: we optimize what we can measure. CPL is easy to measure, so it becomes the scoreboard. But it answers a question your CEO never asked. The one they do ask, "Which of our target accounts did we actually reach?" sits one level below CPL, and almost no ad report ever goes there. Not because teams are lazy. Because the data breaks before the campaign even runs. Buyers sign up to LinkedIn and Meta with personal emails. So a clean list of 500 work emails matches at 40%. LinkedIn won't serve until 300 accounts match, which means the list you built your entire strategy around often can't run as a campaign at all. And the platform never tells you who fell out. For a decade we papered over this with targeting filters: pick a job title, pick a company size, hope for the best. That era is ending. As those filters lose precision, the teams still winning are the ones moving from targeting to identity advertising to named contacts, and reporting at the person level, not the impression level. That's the shift worth understanding right now. And few people are better to learn it from than Dag Holmen. Dag H. is CMO/CRO at ContactLevel and co-founder of Buyerfeeds | Intent Data, an identity graph of 1.84B contacts ingesting 30B+ intent signals a day. He doesn't just run this playbook; he builds the data layer it depends on. (𝗙𝗼𝗿 𝘀𝗰𝗮𝗹𝗲 𝗼𝗳 𝘁𝗵𝗲 𝘂𝗽𝘀𝗶𝗱𝗲: 𝗺𝗼𝘃𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗮𝗰𝘁𝗟𝗲𝘃𝗲𝗹'𝘀 𝗼𝘄𝗻 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗳𝗿𝗼𝗺 𝗽𝗮𝗴𝗲 𝗽𝗼𝘀𝘁𝘀 𝘁𝗼 𝘁𝗵𝗼𝘂𝗴𝗵𝘁-𝗹𝗲𝗮𝗱𝗲𝗿 𝗮𝗱𝘀 𝘁𝗼𝗼𝗸 𝗖𝗧𝗥 𝗳𝗿𝗼𝗺 𝟬.𝟰% 𝘁𝗼 𝟭.𝟴%.) 𝗢𝗻 TripleDart 𝗖𝗼𝗮𝗰𝗵, 𝗘𝗽𝗶𝘀𝗼𝗱𝗲 𝟭𝟴, 𝗵𝗲'𝘀 𝗼𝗽𝗲𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗯𝘂𝗶𝗹𝗱 — 𝗹𝗶𝘃𝗲: – Getting a list from 30% to 90% match before it hits the platform – One campaign structure that carries demand gen and named accounts together – Reporting that names every person who saw an ad, and how often the exact view he hands to sales Then he'll share the workflows at the end, so your team can run it without him. 📅 𝗧𝘂𝗲𝘀𝗱𝗮𝘆, 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟭 · 𝟳:𝟯𝟬 𝗣𝗠 𝗜𝗦𝗧 / 𝟮:𝟬𝟬 𝗣𝗠 𝗨𝗧𝗖 / 𝟰:𝟬𝟬 𝗣𝗠 𝗖𝗘𝗦𝗧 🔗 Register (limited spots): [link] If you've ever been asked which accounts your budget reached and had to guess, this is the hour that fixes it. CC - Shiyam Sunder Sudharshan Mahesh Manoj Palanikumar Jayakumar Muthusamy (JK) Abishek Balaji

On September 1st, I'm breaking down how Contactlevel uses Contactlevel to convert cold prospects into demos. 🙏 What I will be covering in this session with TripleDart: 1. Our internal multi-platform ad strategy. 2. How to set up an evergreen growth system that runs on autopilot. 3. How to leverage intent data to speed up demand capture. You will leave this session with a playbook that you can follow to grow your business using paid ads. (in a consistent and predictable way) Register here 👉 https://lnkd.in/gUnnUm7T

Two years ago I made a bet: grow Fibbler with LinkedIn Ads as the main channel and no external capital. We're now at $1.15M ARR. No outbound. No sales team. Just a paid channel some people say is "too expensive" and "impossible to measure." Thought leader ads have been a huge part of that. I took my best-performing organic posts and put budget behind them, distributing them to relevant people outside my network. Signups jumped from 15 a week to 50+. Today we're at 100+ every single week. On Tuesday I'm opening up my entire ad account, together with TripleDart and their coach series. ✅ Which posts I choose to promote. ✅ How personal-profile ads stack up against company-page creative. ✅ The cold vs. retargeting split. ✅ And the reports I use to tie all of it back to pipeline. You'll leave with a structure you can rebuild in your own account. 📅 Tuesday, July 28 · 4:00 PM CEST Link to register in the comments 👇

𝗧𝗼𝗱𝗮𝘆 𝘄𝗲 𝗮𝗿𝗲 𝗹𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗧𝗿𝗶𝗽𝗹𝗲𝗗𝗮𝗿𝘁 🎉👇🏼 We grew to $7M+ ARR profitably in 4 years, without raising a rupee, by turning marketing playbooks into systems. Not a new website. A new company. 250 tech companies have run those systems. GE, Cognizant, WeWork, Glean, Zoho and others. We built Slate, our own AI native platform, because nothing on the market could run the work the way we needed it run. And from today, TripleDart is a GTM Engineering company. Services, rebuilt as software. Here is why now. For twenty years, marketing services were sold on capacity. More people, more hours, more retainers. That model was not wrong. It was correct for a world where the work could only be done by hands. That world ended. The best marketing teams today are not staffed. They are engineered. The question is no longer how many people you can put on a problem. It is how fast the system around them learns. Agencies sell you capability. Software sells you a tool and leaves the work to you. Neither one gives a CMO what they actually need, which is a machine that gets cheaper per result every month it runs. So we built the third thing . Here is how it works. Every playbook we ran across 250 companies became a system. Every system became a workflow. Every workflow runs on Slate. SEO programs that compound while you sleep. Campaigns that ship in days instead of quarters. Reporting nobody has to chase. Senior operators still make every decision that matters. The drag around them is gone. We are not an agency with an AI page on the website. We do not sell software and wish you luck. We engineer the GTM system, we run it, and we hand you the compounding. Here is what happens next. The website is live today. The deck, the pricing, the bundling, the roles we hire for, all of it moves behind this. We are hiring engineers and operators who want to build the machine rather than staff it. If you are a CMO who is tired of buying hours, come look at what we made. Check TripleDart [.] Com Manoj Palanikumar Mahesh Sundararaman Jayakumar Muthusamy (JK) Sabarinathan Rajeswaran Sudharshan Narasimhan