LinkedIn Personalization Using Multi-Layer Research

AI SDRs promise personalization at scale but many offer spam at scale. One key difference has led Valley to produce top 1% messaging. Building human psychology into our message generation flow. Most tools that offer personalization at scale will scrape LinkedIn + a website and call it a day- "Hello {FirstName} noticed {AI one liner about promotion} would love to chat with you about {value proposition}." PS. {question about if they’ve gone to the most popular restaurant in the town where they’ve live for the past 14 years}. A message Valley generated yesterday: "𝘏𝘦𝘺 𝘛𝘩𝘰𝘮𝘢𝘴 - 𝘴𝘢𝘸 𝘺𝘰𝘶 𝘤𝘳𝘶𝘴𝘩𝘦𝘥 5 𝘉𝘰𝘴𝘵𝘰𝘯 𝘮𝘢𝘳𝘢𝘵𝘩𝘰𝘯𝘴. 𝘙𝘦𝘴𝘱𝘦𝘤𝘵. 𝘝𝘢𝘭𝘭𝘦𝘺 𝘩𝘦𝘭𝘱𝘴 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘦 𝘰𝘶𝘵𝘳𝘦𝘢𝘤𝘩 𝘴𝘰 𝘺𝘰𝘶 𝘤𝘢𝘯 𝘧𝘰𝘤𝘶𝘴 𝘰𝘯 𝘤𝘭𝘪𝘦𝘯𝘵𝘴 (𝘢𝘯𝘥 𝘮𝘢𝘳𝘢𝘵𝘩𝘰𝘯𝘴). 𝘓𝘔𝘒 𝘪𝘧 𝘺𝘰𝘶 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘤𝘩𝘢𝘵 𝘮𝘰𝘳𝘦" Behind the scenes: • 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗟𝗮𝘆𝗲𝗿: Found marathon completions - found he attended Ultra Miami, Oktoberfest and other festivals - previous cities - exact timeline of achievements • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗟𝗮𝘆𝗲𝗿: Matched my writing style - single word sentences - abbreviations - natural pauses - parenthesis to communicate sub context • 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿: Linked personal achievement to business value - no forced small talk Valley also justifies every message it generates - here is what it had to say: 𝘛𝘩𝘪𝘴 𝘪𝘯𝘪𝘵𝘪𝘢𝘭 𝘮𝘦𝘴𝘴𝘢𝘨𝘦 𝘶𝘴𝘦𝘴 𝘢 𝘤𝘢𝘴𝘶𝘢𝘭 𝘵𝘰𝘯𝘦 𝘢𝘯𝘥 𝘥𝘪𝘳𝘦𝘤𝘵𝘭𝘺 𝘳𝘦𝘧𝘦𝘳𝘦𝘯𝘤𝘦𝘴 𝘛𝘩𝘰𝘮𝘢𝘴'𝘴 𝘪𝘮𝘱𝘳𝘦𝘴𝘴𝘪𝘷𝘦 𝘮𝘢𝘳𝘢𝘵𝘩𝘰𝘯 𝘢𝘤𝘩𝘪𝘦𝘷𝘦𝘮𝘦𝘯𝘵𝘴 𝘵𝘰 𝘦𝘴𝘵𝘢𝘣𝘭𝘪𝘴𝘩 𝘳𝘢𝘱𝘱𝘰𝘳𝘵. 𝘐𝘵 𝘣𝘳𝘪𝘦𝘧𝘭𝘺 𝘪𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦𝘴 𝘝𝘢𝘭𝘭𝘦𝘺 𝘢𝘯𝘥 𝘪𝘵𝘴 𝘬𝘦𝘺 𝘣𝘦𝘯𝘦𝘧𝘪𝘵 𝘰𝘧 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘯𝘨 𝘰𝘶𝘵𝘳𝘦𝘢𝘤𝘩, 𝘢𝘭𝘭𝘰𝘸𝘪𝘯𝘨 𝘩𝘪𝘮 𝘵𝘰 𝘧𝘰𝘤𝘶𝘴 𝘰𝘯 𝘤𝘭𝘪𝘦𝘯𝘵𝘴. 𝘛𝘩𝘦 𝘴𝘰𝘧𝘵 𝘤𝘢𝘭𝘭 𝘵𝘰 𝘢𝘤𝘵𝘪𝘰𝘯 𝘪𝘯𝘷𝘪𝘵𝘦𝘴 𝘧𝘶𝘳𝘵𝘩𝘦𝘳 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘢𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘣𝘦𝘪𝘯𝘨 𝘱𝘶𝘴𝘩𝘺. To do this - we called 5 different research agents - summarized 10s of thousands of lines of research - called 6 different reasoning models to predict the most resonant value proposition - and understood multiple inputs on my personality to spit out something identical to what I would write myself. All of this happens in the background - so by the time I woke up today - the guy who received this message had already booked a call on my calendar.

Personalization is not just about using a prospect's name. To provide a truly personalized experience, you should learn your prospect's language, communication style, and what's important to them right now. Sound complicated? It's really not. Everyone utilizes social media. Learning about your prospects has never been easier. The vast majority of decision-makers, executives, and C-suite members use some form of social media every day. They actively absorb content, watch trends, learn new skills, and find areas of opportunity. You can start right here on LinkedIn. Look at their profile and see what they're posting, commenting on, and liking. Is there a common thread to their messages? Maybe they like data, facts, and figures. They could discuss their vision, the future of their industry, and current concerns. Do they post content or write a blog? Read it. It's the best way to meet them. This is their language. Communicate with them the way they communicate with others. Note the topics they discuss or engage with; these are your lead-ins to having a conversation with them. Join groups related to their industry, learn the hot topics and their jargon and acronyms. Your research should extend beyond the person, the company, and their current role. It's about tailoring your message to them and engaging them like a coworker or trusted partner.

Everyone's been sold the same lie: “Spend more time personalizing to get better results.” Here’s what actually happens: Your SDRs waste 5 min per prospect digging through LinkedIn, find nothing useful for 90% of them, and end up defaulting back to generic templates. Personalization is a 1:1 game. You can’t scale it. Relevance is different. Relevance multiplies. One account insight → 10 conversations One company challenge → Every stakeholder One initiative → Entire buying committee The best SDR teams have already made this shift. They’re booking more meetings with less research time. Stop personalizing for people. Start building relevance for accounts using an Account-Based System. What’s an Account System in outbound? Start with company-level insights first, then apply them to the prospects inside the account: Adapt to Above the Line and Below the Line prospects. If your product touches multiple teams, you go both horizontal and vertical inside the account, a multithreaded approach, not just single-thread. With this, it’s faster to spot real initiatives or challenges for each account, then build a strong POV (point of view) around how you can help. That makes your outbound relevant at scale. Example strategy: Identify company strategic initiatives/pain points in under 10 minutes → create a strong POV → reuse insights across multiple prospects in the same account. Here are different approach examples CEO interviewed on a podcast → use it to reach their direct reports. CRO just got hired → leverage with the CEO and front-line sales managers. Bottom-up: talk to end-users about current challenges/tools → use that with front-line managers → Economic buyer. VP of CS just hired 2 CSMs → name-drop. Ask for referrals inside the account. With that, you’ve got (almost) infinite ways to outbound an account. Because if the prospects you’re chasing aren’t active on LinkedIn, you’re just wasting time. And in 2025, with tools like n8n, Clay or AI-driven solutions make this faster and scalable, even if your prospect accounts aren’t active on LinkedIn. "But Elric, does this work in SMB and Mid-Market?” Yes. It works there too Example: Owner sells to single-location restaurants; they only have one contact for most accounts. They adapt the talk track by segment: Restaurant has: A website + uses Uber Eats/DoorDash → focus on the pain of 3rd-party apps (30% revenue cut, no customer data). A website + no delivery → educating them about missed revenue opportunities. They personalize for business maturity, not the owner. -- Follow me 👨🍳 Elric Legloire for daily outbound recipes.

▶️ 4 Advanced Ways to Optimise Your LinkedIn Profile Using AI (and Actually Get Results) Most professionals still treat LinkedIn like a digital CV. That’s outdated. In 2025, your LinkedIn profile is a conversion asset, a data-driven ecosystem where AI can help you build authority, trust, and inbound opportunity at scale. Here’s how to optimise every layer of your profile using AI 👇 1️⃣ Rebuild Your Headline and About Section with ChatGPT-4 (Properly) Most prompts fail because they lack context. You’re not asking for “copy”; you’re instructing a strategic narrative rewrite. Try this: “Act as a senior LinkedIn strategist and brand copywriter. Rewrite my LinkedIn headline and About section to attract [ICP] and convert profile visitors into leads. Optimise for SEO visibility, clarity, and authority. Return three variants using different positioning frameworks — credibility, outcome-driven, and narrative-led.” Then feed the outputs into ChatGPT’s Advanced Data Analysis mode to test which version drives better engagement metrics using previous post data. 2️⃣ Use Atlas to Run an AI-Based Profile Audit Atlas (OpenAI’s research browsing model) allows you to analyse your entire digital footprint in seconds. Prompt it with: “Analyse my LinkedIn profile and recent posts. Identify semantic patterns, tone inconsistencies, and credibility gaps. Suggest improvements aligned with [industry/role] positioning.” Atlas doesn’t just read your copy, it interprets audience sentiment across your posts, comments, and endorsements. Think of it as an SEO crawler for personal branding. 3️⃣ Automate Prospect Research with Apollo + ChatGPT Integration Instead of connecting blindly, use Apollo.io’s AI filters to identify high-intent leads, filtered by role, behaviour, and engagement patterns. Then use ChatGPT-4 to contextualise outreach: “Using Apollo’s data for [company/contact], draft three first-message options that reference their recent content or pain points. Keep tone: consultative, concise, and value-led.” This builds relevance at scale. 4️⃣ Generate Visual Consistency with Nano Banana + ChatGPT Prompt Engineering Your visuals should express your positioning before you speak. To maintain brand coherence: Feed ChatGPT-4 this system prompt, “You are an expert Nano Banana prompt engineer. Create 3 image prompts for LinkedIn carousels matching the tone and identity of [brand]. Include direction for lighting, background, composition, and camera lens type. Ensure outputs align with LinkedIn’s visual standards for clarity and contrast.” Use these prompts directly in Nano Banana to build image templates for campaigns, thought leadership posts, or branded storytelling. LinkedIn has evolved. Those who learn to integrate AI strategically won’t just attract more opportunities, they’ll own the attention economy!

Most B2B teams call their ICP "Series B SaaS, 50-200 employees. Here's how we build a list of 500 hyper-qualified accounts for 110+ B2B clients from scratch instead: Series B SaaS with 50-200 employees is not an ICP. That's a category. A category tells you who could buy. An ICP tells you who is ready to buy, why now, and how to reach them. Step 1: Define the main characteristics, not just the firmographic and tech data We start with one question: what has to be true for this company to need you right now? ↳ Hiring 3+ SDRs in the last 90 days? ↳ Just raised Series B and hasn't built outbound yet? ↳ Using a competitor tool that just increased pricing? ↳ Expanding into a new market? Step 2: Layer the signals on top of it ↳ Pull the base list: company size, industry, funding stage from Apollo + LinkedIn ↳ Layer 1: hiring signals via Clay + LinkedIn (open SDR/AE roles = building sales) ↳ Layer 2: funding signals using(raised in last 12 months = budget + growth mandate) ↳ Layer 3: tech stack (using tools that indicate they're scaling outbound) using BuiltWith ↳ Layer 4: website traffic spike via RB2B , Vector 👻 , or Factors.ai (in-market = evaluating now) ↳ Layer 5: LinkedIn engagements captured by Trigify.io Each signal alone is noise itself. But we combine them to identify the 3% of your market worth reaching out to this week. Step 3: Tier and score before a single email goes out Not all 500 accounts are equal. ↳ Score 1-10 via Clay + Claude based on signals on the companies and contacts ↳ Tier A (8-10): immediate outreach with a personalised sequence. ↳ Tier B (5-7): LinkedIn connect + content nurture first via webinars, ads. ↳ Tier C (below 5): onhold unless they come inbound. We do this for every client before we write a single line of copy and map this out to the client’s GTM motion of how they run their sales-led, product-led motion. Because the best copy in the world won't convert an account that isn't ready. When we build your ICP this way with signals, actual characteristics (not just firmographics), and differentiate the goal of reaching out to them, it consistently outperforms the ones that don't. Regardless of the offer. Regardless of copy. How are you currently qualifying accounts before your campaigns go live? P.S. We build these outbound systems for 110+ B2B GTM teams at Growth Today. If you want to see how we'd build yours, DM me.

I've watched 7 outbound teams go from <2% to +5% reply rates in under 60 days. All of them made the same shift: Static lists to signal-triggered outreach, heavy on tiering. The old way: → Generic "I noticed you're hiring" openers → Zero context on timing or need → 1-2% reply rates if you're lucky → Spray and pray to static lists The new way: → Signal-triggered outreach → 5-8% reply rates consistently → Hyper-relevant context in every message → Perfect timing based on actual buyer behavior So what signals actually move the needle? The ones we've seen work consistently: → Leadership changes (new VP Sales = new priorities) → Social engagement (commented on relevant content) → Funding announcements (budget just opened up) → Job postings (they're hiring for a role you solve for) → Tech stack changes (competitor install/uninstall) Although, the best signals are *always* gonna be hyper-relevant and custom to YOUR offer, eg. for Midnight Labs we've been monitoring dark web activity, amongst many other niche platforms for 'enterprise evidence'. But signals aren't just about personalization - they're about prioritization. Not every account showing intent deserves the same motion. 𝐓𝐢𝐞𝐫 1 - 𝐇𝐢𝐠𝐡 𝐢𝐧𝐭𝐞𝐧𝐭 + 𝐡𝐢𝐠𝐡 𝐟𝐢𝐭: Multiple signals firing. These get the full omni-channel treatment: cold call, personalized email, LinkedIn touchpoints, maybe even direct mail. You're investing real time here. 𝐓𝐢𝐞𝐫 2 - 𝐌𝐞𝐝𝐢𝐮𝐦 𝐢𝐧𝐭𝐞𝐧𝐭 𝐨𝐫 𝐟𝐢𝐭: One or two signals. Personalized sequences, but more automated. Still relevant context, just not the white-glove approach. 𝐓𝐢𝐞𝐫 3 - 𝐋𝐨𝐰 𝐢𝐧𝐭𝐞𝐧𝐭, 𝐛𝐫𝐨𝐚𝐝 𝐟𝐢𝐭: Part of your TAM, but no active signals yet. Nurture at scale. Keep them warm until they light up, or run a more generic sequence. The magic happens when you combine tools like Clay with this tiered framework. Example workflow: • Trigify.io catches a prospect engaging with competitor content • Clay enriches their company data and scores them by fit + signal strength • High-tier accounts get routed to your AEs for multi-channel outreach • Lower tiers flow into automated sequences with relevant personalization • Everything goes out within 24 hours of the signal • Same ICP. Different treatment based on actual buyer behavior. We built this exact system for a tech (SaaS, specifically) client last quarter. Before: 1.8% reply rate, generic messaging, one-size-fits-all After: 6.2% reply rate, signal-based tiering, right effort on right accounts We've compiled 150+ sales signals into a library - categorized by strength, funnel stage, and trigger type. Each one includes when to use it, how to action it, and which data sources to pull from. Comment "Signals" and I'll send it over when it's live.

Here's a workflow I built that turned 43% of accounts that engaged with LinkedIn ads into qualified meetings. The problem was when someone engaged with your LinkedIn ad, you get: Company name, size, industry. But that’s not enough if you want to act on engaged data. So I added 3 layers of enrichment. Layer 1: Pull Engaged Accounts I use Fibbler to sync LinkedIn Ads engagement into HubSpot. Here’s the engagement threshold filter I use: Retargeting campaigns: 2+ engagements in 7 days, OR 1 click This cuts volume by 60-70%. You want fewer, higher-quality accounts. It takes a few tries to get the right filters in place but it’s definitely worth stressing about. Layer 2: Verify ICP Fit Once I have the engaged accounts inside HubSpot, they’re synced to Clay. Ideally you want to get this step right even before running the ad. But sometimes non-ICP folks sneak in. Sometimes it’s just human error. And other times its because somebody liked your ad and people in their network saw the ad and so on. So I like to ensure companies that engaged with my ads are an ideal fit for us. And then I enrich the company to find people who fit the personas for whom we showed the ads. Layer 3: Enrich Contacts with FullEnrich Once I have the names of the people I want to reach out to in each of these engaged accounts, I use FullEnrich. Traditional tools check one database and I used to get a find rate of 30-40%. FullEnrich checks 20+ data providers sequentially which gives me a find rate of ~83%. Best part is I only pay for verified contacts. I take a multi-contact strategy. I enrich 2-3 contacts per company: Decision-maker, Practitioner, and the Champion. Why this worked for me: - On the tech side: The real leverage comes from combining ad engagement with direct outreach. You're only enriching ICP-fit companies and FullEnrich checks 20+ providers until it finds accurate contact info at 83% coverage. - On the alignment side: When SDRs reach out, prospects are already aware of your brand. SDRs focus on selling value and answering objections, not finding contact info or explaining what the brand does. - On the fundamental side: We had a strong offer that hit prospects like a hammer on the nail. If we got this wrong, none of the above would’ve mattered. There’s no shortcut to this unfortunately. This workflow runs like clockwork every week. And all I ever have to worry about is nailing the good stuff like creatives, messaging, and the offer. #FullEnrichPartner

I built an entire outbound engine in Clay that generated 90 qualified leads from just 1,200 prospects, with a 7.5% lead rate. The VP of Sales couldn't believe it … Here's exactly how I did it (and why traditional outbound methods don't even come close): 1. SIGNAL TRACKING BEATS TRADITIONAL OUTREACH As soon as I started working with this client, I immediately shut down their 5,000-contact-per-month cold outreach program, which only got a 0.5% response rate. Instead, I built Clay workflows that track specific buying signals: → New C-Level hires (3x more likely to buy in first 90 days) → Series B/C funding announcements (6x higher response rates) → Competitor contract expirations (11% positive reply rate) The first signal-based campaign we ran resulted in 19 meetings within 2 weeks. 2. MULTIPLE CHANNELS, ONE WORKFLOW Last quarter, I tested similar messaging across different channels: → Email-only campaign: 1% response rate → Clay multi-channel approach: 13% response rate You can use Clay to power any GTM motion in your playbook: → ABM ads on LinkedIn → Gifting campaigns to key decision-makers → Inbound lead enrichment for faster qualification → Event invitations to high-fit prospects in specific locations → Content engagement tracking to identify warm leads → Direct mail campaigns 3. DEEP PERSONALIZATION AT SCALE Previously, the SDR team was spending at least 15-30 minutes researching each single prospect. It’s impossible to scale like that. Now Clay is doing the following: → Find podcast appearances and speaking events → Extract personalized data points from LinkedIn activity → Identify content they've published or engaged with → Build custom gifting lists for high-value prospects One SDR received this reply recently: "This is the first cold email I've responded to in 5 months – how did you know I just talked about this exact problem?" 4. SMALLER BATCHES, BIGGER RESULTS I reduced outbound targets from 2,000 to 500 perfect-fit accounts. The results: → Sales cycle: 73 days → 40 days → Average deal size: $27K → $41K → Close rate: 9% → 19% Clay provided over 15 relevant data points per account to identify ideal fits, removing guesswork completely. 5. AUTOMATED RESEARCH ENGINE I documented every research step the SDRs performed manually, then built a Clay workflow that: → Identifies decision-makers at target companies → Surfaces recent social activities and career changes → Finds content they've authored or been featured in → Scores accounts based on 7 intent signals → Builds targeted lists for event invites and ad campaigns. This system saved the sales team over 35 hours per week, allowing them to focus entirely on conversations with prospects instead of manual research. _________ The game has changed. Traditional outbound is dying while a signal-based approach is the future. Clay isn't just another tool – it has become the entire GTM backbone that powers every touchpoint, from first engagement to expansion.

Our 11-step AI outbound engine has helped generate over $14M in client revenue.   Outbound only started working consistently for us once we stopped treating it like a messaging problem.   Most teams jump straight to sequences. But the real problems show up much earlier.   So we built a system that starts with clarity and ends with booked meetings.   Here is the step by step breakdown:   STEP 1: DEFINE TARGET ACCOUNTS We start by deciding which accounts actually matter. Not leads or job titles, but companies we genuinely want to win. We use ICP and market research with Google's Gemini 3 to set clear boundaries from day one.   STEP 2: DEFINE ICP DATABASES Once the target is clear, we choose the right data sources that match the ICP. We use Apollo, ZoomInfo, and Ocean.io to build accurate account and contact coverage. If the inputs are wrong, everything downstream breaks.   STEP 3: PUSH TO CLAY All accounts and contacts are pushed into Clay. This becomes the central control layer for enrichment, scoring, routing, and automation. Decisions live in one place instead of across tools and spreadsheets.   STEP 4: LAYER SPECIALIZED ABM SIGNALS We layer in account and intent signals to understand priority. This includes data from RB2B, Bombora, G2, and LinkedIn signals. → A single signal rarely means buying intent → Patterns across signals change who moves first   STEP 5: CLEAN AND ENRICH DATA Before writing any messages, we fix the data. Missing fields, broken records, and incomplete context are resolved inside Clay. Personalization only works when the data underneath is reliable.   STEP 6: CHOOSE CHANNELS We decide how each segment should be approached. → Email only → LinkedIn only → Or multi-channel Channel choice is driven by context, not habit.   STEP 7: CREATE PERSONALIZED MESSAGING Only after accounts, signals, and channels are set do we generate messaging. We use Clay AI workflows to create messages tied to why the account is being contacted now. This keeps outreach relevant instead of generic.   STEP 8: LAUNCH OUTREACH Outreach is executed across the chosen channels. We send messages using Smartlead, HeyReach.io, and Instantly.ai. At this point, execution is automated. The thinking already happened upstream.   STEP 9: SYNC ACTIVITY AND REPLIES All outreach activity and replies are synced back automatically using Clay. This keeps the system accurate without manual tracking.   STEP 10: PUSH RESPONSES TO CRM When prospects reply or show interest, responses are pushed into HubSpot. Pipeline stays clean and visible for the team.   STEP 11: NOTIFY SALES Sales is notified in real time through Slack. → Not when emails are sent → Not when links are opened → Only when replies show real intent   That is the engine that helped our clients get 14M+ in client revenue...   It is not built to send more outbound.   It is built to decide who matters, why now, and what action makes sense, then close the loop all the way into the CRM.

Building a successful personal or company brand on LinkedIn requires you to think of it like a product - you need a tech stack. Most people think “posting more” is a strategy. They churn out carousels, write a few thought leadership pieces, and hope for the best. But serious growth on LinkedIn (whether as a founder, marketer, or creator) isn’t about random tactics. It’s about building a system that covers every layer: strategy, content creation, distribution, analytics, and hidden edges. Here’s how I think about it: 1 Strategic Layer Before you ever hit “post,” you need to know: - Who exactly are you talking to? - What topics actually resonate and build trust? - Where are the gaps your brand can own? Semrush → Understand trending keywords and content gaps. SparkToro → Find what your audience reads, listens to, and follows — gold for positioning. BuzzSumo → Identify which ideas or formats already win engagement in your space. This is about using data to figure out what’s working, what competitors are doing and finding your unique edge. 2. Content Creation Layer Once you know the strategy, you need to create high-quality content consistently. Canva → Easily produce polished visuals, carousels, and brand assets even if you’re not a designer. ChatGPT → Brainstorm hooks, outline posts, or rewrite copy for clarity and punch. Riverside → Record crisp audio and video interviews or thought leadership pieces that feel personal and human. The key here is create fast enough to stay relevant and gather feedback for iterating but good enough to actually build authority. 3. Distribution Layer Even the best content is useless if no one sees it. Hootsuite & Buffer → Schedule posts, repurpose across channels, and stay consistent without burning out. You can use these to plan “content waves,” align with launches or events, and ensure your message lands exactly when it should. 4. Analytics & Optimization Layer What gets measured, gets improved. Shield → Deep analytics built specifically for LinkedIn. See which posts drive reach, engagement, and real business outcomes. Google Analytics → Track traffic coming from LinkedIn to your website or landing pages. Typeform → Collect qualitative feedback: Why did people follow you? What content do they want more of? Most creators fly blind or look at LinkedIn’s default analytics. The best ones learn, iterate, and compound. 5. Underrated Gems These tools aren’t on everyone’s radar but they give you an edge. Granola → Find and track the best-performing posts in your niche. Spot patterns and build on proven ideas. Kleo → AI-based brand positioning insights. Helps you stay differentiated in crowded feeds. Creator Match 🧩 → Connect and collaborate with other creators who complement your audience and expand your reach faster. —— It’s not about using every tool in this stack. It’s about designing a system that fits your growth goals and creates a moat around your brand. Goal: be unforgettable.