Sales Intelligence Gathering

There's one habit that separates struggling AEs from elite sellers: They treat executive meetings like product demos instead of strategy sessions. While average reps show features and ask basic questions, elite sellers arrive with insights that reshape how executives think about their business. Here's the exact 5-step intelligence framework that turns executives into champions: 1. Survey Their Current Team First Hit LinkedIn Sales Navigator. Filter for team members active in the past 30 days. Send tailored messages focused on outcomes, not features. Ask about pain points and advice for approaching their exec. Your exec opener: "After talking with 5 of your sales reps in the East, it sounds like AE-created pipeline is a big focus. What are the biggest risks you see to pipe build in the next 3-6 months?" 2. Analyze Their Customer Stories Study their case studies and customer logos. What problems do they solve? Who do they serve? What gets mentioned repeatedly - and what's conspicuously absent? Your exec opener: "You've got impressive logos - Gong, Salesloft, Drift all using your platform. Given that renewals are getting leaner across SaaS, what are the 2 biggest churn risks you're watching next quarter?" 3. Mine RepVue and Glassdoor for Team Intelligence 71% vs 21% quota attainment tells different stories. Use filters and search functions to find specific insights about team dynamics and performance gaps. Your exec opener: "Building pipeline has been brutal in 2025. How are you thinking about attainment risks in the next 3 months?" 4. Study Product Reviews for Competitive Gaps G2 and Capterra reviews reveal what customers love - and what they're missing. Look for gaps your solution fills. Your exec opener: "Your customers rave about functionality and ease of use on G2. The only complaint was insufficient CSM time. Is this a top risk you're addressing this quarter?" 5. Consume Their Content Podcasts, speeches, interviews. Find their personal stories and philosophies. Reference their own ideas to open conversations. Your exec opener: "You made a brilliant point about sales-marketing alignment on Kevin Dorsey's podcast. How do lead conversion issues and slower deal velocity rank as risks you're discussing with marketing?" — This level of preparation doesn't just impress executives. It positions you as a strategic advisor, not another vendor. Your first meetings will be 5x better because you're leading with insight, not interrogation. PS -Want these frameworks delivered weekly? Join 7k+ sellers in my newsletter - details in bio.

I’ve taken ~400 sales calls this year. All recorded via Fathom. Instead of letting them collect dust, I turned them into a sales intelligence system that gives me daily insights into what buyers actually think. Here’s the 2-step setup: 1/ Fathom transcripts → document I exported every Fathom transcript from 2025 into one Google Doc. This became the brain for anything I want to ask or analyze. 2/ Create a custom GPT Then I uploaded that doc into a custom GPT trained on all my calls with these instructions: ### ROLE & PURPOSE You are a Sales Intelligence GPT trained on GrowthPair’s sales call transcripts. Your job is to analyze large volumes of transcripts (100+) and answer whatever questions the user asks,  whether high-level, pattern-based, strategic, or exploratory. You must be able to: - Identify macro-level insights - Detect patterns across many calls - Extract data-backed themes - Summarize findings when requested - Provide recommendations if asked - Answer specific questions about trends, objections, FAQs, buyer types, pricing sentiment, etc. ### CORE BEHAVIOR 1. Never hallucinate or infer facts not present in transcripts. If you can’t confirm something, say so clearly. 2. Prioritize pattern recognition. When analyzing multiple transcripts, focus on recurring themes, not isolated anecdotes. 3. Respond in the style requested by the user. If they want: - A list → give a list - A narrative summary → give a narrative - A McKinsey-style breakdown → give structured analysis - Short bullets → keep it tight - Creative recommendations → deliver options - Raw extraction → give pure data Your format depends on the prompt. 4. Provide strategic recommendations only when asked. If the user wants insights only, don’t offer recommendations. ### INTERNAL CONTEXT YOU MUST KNOW [add your company information here] ### FINAL BEHAVIOR SUMMARY - Know GrowthPair deeply. - Analyze transcripts at scale. - Extract macro-level insights when requested. - Adapt your format to the user’s ask. - Never hallucinate. - Be structured when needed, concise when needed. - Always prioritize clarity and accuracy over assumptions. - Answer any type of strategic, tactical, or analytic question about the dataset. — That’s it. Two steps. And now I can ask questions like: - What are the top objections in sales calls? - Which pain points come up most often? - Why do companies choose offshore talent? - What themes show up between urgent vs non-urgent buyers? The GPT can now give me data-backed answers in seconds. Some outputs I’ve already used: - Built an FAQ website page from real buyer questions - Improved messaging and positioning - Identified common objections + how to address them If you’re sitting on hours of sales calls, this is one of the easiest ways to turn them into real, usable insight. If I were a marketer at a B2B startup, I’d ask my sales team for their Fathom credentials immediately and start building this.

93% of sales teams are wasting their call recordings. They capture hours of conversations only to let critical revenue intelligence gather digital dust. Most "analysis" is nothing more than random note-taking that misses the patterns separating closers from the rest. After analyzing thousands of sales conversations, I've identified what the top 7% do differently: The 5-Layer Strategic Analysis Framework 1. Conversation Flow Mapping ▪️ Track talk-to-listen ratios (aim for 30:70) ▪️ Identify pattern interruptions where prospect engagement peaks ▪️ Map question sequences that uncover genuine pain points vs. surface-level needs 2. Objection Categorization ▪️ Price objections often mask deeper value perception issues ▪️ Timeline hesitations typically reveal priority conflicts, not budget constraints ▪️ Technical concerns usually indicate gaps in your pre-call qualification 3. Linguistic Pattern Recognition ▪️ Note when prospects shift from "if we..." to "when we..." language ▪️ Monitor changes in personal pronouns (they/we) signaling stakeholder alignment ▪️ Track future-tense verbs that indicate implementation thinking has begun 4. Momentum Markers ▪️ Identify exact moments when energy shifts (positive or negative) ▪️ Document specific phrases that trigger deeper engagement ▪️ Catalog the questions that consistently lead to next steps 5. Competitive Intelligence Collection ▪️ Catalog indirect competitor mentions that reveal awareness gaps ▪️ Document feature comparisons that signal what's truly valued Note pricing anchors that reveal budget frameworks The harsh truth?  Most sales leaders think they're doing call analysis when they're really just performing digital hoarding. Your competitors who are systematically decoding these conversations are quietly stealing your deals while you're still listening to random call snippets. Is your team part of the elite 7% that turns conversation intelligence into revenue, or are you just wasting storage space with recordings nobody meaningfully analyzes? #SalesStrategy #ConversationIntelligence #RevenueOptimization #SalesLeadership

I Just Connected Social Listening Directly Into Claude AI. This Changes Everything. With Trigify.io's new MCP feature: Ask Claude a question → Get instant answers from live social data No dashboards. No exports. No manual work. Here's what just became possible: I can now ask Claude: "Show me LinkedIn posts from VP Sales talking about pipeline challenges this week" "Find Reddit threads where people are complaining about [competitor]" "Which podcasts mentioned [industry trend] in the last 30 days?" And Claude pulls real-time data from: ✅ LinkedIn (posts + profiles) ✅ Twitter/X (posts + profiles) ✅ Reddit (keyword posts) ✅ YouTube (videos + channels) ✅ Podcasts (episodes + keywords) This is what we use it for at Sales Titans: 1. Competitor Intelligence "Show me posts where companies mention switching from [competitor] to another solution" Claude pulls the data, summarizes trends, and highlights patterns I'd never catch manually. 2. Trigger-Based Prospecting "Find LinkedIn posts from sales leaders discussing hiring challenges" Instead of scrolling LinkedIn for hours, Claude shows me everyone talking about the exact pain point we solve. 3. Market Research in Real-Time "What are the top 5 concerns mentioned about [topic] on Reddit this month?" Instant market insights without surveys or focus groups. Why this matters: We used to spend hours manually researching prospects context before calling. Now? Claude + Trigify does it in seconds. Our AI agent already tells us prospect pain points before we dial. Now it's pulling live social signals to make those insights even sharper. This is the future of sales intelligence: AI agents that don't just analyze data—they actively hunt for it across the entire social web. No dashboards. No manual exports. Just ask a question and get answers. Social listening just became conversational. And it's going to make signal-based selling exponentially more powerful. Are you using AI to pull live market intelligence yet?

In the AI Era, Sales Teams Are Becoming Product's Secret Intelligence Network The rapid pace of AI innovation is turning the traditional Product-Sales relationship upside down. What I'm seeing now is that sales teams have emerged as essential early warning systems for Product leaders surfing the tsunami of AI disruption. 1) Competitive Intelligence: Sales reps are often the first to hear "We're looking at [new AI solution]" - giving Product teams critical market signals months before these competitors appear in analyst reports. 2) Use Case Discovery: When customers adopt AI products unexpectedly, Sales teams capture these emerging patterns, revealing valuable opportunities that Product research might miss entirely. 3) Trend Detection: The best Sales-Product partnerships now include structured channels for sharing "weak signals" - those subtle shifts in customer conversations that often predict major market turns. 4) Feedback Acceleration: In AI product development, waiting quarters for formal feedback loops are too slow. Forward-thinking teams now leverage Sales insights to make weekly course corrections. 5) Market Testing: Leading Product teams are equipping Sales with strategic questions to test hypotheses about what capabilities will matter most in 6-12 months. Gone are the days of "Product builds, Sales sells." The most successful teams I've worked with have moved to a model of "continuous market sensing" - where Sales becomes Product's eyes and ears in a rapidly evolving AI landscape. How is your Sales team helping detect emerging AI competitors and use cases? What signals are you finding most valuable? #ProductStrategy #AI #SalesIntelligence #MarketInsights #ProductInnovation

Let's address the elephant in our revenue space: Are AI agents real or just another tech buzzword? After years of building AI solutions, Waseem Alshikh's recent breakdown of agent types perfectly captures where we really stand - and, more importantly, where the true value lies. The reality is that AI agents aren't some futuristic concept—they're already reshaping how revenue teams operate. At Aviso AI, we moved beyond the hype, understood the distinct types of agents, and built real-world applications for modern GTM teams. These applications are designed not only to improve the productivity of knowledge workers but also to help our customers generate new revenue opportunities. Here's how we're actually leveraging different types of LLM frameworks: 1️⃣ Basic LLM Agents The High-Volume Task Masters Aviso’s Agentic Workflows leverage open-source LLMs to generate personalized emails and multi-channel sales sequences. These agents excel at handling high-volume tasks with consistent quality, freeing up time for strategic work. 2️⃣ Chain of Thought Agents The Problem Solvers By integrating LangChain, Aviso’s agents analyze complex RFPs, earnings calls, and reports. They provide logical insights and actionable recommendations, empowering teams to make confident, high-stakes decisions. 3️⃣ RAG Agents (Retrieval-Augmented Generation) The Intelligence Gatherers Aviso’s Ask Anything tool synthesizes data from CRM, calls, emails, and more to answer deal-specific queries. These agents turn data overload into strategic advantage by providing contextual, real-time insights. 4️⃣ ReAct Agents (Reasoning and Action) The Real-Time Revenue Coaches Aviso’s MIKI analyzes data and provides actionable guidance, executing the next-best actions instantly. They act like your always-on AI Chief of Staff, helping teams save time and make better decisions in real-time. 5️⃣ Planning Agents The Strategic Orchestrators Aviso’s Agentic Workflows help map paths to quota, perform account planning, and create sustainable growth strategies. These agents turn complex revenue challenges into clear, actionable plans for scaling success. Aviso's AI Brain, the mastermind behind Agentic Worfklows, orchestrates these agent types in harmony, each playing its crucial role in the revenue ecosystem. What makes this approach powerful isn't just the individual capabilities - it's how these agents work together to solve complex revenue challenges. The discussion around AI agents needs to evolve from "Are they real?" to "How do we deploy them effectively?" Because when implemented thoughtfully, they're not just tools - they're transformative partners in revenue generation. #AI #RevenueIntelligence #AIAgents #FutureOfSales

Intent data is a scam, if you treat it like magic. Here's the playbook most teams run: Buy a subscription. Get a flood of "high intent" alerts. Push the list to sales. Watch it gather dust. Here's why: A single signal is worthless. One review category visit? Could be a competitor, a bored analyst, or a bot. Sales calls, gets ghosted, and decides intent data is a hoax. The real move is convergence. You want three or more signals, different sources, close together in time, real people attached. Pricing page visit G2 review LinkedIn job change Recent funding Same week. Stack your signals like this: 1. Only focus on ICP-fit accounts. 2. Layer in account signals, hiring, funding, tech changes. 3. Add contact-level signals. 4. Check recency. Last 7 days is hot. Six weeks ago is an ice cube. Score it by tiers: Tier 1: All the boxes ticked, all signals firing, all in the past week. Immediate, personalized outreach. Tier 2: A couple signals, some fit. Nurture and watch. Tier 3: Weak or stale signals. Do nothing. Intent only works if you treat it like intelligence, not leads. Convergence is the only way sales will trust the data, and the only way you'll ever see pipeline move. Stop buying magic beans. Start building signal convergence. That's how you make intent work.

Shortly after raising our $38M Series B, we realized we had a problem: we had no system for surfacing what our reps were learning from the market. Reps talk to prospects every day. They hear people are saying. They see objections evolving. They notice when the buying committee changes. But that intelligence was disappearing. Yes, you can watch sales calls. That takes hours. Yes, you can ask people. But it’s random and incomplete. We needed a way to systematize it. That’s when I started running situation rooms. Here’s how they work: - First 5 minutes: everyone fills out a form together (blockers, customer intel, priorities) - Next 5 minutes: everyone reads - Rest of the hour: we talk through what’s surfacing The form is public. Anyone can read it. Engineering sees what sales is hearing. Marketing sees the patterns across deals. The repetition matters. Every week, the same format. The same questions. It keeps critical information top of mind. And it creates space for knowledge transfer that doesn’t happen anywhere else.

Adem Manderovic (7x Sales Leader and 2x CRO) showed me how to actually use intent data. Most teams get it wrong - they try to guess who’s ready, instead of gathering insights first. Here’s how Adem does it (and how I’ve started doing it too): Yesterday, a mid-market company popped up in our Dealfront feed. ✅ Clear ICP match ✅ Visited multiple pages about The B2B Incubator 🚫 No form submission yet A marketer sold on the 'intent' dream might try and score them, enrich them, and send them over to sales Sales (with 'meetings targets') would push as hard as they good for a meeting. If they're not in-market (and there's a good chance they're not - 95:5 rule), the conversation likely ends. No account intelligence gathered No ability to follow up No relationship built Another small blow for sales and marketing alignment ________ So what to do instead? Instead, Adem showed me how to catalogue them first. We contact (not to sell or book a meeting), but with a few simple questions: + “Who’s your current provider?” + “What’s working - and what’s not?” + “When does your contract end?” + “Can we reconnect closer to renewal?” In 5 mins we learned: + They’re currently with a competitor + They’re happy overall, but have a key feature gap we can fill + Renewal’s in 6 months And importantly, we have their permission to follow up then This is cataloguing. It’s outbound designed to align with the 95:5 rule - so you build trust and gather intelligence, instead of chasing ghost meetings. It's sales planting seeds for tomorrow, just like marketing does. And unless you're selling Zoom in the pandemic, sales is going to come across some people that are not in-market now. _____ Intent data isn’t a deal trigger. It’s your cue to start a commercial conversation. We’re teaching cataloguing as part of a full end-to-end system for sales, marketing and customer success inside Chief Revenue School #DemandGen #B2BMarketing #Outbound #Cataloguing

We helped one SaaS client grow from 11 demos to 137 demos in nine months (a 1,145% increase in qualified pipeline). And we didn’t do it by chasing keywords. We did it by aligning SEO to revenue. Here’s what that actually looks like 👇 We start by listening to real people through... ✓ Sales call recordings - to hear what prospects are asking and how sales is answering. ✓ G2, TrustPilot, Reddit, and Slack communities - to uncover the unfiltered truth about your product. ✓ Customer interviews and surveys - to learn what customers value most after they’ve converted. Then, we go beyond Google’s view of demand. We cross-reference SERPs with AI search results (ChatGPT, Perplexity, etc.) to see what’s missing, what’s misunderstood, and where your brand deserves to be cited. Before we touch a sitemap, we sit down with sales, customer success, and product to gather intel: ✓ Which objections slow deals? ✓ Which features close them? ✓ What differentiators reps wish they could articulate better? Then, we build content that does that job for them. And it doesn’t stop there. Every insight feeds back into the system! ✓ CRM data shapes quarterly SEO priorities. ✓ Attribution dashboards flag content that drives revenue, not just visits. ✓ Those learnings feed back into sales decks, nurture flows, and product messaging. Most agencies deliver deliverables. We deliver systems that learn. SEO that drives traffic is nice. But SEO that drives deals? That’s where the real compounding growth begins.