Using Data to Segment Sales Leads

If you run an early stage B2B SaaS under $3M ARR, here's something you'll find insanely useful. Takes about 8 hours. Absolutely worth it. Here's what you need to consolidate: - Call recordings from every sales call in the last year. - List of every inbound meeting signup (emails). - List of every user who bought (emails and website). - List of every user who churned. - List of every user who expanded. - List of users with the longest retention. You'll get this from Stripe, HubSpot (or whatever CRM), and your marketing databases. Also create one long doc of all your marketing copy and company context. Site copy, one-pagers, decks, meeting notes. Everything. Now enrich the shit out of this data. Use any full-scope enrichment tool. Not just contact info. Persona and company details. I used Freckle.io → Job profile of buyers → LinkedIn profiles → What the company does → Industry and category → Revenue and funding → Company age Feed everything into a Claude Project or GPT. Find these patterns: 1. Which customers retained longest? ACV, LTV, industry, what they actually do. 2. Which customers churned fastest? ACV, LTV, what they do. 3. Which job profiles bought and which expanded? 4. Conversion rate on sales calls. Total, by channel, by user category. Use this data to figure out who your best customers actually are. For Valley, we assumed it was B2B businesses whose audience is on LinkedIn. That's true. But it's super broad. Turns out, our happiest users are: → Marketing and growth agencies → GTM and lead gen agencies → Consulting companies → Recruiting and staffing orgs → Tech startups In that order. Here's the kicker: Lead gen agencies spend 6x more on Valley because they love the product. Which means I should be willing to spend 6x more to acquire lead gen companies as customers. Now you know which segment to go all in on in 2025. -> Find customers who churned but are actually great fits. Call them with a no-brainer return offer. -> Formulate all future product development based on them. Hyper-focus on what matters. Bonus moves: Use sales call recordings to find common objections. Solve them in your marketing. Name your best customer segment Carol. (Read Jason Cohen's essay on selling to Carol) Make a list of value points Carol cares about. From common sense and sales calls with Carols. Create a list of deal breakers for Carol. Things that make her not buy. Find key inciting events that made Carol decide to buy your solution. What this tells you: Value points and deal breakers → tells product team what to build and what not to build. Key events that made Carol buy → become your signals to identify similar customers and use in marketing copy for higher conversion. Don't do this if you're below $500K ARR. Not worth it yet. You don't have enough data. Takes 8 hours. But it's absolutely worth it. Now go all in.

55% of sales leaders witnessed increased lead conversions with intent data, a stat that marks a new era in the art of sales and marketing. 🔍 A Personal Tale: From Data Jungle to Targeted Strategy 🔍 I once partnered with a client who was overwhelmed by a deluge of intent data from Bombora. Picture navigating a dense jungle without a map. The data was vast but unstructured, not effectively mapped to accounts. I was reminded of Craig Rosenberg's words - "The key on intent is fit comes first." 💡 Turning Complexity into Clarity: The Role of Context Our quest was clear: to cut through this jungle and find a path. We initiated a meticulous cleanup, aligning intent data with specific accounts. Then, we took a pivotal step further by focusing on contextual intent data. 🧭 Unlocking the ‘Why’ Behind the Data Contextual intent data is like a compass in uncharted territory. It goes beyond identifying interested accounts; it's about grasping the reasons behind their interest. This deeper understanding enabled us to tailor our approach, addressing the specific needs and challenges of each account. 🌈 The Outcome: Precision-Driven Sales and Marketing Success The transformation was remarkable. Sales dialogues became more focused and resonant. Marketing campaigns struck a chord, addressing the unique context of each account's journey. 🛤️ A 5-Step Blueprint to Mastering Contextual Intent Data Data Harvesting: Collect intent data with an eye for the underlying context of each interaction. Intelligent Mapping: Align this data with specific accounts, illuminating your path through the data forest. Tailored Tactics: Customize your outreach based on the nuanced context of each segment. Adaptive Campaigns: Launch dynamic, context-sensitive campaigns that connect deeply with each account's narrative. Strategic Refinement: Continuously evolve your strategies, responding to the ever-shifting landscape of intent signals and contexts. 📈 Beyond Just Data Points: Contextual intent data isn't merely a collection of information; it's a storytelling tool. It's about transforming raw data into compelling narratives that not only reveal who is ready to buy but also why they are on this journey, creating more meaningful and effective sales and marketing engagements. Step into the world of contextual intent data and watch your sales and marketing narratives change from abstract data points to stories that connect and convert. #ContextualIntentData #SalesInnovation #MarketingTransformation #DataDrivenDecisions #BusinessGrowth #B2Bmarketing #ABM #accountbasedmarketing #METABRAND #IndustryAtom

How I Went From Reporting Numbers to Driving Strategy Last week, my post about failing a data analyst interview reached over 18,000 impressions. Many of you asked, "How exactly are you bridging that gap?" Here's the honest breakdown, no fluff, just what's working. THE PROBLEM I IDENTIFIED: I was stuck in descriptive analytics (what happened?) while businesses needed prescriptive analytics (what should we do?). I could tell you sales dropped 15% last quarter. But I couldn't explain: • WHY it dropped (diagnostic) • WHICH customers might churn (predictive) • WHAT actions to take (prescriptive) That's the gap I'm closing. WHAT I'M LEARNING: Instead of just mastering more tools, I'm learning strategic frameworks that change how I view data: 1. RFM Analysis (Recency, Frequency, Monetary)* Segments customers into Champions, At-Risk, Lost, and Potential Loyalists.   Example: "These 12% of customers generate 34% of revenue but haven't purchased in 60 days; a retention campaign is needed." 2. Customer Lifetime Value (CLV) Predicts the long-term value of customer segments.   Shifts focus from single transactions to relationship value. 3. Cohort Analysis Tracks customer groups over time and reveals retention patterns.   Example: "Q1 customers have 40% better retention than Q3; what did we do differently?" 4. Churn Prediction Identifies at-risk customers before they leave.   Example: Customers with 3+ support tickets and expiring contracts have a 67% churn risk. 5. Market Basket Analysis Reveals products bought together for cross-selling strategies. Example: 80% of customers who buy Product A also buy Product B within 30 days THE MINDSET SHIFT: Before: Looking at data and asking, What can I calculate? Now: Looking at business challenges and asking, What data do I need to solve this? I've learned to think in four levels: Level 1 (Descriptive): Sales decreased 15% Level 2 (Diagnostic): Top 3 customers cut orders by 40% Level 3 (Predictive): We'll likely lose 2 more major customers in Q1  Level 4 (Prescriptive): Launch a targeted retention campaign. Estimated ROI: 3.5x" Most analysts stop at Levels 1-2. The job market rewards Level 3-4 thinking. RESOURCES HELPING ME: Learning: • Kaggle Learn - Free short courses   • Mode Analytics SQL Tutorial - Advanced SQL techniques   • StatQuest YouTube - Statistics explained simply   • Google Data Analytics Certification - Solid foundation  Practice: • Kaggle datasets - Real messy data to work with   • Maven Analytics - Free datasets with business context   Currently reading Storytelling with Data by Cole Nussbaumer Knaflic  TO EVERYONE WHO REACHED OUT: Your messages reminded me that I'm not alone in this journey. My challenge: Pick ONE framework, find a Kaggle dataset, build something this weekend, and share what you learned. Let's level up together. #DataAnalytics #CareerDevelopment #LearningInPublic #DataScience #BusinessIntelligence

I partnered with Bombora to integrate intent data into UpLead, and it's transformed how our 4,000+ B2B customers target prospects. Here are 3 ways intent data helps you find ready-to-buy prospects (with real examples from our customers): 1. Identifying active buyers before your competitors do - Traditional outreach relies on static firmographic data, often missing the crucial timing element - Intent data analyzes online behavior to spot companies actively researching solutions like yours - Example: A SaaS customer of ours increased their qualified lead rate by 215% in just 3 months by focusing on high-intent accounts identified through our platform Why it works: - You're reaching out when prospects are already in a buying mindset - Your message aligns perfectly with their current needs and research - You get ahead of competitors who are still using outdated outreach methods 2. Personalizing outreach based on specific pain points - Generic outreach messages often fall flat, even when sent to the right people - Intent data reveals not just that a company is in-market, but what specific topics they're researching - Example: An enterprise software company using UpLead's intent data tailored their pitches to address the exact challenges their prospects were researching, resulting in a 40% increase in response rates Why it works: - Your messages resonate more deeply because they address current, specific needs - Prospects perceive you as more knowledgeable and relevant to their situation - You can prioritize different product features or use cases based on the intent signals 3. Optimizing your sales team's time and resources - Sales teams often waste time on prospects who aren't ready to buy - Intent data helps prioritize outreach to companies showing strong buying signals - Example: A B2B agency using our platform reallocated their SDR efforts based on intent scores, resulting in 50% more booked sales calls without increasing headcount Why it works: - Your team focuses on the warmest leads, increasing efficiency - You reduce time wasted on prospects who aren't in a buying cycle - Sales and marketing efforts align more closely with market demand BONUS: Combining intent data with other UpLead features. Intent data becomes even more powerful when combined with our other offerings: - 95%+ accurate contact data ensures you're reaching the right people within high-intent companies - Real-time email verification reduces bounces and improves deliverability to these hot prospects - Direct dials, including mobile numbers, help you quickly connect with decision-makers in active-buyer companies TAKEAWAY By leveraging intent signals, you're not just reaching out to more prospects but you're engaging with the right prospects at the right time with the right message.

Through the years, I’ve spent more than 300 hours looking at website visitor identification data. Many companies have no idea how to use this goldmine of information. So let’s fix that! Here’s what I’ve learned are the most powerful ways to leverage this data for new conversations, lead gen, and pipeline growth. ✅ CONFIGURE CUSTOM FILTERS Raw data = noise. Custom filters = signal. Don’t try to use the data out of the box. Instead, configure custom filters to more efficiently find your most qualified prospects. 🎯 IDENTIFY THOSE WITH STRONG INTENT SIGNALS Among those in your filtered segments, identify those showing strong intent signals. Who is reviewing your product, solution, or services pages? Who is checking out your pricing? 🔎 IDENTIFY THOSE ON YOUR LANDING PAGES Check who’s visiting your landing pages but not submitting a form. Most companies ignore them. It’s actually shocking how many companies don’t use the data in this way. 🏙️ BLEND FIRMOGRAPHICS + BEHAVIOR Among those who are showing intent or going to your landing pages, filter for those with the right firmographics such as industry, company size, financial performance, etc. 👩🦰 ROUTE TO THE RIGHT PERSON Make sure responsibilities are clear. Who should reach out to qualified accounts? Be sure they are informed in a timely manner. And, importantly, ensure accountability. 🗓️ REACH OUT THE SAME DAY OR THE NEXT DAY If you’re going to reach out, do so the same day or no later than the following business day. Any longer and your response rates will decline significantly. ✍ PERSONALIZE YOUR OUTREACH This is the most important aspect of your outreach program. Take the time to customize your outreach messages. Treat them like a real human being. Strive to resonate more deeply with them. This is your best bet for boosting response rates even higher. 🤝 BE HELPFUL (NOT SALES-Y) When you reach out, simply ask how you can help. Nothing more. Whatever you do, don’t try to sell them anything. Just see if you can answer any questions they may have. Does it work? Our team at Stratabeat noticed a company looking at our SEO blog posts and then our B2B SEO services page and then left the website without submitting a form or otherwise contacting us. We reached out to the CMO right away to simply ask if he had any SEO questions that we could help with. Just trying to be helpful. He responded within 60 seconds; we chatted; then we met with the marketing team; and then it turned into a $400,000 client relationship. Website visitor detection is low-hanging fruit for generating greater conversations, leads, pipeline, and revenue. It’s all about timing and relevance. And trying to be helpful. Used right, it’s the fastest shortcut from traffic to qualified conversations. Let’s Destroy Mediocre Marketing!

If I was running ABM at a fast-growing security company (like Wiz, Snyk, or Netskope), here's how I'd avoid wasting money on bad-fit accounts. 👇 AI Segmentation. Most companies segment by industry. They say something like: "We target Tech, Retail, and Hospitality companies with 1,000+ employees." Motel 6 and Airbnb show why this breaks. Same firmographic profiles. But very different business situations, needs, and priorities when it comes to information security (or any tech purchase). You wouldn't sell to them the same way. AI Segmentation helps you uncover and target the highest value segments for your business, beyond basic industries. Here's how I would do this for a security company: 1.) Segment on business situation (not industry). -- Analyze your best customers (high NRR, high ACV). -- Group by specific situations that align to your value prop. e.g. Security Maturity Level, Security Use Cases, Compliance Sensitivity, etc.  -- Find the *natural* clusters based on value, not generic industry labels. 2.) Identify segments with AI. -- Use Keyplay AI to categorize every account in your market. -- Backtest segments against historical data to find which segments have the highest NDR, ACV, and Win Rates. -- Find new ICPs, outside generic vertical groups. 3.) Action the data -- Create ABM plays at intersections with highest win rates. -- Develop content specific to each segment combination (e.g., "Cloud Security for Advanced DevSecOps Teams in Retail") -- Refine your segmentation models as you grow. This process can reduce non-ICP Spend (waste) by 20-30% and help you find thousands of net new target accounts. Don't just throw your budget at industries. Find the segments where your solution resonates most, where you win often, win fast, and win big. That's strategic segmentation. p.s. If you want me and my team to kick-start this process for you, we're offering a free strategic segmentation analysis to CMOs at SaaS security companies with >$20M ARR. Get your report here --> https://lnkd.in/gMezS4Zk #ABM #ICP

If I were a B2B founder and needed to get more appointments without spending more on inbound/ads/lead acquisition, here’s exactly how I’d tackle it: 1) Organize your Leads First, I’d dig into every database, spreadsheet, trade show list, and CRM I own. Anywhere I’ve ever gotten a lead, I’d pull that data together. (Bonus: old customers are now leads) • Consolidate: If you haven’t touched a lead in 6+ months, it’s time to resurrect it.  • Verify Numbers: Don’t have a phone number? I’d grab one using a service like ZoomInfo or any other B2B data provider.  • Segment & Label: Separate them by industry or past engagement level so I can track which groups respond best once the calls start.  • If I wanted to go next level, I’d use tools like Clay to get next level intelligence on my leads By the end, I’d have a single, polished list—my sleeping giant—ready for re-engagement. 2) Launch an AI BDR  Next, I’d create an AI-based BDR to do the heavy lifting, because calling 1,000 leads manually is a full-time job (and I don’t want my team burning out).  • Automated Outreach: This AI sales rep would call every number on my list—faster than any human SDR.  • Qualification on the Spot: The AI can ask critical questions to figure out whether the lead is a good fit.  • Calendar Booking: For leads that show interest, the AI BDR instantly drops a meeting on my sales team’s calendar, saving a lot of time in back-and-forth emails. Instead of letting my leads slip through the cracks, the AI BDR works around the clock, ensuring each one gets the immediate attention it needs. 3) Clean Your Data & Rinse, Repeat  Finally, I’d treat my lead list like a living, breathing organism that needs constant care:  • Follow-Up: For leads that need a little nurturing, I’d set them aside for a second or third call cycle.  • Eliminate Dead Ends: Quickly remove numbers that bounce, are disconnected, or lead nowhere.  • Optimize: Over time, I’d refine my approach—tweaking call scripts, call times, and questions based on what resonates most with each segment. This cycle of calling, cleaning, and improving ensures I’m always getting a solid ROI on every lead. 👉 Btw if you’re a B2B founder who wants help with this, let’s have a chat: https://lnkd.in/gQEEn7H8

Signals show you 5% of the market ready to buy. But many GTM teams aren't actioning them correctly. I did a webinar yesterday with Spencer Hardey, MBA (HG Insights) and Matthew Volm (RevOps Co-op), where we dove deep into signals. I presented this graphic, which lays out 13 steps from signal capture (easy) to signal conversion (where the $ is). Here's how it works: 1/ Capture Pull signals from as many sources whilst minimizing tool spend. - 1st party (e.g. CRM, website visits, MELs) - 2nd party (e.g. review sites, partner networks, LI engagement) - 3rd party (e.g. technographic, news, social activity) 2/ Aggregate Centralize all signal data into one orchestration layer. → Tool options: Clay, Cargo 🧱, n8n, Freckle.io 3/ Normalize Ensure you have all properties needed to digest the signal. Fill gaps and standardize data like: - Company domains - LinkedIn URLs - Job titles Use these to deduplicate. 4/ Enrich Layer on company + contact enrichment data. → Tool options: Apollo, Clay, BetterContact, Findymail, AI Ark 5/ Lookup A signal from an account already being worked vs a net-new account, need to be treated differently. Check every signal against your CRM to avoid duplicates and respect ownership. 6/ Qualify Only send ICP-fit signals to your reps or you risk losing trust. Use enrichments to qualify companies + contacts. Many teams skip this then complain about signal fatigue. → Tools: Clay, Claude, ChatGPT, Perplexity 7/ Score Use firmographics, technographics, and account-fit data to score every account. No matter how complex the backend logic, I’m a big believer in simplifying the output. Our preference: - Tier 1 - Tier 2 - Tier 3 - Unqualified Easy for reps to interpret. And allows for logical routing (e.g. Tier 1s get a manual touch) 8/ Segment Split accounts into relevant segments. Typically using: - Size - Industry - Location - Business type 9/ Route Keep a live list of your active reps in orchestration tool. Use segments to assign each signal to a rep. 10/ Sync Push enriched, scored, routed leads into your CRM. Update: - Companies - Contacts - Custom events (HubSpot) - Custom objects (Salesforce) This data model lets you spot accounts showing multiple signals at once. 11/ Activate Ways to action signals: → Retargeting ads: Google Ads, LinkedIn Ads → Automated outbound: Instantly.ai, HeyReach.io → Connection requests: HeyReach, Sales Navigator → Slack alerts for high-intent signals → Manual outreach: Nooks, Apollo → CRM tasks 12/ Track 2 main ways we add extra visibility: → Rolling up 1st- and 2nd-party signals to awareness stages: - Identified → Aware → Interested → Considering → Selecting → Extensive CRM reports - e.g. overdue signal tasks per rep 13/ Enablement Where sales leadership steps in to create the playbook: - Custom sequences - Summary digests - Rep onboarding - Dashboards - Call scripts Signals aren’t overhyped. But they aren't easy to activate.