How to Use Data for Targeted Outreach

80% of outreach fails because it lacks real relevance. Let’s fix that. Here's how to use strategic relevance to generate replies with buying intent: You can’t win replies by dropping someone’s name or company in your outreach. That’s not real relevance. Prospects see right through it – and hit delete. The only way I see real engagement now? Precision. You have to reflect their exact situation so well, it feels like you’ve been in their meetings. Here’s how I do it step by step: 1/ Mirror their pain → I use data to spot the actual pain they’re living with. Not a guess. Not general. Something you can prove with public signals. 2/ Show you know their world → I write a message that feels like I’m an insider. I bring up a challenge so specific, they wonder, “How did you know?” 3/ Drop a quick story → I share a mini-story about someone in the same spot. Not a case study. More like, “Most teams hit a wall when they reach 50+ controls – but the ones who win do X instead.” 4/ End with curiosity → I don’t pitch. I don’t ask for a meeting. I ask a low-pressure question that’s hard to ignore: “Are you seeing the same pattern?” or “Want to see how Company X solved this?” Let’s compare: ❌ “I noticed your company is expanding. Let’s chat!” ✅ “Your move into regulated markets while juggling compliance backlogs is a big opportunity – and a risk your competitors miss. Most companies try to scale manual reviews, but the ones who win automate evidence collection to cut bottlenecks fast. Curious if you’re seeing the same thing?” See the difference? The first is generic noise. The second is a mirror of their reality. That’s what gets replies. Key lessons I’ve learned: - The quality of your message comes from the quality of your data - Your story must match their situation, not just their industry - Share insights most people in their shoes miss - You’re aiming for a reply, not a meeting Curious how you’re using data to drive real relevance?

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.

Survey data often ends up as static reports, but it doesn’t have to stop there. With the right tools, those responses can help us predict what users will do next and what changes will matter most. In recent years, predictive modeling has become one of the most exciting ways to extend the value of UX surveys. Whether you’re forecasting churn, identifying what actually drives your NPS score, or segmenting users into meaningful groups, these methods offer new levels of clarity. One technique I keep coming back to is key driver analysis using machine learning. Traditional regression models often struggle when survey variables are correlated. But newer approaches like Shapley value analysis are much better at estimating how each factor contributes to an outcome. It works by simulating all possible combinations of inputs, helping surface drivers that might be masked in a linear model. For example, instead of wondering whether UI clarity or response time matters more, you can get a clear ranked breakdown - and that turns into a sharper product roadmap. Another area that’s taken off is modeling behavior from survey feedback. You might train a model to predict churn based on dissatisfaction scores, or forecast which feature requests are likely to lead to higher engagement. Even a simple decision tree or logistic regression can identify risk signals early. This kind of modeling lets us treat feedback as a live input to product strategy rather than just a postmortem. Segmentation is another win. Using clustering algorithms like k-means or hierarchical clustering, we can go beyond generic personas and find real behavioral patterns - like users who rate the product moderately but are deeply engaged, or those who are new and struggling. These insights help teams build more tailored experiences. And the most exciting part for me is combining surveys with product analytics. When you pair someone’s satisfaction score with their actual usage behavior, the insights become much more powerful. It tells us when a complaint is just noise and when it’s a warning sign. And it can guide which users to reach out to before they walk away.

For years, ABM followed an inefficient playbook: -Identify target accounts based on researched topics -Send broad messaging -Nurture with generic content -Hope for the best Marketers cast a wide net, often unsure if the right message landed at the right time. In developer-focused marketing, this approach doesn’t work anymore. 💡 Why This Fails for Developers: Developers focus on your product and their problem, not marketing content. Here’s why traditional ABM falls short: 🚫 No Real-Time Insights: Traditional ABM relies on signals like website visits or downloads. Developers skip marketing pages and go to docs or GitHub. Without real-time signals, tracking their buying journey is impossible. 🎯 Misaligned Outreach: Generic messaging fails. Developers have specific needs, and without knowing who’s exploring your product, outreach becomes guesswork. ⏳ Missed Timing: The buying journey starts with a problem—they're ready to buy when engaging with your product. Traditional methods risk outreach that’s too early or too late. Here’s what modern DevGTM ABM looks like: (Powered by Reo.Dev’s real-time intelligence, this playbook helps design effective outreach campaigns.) 🔍 Step 1: Leverage Real-Time Intent Signals Developers engage with product docs, explore GitHub, and compare alternatives—often without visiting marketing pages. Action: Capture intent signals in real-time. Focus on interactions like code engagements and documentation views to gauge their buying stage and customize messaging. 📩 Step 2: Create Personalized Outreach Generic outreach falls flat. Developers have specific needs. Action: Use Reo.Dev data to identify the features your prospects explore. Build messaging that addresses their challenges and integrates with their tools. ⏰ Step 3: Time Your Outreach Strategically Reaching out too soon or too late means missed opportunities. Developers research deeply before deciding. Action: Monitor high-intent signals to know when they’re in the decision-making phase. Align outreach with these moments for better engagement. 🔑 Step 4: Operate with Data-Driven Precision Outdated methods rely on guesswork. Today’s ABM campaigns are built on actionable data. Action: Use Reo.Dev to track which accounts progress through the funnel and why. Adjust strategy based on insights, focusing on high-engagement accounts. 📊 Step 5: Continuously Refine and Adapt ABM isn’t static—it requires constant adjustment, especially for developers exploring new tools. Action: Regularly revisit account signals and messaging. Stay updated on trends and evolve outreach to meet changing needs. The old ABM way—broad targeting and generic messaging—falls short for developer-focused teams. The new way uses real-time data, personalized messaging, and precise timing for meaningful engagement that drives conversions. Ready to bring this strategy to life? Reo.Dev is designed for DevGTM teams to create data-driven ABM strategies tailored to the developer journey.

As cold outreach continues to fall well short of expectations, many smart companies and AEs are now shifting their SDRs time to focus on RESEARCH vs. outreach as the core part of their role. Helping teams really tighten up the ideal customer profile with insights that reveal relevance and drive higher engagement rates. 🔸 Research on a target company’s strategic objectives and current events using 10K’s, press releases, recent articles, earnings announcements, posts on social media from their execs, M&A transactions, etc. 🔸 Research on their current partner ecosystem and the overlap with any complimentary applications where integrations and business outcomes can showcase a natural, obvious fit. 🔸 Research on the tenure and turnover of the department their AE is targeting to identify new champions, thought leaders, early adopters, and what is currently top of mind to them. 🔸 Research on clues of intent through both 1st party data (e.g. webinar lists, event lists, etc.) or 3rd party data (e.g. 6sense, etc.) to prioritize accounts and identify the right people at those companies to engage. 🔸 Research on relationships into target accounts that may not be visible or obvious through LinkedIn using clues like work history, board seat overlap, and 3rd-degree relationships to reveal where trust already exists. 🔸 Research on peer groups where existing customers can help evangelize successes and further qualify the best target prospects. Shifting time wasted on cold outreach to intelligence gathering to arm their AEs and the marketing team with insights that deliver better top-of-funnel results.

Here’s how to turn website traffic into leads — regardless of which deanonymization platform you use. Everyone loves the idea of uncovering who’s visiting their site… But most teams struggle with what to actually do with that data. You get insights like: - Who’s visiting - What pages they’re viewing - How long they’re sticking around - But raw intent data is only valuable if you can act on it. So I broke down how we take this type of data and turn it into high-converting outbound — in a repeatable, scalable way. Here’s the play: 1. Push the data into your workspace  Start with the essentials — company, contact name, title, LinkedIn URL. 2. Filter for ICP + decision-makers Run logic to confirm: Is this a B2B company? Is the contact a decision-maker? 3. Enrich missing fields If the contact email is missing, use waterfall enrichment to fill the gaps with verified data. 4. Personalize the outreach Scrape recent LinkedIn posts Check for relevant keywords (e.g. AI, growth, etc.) Generate a custom first line referencing their content 5. Add buying signals Layer in signals like: New role Actively hiring Recent funding 6. Run a tailored AI prompt Use AI to read their company description and generate 2–3 use cases specific to their business. 7. Add a personal touch at scale Generate a custom image with their profile picture and a personalized message — all automated. Once it’s set up, this becomes a self-running, personalized outbound engine. Check out the full video workflow below ⬇️

If 2024 taught us anything about Cold Email, it’s this: 👇 General ICP Outreach isn’t enough to drive results anymore. With deliverability getting tougher every day, there’s only one way to make outbound work: → Intent-Based Targeting Here’s how we do it at SalesCaptain to book 3x more demos ⬇️ Step 1️⃣ Identify High-Intent Triggers The goal? Find prospects showing buying signals. ✅ Website visits – Someone browsing pricing or case studies? (We use tools like RB2B, Leadfeeder, and Maximise.ai). ✅ Competitor research – Tools like Trigify.io reveal when prospects engage with competitor content. ✅ Event attendance – Webinar attendees or industry event participants often explore new solutions. (DM me for a Clay template on this) ✅ Job changes – Platforms like UserGems 💎 notify us when decision-makers start new roles (a prime buying window). ⚡️ Pro Tip: Categorize triggers: → High intent: Pricing page visits → Medium intent: Engaging with case studies This helps prioritize outreach for faster conversions. Step 2️⃣ Layer Intent Data with an ICP Filter Intent data alone isn't enough, you need to ensure the right audience fit. Tools like Clay and Clearbit help us: ✅ Confirm ICP fit using firmographics ✅ Identify the right decision-makers ✅ Validate work emails ✅ Enrich data for personalized messaging ⚡️ Key Insight: Not everyone showing intent fits your ICP. Filter carefully to avoid wasted resources. Step 3️⃣ Hyper-Personalized Outreach Golden Rule: Intent without context is meaningless. Here’s our outreach formula: 👀 Observation: Reference the trigger (e.g., webinar attended, pricing page visit) 📈 Insight: Address a potential pain point tied to that trigger 💡 Solution: Share how you’ve helped similar companies solve this pain 📞 CTA: Suggest an exploratory call or share a free resource ⚡️ Pro Tip: Use tools like Twain to personalize at scale without landing in spam folders. 📊 The Results? Since focusing on intent-based outreach, we’ve seen: ✅ 3x Higher Demo Booking Rates 📈 ✅ 40% Reduction in CPL (focusing on quality over quantity) ✅ Larger Deals in the Pipeline with higher-quality prospects It’s 2025. Let’s build smarter, more profitable campaigns. 💡 Do you use intent signals in your outreach? Drop me a comment below! 👇

Stop pretending that a single data point from a prospect's LinkedIn profile defines them. When you do that, your outreach sounds like you're reading the news to them - "Congrats on XYZ" or "I see that you're the [title] at ABC." You can't tell your prospects sh*t they already know and expect them to care. When you use a single data point as the cornerstone of your outreach, it's a telltale sign that you don't really understand your buyers, the challenges they're facing, or the opportunities they are excited about. Elite sellers understand how to uncover a more complete view of their prospects. More importantly, they understand how those data points come together. My go-to way to understand how prospects are interacting with me across multiple channels is the Members Dashboard in Common Room The 3 things I love most about this dashboard are: 1. It ranks prospects based on their overall impact in my ecosystem. I can see which people or orgs are most engaged with my content, across multiple channels, in a meaningful way. 2. I can get a view beyond LinkedIn. I have my YouTube, X (Twitter), and Company LinkedIn pages integrated as well as Slack for my Business Book Club community AND HubSpot. I can pull in so much data that is relevant to me and the folks interacting with me to figure out what matters TO THEM! P.S. The enterprise integrations are even better than the stuff I use as a solopreneur. It's impressive. 3. The tags. For instance, the first person in this list is tagged as an economic buyer [image]. This happens automatically. I didn't have to do that work. They are also tagged as a pioneer meaning they are the first person from that org to engage with my content. What this quick view tells me is that I have an economic buyer, a CRO, who is new in seat and is talking online about building a tech stack. They are engaging with me across LinkedIn & they are a member of my Slack community. The timing is ideal to connect to better understand their vendor selection process. You can filter to only see economic buyers or other tags or filter to only view specific channels that you might know are where most revenue is attributed. The result? Instead of reaching out to a prospect with disingenuous personalization, I have an immediate view of the conversations they are having across social channels that relate to me. It's advanced social listening + identity resolution + person-level AND account-level AND org-level enrichment based on a multitude of signals. It's a true 360 view. It allows me to have a more complete view of what's going on in a prospect's world before I reach out which increases engagement and conversion rates significantly. If this has sparked your interest, read this Blog about how to uncover the person behind the data points: https://lnkd.in/gEv26z6k

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

Customer marketers don’t get enough credit. They’re the ones behind advocacy programs, customer reviews, and persuasive customer stories. But connecting their work to pipeline and revenue? That’s where it gets tricky. It’s easy for their work to feel undervalued. But the entire conversation changes when customer marketers can tie what they do to business outcomes. So, how do we reach customer marketers in a way that resonates? Micro-campaigns designed to meet them where they are. Here’s how I’d use Common Room to create a micro-campaign for customer marketers at enterprise software companies: - Audience: customer marketers at companies that recently launched a product or shared a customer story - Signals: Companies sharing case studies or scaling review programs on LinkedIn Common Room helps track these signals in real-time, making it easy to spot the perfect moment to reach out. From there, your outreach could look like this: “Hey [Name], I saw [specific announcement]. The way [Company] highlighted [customer outcome] caught my eye. One trend I’ve noticed: customer marketers rely on UserEvidence to create customer proof that builds trust and converts interest into pipeline. For example, [Example Company] drove Y% growth from X customer stories. Given your focus on [initiative], I’d love to share how we could help scale similar results.” The key? It’s not a pitch. It’s building on the story they’re already telling. With Common Room and UserEvidence, running micro-campaigns becomes simple and drives results you can actually measure.