Customer Intent Analysis

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Your message aligns perfectly with their current needs and research - You get ahead of competitors who are still using outdated outreach methods 2.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.

"Talk to customers" is classic startup advice. But not enough folks teach you how to talk to users in a way that gets you actual insights. Since launching Decagon and raising $100M over 3 rounds, we’ve learned a lot, especially about GTM. Here's how we've adapted our customer conversations to go beyond surface-level excitement and uncover real signals of value. We benchmark around dollars when discussing product features. Why? Because it’s easy to run a customer interview where the customer seems thrilled about a new idea we have. But excitement alone doesn’t tell you if a piece of feedback is truly valuable. The only way to find out is to ask the hard questions: → Is this something your team would invest in right now? → How much would you pay for it? → What’s the ROI you’d expect? Questions like these don’t allow for generic answers—they'll give you real clarity into a customer's willingness to pay. For example: say you float a product idea past a potential user. They're stoked by it. Then you ask how much they'd pay for said product—and the answer is $50 per person for a 3-person team. Is that worth building? It might be, depending on the outcome you're shooting for. But if your goal is to build an enterprise-grade product, that buying intent (or lack thereof) isn't going to cut it. If you'd stopped the interview at the surface-level excitement, you might have sent yourself on a journey building a product that isn't viable. By assessing true willingness to pay you can prioritize building what users find valuable versus what might sound good in theory. Get to the dollars as quickly as you can. It’s an approach that has helped us align our roadmap with what customers truly need and ensure we’re building a product that has a measurable impact.

It's easy to fall into the "doing things just to do them" trap in demand gen and ABM.  👉🏾 Launching campaigns because "it's our typical approach." 👉🏾Creating content because "we have to."   👉🏾 Chasing every lead with the belief that "more is always better." But with AI and automation making it easier than ever to produce generic content, it's even more crucial to pause and ask, "Why?" ✔️Why this campaign? ✔️Why this content? ✔️Why this account? ✔️Does it truly align with our ideal customer profile (ICP)? ✔️Does it resonate with their needs and challenges? ✔️Does it get results on our goals? Generic #ABM is just...marketing. And generic #demandgen is a waste of resources. 👉🏾 To break the autopilot cycle, be specific about your ideal customer. Use tools like 6sense or ZoomInfo to gather rich data, going beyond basic demographics to understand their firmographics, technographics, and psychographics.  👉🏾 Then, map your content to the buyer's journey. Don't just create content for content's sake. Use tools like HubSpot or Marketo to address their pain points and provide real value at each stage. 👉🏾 Analyze intent data. Tools like Bombora or G2 Buyer Intent can tell you which accounts are actively researching solutions like yours, allowing you to focus your ABM efforts on those showing high intent. 👉🏾 Don't forget to make it a personalized experience. Use AI-powered platforms like Persado or Phrasee to tailor your messaging to individual accounts and show a deep understanding of their needs. 👉🏾 Finally, measure what matters. Track metrics that align with your goals, not just vanity metrics. Tools like Google Analytics or Bizible can help you measure the true impact of your ABM and demand gen efforts. 👉🏾 And most importantly, find someone to challenge your thinking. A colleague, a mentor, even a (kind!) competitor. Someone who asks: ✔️Why are we targeting this account? ✔️Will this content truly resonate? ✔️Does this campaign align with our overall strategy? Break free from autopilot, be intentional, and be strategic. Then, watch your ABM and demand generation results grow. What tools or strategies do you use to focus on the "why" behind your marketing? #b2bmarketing #marketingstrategy

Successful companies deploy AI to help their people create more value. Companies that fail deploy AI to avoid paying people to create value. Clients expect AI’s ROI to come from cost reductions, but bigger wins come from turning cost centers into revenue generators. A large airline client expected AI to reduce its customer service costs. We implemented AI to detect customer intent and deliver outcomes faster. Productivity improved, but instead of laying people off, we deployed a sales coach into select agents’ workflow. One model gives every customer a rating based on how likely they are to buy an upgrade and predicts the top upgrades to recommend. A second model generates a personalized pitch for the customer service agent to use. We ran a 3-sided experiment: 1️⃣ One group of customer service agents kept working on the AI intent-outcome augmented workflow. 2️⃣ A second group was given a generic script and discretion to pitch upgrades without the AI coach. 3️⃣ A third group was given the AI sales coach and discretion to decide when to accept its recommendations and which upgrade to pitch. After 3 months, the second group had an 8% upgrade pitch success rate, and the third group had a 31% success rate. In the first month, the second group pitched more upgrades than the third, but that switched in months 2 and 3. People do not immediately trust AI. They need to see it function reliably before they truly integrate it into their workflows and trust its output. Giving customer service agents discretion was critical for adoption. As the initiative scales to the entire customer service team, the airline expects to make significantly more money from upsells than it would have saved with layoffs. We reclaimed time with the AI intent-outcome agent and used the opportunity to create a new revenue stream for customer service. We found that when customers quickly go from “I have a serious problem,” to “Hello, thanks for calling support, how can I help?” to “Wow, that was an easy fix,” they’re more receptive to upsells. Businesses that win with AI are reorchestrating workflows and finding new ways to create value. Others don’t see these opportunities, so their only option is cost-cutting.

Sales and marketing leaders’ obsession with “intent” is undermining their Account-Based GTM strategy. Here are the 3 biggest mistakes GTM teams are making on intent (and how to use intent effectively): 1. Intent is not magic Unfortunately, intent has been marketed as if it’s magic. As if it can 100% accurately identify ALL companies that have a qualified opportunity. It cannot. Intent is simply an indication of interest and engagement on a *topic* related to the product you sell. At its best, vendors should utilize good sources and strong algorithms so that the level of confidence in the signal is clear. But often, in the interest of showing huge volumes of intent, vendors end up stretching the signal to cast as wide a net as possible and generate a large amount of false positives. 2. Intent is not your Ideal Customer Profile (ICP) I see this every day. Sales and Marketing teams get a list of high intent accounts and then “go after them.” This is counterproductive and wasteful because not all high intent accounts are in your ICP. The whole purpose of an account-based GTM is to align Sales and Marketing resources to accounts that have the highest LTV and thus generate the greatest enterprise value. This means being ultra clear on your ICP and avoiding the “intent temptation” of going after accounts that are interested in your solutions but are not in your ICP. Just because someone WANTS something doesn't mean they can or should buy it. 3. Intent should not be used in isolation from other data sets Intent only becomes powerful when it’s focused on your ICP and combined with other important data sets. Used in isolation, without other signals, you will never maximize your investment in intent. If tech companies want to increase the power and benefit of intent, they first need to combine intent with technographic data. Overlay the list of high-intent accounts with a list of companies that have the technologies your customers need to have and your hit rate on demand gen will improve significantly. The more robust solution to integrating intent into your broader GTM is to model it, with all other relevant data (firmographics, technographics, website engagement, Sales and Marketing engagement, etc) against closed won opportunities over the last 2 years. This will give a relative weighting for each data feature and intent keyword such that intent can be integrated into a more accurate score to represent propensity to buy soon. TAKEAWAY: Addressing the above issues are intended to arm you against what we’ve all heard many times, “This intent is BS, I called an account and they’re not ready to buy!” Don’t expect magic. Intent can't make a bad account great. But if you understand how intent relates to your ICP, and then use it in conjunction with other data sets, it becomes a powerful part of your account-based go-to-market strategy.

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

What I learned after 100+ failed outbound campaigns (at a $400,000 MRR agency): Most flop because they're aimed at people who were not going to buy anyway. (Too) many companies still run outbound like so: - Pull a lead list from their CRM or a generic B2B database - Fire off 100 cold emails/week to “hit quota” - Hope and pray something sticks And they have no idea why prospects are on their list in the first place. If you’re not starting with the right inputs, it doesn’t matter how good your cold email is. It’s still a shot in the dark. One way to fix this is through intent data: For example, here are some signal plays we run for ColdIQ and our clients: 1. First-party intent: Who’s visiting your website Not everyone fills out a form, but that doesn’t mean they’re not interested. We use tools like Instantly.ai and Vector 👻. They track anonymous visitors and identify who’s checking out our content, landing pages, or product pages. This gives us a warm list of people who are already aware of us. Even if they haven’t raised their hand yet. First-party intent can also come from: - Product usage (e.g: Common Room, Pocus) - Social engagement (e.g: Teamfluence™, Trigify.io) 2. Second-party intent: Champion job changes Let’s say someone loved your product at their old company. They just switched jobs. Now they’re in a new buying position, possibly with budget and urgency. Tools like Common Room and Unify help us track job changes across our network and historical CRM contacts. We can re-engage with a hyper-relevant message, right when they’re getting settled in. Second-party intent can also come from: - Review sites (e.g: G2, Capterra) - Affinity signals (e.g: Crossbeam, WorkSpan) 3. Third-party intent: Research at scale Most often, you need to go outbound into entirely new territory. That’s where third-party data comes in. Pulling insights from: - hiring trends (e.g: LoneScale, Mantiks, PredictLeads) - tech stack changes (e.g: BuiltWith, Similarweb) - funding rounds (e.g: PitchBook, Crunchbase) or from custom AI agents (e.g: Relevance AI, Claygent) We use Clay to build many of these workflows: - Filter for buying signals - Enrich contacts in real-time - Score and segment dynamically - And combine multiple data sources The result? You’re increasing your odds of reaching out to the right person, with the right message, at the right time. Better targeting = better reply rates = better pipeline. Whenever your outbound is underperforming, start by reviewing your data strategy. What intent signals are you tracking in your GTM motion right now? Would love to hear what’s working for you 👇

Two e-commerce platforms launch an AI shopping assistant before Black Friday. A customer types: “I need a laptop for my daughter. She’s starting design school. Budget under $1,200.” Platform A responds instantly: “Here are the top 5 best-selling laptops under $1,200.” Fast. Clean. Confident. It ranks by popularity and price. It looks smart. Platform B responds differently. It breaks the request down: ✅Design school → likely needs a strong GPU ✅Creative software → higher RAM requirements ✅Student → portability matters ✅Budget constraint → trade-offs required It evaluates options: Option 1: Better GPU, heavier Option 2: Lighter, weaker graphics Option 3: Upgrade RAM within budget Then it responds: “For design school, GPU performance matters more than screen resolution. I recommend Model X with 16GB RAM. It’s slightly heavier, but it will handle rendering tasks better. If portability is critical, here’s an alternative.” That’s not just an answer. That’s structured reasoning. This is the difference between: ✔️Large Language Models (LLMs) ✔️Large Reasoning Models (LRMs) LLMs are exceptional at: 🔹Generating fluent responses 🔹Ranking based on patterns 🔹Answering fast But most are doing advanced pattern matching. LRMs: 🔹Break problems into components 🔹Interpret intent, not just keywords 🔹Evaluate trade-offs 🔹Adapt under constraints They operate as: Intent → Decomposition → Trade-offs → Recommendation Customers don’t just want options. They want guidance. So as PMs, don’t ask: ❌ “Can the model return results?” Ask: ✅ Can it reason about the customer’s intent? ✅Can it explain trade-offs? ✅Can it adjust when the budget or constraints change? 👉Speed converts. 👉But reasoning builds trust. Note - This post is part of my ongoing Agentic AI Playbook series. Follow #AgenticAIForPMs to read more. #AgenticAIForPMs #AgenticAIPlaybook #ProductLeadership #AIProductManagement #AgenticAI

In a proactive CS model, the strongest indicators of customer health aren’t what customers say. It’s what they do. Adoption patterns. Logins. Product depth vs. surface-level usage Feature usage. In-product engagement. Support behavior. Community and Academy activity. Moments of friction we can see but they may not articulate yet. Behavioral signals are the new voice of the customer. In a reactive model, these signals are interesting. In a proactive model, they’re essential. In a predictive model, they become the operating system. When paired with intent-based playbooks, they unlock a predictive model that scales far beyond traditional coverage. Customers are telling us everything… long before they ever say anything. When we use these signals to guide where we show up, how we show up, and when we intervene, customers feel supported long before they even have to ask. That’s how you drive adoption, reduce risk, and build loyalty at scale. And that is the real power of predictive CS.

I've seen reps burn through 6 months of quota chasing the wrong accounts. All because they thought "bigger is better." Then I did the math on my wins. ✓ 91% had certain sales tools ✓ 74% were actively hiring for revenue roles ✓ 67% had recently raised funding or gone public in the last year This wasn't luck. There was a clear pattern. Enter: the power of Ideal Customer Profile meets intent data. Here's a framework you can live by: 𝗗𝗲𝗳𝗶𝗻𝗲 𝘆𝗼𝘂𝗿 𝗜𝗖𝗣 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮 (𝗻𝗼𝘁 𝗴𝘂𝘁 𝗳𝗲𝗲𝗹𝗶𝗻𝗴𝘀) * What's the average deal size of your wins? * What industries close fastest? * What technologies predict success? 𝗟𝗮𝘆𝗲𝗿 𝗶𝗻𝘁𝗲𝗻𝘁 𝗼𝗻 𝘁𝗼𝗽 𝗼𝗳 𝗜𝗖𝗣 𝗺𝗮𝘁𝗰𝗵 * Are they researching your category? * What's their buyer committee doing? * When are they in active evaluation? 𝗦𝗰𝗼𝗿𝗲 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝘀 𝗯𝘆 𝗯𝗼𝘁𝗵 * High ICP + High Intent = Chase hard * High ICP + Low Intent = Nurture * Low ICP + High Intent = Qualify fast or pass We use Demandbase to detect intent, orchestrate plays and engage the right accounts - at exactly the right moment. Result? No more wasting time on "exciting" logos that would never close. Started focusing on accounts that fit the profile AND showed buying behavior. Bottom line: Spray and pray is dead. Precision targeting wins.