Using Market Intelligence

Last week, I shared how Gen AI is moving us from the age of information to the age of intelligence. Technology is changing rapidly and the way customers shop and buy is changing, too. We need to understand how the customer journey is evolving in order to drive customer connection today. That is our bread and butter at HubSpot - we’re deeply curious about customer behavior! So I want to share one important shift we’re seeing and what go-to-market teams can do to adapt. Traditionally, when a customer wants to learn more about your product or service, what have they done? They go to your website and explore. They click on different pages, filter for information that’s relevant to them, and sort through pages to find what they need. But today, even if your website is user-friendly and beautiful, all that clicking is becoming too much work. We now live in the era of ChatGPT, where customers can find exactly what they need without ever having to leave a simple chat box. Plus, they can use natural language to easily have a conversation. It's no surprise that 55% of businesses predict that by 2024, most people will turn to chatbots over search engines for answers (HubSpot Research). That’s why now, when customers land on your website, they don’t want to click, filter, and sort. They want to have an easy, 1:1, helpful conversation. That means as customers consider new products they are moving from clicks to conversations. So, what should you do? It's time to embrace bots. To get started, experiment with a marketing bot for your website. Train your bot on all of your website content and whitepapers so it can quickly answer questions about products, pricing, and case studies—specific to your customer's needs. At HubSpot, we introduced a Gen AI-powered chatbot to our website earlier this year and the results have been promising: 78% of chatters' questions have been fully answered by our bot, and these customers have higher satisfaction scores. Once you have your marketing bot in place, consider adding a support bot. The goal is to answer repetitive questions and connect customers with knowledge base content automatically. A bot will not only free up your support reps to focus on more complex problems, but it will delight your customers to get fast, personalized help. In the age of AI, customers don’t want to convert on your website, they want to converse with you. How has your GTM team experimented with chatbots? What are you learning? #ConversationalAI #HubSpot #HubSpotAI

One thing I push early-stage B2B founders to do (and it’s harder than it sounds) is to really understand — and quantify — the value you deliver to customers. Very few can put a dollar number on it.💡 Try to estimate the value your product creates for a customer in real dollars ($Z) 💰 Once you do that, , you can ask a few important questions to qualify how robust and urgent the value proposition really is: ▪️ Is $Z actually meaningful in the context of the customer’s business? (If it’s a rounding error for them, say <2% of top line, selling will be painful 😬) ▪️ Can you show or prove $Z quickly, or are you asking the customer to take a leap of faith? Quantifying value proposition also helps with 💵 pricing and 📐market size, which many founders struggle with early on. Example 1: cost / time savings ⏱️ - Say you’re selling software that saves a RevOps team ~5 hours per week. - Fully loaded cost is ~$80/hour → ~$20k/year in savings. That’s your $Z. - If you’re saving time or money, customers will often pay ~10–20% of that value. So a ~$2–4k ACV is a reasonable first pricing hypothesis 🎯 Example 2: revenue generation 📈 - Now say your product helps a sales team close 2 extra deals per quarter. - Each deal is worth ~$50k → ~$400k/year in incremental revenue. That’s $Z. - When you’re directly helping customers generate revenue, they’re often willing to pay more — say ~20–30% of the value. That points to an $80–120k ACV range (assuming you can prove the value). More importantly you can use $Z to estimate market size.  📐 Start bottoms up. Market = X customers × $Y ACV = market size Where: ▪️ $Y ≈ 10–20% × $Z (for cost/time savings) ▪️ $Y ≈ 20–30% × $Z (for revenue generation) Finally, pressure-test the assumptions: ▪️ Are we being precise about who “X customers” actually are? Do I need to sell a story where I start with a small #X and then expand? ▪️ Does $Y line up with real budgets and comparable spend? ▪️ Can we acquire customers for less than ~$Y/3? ▪️ Do we need more product to credibly charge $Y? You don’t need perfect answers early but a strawman that allows YOU to understand why you are willing to spend the next 10 years of your life working on something. 🚩

Why is no one saying this? If you’re in marketing, stop thinking your career growth will come from managing more people. Growth now comes from rethinking how AI can reshape marketing teams and processes. Roles are collapsing into each other and while it looks different at every company, the future is going to be the same everywhere: the full-stack marketer. If you are looking for a new role, here is how you can take advantage of this shift: 1/ Become the master of distribution. Know your audience, your story, your channels. Know when to test, what to test, and what the results mean. If you have these fundamentals AI will only make you better. 2/ Learn to vibe code Don't just use chatGPT to draft your content, use AI to build the processes that orchestrate your marketing efforts. And that will mean getting seriously good at at prompting and understanding how tools can connect. 3/ Stop hiding behind your CV and build your personal brand. Share your thinking and remember that people hire people they know, not the 89th resume in the pile for the role that just got way more competitive. Network like you mean it, dm, comment and get on phone calls. 4 / Fractional + agency work is going to be how MANY companies will start their AI transition. If you can't find an in house role that will let you become a full stack marketer, a portfolio role may be the opportunity you need to reposition yourself as a leader in AI marketing. 5/ Embrace the identity shift This is a first-mover advantage moment. Position yourself as a full stack marketer because soon very few companies are going to want that super specialized role. And lastly an Interview tip: If you’ve got 60 seconds, tell them three ways you’ve used AI to transform your workflows. Then flip it on them: “What’s your AI strategy for workflows and for product?” If they don’t have one, that might be a company you don’t want to bet your career on. I’m putting together a Prompting Playbook for Marketers right now... what’s the #1 prompt you wish you had in your toolkit to help you embrace that fullstack marketer identity

For twenty years, buyers Googled. Got ten blue links. Built their own spreadsheets. The search engine pointed. They decided. That era is done. G2 just dropped their latest Insight Report on the Answer Economy. 1,000+ B2B buyers surveyed. It confirms what I’ve been shouting about for 18 months: buyers moved from reference to inference. They want answers delivered. No friction. No tabs. No spreadsheets. 51% now start their software research with an AI chatbot more often than Google. Read that number twice. First impressions left your homepage. They now happen inside ChatGPT, Gemini, and Claude. Often in rooms your brand isn’t even in. If the AI can’t find you, or misrepresents you, you’re not in the consideration set. You don’t exist. The numbers that stopped me cold: → 69% chose a different vendor than expected because of AI → 33% bought from a brand they’d never heard of before AI surfaced it → 8 in 10 say AI accelerated their purchasing decision → 45% say review site citations are the #1 trust signal in an AI answer → 41% use Deep Research tools regularly That last one hit hardest. Buyers are running structured, multi-source vendor evaluations inside AI tools. What took a team days now takes one buyer an afternoon. Your sales team is walking into calls with prospects who already have an AI-generated shortlist. The only question that matters: are you on it? The brands winning AI citations are doing something boring. They’ve spent years executing fundamentals most teams treated as optional: ✅ Consistent review generation ✅ Third-party presence and community participation ✅ Content distributed across every relevant surface ✅ Clear, consistent positioning everywhere Answer Engine Optimization is what happens when you execute the basics relentlessly for years. Nothing more. Nothing less. If you lead GTM, marketing, or revenue at a B2B company and you haven’t read the G2’s new report yet, make it the most important thing on your list today. https://bit.ly/4cf9enu 

The most important competence for building a sustainable DTC strategy: Data-Driven Customer Insights. Over the last decade direct-to-consumer marketers have suffered a 15% CAGR in CPM inflation for digital #advertising, according to research by Frederic Fernandez & Associates, dramatically increasing cost per acquisition. #DTC companies hence need to much better understand their target consumers, their path-to-purchase metrics, barriers/ drivers/ triggers & 4Ps preferences, and design a new omnichannel acquisition strategy. In my view, its time for DTC companies to build truly immersive and personalized customer acquisition strategies based on data driven customer insights. Data-driven customer insights are essential in the following 5 marketing areas: 🙋 Understanding Customer Behavior: To create personalized experiences, brands need to understand their customers' behaviors, preferences, and pain points. #Data analytics enables companies to track and analyze customer interactions across all touchpoints, providing deep insights into their journey and decision-making processes. 🎯 Personalization at Scale: Leveraging customer data allows brands to segment their audience and deliver tailored content, offers, and recommendations. This level of #personalization can significantly enhance customer satisfaction and loyalty, as consumers are more likely to engage with content that is relevant to their needs and interests. 📢 Optimizing Marketing Efforts: Data insights help brands to optimize their #marketing strategies and campaigns. By analyzing which tactics are most effective, companies can allocate resources more efficiently and improve their return on investment. ❤️ Enhancing Customer Engagement: Real-time data analysis enables brands to engage with customers at the right moment with the right message. This timely #engagement can drive higher conversion rates and foster a stronger emotional connection with the brand. 📈 Continuous Improvement: Data-driven #insights provide a feedback loop that allows brands to continuously refine their products, services, and customer interactions. This iterative process helps in adapting to changing customer expectations and market trends. By investing in data collection, advanced analytics, and skilled personnel, #DTC companies can create truly immersive and personalized customer experiences that drive engagement and loyalty.

In marketing, choosing the right campaign strategy — such as whether to reach customers through SMS or email — is critical. These decisions shape how effectively brands connect with their audiences. In a recent tech blog, Klaviyo’s data science team shared how they used uplift modeling and counterfactual learning to help marketers deliver more personalized campaigns at scale. The team began with a simple but powerful insight. Instead of defining audience segments first and then randomizing within each group to test different strategies, it’s mathematically equivalent to randomizing treatments first and segmenting afterward. In practice, this means you can run a single randomized experiment — for example, comparing SMS versus email — across the entire audience, and later analyze how different subgroups responded to each treatment. Building on this foundation, the team applied uplift modeling to estimate how each recipient would respond under different treatments. The result is a system that predicts which customers are more likely to engage via SMS versus email — and automatically personalizes campaign delivery accordingly. The team ultimately turned this approach into a product feature, empowering marketers to design smarter, data-driven strategies with minimal manual testing. It’s a great example of how causal inference and machine learning can go beyond analysis — directly shaping how real-world marketing decisions are made. #DataScience #MachineLearning #UpliftModeling #CounterfactualLearning #Personalization #Marketing – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gBgBiTJj

Is your team is losing more deals or hearing “no decision” too often? I’ve been doing win/loss reviews with my clients, and some patterns are too clear to ignore. 🪄 The more you know, the more you win! In deals that closed, sellers knew more than five buying influencers. In losses, they knew about two. We call that multi-threading, but really it just means knowing everyone who matters. If your sellers do not, the odds are already against them. 🪄 No decision is still a decision More deals are ending with no decision. Buyers are exhausted. They have compared too many options, read too many AI-generated suggestions, and now everything looks the same. When they do not feel confident, they freeze. They stick with the pain they know instead of the change they do not. 🪄 Dump the BANT discovery Ghosting after discovery calls is out of control. Sellers tell me, “They were so engaged!” Then nothing. Buyers were interested, but the discovery call did not teach them anything new. It did not add value or show insight. So they went cold. Want to stop the ghosting? Make your discovery call the one meeting that actually helps them think differently. Always agree on a clear next step. 💥 What sellers need to know now The best sellers today know: What a day in the life of their buyer looks like What insights bring real value How to guide a team to consensus How to build buyer confidence Here is the truth. Most buyers do not know how to buy from you. This may be their first time buying a solution like yours. They might not even know how to get it approved internally. Your sellers’ job is to guide them through it. Share examples, insights, and stories from other companies that made similar decisions. Those sticking to outdated methods are losing deals. Those meeting customers where they are and bringing useful insights are winning. If you are the CEO, ask your leaders: ✅ Is our ICP still aligned with our best-fit customers? ✅ Are our personas actually involved in recent wins? ✅ Has our customer journey changed? ✅ Does our discovery process impress or bore buyers? AI has changed the way buyers buy. Has it changed the way your sellers sell? Use AI to learn and prepare, but let it make your team more human, not less. Your turn: What are you seeing in your own win/loss reviews right now? Where are your sellers getting stuck: multi-threading, no decision, or discovery? If this resonates, share it with a CEO or sales leader who needs to see it. 🪄 I'm Alice Heiman and I help CEOs drive revenue the easy way.

Leveraging Data Analytics for Competitive Advantage: Strategies for Startups to Stay Ahead of the Curve 📊 Hi everyone! Ankita here, excited to dive into how data analytics empowers startups to make smarter, faster decisions. Today, data is the fuel that drives competitive success, enabling even lean startups to punch above their weight. Why Data-Driven Decisions Are a Game-Changer With the right data strategies, startups can optimize nearly every aspect of operations. Here’s how: 🌟 Discover Core Customer Needs: Understanding what resonates with customers saves time, boosts loyalty. Tip: Use segmentation analytics to group audiences by shared traits, helping prioritize features that convert. 🌟 Anticipate Market Trends: Analytics helps startups not just keep up but also anticipate shifts, gaining a first-mover edge. Tip: Use tools like Google Trends or sentiment analysis for real-time insights. 🌟 Drive Personalization: Personalization enhances connections, achievable at scale through analytics. Tip: Use AI-driven engines to tailor recommendations, email, and content based on user behavior. 🌟 Boost Marketing ROI: Insights reveal which marketing efforts work and which don’t. Tip: Track CPC, conversion rates, and CLV to pinpoint high-ROI channels. 🌟 Streamline Operations: Internal data exposes bottlenecks, enabling more efficient operations. Tip: Monitor metrics like task completion time and use workflow automation tools. 🌟 Reduce Churn: Analytics reveal why customers stay or leave, enabling proactive retention strategies. Tip: Cohort analysis uncovers traits in long-term customers, boosting satisfaction. 🌟 Improve Financial Forecasting: Data-driven forecasts support strategic scaling choices. Tip: Use dashboards to track MRR, cash flow, and runway for a clear financial picture. 🌟 Gain Competitive Insights: Competitor benchmarking helps startups surpass industry standards. Tip: Use intelligence tools to monitor key metrics like pricing and customer reviews. Moving Forward Startups have more data than ever. By harnessing analytics, we can fuel smarter decisions, increase efficiency, and strengthen customer ties. A solid data strategy isn’t a luxury—it’s a vital advantage today. What insights have transformed your startup? Let’s discuss and grow together! 💡 #StartupGrowth #DataAnalytics #CompetitiveAdvantage #CustomerInsights #OperationalEfficiency #FinancialForecasting

AI is making marketing execution cheap. Dangerously cheap. If your value is tied only to ‘efficiency’: • Generating ad copy variations • Pulling basic performance reports • Setting up templated campaigns ...you're in a race to the bottom, and AI is winning. The cost of doing these things = plummeting towards zero. But this isn't a threat, it's the biggest opportunity we've seen. As AI handles the 'how', your strategic brain—defining the 'what' and 'why'—becomes exponentially more valuable. Specifically: 1. Getting the Business: Truly understanding client pain points, values, goals, resources, the market, and tying marketing to actual business results (think Customer Lifetime Value impact, not just leads). 2. Asking Tougher Questions: Moving past "How do we lower CPA?" to "Are we targeting the right people for long-term profit?" or "Which campaigns bring incremental customers vs. capturing existing demand?" 3. Proving Real Impact (Incrementality): Showing how your strategic thinking leads to growth that wouldn't have happened otherwise (e.g., using holdout tests). This is the new proof point. 4. Connecting the Dots: Weaving marketing strategy with sales, product, and finance – being the strategic glue AI can't provide (yet), like ensuring campaigns align with sales capacity or product launches. How to start adapting: • Gut-check your value prop: Is it actually strategic or just execution in disguise? • Get uncomfortably close to your clients' real business (P&L, not just marketing stuff). • Stop hiding behind vanity metrics. Help prove incremental lift. • Dare to ask the questions that might kill a campaign but save the business. Made me think. Ignoring this shift isn't just risky, it's choosing commodity status. Execution is the new baseline. Strategic direction? That's where the real alpha is.

How precise does your b2b marketing data get? Lately, I've been exploring technographic data – a tool that’s reshaping how I approach marketing personalization. Technographic data refers to information that describes a company's current technology stack, including the software, platforms, tools, and technologies they use to run their business. This data can reveal insights into a company's operations, technological capabilities, preferences, potential needs, and challenges. For instance, knowing a company uses WordPress not only tells us about their web platform choice, but can also indicate specific challenges they face, from security concerns to customization issues. Rather than a generic pitch, you can address their unique situation with a solution that fits perfectly. 👉 This level of personalized outreach doesn't just get attention; it positions you as an expert. You're already halfway through the door by demonstrating an understanding of their tools and challenges, shifting from a cold outreach to a meaningful conversation starter. #DigitalMarketing #TechnographicData #Personalization #MarketingStrategy