Using Market Intelligence

Most founders still follow the old playbook: 1. Have idea 2. Fall in love with idea 3. Build for months 4. Launch 5. Discover no one wants it 6. Repeat The AI validation approach flips this completely: 1. Have idea 2. Validate mercilessly 3. Kill it if data says no 4. Only build what people already want The hard truth? 90% of businesses fail because founders build products nobody wants. But that was before AI. I’ve wasted millions on bad business ideas. Now I can kill bad ideas in 72 hours, not 12 months. Here's my 5-step AI validation framework that saves you time, money, and heartbreak: 1. Problem Verification AI tools like Perplexity can search for people actively complaining about the problem you want to solve. Feed Reddit threads, forum posts, and review sites into ChatGPT. Let it extract patterns of pain. No real pain = dead idea. 2. Market Size Analysis If the pain is real, check if enough people have it. Let AI analyze Google Trends, search volume, and TAM (total addressable market) data. Create detailed spreadsheets of potential users. Too small = dead idea. 3. Competitor Assessment Feed AI your top 5 competitors' websites, pricing pages, and customer reviews. Ask it to identify gaps and oversaturation. Create a map of what's missing in the market. No clear advantage = dead idea. 4. Zero-Cost MVP Design Most founders build full products before validation. With AI, create "fake door" tests instead. Build a landing page that looks real. Create AI-generated mockups of your product. Run $50 of ads to see if people try to buy. No buyers = dead idea. 5. Early Adopter Interviews For ideas that survive steps 1-4, use AI to: - Draft perfect outreach messages to potential customers - Generate interview questions that reveal true buying intent - Analyze interview transcripts for patterns No enthusiasm = dead idea. This isn't just faster. It's an entirely different game. Come to my free AI masterclass and I'll show you my system for validating ideas and building profitable businesses in weeks, not years: https://buff.ly/THAXwgV

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 

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.

One of the first things every sales leader shares with me is their dashboard. But here's the problem, your CRM is lying to you. Sales velocity. Pipeline growth. Productivity metrics. All the dashboards look great. And yes, those numbers do tell part of the story. Sales engagement analytics can show which touchpoints convert to revenue. They help visualize how activity impacts the top line. All important. All useful. But that's also where the problem lies, because each of those metrics focuses on your team and their activity. Meanwhile, the buyer has quietly taken control of the process. - Ghosting is up. - “No-decision” outcomes are up. - Deals are stalling or getting pushed due to “timing.” - Meetings are being canceled—or never happen at all. And initial contact? Harder than ever. Why? Because buyers want less interaction with sales and more access to information, on their terms. If your team is seeing more no-shows, more silence, and more deals stuck in limbo, your outreach strategy may not be broken… It may simply be misaligned. Today’s buyers are already 70% or more through their journey before a salesperson ever gets involved. So instead of obsessing over how much your reps are reaching out, start looking at something far more revealing: How are buyers engaging? Ask yourself: ·       Are prospects initiating conversations or requesting meetings to clarify what they’ve already learned? ·       Are they asking for content, data, or insights? ·       Are they engaging with your posts and asking thoughtful questions? ·       Are they consuming content designed for their stage of the buying journey? If the answer is no, it’s not a pipeline problem, it’s a relevance problem. The most effective salespeople today don’t look like salespeople at all. They look like industry experts, educators, and trusted guides. They don’t push deals forward, they pull buyers in. So how do you increase buyer engagement? A few practical shifts: ·       Create content that answers buyer questions, not product questions ·       Share insights that help prospects make better decisions—even if they don’t buy from you ·       Design content for each stage of the buying journey, not just top-of-funnel awareness ·       Replace “checking in” messages with context, perspective, or data ·       Make it easy for buyers to self-educate before they ever talk to sales Which means your strategy shouldn’t just measure how often your team contacts prospects, it should measure how often prospects contact your team, and what triggered it. That’s where the real truth lives. #sales

The market research industry is being torn down & rebuilt. No one is talking about it loudly. 92% of organizations are flying blind, with garbage data. Here's the no-BS explanation: Survey fatigue has reached critical mass: • Response rates dropping to 5-15% • 81% of consumers ready to unsubscribe • Declining quality as participants rush through The say-do paradox is more real than ever: • 83% of organizations face this problem • Customers post-rationalize their decisions • Social desirability bias = polished lies Traditional survey-based methodologies are increasingly failing to capture authentic customer insights, and it sucks because millions of dollars are wasted. The organizations succeeding in this environment are those that acknowledge the limitations of traditional methods and actively experiment with hybrid approaches. What's working: Ethnographic research (8.5 effectiveness) Behavioral analytics (8.1 effectiveness) Real-time feedback systems (7.2 effectiveness) Passive data collection (7.8 effectiveness) Heatseeker combines these into one platform. Our market experiments get to participants in their environment, passively collecting data based on what they do in real time. The future of market research lies not in abandoning surveys entirely, but in using them as one component of a broader, more sophisticated approach to understanding customer behavior. The conversation across Reddit, LinkedIn, and industry forums is clear: the time for relying solely on what customers say they want is over. The future belongs to understanding what they actually do. What's your experience with survey fatigue? Are you seeing response rates drop in your organization?

Two important sources of great insight that most marketers tend to miss out are: (1)Win-loss analysis (2)Existing customers (1) A win-loss analysis having accurate and comprehensive data (qualitative as well), can throw insights that are rarely documented anywhere else. Even secondary research will not give you that data. Analyzing these can provide trends about geographies, solution areas, industry, company type/ size, the audience involved in decision making, sponsoring, influencing, objections, FAQs, challenges, your USPs and probably your sweet spot. 💡 Example: In one former role, we realized that we were winning more new logos in non-banking finance apps in a particular market and that too primarily for a particular functionality which we werent highlighting much globally. 💡 Additionally, some cases highlighted that prospects were actively liking a particular type of content we were regularly promoting/ distributing and this was purely due to qualitative win-loss analysis that would never show in any numerical analysis (2) Customer interviews: Talking to existing customers ( in a non-sales situation) can help you to get a real understanding of how they use your product, what exactly is their Aha moment, what challenges they face, what's something they'd love to have and so on. Apart from helping the product teams, this provides useful fodder to address gaps in positioning, improve customer retention or help strengthen the buyer's journey. 💡 Example: In one former role, one reason our enterprise customers went gaga over us was our white glove onboarding and the overall post-sales process (typically handled by customer success). This helped us craft a niche bottom of the funnel campaign around this theme. ➡️ To conclude, the above can then be used in your marketing plans to capitalize on strengths, reducing weaknesses, determining areas to avoid, identifying opportunities etc. Eventually, you'll see a marked reduction in sales cycles and better retention as marketing would have taken care of most obstacles hindering the sales process. #B2BMarketing

Analytics aren’t just numbers; they’re your roadmap to publishing growth. Data isn’t power, it’s potential. For publishers, the real value lies in transforming raw metrics into repeatable growth strategies that drive audience retention, revenue, and #SEO performance. Too often, publishers collect vast amounts of data but fail to extract meaningful takeaways. The key is understanding what content resonates, how audiences engage, and where opportunities for growth exist. Collecting data is easy; extracting insights is not. Without clarity, metrics like pageviews and bounce rates become distractions. For example, a 40% drop in returning visitors isn’t just a traffic issue—it’s a retention red flag. By using the right tools and refining strategies based on real data, you can turn numbers into growth. Here are actionable strategies to turn data into action: 1. Know Your Audience Beyond Pageviews Pageviews alone don’t tell the full story. Instead, track return visitors, time on page, and scroll depth to measure true engagement. Tools like Google Analytics 4 (GA4) and Parse.ly provide deeper insights. Cohort analysis can reveal trends, millennials may prefer video, while Gen X engages more with newsletters. For example, if mobile traffic spikes by 20% after 8 PM, push breaking news via mobile notifications to capture that audience in real-time. 2. Optimise Content Performance with Behavioural Data Understanding why some content performs well helps you replicate success. Use @Google Search Console and Semrush to analyse search visibility and Hotjar Digital Marketing Company to track user interactions. For example, if "AI in media" gets 3x more shares than "content trends," double down on AI-related content. Additionally, A/B test headlines (e.g., “5 Growth Hacks” vs. “Proven Tactics”) to see what improves click-through rates. 3. Track Conversions, Not Just Traffic Traffic alone doesn’t guarantee success—conversions do. Set up goals in GA4 to measure newsletter sign-ups, paid subscriptions, or product purchases. Identify which referral sources drive the highest conversion rates, and adjust your strategy accordingly. For example, premium subscribers from "how-to guides" tend to have a 15% higher lifetime value than general news readers, meaning content type matters when driving long-term revenue. To scale what works, automate reporting with Power BI Visualization or Looker Studio to save 10+ hours per month. Analytics only matter when they drive actions. The biggest mistake any publishers can make is to treat data as a report card instead of a playbook. Start by auditing one content category this week, setting up a conversion goal in GA4, and A/B testing a headline. Data doesn’t lie, but it won’t work unless you do something. What analytics tools are you using to grow your publishing efforts? Share your go-to platforms in the comment below. #DigitalPublishing #SEO #ContentStrategy #AudienceGrowth #DataAnalytics

Most startups don’t fail because founders lack effort. They fail because they start with unvalidated assumptions. Research consistently shows that lack of market need is one of the top reasons startups collapse. The real advantage at the idea stage is not speed of building. It is precision of validation. Bootstrapping Playbook for Idea-stage Founders - At the center of this framework is a simple but disciplined approach: 1) Find Your Edge: What's your domain expertise? Your unfair advantage? Pinpoint a pain point only you can solve. 2) Validate Mercilessly: No code. No outsourced MVP. If the idea doesn't validate? Discard. Start over. 3) Learn from Success: Study structured Case Studies, not anecdotes. Absorb lessons. 4) Refine Your Thesis: Iterate with real customer feedback loops. Is this idea strong enough for a decade of your life? 5) Immerse in Customers: Talk to at least 50 Ideal Customers. Understand their world. 6) Nail Positioning: Refine your precise positioning based on customer feedback. 7) De-risk Your Market: Master Market Sizing and Competitive Analysis. Avoid walking into a noisy market blind, hoping for funding. This is not about inspiration. It is about eliminating false positives early. The Core Principle: Validate Before You Build - Idea-stage founders often confuse motion with progress. But the real sequence follows a clear order. First, you define your edge by clarifying why you are the right person to pursue this idea. Next, you talk to real customers rather than relying on friends or assumptions. You then run structured validation before building anything, without writing code or creating an MVP. After that, you eliminate weak ideas quickly based on what you learn. Finally, you strengthen only the ideas that survive evidence. If your idea cannot survive structured scrutiny, it should not survive into development. Come talk to me at a free mentoring roundtable and ask questions of the 1Mby1M AI Mentor: https://lnkd.in/g3VwPX_S

The marketing curse 😂 The fix: Dara Denney's 5 step framework that brings data and creatives team together, battle tested with over $100M of ad spend. Dara's take: Creative freedom is a myth. “You need to attack the sources of ambiguity within the creative process. This is the secret to building high performing creative teams" 1. Remove ambiguity with SOPs "The most ambiguous parts of the creating process have the biggest impact on performance" Think of all the ambiguity that exists in your creative production workflow: Research: Who is conducting competitor research? Where is the team documenting customer reviews, and how are you using the performance data you’ve collected? Roadmap: Is everyone clear about the goals and tasks in your creative production pipeline? Or does every new request feel chaotic? Performance: Does your designer know why the last ad bombed? Is data on performance understood or locked in some spreadsheet? To remove ambiguity, Dara suggests formalizing the creative project lifecycle stages research, execution, review, client submission, and launch—for streamlined creation. She calls these stages Standard Operating Procedures (SOPs). 2. Hire a dedicated Creative Strategist Creative strategists remove ambiguity from the creative process by doing the hard work of understanding customer psychology, the competitor landscape, deep s of performance data, and uncovering the strategic problems that ads need to solve. Without a creative strategist, your growth and creative teams become disconnected. For in house teams, this leads to internal politics, mistrust between teams, and low output. 3. Make data accessible AND exciting Not sure which metrics to narrow down on? Focus on your primary KPIs, such as spend, purchases, and cost per lead. These metrics will give you a good understanding of your campaign's performance. Additionally, look at storytelling KPIs, like drop off rates, average video watch time, hook and hold rates, and CTRs. Use a visual analytics platforms to make the data accessible and interesting for your creatives (that's what Motion (Creative Analytics) does btw) 4. Roll out a sprint structure Here's a simple structure you can start with: - Monthly roadmaps, metric checkpoints, bi-weekly retros - Keep the process on track with daily stand-ups Regularly analyze ad formats and metrics as a team during your live sessions and set up a Slack channel for sharing high and low performing ads where you can chat async on what you're seeing 5. Build a data driven creative culture You need to embed Creative Strategy into your org culture. Start all brainstorms with a data download. Ex: share CI research, customer insights, past performance but make sure you start from data or bring it into how you operate. To keep momentum up, create a "win" Slack channel to celebrate learnings and top performing ads and conduct monthly retros to keep the team aligned and engaged with data.