Customer Pain Point Identification

I once sat next to a rep making $750K a year. He wasn’t friendly. He didn’t build rapport. He wasn’t even likeable. Yet he closed deals most “charismatic” sellers could only dream of. His secret? He only asked Quantifiable Questions. Here’s the exact sequence of questions he used: 👉 START WITH TIME  “How long does it take you to [calculate commissions each quarter?]”  Buyer: “About 30 hours.” 👉 STACK THE COSTS Time cost → “So that’s ~4 full workdays. What’s your time worth? Or better, what could you have done with that time instead?” (Unlocks bigger, business initiatives they didn’t get to.) Error cost → “How often do mistakes happen? Have you ever overpaid? By how much?” Buyer: “Yeah, we overpaid one rep $20K last year. Underpaid another one.” Ripple effect → This is where it gets heavy. “What happens when reps don’t trust their comp?” Buyer: “They re-run the numbers themselves. Probably another $5K in wasted time per quarter. Per rep.” Don’t stop here though. There’s more quantifiable pain to find. Push further: “How distracted do they get? Ever had someone leave over comp errors?” Buyer: “Yes.” Rep: “What’s your hiring cycle cost? Recruiters? Ramp time? Buyer: 20K agency fee + 200K ramp quota while the seat remains open. 👉 MAKE THE MATH UNDENIABLE  “30 hours + $20K in errors + $5K in lost productivity + 20K agency fee + 200K missing ramp. That’s $240K a year tied to comp. Do I have that right?” At that point, the buyer isn’t looking at a “spreadsheet headache.” They’re staring at a $240K business problem. — Small talk won’t win you deals. Neither will charisma. Feelings don’t get funded. Math does.

A VP of Sales once told me, word for word: "My reps sound like donkeys on the phone." Funny line, real quote, and completely useless for a deal. No CFO on earth funds "less donkey." You can't build a business case out of adjectives. So I asked one question: "What metric is suffering most as a result of that?" His answer changed the whole conversation: "Close rates. We're at 30% and we're supposed to be at 33%." Now there's a deal. Three points of close rate, applied against his pipeline and deal sizes, is worth real money. The rest of the sales cycle was me proving I could move that number. Here's the part most reps miss. Some metrics your buyer names have no dollars attached. Product adoption. NPS. Engagement scores. When you hit one of those, you're one layer short. Find the metric behind the metric: "What's driving you to prioritize this among everything else you could be working on?" Watch how it plays out. A buyer says product adoption is low. No dollars there yet. One layer deeper: customers who don't adopt, churn. Gross retention is sitting at 77% on a $10 million book, so $2.3 million walks out the door every year. Lift that renewal rate to 80% and you've found $300,000. Now the deal has a number. Same thing with "employee engagement is down." Behind it: first-year engineering turnover. Which means paying recruiters to refill the same seats, burning salary on people who quit right as they get productive, and shipping slower because the team keeps resetting. Two questions to steal: "What metric is suffering most as a result of this?" "What's driving you to prioritize improving it right now?" Keep asking until the metric has dollars attached. Dollars get deals funded. Adjectives get deals stalled. P.S. Quantifying the problem is one of the 11 skills our research tied directly to bigger deal sizes. See the full breakdown → https://lnkd.in/g63fcp2D

Do buyers prefer to learn from a vendor live during a virtual meeting, or are they just as happy receiving a PDF to read on their own? One of the variables I’ve been studying to answer this is mind-wandering. It’s not always bad for the brain to drift (it can support creativity), but during critical commercial moments, it can be detrimental because attention often leads to memory, and memory leads to decisions. In a study I conducted, buyers’ brain data showed they mind-wandered more during live Zoom calls than while reading an eBook. The virtual meetings also triggered greater fatigue. Yet when I asked participants which format they preferred, there was no statistically significant difference between what they said they liked and what their brains actually responded to. In the chart below, the x-axis shows preference as stated explicitly. The y-axis reflects valence—how much participants actually liked the medium, based on implicit brain signals (EEG and ECG). This is a reminder that stated preference is not always predictive of performance. For anyone sharing content with business audiences: don’t rely solely on what people say they prefer. Keep track of what they remember and when they act. Let behavior (not just opinion) guide your strategy.

Why do prospects choose your company despite competitors offering steep discounts of up to 70%? In every competitive deal, there comes a moment of truth. Your prospect tells their current vendor they're considering a switch. Suddenly, the desperate discounting begins. 50%, 60%, sometimes 70% off. Most sales reps lose at this moment. But elite sales pros win anyway. How? Through what I call the 10X Pain Method. It's a systematic approach to quantifying pain at 10 TIMES the cost of your solution. Here's the framework: 1️⃣ Identify the fundamental pain points in discovery 2️⃣ Make prospects RELIVE painful experiences through detailed questioning 3️⃣ Expand from direct costs to include indirect and opportunity costs 4️⃣ Attach concrete numbers to emotional situations 5️⃣ Set landmines against competitive discount tactics For example, if your solution costs $80K, you need to uncover at least $800K worth of pain. For instance, let’s say I sell a global HR platform. Here's the exact language I use: "Tell me EXACTLY what happened the last time this occurred?" "What did your employees say when they didn't get paid correctly?" "What were the downstream effects of that?” "If this happened again…what would happen?" Then the crucial step… quantifying: "You said 4 employees were so fed up after the 4th time it happened they started grumbling about quitting. If this causes you to lose just four GOOD employees, at a replacement cost of $200K each? that's $800K in direct costs alone to replace good talent.” Next, I set landmines. "When you tell your current vendor you're considering us, they'll likely slash their price by 70%. But will that solve the fundamental problems you’re currently dealing with?" The prospect now has a framework to evaluate the situation beyond just price. This method works because it addresses the real reason prospects don't switch (what behavioral economists call "omission bias") it feels safer to stick with a known bad situation than risk making a change. By properly quantifying pain, you shift the equation. — Want to grow your sales so fast it feels illegal? Don’t miss this: https://lnkd.in/gYaBjpf2

Certainly, while wishlists have emerged as a valuable tool for gauging consumer interest, there are several other methods and metrics that e-commerce platforms can use to measure consumer interest: 1. Cart Abandonment Rate: Observing how many customers add products to their carts but don't complete the purchase can provide insights into potential hesitations or barriers. 2. Product Views: The number of times a product is viewed can indicate its popularity or interest level. 3. Time Spent on Page: Monitoring the average time consumers spend on product pages can hint at their level of interest. 4. Product Reviews and Ratings: A high number of reviews or ratings, even if mixed, can signify strong interest or engagement with a product. 5. Search Query Analysis: Observing which products or categories users are searching for on the platform can indicate trending interests. 6. Social Media Engagement: Shares, likes, comments, and mentions related to products can provide insights into consumer preferences. 7. Referral Traffic: Analyzing traffic from external sites or social media can show where the interest is coming from and which products are driving it. 8. Customer Surveys and Feedback: Directly asking customers about their preferences or interests can yield detailed insights. 9. Sales Data: A straightforward metric, but analyzing which products are selling the most can clearly indicate consumer interest. 10. Click-Through Rate (CTR): Observing how often people click on a product after seeing it in a recommendation or advertisement can be a strong indicator. 11. User-Generated Content: If consumers are posting pictures, videos, or blogs about a product, it showcases genuine interest and engagement. 12. Repeat Purchases: Products that are frequently repurchased can indicate high levels of satisfaction and interest. 13. Customer Service Inquiries: The number and nature of questions related to a product can offer insights into areas of curiosity or concern. 14. Heatmaps: Tools that show where users most frequently click, move, or hover on a page can help in understanding which products or sections grab their attention. 15. Newsletter and Email Open Rates: If consumers are frequently opening emails about specific products or categories, it can be an indication of their interest areas. 16. Retargeting Campaign Success: The conversion rate of retargeting campaigns can provide insights into the residual interest of consumers after their initial interaction. By leveraging a combination of these methods, brands can gain a comprehensive understanding of consumer interest, helping them to tailor their offerings and marketing strategies more effectively. #ecommerce #LinkedInNewsIndia

92% of B2B buyers only purchase from vendors already on their "day-1" shortlist. Yet most B2B marketers have no clue if they're making that list. Share-of-search? Only shows the 5% actively buying today. Pipeline data? You've already won or lost by then. Social metrics? Vanity numbers that don't predict deals. Here's what actually works: survey-based brand tracking. It's the only way to measure what matters most, whether you're in the minds of the 95% who aren't buying yet but are forming tomorrow's shortlists. How many people do you need to survey? Precisely targeted 100 category buyers is enough for most. A 100-respondent B2B survey gives you: → ±9.8% margin of error (plenty tight to spot meaningful shifts) → can be 5-10% of your total addressable market (way higher than consumer research) → Stable directional insights Google and Bain found that shortlists have shrunk from 6 suppliers to 3.5 today. And 62% of decision-makers finalize that list without ever talking to sales. Miss the initial cut? Less than 10% chance to win the deal. What to track in brand tracking surveys, three metrics matter: 1. Unaided awareness: "Name vendors you'd consider for X" 2. Aided awareness: "Have you heard of...?" 3. Preference: "If you had to decide today...?" Add 2-3 perception questions tied to your positioning. Run it quarterly if you're in a competitive category, semi-annually otherwise. Open-ended questions reveal why you're making (or missing) shortlists: "Credible enterprise-grade security" vs "Too expensive" "Innovative technology" vs "Unproven startup" That's your roadmap for messaging, product, and go-to-market. While your competitors chase vanity metrics, you'll have a direct line to what actually drives deals (mental availability among future buyers). Survey-based brand tracking isn't just measurement. Its competitive intelligence disguised as market research.

𝗧𝗵𝗲 𝗡𝗕𝗗-𝗗𝗶𝗿𝗶𝗰𝗵𝗹𝗲𝘁 𝗠𝗼𝗱𝗲𝗹 – 𝗣𝗮𝗿𝘁 𝟳 (𝗕𝘂𝘆𝗶𝗻𝗴 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 + 𝗕𝗿𝗮𝗻𝗱 𝗣𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲) A quick review – the NBD-Dirichlet model has two key parts: 1️⃣ The 𝗡𝗕𝗗 part that describes the average frequency of individual buyers and how those purchase frequencies are distributed across all category buyers. Essentially, what does the mix of heavy and light buyers looks like. 2️⃣ The 𝗗𝗶𝗿𝗶𝗰𝗵𝗹𝗲𝘁 part that describes how individual buyers hold unique preferences for different category brands and how those brand preferences are distributed across all category buyers. The Dirichlet model gives you relative brand preferences across all buyers. This is what you would get if you did a "Brand Preference Survey" across your market. --- We might do a survey of brand preference and find: • Brand A ➜ preferred 40% of the time • Brand B ➜ preferred 37% • Brand C ➜ preferred 23% But if we look at actual relative market share based on total units purchased, we find: • Brand A ➜ 25% market share • Brand B ➜ 41% • Brand C ➜ 34% Buyer preference DOES NOT translate into overall market share ranking. This is illustrated in the graphic below 👇 --- 𝗪𝗛𝗬 𝗗𝗼𝗲𝘀 𝗢𝘃𝗲𝗿𝗮𝗹𝗹 𝗕𝗿𝗮𝗻𝗱 𝗣𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 ≠ 𝗢𝘃𝗲𝗿𝗮𝗹𝗹 𝗠𝗮𝗿𝗸𝗲𝘁 𝗦𝗵𝗮𝗿𝗲? Because different buyers have different purchase frequencies. Market share emerges from the combination of purchases made by both light and heavy buyers based on individual relative brand preferences. If heavy buyers prefer different brands than light buyers, then brand preference will not translate into total units sold. Example: the largest CRM customers (heavy buyers) disproportionately prefer Salesforce (80%+) over the average of all CRM buyers (~35%) As a result, Salesforce has: 👉 20x more revenue than Hubspot 👉 5x more market share 👉 But only ~150,000 accounts vs. Hubspot with ~216,000 accounts More heavy buyers (large enterprises with high seat counts) prefer Salesforce...while more light buyers (SMBs with small seat counts) prefer HubSpot. This is an example of why we need to look at BOTH purchase frequency (or size) PLUS brand preference to understand how relative 𝗠𝗮𝗿𝗸𝗲𝘁 𝗦𝗵𝗮𝗿𝗲 emerges! ✅ It's this combining of the two halves of the model (𝗡𝗕𝗗 + 𝗗𝗶𝗿𝗶𝗰𝗵𝗹𝗲𝘁) that gives the model its power to describe actual market outcomes! --- A note on how this applies to B2B: The NBD-Dirichlet model tends to be talked about ONLY in the language of B2C CPG products (how often do people buy a box of laundry detergent) But heavy vs. light buyers equally applies to areas like B2B SaaS, where heavy vs. light describes things like average seat counts. --- Next, in 𝗣𝗮𝗿𝘁 𝟴, I examine the significant limitations and narrow contexts where you can apply the NBD-Dirichlet model. For example, it cannot tell you ANYTHING about "How Brands Grow" (in spite of books with such dubious titles)

Here's how my SMB AEs that earn $20,000-$50,000/month quantify pain during discovery without sounding salesy. I've spent 12+ years in sales and have coached hundreds of AEs. The #1 issue sellers still struggle with is getting prospects to open up about their pain and quantify it. There's a concept in psychology called recognition vs recall. Recall is pulling something out of your brain from scratch. Recognition is when someone presents it to you and you go, "yes, that's it." Think about a fill in the blank test vs a multiple choice test. Your brain can recall information easier once it recognizes it. When you ask a prospect about their pain and try to get them to quantify it, that's a fill-in-the-blank test. When you describe a problem you've seen with companies like theirs and ask if that sounds familiar, that's multiple choice. So here's how to do it: → MOP+I+QP + Something Else MOP (Menu of Pain):  You share a problem you've seen with companies similar to theirs. You're pulling from real patterns you've observed across your deals. I (Impact):  You attach the consequence of that problem. What happens because of it? What breaks down? What gets worse? QP (Quantifiable Pain):  You put a number on it. A percentage, dollar amount, time cost, etc. Something measurable that came from other companies you've worked with. Something Else:  You ask if that's what's happening for them, or if it's something different. It keeps you from "leading the witness" and gives them permission to correct you. P.S. I'd recommend using my Pain Peeler™ worksheet over here (my AEs are using this): https://lnkd.in/eD8hfPQz

Your customers are lying to you. Not intentionally. They just don't know what they actually want. Last week, a SaaS founder showed me their on-site survey data. 87% of respondents wanted "more features." Their session recordings told a different story: visitors couldn't find the features that already existed. This happens everywhere... what people *say* and what they *do* are different universes. For example, during a recent project with Adobe, we discovered something shocking: survey respondents begged for advanced filters. But heatmaps showed they never clicked the basic filters already there 🤦🏻♂️ We ignored the surveys. We followed the behavior. Revenue jumped. In my latest book "Behind The Click," I detail the psychological forces where users aren't conscious of their real motivations: ↳ They tell you they want lower prices. Their behavior shows they'll pay more for convenience. ↳ They say they want more options. Their behavior shows paralysis with current choices. That's why The Good | Digital Experience Optimization's methodology ignores opinions. We observe actions. We measure what users *do*. Not what they *claim*. Otherwise, despite endless survey data, conversion rates will stay flat. Your survey says users want faster checkout. Your data shows they abandon at shipping options. Different problems. Different solutions. Don't optimize for fictional preferences. Watch what users do for one hour. Learn more than 1,000 survey responses can teach you. Behavior beats intention every single time. Trust their clicks, not their words.

Recent research with real on-your-land building customers revealed something striking: Two customers with the same builder. One rated their experience 2/5. The other 5/5. The difference? How long they waited for answers about their land. Permits. Utilities. Geotechnical reports. Septic requirements. When customers can't get clear answers, they don't blame the county. They blame you. The low-rated experience: → Waited over a week for responses → Discovered tens of thousands in unexpected costs → Blindsided by jurisdictional requirements → Utility estimates were off by 50-100% → Would not refer the builder The high-rated experience: → Got responses within 24-48 hours → Knew cost expectations upfront → No jurisdictional surprises → Already planning their second build Same company. Different rep. Completely different outcome. Here's the pattern across multiple builders: Customers who rated their experience poorly mentioned: - Hidden costs discovered at closing - County-specific requirements nobody warned them about - Utility estimates that were wildly inaccurate - Geotechnical, septic, or engineering surprises High-rated customers? None of these surprises. Because someone did the homework upfront. The business impact: - Low-rated customers don't refer. High-rated customers become repeat buyers and referral engines. - Your CAC for dissatisfied customers: Complete loss. - Your CAC for delighted customers: $0 for the next build, $0 for every referral. In on-your-land building, communication problems aren't about response time. They're about having the right information before the customer asks. When your team guides customers through discovering what their land requires: "Here's the process for your county. We'll uncover requirements together. As we find each answer, I'll give you accurate costs and realistic timelines." You eliminate most anxious follow-ups. When customers feel abandoned in that discovery process, your team spends months firefighting while they spiral from anxiety → resentment → bad reviews. Top-performing on-your-land builders do three things differently: - Know jurisdictional requirements before contract signing - Have accurate cost ranges for utilities, permits, and site work by county - Do comprehensive due diligence upfront instead of discovering issues mid-process Struggling builders discover these things with the customer in real-time. That's the difference between referral engines and revenue leaks. Here's my question for on-your-land builder executives: How many of your communication fire drills are actually due diligence gaps? What's the revenue impact of turning dissatisfied customers into repeat buyers and referral sources? The difference often isn't construction quality. It's whether someone did the homework upfront We help on-your-land builders eliminate these surprises through comprehensive land due diligence. If your team is spending more time firefighting than building, let's talk.