Data Analytics In Sales

It’s time to stop thinking like it’s 2005. Correlation may flatter your GTM story, but only causation proves impact. More than 80% of companies missed their sales forecast in at least one quarter over the last two years (Gong, 2024). In H1 2024, 49% of companies missed their revenue goals (GTM Partners Benchmark Report, 2024). At the same time, executives keep putting faith in attribution models that only tell a sliver of the story. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: too often, data is interpreted in ways that confirm existing assumptions rather than test them. Harvard Business Review found that sales leaders are frequently blindsided by overinflated forecasts driven by “all-too-human behavior” (Harvard Business Review, 2019). GTM Partners research shows that poor data quality can cost companies up to 25% of annual revenue, yet 60% don’t even measure these costs. That’s value leakage every CFO cares about. It’s time to fix this. Here are 5 ways to make GTM decisions actually data-driven: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗻𝘂𝗹𝗹 𝗵𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀: Harvard Business Review notes that “consistently accurate sales forecasts are rare because many companies fail to align their sales and marketing departments.” Assume your campaign 𝘸𝘰𝘯’𝘵 work—then try to prove yourself wrong.     2. 𝗥𝘂𝗻 𝗽𝗿𝗼𝗽𝗲𝗿 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆 𝘁𝗲𝘀𝘁𝘀: Compare your marketing results to a control group to see the actual lift your efforts create. MIT Sloan warns that confirmation bias leads us to “interpret ambiguous facts in light of preexisting attitudes.” Stop crediting natural growth to your LinkedIn ads.     3. 𝗕𝘂𝗶𝗹𝗱 𝗿𝗲𝗱 𝘁𝗲𝗮𝗺𝘀 𝗳𝗼𝗿 𝗺𝗮𝗷𝗼𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: MIT Sloan recommends bringing together “different perspectives on the same issue” because organizational biases cloud interpretation. Create space for contrarians—the risks of blind spots are too expensive to ignore.     4. 𝗧𝗿𝗮𝗰𝗸 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝙖𝙣𝙙 𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀: Research shows the average B2B buyer has ~31 touchpoints with a brand before deciding (Dreamdata, 2024). Your last-touch attribution is missing most of the story.     5. 𝗣𝗿𝗲-𝗿𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀: Record in advance your testing methodology and success criteria. This prevents “analysis after the fact” bias and ensures accountability when results don’t fit expectations. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: If your data never challenges you, it’s not science; it’s storytelling. The companies that break through are the ones willing to let the data argue back. What’s the most obvious confirmation bias you’ve seen in GTM? #GTM #MarketingLeadership #causalinference  

Just watched a sales leader lose 5 of his top reps after spending months perfecting a "winning" sales methodology that his team HATED. After 18 months of work, the CEO killed his career with six words: "Your team keeps missing their numbers." After analyzing 300+ sales teams and thousands of reps I've identified the exact leadership framework that separates 90%+ quota attainment from the industry average of 60%. The BIG missing piece that most sales leaders miss? Stop running meetings as status updates. And start treating them as PERFORMANCE ACCELERATION ENGINES. Here is the GOLDEN Leadership framework: GROWTH MINDSET: Start every meeting with these 3 strategic elements. → Team member shares industry insight or sales technique (creates learning culture) → Discuss application to current deals (makes learning actionable) → Rotate presenters weekly (builds leadership skills company-wide) This approach increased team knowledge retention by 72% across my client base. OPTIMIZATION SESSION: Have top performers demonstrate and teach these 4 specific skills. → Objection handling techniques (with exact language used) → Discovery questions that uncovered hidden needs → Email templates that generated 80%+ response rates → Closing language that accelerated decisions Use this exact script: "Jeff, you closed that impossible deal with [company]. Walk us through exactly how you handled their [specific objection] so the team can replicate it." LEADERBOARD ACCOUNTABILITY: Create what I call the "Performance Matrix" with columns for. → # of Booked Discovery Calls (activity metric) → New opportunities generated (pipeline metric) → Percentage to monthly target (results metric) → Weekly win or learning (growth metric) DATA & DEVELOPMENT: Each rep inputs and shares three critical elements. → KPIs for the week (leading indicators - 100% controllable) → Sales results (lagging indicators - what they actually sold) → Wins or learnings (development indicators) EXECUTION: Randomly select an AE to role play live. → Use a jar or spinning wheel to pick sales scenarios → Focus on objections, cold calls, or tough situations → Play the difficult prospect yourself → Provide immediate feedback and coaching This gets your team sharper before they jump into their day, and knowing they might be selected drives preparation. NEXT LEVEL MINDSET: End with motivation to conquer the week. → Short visionary speech or gratitude to the team → Positive reinforcement → Ensure they leave with the right mindset This is what they'll remember as they enter their next task or meeting. "REAL RESULTS from this framework: ✅ An IT services client increased sales by 37% in just 30 days ✅ Average rep retention improved from 18 months to 36+ months ✅ Team productivity increased 42% with the same headcount ✅ Top performers stopped taking recruiter calls Hey sales leaders… want a deep dive? Go here: https://lnkd.in/e2iZ7Rmv

Your data problem didn't start in your warehouse. It started in that free-text 'Region' in your ERP. Spending $1M modernizing your stack, won't fix your data. Everyone wants accurate data. But when you dig you realize their processes were never built to produce good data. They’re trying to analyze chaos. A few months ago, we were talking to finance company. They’d just spent 14 months modernizing their stack. They hired the data engineers. Millions spent. Hundreds of dashboards. And yet: “Revenue” in Salesforce included refunds. “Customer” in Marketing meant prospects too. Operations had 15 different “regions” spelled 8 different ways. The tech wasn’t broken. The process was. Their CRM, ERP, and sales systems were designed for convenience, not for data. Every time a sales rep skips a CRM field.. You create a leak in your data foundation. Until your warehouse is garbage. If your processes weren’t designed with data in mind nothing will save you Here is how to go about stopping bad data 1. Design Every Process as if Data Were the End Goal If you’re setting up a CRM, ERP, or even a Google Form, build it like a data engineer would. Even if it's yet. Develop a process with data in mind. As down the line, you will need, and rather than waiting 3-5 months to get data. Replace free-text fields with controlled dropdowns. Enforce mandatory fields that align with business-critical metrics. Executives say they want clean data but approve workflows that guarantee mess. In my opinion, data should be clean from the source. Becuse if it's not, managing pipelines, modelling becomes a nightmare. And even that can't save it 2. Treat Metrics Like Products Agreeing on definitions is not easy at all. People change, leave. 2 VPs can't agree on it so they create their own spreadsheet. Every metric you report on should have an owner, version history, use case and single definition across the company. If found in a situation can't agree, ask "What finding this info enables you" If can't answer it, archive it. Or if can't agree on metric. Seperate and define clear use case where each. 3. Asssign Owner & Build Feedback Loops Bad data comes from the frontlines, reps skipping CRM fields, creating custom objects in Salesforce. Assign owners of the metrics. Answer: Who owns the data? Who manages the inputs? Who's keeping operational systems clean? (Data stewards) If no one is accountable or owns it, how do you thing it will get fixed. Tie accuracy to incentives. 4. Enforce Standards, Not Opinions Everyone uses their own definition of “good data” Define how data should look: formats, naming, validation rules. If “Region” is free-text in CRM, you’ve built chaos by design. 5. Data quality isn’t a project or a one-time thing Start where it's most important. Track exceptions, expose results, fix patterns. Embed it in the system, so it's proactive rather than reactive.

Sales growth is not a matter of luck; it stems from structured processes, consistent execution, and a deep understanding of customer behavior. The attached framework offers a practical breakdown of the key drivers that enable businesses and sales professionals to accelerate performance sustainably. One of the most valuable insights is the “4 Multipliers of Sales Growth” model: Leads, Conversion Rate, Average Deal Size, and Retention Rate. Sustainable sales growth occurs when organizations enhance each of these areas simultaneously. Simply increasing lead volume is insufficient if conversion rates are low or customer retention is lacking. High-performing sales organizations concentrate on the entire sales ecosystem, rather than focusing on a single metric. The framework also underscores the significance of process-driven and data-driven selling. Top sales teams consistently adhere to structured sales methodologies, track key performance indicators, and refine strategies based on measurable outcomes. Metrics such as Customer Acquisition Cost (CAC), Lifetime Value (LTV), Win Rate, and Sales Cycle Length provide essential insights into profitability and operational efficiency. Another critical lesson is the shift toward customer-centric selling. Modern buyers seek value, trust, and understanding rather than aggressive pitches. The “70/30 Rule” emphasizes that effective sales professionals allocate more time to listening than speaking. Successful sales conversations are built on smart questioning, active listening, and addressing real business problems. The framework also highlights the need for diversification in lead generation through inbound marketing, outbound prospecting, referrals, partnerships, and paid campaigns. Relying on a single source of opportunities can limit growth and increase risk. Ultimately, successful sales growth is achieved through a blend of strategic lead generation, disciplined execution, customer trust, and continuous optimization. The strongest sales organizations prioritize creating long-term value, fostering strong relationships, and enabling scalable growth.

Legacy enrichment tools treat firmographic data as a checkbox exercise. Sales reps spot the obvious mistakes, and RevOps teams take the blame. At Kernel, we treat each data point as its own product. Our product team uses proprietary tooling, search agents, and reasoning agents to constantly improve core firmographic accuracy over time. For every core firmographic data point, we also assign a dedicated product owner. Headcount is one of the most critical data points to anchor the CRM, territory planning, and market segmentation around. Katie Peachey owns it end-to-end: structured and unstructured data sources, heuristics, exceptions, edge cases, and explainability. Our process replicates how a RevOps professional would establish firmographic data accuracy with unlimited time, based on foundational AI-native master entity data, account identity resolution, agent searches, and our reasoning agents. What does this approach prevent? ➡️ Entity confusion. Mosaic, the VC, vs. Mosaic, the biotech is an example of where generic AI tools merge or mix entities. ➡️ Single-source heuristics. Relying on LinkedIn alone can be wildly misleading. Upwork is a perfect example. ➡️ Stale databases. Fast-growing companies like Anthropic can double headcount long before a static vendor refreshes. ➡️ Missing primary data. Most systems do not have the workflows or expertise to find and validate it at scale. 🚨 Sales reps messaging RevOps with obvious mistakes and undermining credibility in CRM data. Here's how this accuracy translates into value at enterprise scale: ✅ Accuracy you can trust across hundreds of thousands of accounts, not just a handful of hand-checked logos enables confident planning at scale. ✅ Fewer escalations from reps who spot obvious errors in territories or TAM. ✅ Explainability. Reps can see why a headcount number is correct and review the underlying evidence and data sources. ✅ Better planning. Cleaner segments, cleaner territories, and fewer missed high-value accounts caused by incorrect data. When core firmographics are developed with ownership, data source evaluation, and feedback loops, RevOps teams finally get the data quality they need to plan with confidence. Our reasoning agent avoids the black box experience that's at the root of broken trust between sales and RevOps. If you want to see the difference for yourself, DM me and we can benchmark Kernel headcount data against whatever you are using today, whether it's your own CRM data, your enrichment vendor, or both. 👉 https://lnkd.in/dRkyzHNw Save your seat for my conversation with Jordan Crawford on the topic next Tuesday: 👉https://lnkd.in/dw7UqBWY

We all know segmentation is key, but it's often limited to company firmographics. So when Clay announced their integration with HG Insights today, I have been geeking out layering in new insights to our ABM list. Instead of rattling off a bunch of features or "what this means," I'm going to explain exactly how I plan on using this to paint the picture: 1) We know which accounts we want to go after, but that's often based on things like their location, size, industry, etc. What we *don't* know is if they currently have a solution in place to solve for what we want to help them with, or if we do, it's either sketchily acquired or probably out of date 2) To create a truly relevant message, knowing what an organization does isn't enough. We need to go beyond that by understanding how they're approaching what they do today, and that execution is often a function of the tech they use 3) Enter Clay x HG Insights. I can now further prioritize our target account list by going beyond firmographics and layering in technographics. What are our prospects currently using? Which competitors are low-hanging fruit that we can beat? Which competitors are going to be uphill battles because they've got a great product? This is a huge help in resourcing our overall GTM efforts. 4) Enrich the rest of the account data appropriately. Find the relevant contacts + engage them in a HIGHLY relevant way based on all of the above. 5) Sit back and watch your win rates increase, sales cycles decrease, and your GTM team (AEs, BDRs, Marketing, etc.) breathe a HUGE sigh of relief as acquisition has just become a lot easier. HG Insights has historically only been used by companies with big budgets because it's not a cheap product. That all changes today.

Clean dashboards can still hide underlying issues. Strong numbers do not always reflect what is actually happening. In one case, campaigns were optimized continuously, yet leads stalled and conversions dropped. The problem was not content or timing. It was the lifecycle structure behind the system. Stages were based on templates rather than real buyer behavior, which created gaps between reporting and reality. Once lifecycle stages were redesigned around actual engagement signals, the system aligned. Sales gained confidence in the data, marketing could identify what worked, and retention became more consistent. HubSpot’s effectiveness depends on how well its lifecycle logic reflects real processes. When that foundation is accurate, automation and reporting support growth. When it is not, they scale confusion. This week’s newsletter explains how to design lifecycle stages that align with real buyer journeys and improve overall system clarity. For teams dealing with inconsistent conversions or unclear lead quality, it is worth a read. #hubspot

Leveraging Voice of Customer (VoC) for Enhanced Sales Outreach   In today's complex B2B sales environment, where buyers demand personalized engagement, sales team's time is your most valuable asset. In the age of the experience economy, where customer experience (CX) outweighs the value of products and services themselves, efficient lead qualification is key to success for B2B teams. With long sales cycles and complex decision-making, focusing on the right prospects can make or break revenue goals. VoC is a strategic asset employed to understand the needs, pain points and expectations of potential buyers. Today’s business buyers expect personalized engagement and often define their solution needs before contacting sales, with some even identifying specific solutions. By collecting and analyzing customer feedback, businesses can prioritize high-quality leads, improve conversion rates, and reduce wasted time on unqualified prospects. Key Benefits of Using VoC for Lead Qualification B2B companies that prioritize VoC-driven lead qualification gain a strategic advantage by fostering stronger relationships with prospects by demonstrating a deep understanding of their needs. Hence, as businesses set up Sales operations, it is important to get real time feedback to: ▪️ Understand customer decision making process to enhance connection & conversion rates ▪️ Update sales messaging to cater to a specific customer persona ▪️ Provide feedback to the business regarding their offerings & positioning ▪️ Prioritize key market segments based on data-driven needs analysis Additionally, strategic use of VoC data helps improve lead qualification process by: ▪️ Refining lead scoring models by incorporating customer concerns and success factors ▪️ Accelerated decision making by addressing objections, pain points early in the process ▪️ Enhancing product/ service to make it a better fit with customer needs What does it take to enable Platforms with the Power of Voice of Customer? ▪️ Creating the right VoC Questionnaire A tailored questionnaire aligned to business needs that offers structured data collection and flexibility for different personas in order to capture product awareness, competitors and pain points for better conversion assessment. ▪️ Driving Implementation Manage the VoC program end-to-end, integrating the program into sales processes, ensuring adoption and alignment with sales objectives. Provide trainings to ensure consistent application by teams ▪️ Analytics and Insights Analyze VOC data to uncover actionable insights for sales strategy and deliver comprehensive reports to enable data driven decisions. Backed by the power of insights, continuously monitor program effectiveness and optimize for better results. Ultimately, leveraging VoC is about shifting from a scattershot approach to a laser-focused, insight-driven strategy that ensures that every sales interaction is meaningful and impactful. Aditi Bansal Sambhavi Ganguly

This is the exact system data teams use when they’re given unfamiliar data and can’t “see” the errors. It’s helped prevent broken dashboards, wrong KPIs, and bad decisions I am sharing so you know what to do when given a new data (Repost to help someone who thinks bad data = just missing values) ♻️ The 6 Signal Data Quality Checks 1️⃣ Completeness: Are values missing where they shouldn’t be? Examples: Customers without email addresses Orders without prices Transactions missing timestamps Sometimes missing data isn’t a data issue. It’s how the business captures it. 2️⃣ Accuracy: Do the values make sense? Examples: Negative revenue 300-year-old customers Orders dated in the future Product prices at £0.00 These usually come from edge cases like refunds, timezones, or default values not being handled. 3️⃣ Consistency: Does the same metric match across systems? Examples: Revenue in sales DB vs revenue in finance Customer count in CRM vs analytics Same KPI calculated differently in two reports If two dashboards disagree, this is almost always why. 4️⃣Uniqueness: Should something appear only once? Examples: Duplicate customers (John Smith vs john.smith) Duplicate order IDs Same transaction logged twice Duplicates quietly inflate metrics and wreck funnels. 5️⃣ Timeliness: Is the data fresh? Examples: Today’s dashboard showing last week Real-time report that updates daily Pipeline broken for days with no one noticing Most teams don’t have a data problem. They have an alerting problem. 6️⃣ Volume changes: Did row counts suddenly spike or drop? Examples: Yesterday: 10,000 orders. Today: 47 Sales data missing entire regions Sudden 500% increase in sign-ups (usually a logging bug) Big swings are often the first sign something broke upstream. Not every check matters equally. Follow the business logic: What numbers leadership relies on What drives money or decisions What gets sent to customers That tells you what to check first. Want to practice finding these issues? SQL Tutorial → https://lnkd.in/eaeTDGWb Python Tutorial → https://lnkd.in/ehz5huHZ Excel Tutorial 1 → https://lnkd.in/eTk9eXSn Excel Tutorial 2 → https://lnkd.in/eQ2TiASk P.S. Which check catches the most issues in your data? Drop 1–6 👇

Here’s why your pipeline might be broken: You can’t fix what you can’t see. Opportunities don’t disappear - they slip through the cracks because they’re hard to spot. I’ve been there, chasing deals that seemed promising, only to realize later I was ignoring bigger opportunities. The biggest issue? -- relying on gut feelings instead of real insights -- ⇢ Top sellers know better - they use clear patterns and buyer behavior to make decisions that matter. What really works: 𝗦𝗽𝗼𝘁 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 It’s not about random guesswork. Winning deals often share trends, like: ⇢ which product features resonate most in calls ⇢ talk-to-listen ratios that improve engagement ⇢ objections raised and how they were handled ⇢ recognizing these patterns gives you a roadmap for success 𝗙𝗶𝗻𝗱 𝘄𝗵𝗮𝘁’𝘀 𝘀𝘁𝗮𝗹𝗹𝗶𝗻𝗴 𝗱𝗲𝗮𝗹𝘀 Not every deal is moving forward. Ask yourself: - are prospects going cold because follow-ups are delayed? - are deals sitting too long in certain stages? - is a specific pain point being ignored? ⇢ Identifying these roadblocks ensures you can address them quickly. 𝗧𝗮𝗶𝗹𝗼𝗿 𝘆𝗼𝘂𝗿 𝗺𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 Relevance is everything. Make your emails and calls about the prospect’s world, not yours. For example: - if they’ve recently hired SDRs, talk about improving ramp time. - if they’re expanding, focus on scalability or compliance. ⇢ Personalization combined with specific triggers makes your outreach stand out. 𝗚𝗲𝘁 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 Acting on the right insights at the right time can make all the difference. Tools like MeetRecord help by: - highlighting trends in winning deals - providing clear next steps based on buyer behavior - surfacing risks early so you can keep deals alive 𝗛𝗼𝘄 𝘁𝗼 𝗮𝗽𝗽𝗹𝘆 𝘁𝗵𝗶𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 Here’s a practical example: Scenario: A deal has been stuck in negotiations longer than it should. 1. Check your call notes in MeetRecord to see what concerns came up last time. 2. Look at what caught their interest earlier - features, benefits, etc. 3. Send a follow-up that highlights what they care about and clears up any concerns. 4. See how they respond and adjust if needed. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 Without visibility into your pipeline, you’re guessing - and guesses waste time. I’ve learned this the hard way. When I started focusing on patterns, tracking key metrics, and using tools like MeetRecord to stay ahead, I saw how much easier it became to prioritize and act. Sales isn’t about hustling harder. It’s about seeing the right opportunities and acting on them.