Using Data Analytics in Sales

A $35M ARR SaaS client came to us asking: “How do we only target the top 5% of fast-scaling companies?” So we built a workflow that does exactly that, and you can have it 🎁 We wanted to help them reach only the companies that are already winning. Not the “maybe one day” crowd, and not just logos. Here’s how we did it 👇 → Step 1: Track fast-scaling companies We use Propensity and SalesIntel.io to monitor who’s hiring fast. (Growth signals don't lie). Filters: employee growth rate, industry fit, recent expansion. → Step 2: Enrich every account Using Clay, we layered in: 1. Firmographics (revenue, funding, HQ) 2. Job data (remote or distributed roles) 3. News triggers (funding, layoffs, expansion, partnerships) 4. Decision-makers in HR, Ops, IT, and Finance → Step 3: Score the top 5% We assigned a score based on: ✅ Employee growth rate ✅ Industry match with proven case studies ✅ Remote hiring signals ✅ Funding or expansion events → Step 4: Personalize every message Every email referenced a real event or result (no generic intros), tools like Twain help us create personalized messaging. Examples: 💸 “Congrats on your Series B! [Client] scaled globally and improved performance 3× with our platform.” 🌍 “Expanding? [Client] boosted productivity 30% while proving ROI to new markets.” ⚙️ “Restructuring? [Client] cut software costs by 70% using analytics from our platform.” Results: - Zero wasted effort on wrong-fit accounts - Outreach that performs, because it’s built on proof and signals - More meetings Building GTM engines like this is what I love 💚 If you want to see how this could work for your ICP, let me know below or DM and I'll reach out. #gtm #outbound #salesops #b2bsales

Your reps get handed Sales Nav. And you wonder why they're missing quota. Most teams use it like a phone book. Random searches, generic messages, zero buying signals. That's not prospecting. That's expensive busy work. I've used this exact 5-step system to generate millions in pipeline. Same workflow my team runs every week. Step #1: Build your sequence FIRST Don't touch Sales Nav until you have a complete 21-day multi-channel sequence. I write mine in Google Docs, then build it in Apollo. Step #2: Create trigger based searches → Less than 1 year in current role (they're making changes) → Job opening increases (expansion or performance issues) → Posted on LinkedIn recently → Company headcount growth Step #3: Research before outreach I use ChatGPT to uncover strategic priorities and quota attainment data. Found a company with 35% sales job growth and only 23% quota attainment? Perfect storm. Step #4: Batch your outreach Set calendar blocks. When I was an AE, 8 hours minimum weekly for cold outbound. Step #5: Make it ongoing Monday: Check searches Daily: Work your tasks Weekly: Add qualified prospects By the way… check your messaging before you start. Here’s what I mean: Generic: "Hey, saw you're hiring..." Researched: "Hey Sarah, congrats on 8 months at TechCorp. If you're like most VPs, you're uncovering dead bodies. Team hitting 32% vs 42% industry average..." Which gets the meeting? Start today: Build ONE search, research 10 prospects, block daily outreach time. — Check out my FULL Sales Nav tutorial here: https://lnkd.in/gtE-FWax

If I was running ABM at a fast-growing security company (like Wiz, Snyk, or Netskope), here's how I'd avoid wasting money on bad-fit accounts. 👇 AI Segmentation. Most companies segment by industry. They say something like: "We target Tech, Retail, and Hospitality companies with 1,000+ employees." Motel 6 and Airbnb show why this breaks. Same firmographic profiles. But very different business situations, needs, and priorities when it comes to information security (or any tech purchase). You wouldn't sell to them the same way. AI Segmentation helps you uncover and target the highest value segments for your business, beyond basic industries. Here's how I would do this for a security company: 1.) Segment on business situation (not industry). -- Analyze your best customers (high NRR, high ACV). -- Group by specific situations that align to your value prop. e.g. Security Maturity Level, Security Use Cases, Compliance Sensitivity, etc.  -- Find the *natural* clusters based on value, not generic industry labels. 2.) Identify segments with AI. -- Use Keyplay AI to categorize every account in your market. -- Backtest segments against historical data to find which segments have the highest NDR, ACV, and Win Rates. -- Find new ICPs, outside generic vertical groups. 3.) Action the data -- Create ABM plays at intersections with highest win rates. -- Develop content specific to each segment combination (e.g., "Cloud Security for Advanced DevSecOps Teams in Retail") -- Refine your segmentation models as you grow. This process can reduce non-ICP Spend (waste) by 20-30% and help you find thousands of net new target accounts. Don't just throw your budget at industries. Find the segments where your solution resonates most, where you win often, win fast, and win big. That's strategic segmentation. p.s. If you want me and my team to kick-start this process for you, we're offering a free strategic segmentation analysis to CMOs at SaaS security companies with >$20M ARR. Get your report here --> https://lnkd.in/gMezS4Zk #ABM #ICP

In June, I talked to a Head of Sales whose team had plateaued. In 3 questions, we unpacked why his team was stalled and fixed it (and check out real numbers improvements at the bottom!): What are the 3 questions: 1. How many OOs do AEs have? 2. How many OAOs do AEs have? 3. What % of meetings get the next call booked during the call? Breaking it down... 1. How many OOs do AEs have? OO - Open Opps - all opps that aren't won or lost in a rep's pipeline. CRM data showed us 63 OOs per rep, on average (felt high to me). 2. How many OAOs do AEs have? OAO - Open Active Opportunities - The number of opps a rep has that are actively being worked (any activity in the last 30 days - this is a generous definition, I prefer a more aggressive 14 days or less measurement). CRM data showed us 28% of OOs had no email or call activity in 30 days, 44% had not meetings in the last 30 days (uh oh, this is really bad). 3. What % of meetings get the next call booked during the call? Booking the next meeting while on the current one is a sign your team is locked in, knows where to take buyers, and is intentionally moving deals forward. CI data showed us 60% of sales calls resulted in booking another call (there's some room for improvement but not a ton). How do we add up the clues to figure out the mystery of this team's growth stall? I've found that in mid-market more than 25-30 OOs results in pronounced decreases in win rate, deal size, and cycle time (the Holy Trinity of Divine Sales Performance). Reps just have too many deals to work them correctly, especially if they have non-closing expectations (e.g. prospecting, service, onboarding, lots of admin, etc). So, they don't email, call, meet with their opps enough. They don't have enough time to be thoughtful and properly prep for meetings. There's no deal momentum. We see all of that in the data. One last insight helped us nail the issue: 63% of OOs were self-sourced. AHA! Moment: Reps are prospecting at the expense of closing. I love a rep with tons of pipeline they self-sourced by knowing the buyer data they need, getting it, contextualizing it in the buyers' POV and their own value prop, and creating compelling and relevant messaging that attracts buyers into conversations. But, there's a point when fear, pressure, sales activity expectations, and managing the org not the individual crosses a line from efficiency productivity to self-defeating busy-work. This team ended up prospecting less which decreased OOs by 15% and a: - 8% increase in conversion - 5% increase in deal size - 10% decrease in cycle time - and...wait for it...45% increase in tier 1 lead volume coming in the pipeline They slowed down to speed up. Less (and higher quality) prospecting led to working the right accounts increasing win rate, deal size and cycle time. From an attainment perspective, the team went from 98% to 102% to 129% over 2 quarters while revenue increased 48%. Less can be more.

99% of GTM leaders don't know who their ICP is. This is because of three main reasons: - They never analyze their closed wons - They never interview their best AEs & CSMs - They don’t document who their ICP actually is Here’s what we do instead using data to validate and refine ICP assumptions: 1️⃣ Survey your best AEs & CSMs Truth is sales and marketing leaders who aren’t on the frontlines often don’t know the nuances of selling. SDR, AEs, and CSMs know: - Which customers light up on discovery calls - Which ones ghost after three emails - Which industries "get it" immediately ↳ Survey them and upload all responses to a Claude project. 2️⃣ Export your CRM deal data and enrich it with Clay We want to assess patterns that make up an existing client base. - Enrich deals with Clay to find firmographic and technographic data.  - Upload the enriched CSV to the Claude project - Prompt it to find patterns in your existing client base: Closed wons are not necessarily the ICP but they tend to be strong indicators. We generate reports based on: - Top sub-industries by deal volume - Top locations by deal volume - Top headcount ranges by deal volume - Win reason analysis (why they actually bought) - Top 3rd-party signals that correlate with buying intent - Other applicable signals 3️⃣ Create a scoring model based on surveys & deal analysis Tier systems are way more usable than point systems for scoring. We define the following: - Tier 1, 2, 3 criteria - Firmographic criteria - Technographic criteria - Account-fit signals 4️⃣ Back-test the model with existing closed won Validate the model with real data: - Build your tiering criteria in Clay. - Run it against your closed-won deals. - Versus your TAM, your closed-wons should disproportionately represent higher tiers. Tier 1 accounts should be allocated more resources than Tier 2 & 3. - Your ad spend should focus on Tier 1. - Your manual prospecting time should prioritize Tier 1. - 1:few and 1:many plays can make up your Tier 2 & 3 strategy. Step 2 would be to create a highly targeted account list and track signals. But the scoring model is your foundation.

Last year, I was working with the manager of a national staffing agency during their Q1 internal review. They had a branch that was off track from the start. After just three months, the branch was significantly behind its revenue and margin targets, and they were worried. We pulled up the dashboard to analyze the situation. On the surface, everything looked decent with the recruiting performance meeting the benchmark. But when we dug deeper, we found the problem: not enough job orders were available to meet their financial targets. Even if they filled 100% of the existing orders (which, as we know, is unlikely), it simply wouldn’t be enough to hit the goal. Next, we broke down where the orders were coming from. The branch was on track with their existing client growth plan, but they didn’t have enough new clients bringing in new orders. Initially, the manager wanted to create a new sales plan that increased the number of prospects and activities the sales rep was responsible for....but this would have been the wrong decision. That’s when we turned to the Sales Activity Tracker. The numbers immediately jumped out: The in-market sales rep was already maxed out with prospecting activities, meetings, and pipeline management. Based on the first-year value of a new client, it became clear that this sales rep literally couldn’t do enough to hit the branch's goal. At that moment, the manager made the tough decision to revise the revenue target and rework the budget to maintain profitability for the year. They also placed the sales rep on a performance improvement plan to focus on improving conversion rates through the sales cycle. In the end, the sales rep didn’t work out, but here’s the silver lining: By identifying this early, they were able to pivot quickly. Rather than holding on to an underperforming sales rep and risking a loss at the end of the year, they made the tough call, restructured, and ended up with a profitable branch. The key takeaway? Know your numbers, track your metrics, and use the insights to make data-driven decisions. It’s better to adjust early than wait until it’s too late.

⚠️ Car dealers 🚗 & OEMs risk losing their competitive edge without investing in digital solutions. Read more here: https://lnkd.in/dYUTPEhk ➡️ As always, just my personal opinion. Please add yours & re-share the post. The automotive retail and aftermarket is undergoing a massive transformation—standing still is no longer an option! 👉 OEMs 🚗 are scaling back their agency model ambitions across different regions. Leading brands like Volkswagen are returning to a dealer-based model, while Stellantis, Ford Motor Company, and BMW Group have halted their rollouts. Polestar is also adjusting its retail approach. 👉 OEMs have expressed a strong focus on expanding aftersales and digital services throughout the entire vehicle and customer lifecycle. This shift could increase market pressure on non-captive service providers and dealer groups. 👉 Dealers 🏪 must diversify their business both horizontally and vertically to tap into new revenue streams. 👉 To remain competitive, digital solutions must connect the dots across multiple data sources. The key lies in digitization 💿, data integration, standardization, and automation. One challenge is the fragmented digital value chain, particularly at automotive retailers. Stakeholders 🚗 operate in siloed and poorly connected systems, leading to a suboptimal customer experience. 👉 Customer Data Platforms (CDPs) can serve as the technical backbone for the stakeholders in the automotive value chain, enabling targeted and improved customer communication throughout the entire lifecycle. 👉 An integrated CDP empowers OEMs, non-captives, and dealers by consolidating customer data from multiple sources 🤝 into a single, comprehensive view. This facilitates personalized communication across all touch points. 👉 Automation & AI 🤖 enable efficient personalization without compromising personal relationships. This strengthens long-term customer loyalty 🌟 and relationship management. 👉 Digital platforms have proven to increase revenue potential per customer. A study by Veact GmbH, one of Europe’s leading CDP providers, found that depending on the vehicle class, revenue per car could increase by 21% for mid-size models and up to 38% for compact-class models. Additionally, workshop productivity could improve by 12%. 👉 VEACT’s approach leverages multiple data sources—including invoices, vehicle details, and service histories—to build comprehensive customer profiles and identify the best target audiences for marketing campaigns. 👉 By creating 360-degree customer profiles, businesses can unlock new sales & service opportunities. 🚀 Is your automotive business ready to accelerate into the future? Let's discuss how a Customer Data Platform can fuel growth, enhance customer loyalty, and drive success in the evolving automotive landscape! 👇 #automotive #automotiveretail #aftermarket #digitaltransformation #OEM #dealership #innovation #VEACT #datamanagement #AI #automation #customerexperience #customerloyalty

90% of tech companies can't predict their revenue 3 months out. I've seen this across 112+ companies. CEOs asking "What's in the pipeline?" and getting shoulder shrugs instead of solid numbers. The problem isn't your sales team. It's your forecasting system. Most companies use: ● Gut feelings disguised as percentages ● Activity metrics instead of outcome predictors ● Historical data without behavioral patterns ● Individual opinions over systematic scoring Here's what actually works: ● Every deal stage has measurable criteria and historical conversion rates. The average deal size, the sales cycle and the buying process. ● Track prospect engagement and micro-milestones between the sales stages, not just sales activities. ● Different deal sizes and sources get different confidence levels. ● Track the predictions to actual bookings and adjust the model. Through my B2B Tech SalesOS framework, we've helped companies achieve 85%+ forecast accuracy within 90 days. The result? The sales leaders and founders sleep better. Teams hit their numbers consistently. Moral goes up. Predictable revenue isn't luck, it's science and science can be taught, measured, and replicated. #techcompanies #sales #consistently #salesleaders #LinkedIn #LinkedInnews

After analyzing $200M+ in sales data across 2,500+ campaigns. I'm sharing my proven framework for scaling outbound success. Current Sales Challenges In 2025: - 79% of sales emails never reach primary inbox - 91% struggle with prospect overload - Only 2% of cold calls result in appointments - Average response rates declining 23% yearly - 51% of quota-hitting reps use social selling My Battle-Tested Scaling Framework: 1. Strategic Targeting - ICP development and refinement - Multi-channel prospect identification - Data-driven lead scoring - Behavioral trigger mapping - Custom audience segmentation 2. Personalization at Scale - AI-powered content generation - Industry-specific messaging - Dynamic template creation - Response pattern analysis - Engagement optimization 3. Multi-Channel Orchestration - Cross-platform integration - Sequential touchpoint mapping - Channel performance tracking - Automated follow-up sequences - Social selling integration My Verified Results Of Q4 2024: - Response rates improved 312% - Sales cycle reduced 47% - Lead quality up 189% - Conversion rates increased 156% - Cost per acquisition down 67% My Enterprise Case Study Of a B2B Tech Company. Before Implementation: - 18 calls per connection - 2.1% response rate - 15 hours weekly on research - $245 cost per qualified lead After Implementation: - 6 calls per connection - 8.9% response rate - 5 hours weekly on research - $76 cost per qualified lead Success isn't about more outreach - it's about smarter, data-driven engagement that resonates with your prospects. Start with personalization and a multi-channel approach. This combination alone improved our clients' response rates by 40%. What's your biggest challenge in scaling outbound sales? #SalesStrategy #OutboundSales #B2BSales #SalesOptimization