Garrett Gray

Why are Referrals becoming the forgotten growth channel? Too many SaaS execs build 2025 pipeline plans without even mentioning them. 😬 Why? ❌ “They’re not scalable.” ❌ “They’re not repeatable.” ❌ “They take too long.” But here’s the thing: ✅ Referrals work. ✅ They close faster. ✅ They convert better. ✅ They cost less. 💸 According to Aaron Ross, inbound and outbound strategies have led for decades. Yet in today’s competitive landscape, word-of-mouth has become equally essential." 👂 🔥 2025 growth leaders will systematize referrals — not sideline them. Full Episode: https://lnkd.in/dEBuSBb9 #SaaS #B2B #SalesStrategy #DemandGen #PipelineGrowth #Referrals #GrowthHacking #affliatemarketing #MarketingStrategy #LeadGeneration #CustomerAcquisition #BusinessGrowth #WordOfMouth #SalesFunnel #Networking #ClientRetention

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At Utah Business's Founder Friday, David Wright shared four core principles that have built Pattern into the company that it is today: Talent-first hiring (Dave personally reviews every candidate packet) Light-touch leadership for top performers Unrelenting outreach — even early stage calls made our biggest clients And the power of asking, learning, then paying it forward Want inspiration for building your own momentum? Read the article 👇 🔗 : https://lnkd.in/gYVzEyrc

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Imagine you work at a mid-market company, and you’re trying to give your sales team AI-powered insights. You connect your LLM to Salesforce, NetSuite, and Zendesk. Three separate MCPs, each one built by a different vendor. Your sales team asks a simple question: 'Are any of my customers at risk of churning? Please consider usage patterns, support sentiment, contract value and any recent interactions' The AI pulls support ticket volume from Zendesk, usage from internal databases, payment history from NetSuite, and engagement data from Salesforce. Then it gets confused. It doesn’t know how to connect a 'contact' in Salesforce to an 'account' in NetSuite to a 'user' in Zendesk. The result? Three separate answers depending on how you phrase the question. Your team loses trust in the system within a week. The fix isn’t better AI. It’s a single data access layer that understands your business context and pre-prepares the right datasets for your LLM in advance. Have you experienced AI giving inconsistent answers? It’s time to expect better. #AI #LLM #datagap

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Most owners think they need “more sales.” What they really need is a $50K Finder Audit...a core part of our Sprint. We dig into pricing, margins, and overhead to uncover hidden value. We routinely find $100K–$500K already in the business. This step alone often pays for the Sprint before we’re even halfway through.

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Finance loves their spreadsheets. Operators know there are hidden profit leaks. Here's why they exist.... The brutal truth is, I’ve seen nearly every Multi-Unit Restaurant Executive struggle with disparate systems giving incomplete information. I watched teams scramble to piece together numbers from POS, inventory, and labor systems, only to realize the data never lined up. The cost was always the same: missed targets, endless finger-pointing, and a nagging sense that something critical was being overlooked. Then came the reliance on Business Intelligence systems that only reported on yesterday instead of predicting today. I’ve seen leaders celebrate a strong weekly recap, only to get blindsided by today’s labor overruns or unexpected inventory swings. We all learned the hard way that yesterday’s story can’t prevent today’s disaster. But the harshest lesson was realizing the seven most common profit leaks actually get overlooked during traditional reporting. I saw seasoned operators pore over P&Ls, convinced they had a handle on every line, yet watched profits drain away from precisely those blind spots - waste, theft, comped meals, untracked overtime, vendor errors, and more. Each time, it was the same gut punch: the leaks were hiding in plain sight. I see this pattern with nearly every Multi-Unit Restaurant Executive and Operations Leader I have as a customer before they realize the importance of a standardized, location-level profit control system that actually reveals what’s hidden. If you are a Multi-Unit Restaurant Executive or Operations Leader and you want to stop revenue bleed, protect your margins, and standardize your profit controls, then grab a complimentary copy of our 7-Point Restaurant Profit Leak Audit Checklist. It reveals how to identify the seven most sinister and preventable profit leaks across your locations so you can finally get ahead of the numbers. Grab your copy of the 7-Point Restaurant Profit Lead Audit Checklist from the Featured Section of my profile: https://lnkd.in/eKefB476

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Most SMB manufacturers don’t have a data team. They don’t have analysts. They don’t have time to stitch together spreadsheets, TMS exports, and carrier emails just to understand what’s happening at their docks. But they do need answers to questions that directly impact cost, throughput, and customer commitments: o Which carriers are consistently late? o Which offices are slowing down freight? o Where detention is truly justified? o Why dwell is spiking? o How next week’s congestion will look? Ascend Cargo Systems gives SMBs these answers automatically - no extra tools, no dashboards to build, no analysts required. Ready to operate like a top‑tier logistics team without the overhead? Let’s talk. #SupplyChain #Logistics #Manufacturing #SMB #SMBManufacturing #SMBOperations #DigitalTransformation #OperationalExcellence #FreightTech #LogisticsTech #ActionableInsights #PredictiveOperations #OperationalIntelligence #ProcessOptimization #NoMoreSpreadsheets #NoMoreGuesswork #AscendCargoSystems

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THE 18-MONTH DEATH CLOCK: How Elite CFOs See the Future Jennifer’s SaaS company was crushing it. MRR up 18% month-over-month. Churn at 2.9%. Sales team hitting quota. The board approved hiring 8 more people. Eleven months later, they were negotiating down rounds to avoid shutdown. What happened? The thing that kills 82% of startups: they ran out of cash without seeing it coming. The brutal math was obvious in hindsight: Monthly burn: $340K Cash in bank: $2.1M Runway: 6.2 months Time to close next funding round: 9-12 months They were dead 3 months before they could raise money, but nobody noticed until month 8. The Forecasting Delusion Most “financial projections” are fantasy dressed up as Excel. Three scenarios: Best case: Everything goes perfectly (never happens) Base case: Things go as planned (also never happens) Worst case: Some stuff goes wrong (always worse than this) CFOs at dying companies had projections. Detailed spreadsheets. Color-coded dashboards. All of them wrong. Why? Because they projected what they hoped would happen, not what statistically would happen given their data. What $10B Companies Do Differently Amazon doesn’t forecast revenue by asking sales leaders “what do you think?” They run Monte Carlo simulations across 50+ variables: Customer acquisition cost trending Churn rate movements Average deal size changes Sales cycle length variations Market condition scenarios They run 10,000 simulations. The result isn’t three scenarios. It’s a probability distribution: 68% chance of landing between $X and $Y, 95% chance between $A and $B. Apple’s CFO doesn’t present one cash flow projection. She presents confidence intervals. “There’s an 85% probability we’ll have $X in cash by Q3, 50% probability we’ll have $Y.” This is how you don’t run out of money. The 87% Accuracy Standard MIT studied 3,400 companies’ financial projections vs. reality. Traditional projections (Excel models with three scenarios) were accurate within 15% just 31% of the time. Companies using probabilistic forecasting: 87% accuracy. The difference? Traditional models assume the future is linear. Probabilistic models assume the future is complex, with interdependent variables creating unexpected outcomes. Jennifer’s company projected $500K MRR by month 12. Linear growth from current $180K. What actually happened: Churn increased from 2.9% to 4.3% (one enterprise customer left) CAC increased from $1,800 to $2,700 (Facebook ads got more expensive) Sales cycle lengthened from 43 days to 67 days (economic uncertainty) Average deal size dropped from $8,400 to $7,100 (customers downgraded) They hit $287K MRR, not $500K. A $213K monthly miss that compounded into disaster. Continue reading this article - https://lnkd.in/gDeAw5-v #AIBIZGURU #AI #AIFinance #CashFlow

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