Elena Verna

Learn how to fine-tune your product to meet market demands with strategic customer conversations and data-driven feedback mechanisms 👇 ➡ 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗲: Brian Long, CEO & Co-Founder at Attentive 🔌 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: Nick Moran, General Partner at New Stack Ventures and Nate Pierotti, Principal at New Stack Ventures ✏ “The Playbook to Finding Product-Market Fit, When Founders Should Begin to Scale, and Lessons from Building Attentive” ⭐ 𝗦𝗮𝘃𝗲 𝘁𝗶𝗺𝗲 𝗯𝘆 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝘁𝗼 𝗮 𝘁��𝗲𝗺𝗲𝗻𝗱𝗼𝘂𝘀 𝗮𝗺𝗼𝘂𝗻𝘁 𝗼𝗳 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀: 💬 "I think that what we got right were two things. One, I think that we talked to a tremendous amount of customers, before we started building things. So we learned to hear their problems, hear their issues, and then start building and assaulting those, you know, rather than kind of just jumping right into build-build-build, so that saved us a lot of time." ⭐ 𝗚𝗲𝘁 𝗾𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝘀𝗰𝗼𝗿𝗲𝘀 𝗳𝗿𝗼𝗺 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗾𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸, 𝘁𝗼 𝗼𝘃𝗲𝗿𝗰𝗼𝗺𝗲 𝗽𝗼𝗹𝗶𝘁𝗲𝗻𝗲𝘀𝘀 𝗯𝗶𝗮𝘀: 💬 "I think you're also looking at things like NPS, you know… because if that number starts dropping, it's often because you're no longer solving the problem." ⭐ 𝗧𝗮𝗸𝗲 𝗮 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝘃𝗶𝗲𝘄 𝗮𝗻𝗱 𝗵𝗮𝘃𝗲 𝗮 𝘁𝗵𝗲𝘀𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗺𝗮𝗿𝗸𝗲𝘁 𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝘄𝗵𝗲𝗻 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝘆: 💬 "I think that we've taken an even longer term focus. We know what can be big. And we know it's dangerous to start getting an ego to think you can do things because it's always very hard. You've always got to think towards taking a longer term view, having a thesis around that view, and then understand how the pieces can come together for success." ───── 👉 Follow me, Alexander Small, for tactics and strategies for building startups from industry-leading Founders, Operators and Investors

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This article from CJ Gustafson, caught my attention: Net dollar retention rates (NDR) are facing a brutal decline. When I read this on Mostly Metrics, I knew I had to dive deeper into what's happening in the SaaS landscape. 📉 The Nasty Declines: The data doesn’t lie—top tech players are feeling the heat. Check out these declines over the past 24 months: Snowflake: -23% Zoominfo: -20% Digital Ocean: -17% Elastic: -13% DataDog: -10% MongoDB: -10%​​. Across a broader set of 32 companies, the average NDR drop is 7%, with a median of 5%. What’s Driving This? 🚀 → Hiring Declines: Tech’s biggest buyers are tightening their belts. Fewer hires mean less need for licenses. CFOs aren’t keen on bulk deals with uncertain headcounts. → Optimization Pushes: Companies are scrutinizing every dollar. They’re cutting back on non-essential expenses and optimizing their existing tech stack. → Buyer’s Remorse: Remember those multi-year contracts signed during the tech boom? Buyers are now questioning those commitments as they hit the tail end of these deals. Future NDR will reveal if companies are doubling down or scaling back. Usage-Based Models: Double-Edged Sword ⚔️ Usage-based billing, once a cheat code for growth, is now a liability in tough times. It’s easier to scale back usage than to break a subscription contract. Companies are "optimizing" their spend, impacting vendors' NDR. Efficiency Metrics 📊 Efficiency remains key. Here’s a snapshot: → CAC Payback: Median is 23 months → Rule of 40: 48% → Revenue per Employee: $398K Revenue multiples are trending slightly down week-over-week: → EV / NTM Revenue: 12.5x (-0.4x w/w) → CAC Payback: 23 months (flat w/w) → Rule of 40: 48% (-6% w/w) → Revenue per Employee: $398K (+$19K w/w) Perspectives: 👉🏽 Risk Adjusted Trade-offs: Long-term contracts are becoming less attractive. Short-term agility is valued over long-term discounts. 👉🏽 Value Metric Linked Contracts: Contracts tied to revenue growth metrics are seeing adjustments as top-line growth stalls. The Road Ahead 🛣️ We’re in for a wild ride. The second half of 2024 will be crucial in understanding how these trends evolve. Companies must stay agile, optimize smartly, and prepare for a potential reckoning in NDR figures. TL;DR Multiples are slightly down, with notable efficiency benchmarks to watch. Keep an eye on hiring trends, optimization pushes, and buyer’s remorse for the next big moves in SaaS Stay tuned, and let’s navigate these choppy waters together!

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Great article here from the team on the challenges of not only selling Health Insurance, but getting paid reliably and on-time from Carriers, FMOs, and SMBs alike. Before starting work with e123, I never fully appreciated or understood just how complex the #commissions management process can be for everyone involved. Excited to be helping smooth this process using modern #technology, and a whole lot of #empathy for the users 💜

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Product Configuration > Quote Generation > Contract Management > Winning the deal > Managing the revenue - all with Maple

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Great dinner and discussion on the cutting edge of outbound automation and signals. Two nuggets from the discussion: 1) Email deliverability is especially critical for GTM motions with large scale outbound automation (usually selling to SMBs and wide TAM). There doesn’t seem to be a silver bullet, and banking on one deliverability vendor / approach is risky. 2) Bulllish on signals and automation for up-market GTM motions, but needs to be done thoughtfully. Likely keeping reps in the driver’s seat and less end-to-end automation.

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There’s a dramatic divide in how GTM teams are setting up signal-driven outbound. Some teams are thrilled with a 2% conversion rate, and others expect 20%. Here’s why: It comes down to whether your motion is for SMBs or MM / ENT. Companies selling to SMBs have a massive TAM. Using Bluebirds, we found ~500k accounts in the US with less than 200 employees. With smaller ACVs and this large of a TAM, SMB motions need to rely on automation to make the economics work. Here are the benchmarks at an account level: - 0.3% meeting rate with just email sequences, no signals - 1% meeting rate with email sequences that are signal-driven (job change, job postings, fundraising, etc.) - 3% to 5% meeting rate with email, call, connection requests and signals It’s difficult to scale beyond these benchmarks without smaller territories and higher-touch rep involvement, which breaks the unit economics. Luckily this works really well if you can automatically touch thousands of accounts per week using tools like Smartleads, Bluebirds, Clay, dozens of inboxes, Orum / Nooks, etc. On the flip side, companies selling to MM / ENT have a much tighter TAM of ~50k accounts. After tiering, reps might be responsible for 100-200 accounts and need to drive $30k+ ACVs. Three VP of Sales running this GTM motion said they expect their reps to convert at a 15-30% meeting book rate, within 2 weeks of working an account. These teams still want signals to improve productivity and know where to focus, but don’t wan’t extreme outbound automation. Their tech stack looks more traditional, using Outreach instead of Smartleads, a moderate number of inboxes, and something like Bluebirds for signals. One approach isn’t better than the other, it just depends on your GTM motion. Feels like this nuance has been glossed over in the signal-driven outbound conversation on LinkedIn.

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Your GTM is broken. We have more knowledge, tools, and AI than ever... So why did 56% of companies miss revenue goals last year? ↳ Misaligned Teams - 60% of companies say sales, marketing, and CS are out of sync leading to wasted hours. ↳ Siloed Tech Stacks - 11,038+ GTM techs on the market - The average enterprise has 23 tools in their GTM tech stack, often not integrated ↳ Lack of Trust - 71% say internal go-to-market clarity is the biggest blocker to business growth than competition or market conditions. Your GTM is Better Together... Decisions about GTM technology, interoperability, team roles, and strategies cannot be made in silos any longer. → GTM Teams are Better Together - GTM-aligned teams are 67% more efficient at closing deals → GTM Tech is Better Together - 82% of tech buyers want software that integrates with existing solutions. They rank ease of integration higher than the cost or security. Source: G2 2023 Buyer Behavior Report → GTM Trust Happens when Teams and Tech work better together. Teams that work better together are 50% more productive (source: Stanford) and drive 21% more profitability (source: Gallup) Join us for Better Together... Learn more and request an invite from the 🔗  in comments #BetterTogether G2 Clari TechnologyAdvice Totango + Catalyst Software ZoomInfo ON24 Vidyard Demandbase MadKudu

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The journey from $1 to $10M in ARR is typically an exercise in finding your first channel-market fit as a GTM/Marketing leader and/or founder. After prioritizing the top 1-3 channels you believe are most likely to be a fit for your product, finding channel-market fit is typically an exercise in experiments and iterations. If you’re flying blind bc you don’t have the systems, processes, and data hygiene setup to measure and test quickly inside a framework, it’s going to be a difficult journey. Check out the podcast I did with the team at Evron for more mistakes to avoid on your journey from $1M to $10M ARR.

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"Earnings Week 💄on 🐷 Twilio up already 🧐, but low volume buying 😉. Except for the #hedgefund shuffle as they are only ones buying or talking it up. Sinch a bit down 🧐 and they're profitable 😳. Wtf? LINK Mobility is up with strong cash position! Bandwidth Inc. up and they love election season 💰. What is the Twilio insiders' game? Stock buyback 😉, but make sure you read terms and conditions💣😂. 👇 To date, Twilio has completed almost $800 million of repurchases from the initial program and is targeting to complete both programs, representing further share repurchases of approximately $2.2 billion, between now and the end of 2024. Subject to legal requirements, price, and economic market conditions. Mar 5, 2024. Or it’s because shareholders not accepting the fact that Jeff Lawson is really gone to the onion fields. And let the #Spam continue. See truth in telecom this week with Noah Rafalko and did you know Twilio is the fourth carrier in 🇺🇸? Alan Quayle brilliant blog and #podcast here https://lnkd.in/euvj7EYF Mobile Ecosystem Forum CNBC #wallstreet TADSummit Infobip Vonage tyntec GMS #cpaas Bird Five9 RingCentral 8x8 SignalWire CTIA Syniverse Tata Communications The Campaign Registry netnumber Eric J. Troutman #tcpa YouMail Inc. Sachem Capital LLC Jim Cramer #madmoney iBASIS Telesign Cisco GSMA Federal Communications Commission #stocks #madmoney Piper Sandler Goldman Sachs #finance #stockmarket #money

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Wrong product roadmap? We spent 3 months talking to SaaS product, sales, and CS leaders (50-200 employees). Misalignment around the product roadmap is common. Visionary executives dictate the roadmap. Loudest voice in the room gets priority. Customer voice, data, and feedback are ignored. Commercial viability is an afterthought. No framework for prioritizing features exists. Gut feel drives feature prioritization. Spending 2 quarters on a roadmap your ICP doesn't want hurts. Competitors focus on what the ICP wants. Prospects choose competitors, shrinking the market. Inbound leads decrease. Win rates decrease. Outbound effectiveness decreases. Churn increases. This is a multi-million dollar problem. Secondary problems include top rep turnover, CSM burnout, and wasted resources. Layoffs ensue. Everything on the roadmap is critical for business growth. Getting it right is hard. A framework is needed to assess commercial viability. Sales/CS takes the brunt of the pain. How do you focus the roadmap on your ICP? #ProductRoadmapStrategy #SaaSProductManagementBestPractices #CommercialViabilityAssessment #FeaturePrioritizationFramework #CreativerseAgency

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Yesterday we launched the next chapter of Pocus to help companies run ALL go-to-market playbooks, powered by AI. After 3 years building with the best leaders in GTM, this launch is a realization of our founding vision to unlock data for GTM teams. Isaac Pohl-Zaretsky and I built Pocus because we saw that go-to-market teams were desperate for accurate & actionable data. We started with PLG companies, who felt this pain acutely with product usage data. As we dug in deeper we realized there was an entirely new GTM motion — Product-Led Sales — that companies needed help building. With our customers and community, we built PLS from a niche strategy to a standard GTM motion. It has been incredible. This next chapter is a natural extension of what we’ve built in Product-Led Sales. In this next chapter of Pocus, we unlock ALL data (not just product usage), to power ALL go-to-market playbooks (not just PLS). We help customers turn all data into revenue, powered by AI. We’ll do this with 3 key product investments 1/ Enrich: unify customer identity across various data sources 2/ Engage: helps sellers reach out to the right users with the right message at the right time 2/ Optimize: help leaders copy & paste their best reps’ workflows Check out the blog Isaac wrote highlighting new features launching today and what’s coming soon (hint - it’s a LOT). We’re also co-hosting a webinar in a few weeks to talk through the state of the market, the trends we’re seeing, and how Pocus will help our customers continue to thrive.  RSVP at the link below.

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Hey RevOps - here's a hack for you to eliminate all the time and money you are spending on your sandbox environment 👇 Test in production. 😏 Follow me for me RevOps hacks...and have a good weekend y'all ✌️ #revops #revenueoperations #salesops #salesoperations #sfdc #salesforce #salesforceadmin #sfdcadmin

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Should every partner be a special snowflake? There's a delicate balance between building the meaningful relationships that make partnerships a unique GTM rhythm and building the operational efficiency to scale them. This is particularly difficult for partner enablement functions. Aaron Howerton and Adam Pasch and I got right into it on this episode of Literal Scratch. Also a little shout to Chris Lavoie, PhD... can't stop thinking about your post on leading indicators.

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Revenue operations, or RevOps, is the latest buzzword taking the business world by storm. But what is it, and why should you care? Let's cut to the chase: the purpose of any business is to make money. RevOps is all about aligning teams with data-driven strategies to boost sales, shorten cycles, and crank up those win rates. More importantly, fix disjointed communication among businesses. Bottom line: the impact of RevOps is massive and can't be ignored. #RevenueOperations #RevOps #DataDriven

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Expect these numbers to skyrocket in the next 12 months. Embedding AI across your stack is quickly becoming a do-or-die for GTM teams. StackOptimise

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Really excited to launch our new ‘Magic’ features at Insight Out. These new features are just the tip of the iceberg, and I can’t wait to continue bringing more magic to your product discovery process, how your team reuses insights, and overall see how Dovetail helps you figure out what to build next. We launched: Channels uses large language model (LLM) and machine learning (ML) techniques to process vast amounts of high-volume, continuous user feedback, and data sets such as support tickets, user feedback, and app reviews. Channels continuously analyzes incoming data and classifies it, enabling you to identify patterns before they become problems and more effectively prioritize your roadmap. Magic summarize automatically breaks down video, audio, text, and documents, extracting topics and key points, complete with timestamps that will take you directly to moments of interest. Magic highlight automatically looks for key and important moments in your data, giving you suggestions for highlights which you can accept or reject. Dovetail gives you a reason for each highlight suggestion, and you can get more specific results by typing in a custom prompt. Magic cluster finds themes in highlights on the canvas view in Dovetail, helping you make sense of a bunch of unstructured data in seconds to help you shorten the time to get to insights in your analysis process. Magic search allows you to ask your data questions and provides deeper insights across all video, audio, text, and documents in your insights hub. Using our improved filtering capabilities, you can now segment data more easily to find the most relevant answers. Magic search summarizes results, complete with citations, enabling you to share evidence-backed insights in an instant. Magic reels automatically shortens videos based on highlights or the summary, showcasing only the key moments. This enables the data to tell its story succinctly and enables stakeholders to play back a trimmed video. Magic redact makes data privacy protection easy by simplifying the process of blurring video, muting audio, and redacting transcripts. Enterprise customers can just select text in a transcript and click Redact to hide things. Most of these features are available to all Dovetail users today. Check out dovetail.com/launch/magic to learn more about our magic launch.

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In case you missed her at the Denver Sales Enablement Summit, Seismic VP of Enablement Irina Soriano outlines the Enablement Value Chain (EVC) in a new article for Sales Enablement Collective. What is the EVC exactly? According to Irina, it's a "proven framework to align your analytics, partnerships, and enablement team operating model in a unified approach that demonstrably improves seller behavior and business outcomes." Learn more at the link:

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Two posts I shared with my team this week about LLM's impact on SaaS. SaaS has always been about solving a problem. Engineers I interview often ask me "Why spend so much time on the LLM stuff?". Aka, "Why are we working on all the hard stuff that is barely working, when there's plenty of problems to be solved the old way?". Brett Queener's tweet about the UI. He talks about a world where most of a software product's user interface will be a prompt or a recommendation. "..its far better user experience than forcing people to hunt and peck across your tabs, list views, form views and dashboard for them to get done what they want to. It also means that what you used to compete on / differentiate on traditionally (I have these capabilities / better easier to use screens / yada yada yada) are kinda irrelevant" Matt Turck's LinkedIn post, "Is SaaS dead?" SaaS vendors are currently perceived as “last generation” despite best efforts to add AI quickly. Customers trying to build themselves, since it's pretty easy to get started. He says what happens next: "* customers realize that “build” is a headache, not always good * OpenAI / Azure etc can’t / doesn’t want to build hundreds of problem specific/ vertical specific apps * Takes time, but legacy and new SaaS companies truly become AI-first (not just marketing), abstract away complexity of deploying LLMs * macro environment eventually rebounds * AIaaS becomes the new SaaS - what is old is new" Both are linked in the comments. #who-moved-my-cheese

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