Ryan Seams

Just built one of the most scalable sequences using Sendspark's Dynamic Video feature. You see there's Account Based Sequencing (ABS) when talking in terms of top of funnel... And then there's Executive Multi-threading 🎯 I built a sequence that I could use to come on top of my reps outreach and level set with their executive team. The best part? I am using video in 4 of the email templates and they will be leveraging AI to... 👉🏻 Show my prospects company website and scroll on it (seems super personalized at first glance) 👉🏻 Say each executives name at the beginning of the video (even more personalized when they click into it) I usually get 12-16% click rates on these types of emails and the best part is I can personalize these videos with AI to a little as 10 or as many at 10,000 execs. Stay tuned for updates on this one! 🤠 #Outbound #Video #Sendspark #SoftwareCowboy #Warmly

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"Our goal is to create operating leverage through AI. ... One of the ways I look at it is, our total people costs will go down, our cost per head will go up, and my GPU per researcher will go up." — Satya Nadella. I finished listening to the BG2 podcast with the Microsoft CEO last night. Extremely interesting conversation with Brad Gerstner and Bill Gurley, and a lot to chew on. I found myself skipping back repeatedly to listen to certain parts again, to make sure I understood exactly what Satya was saying. One example was the quote above, in the context of the long-term implications of AI for the workforce, specifically at Microsoft in this case. I'd be very interested in hearing what everyone thinks of this. Here's that specific portion of the show: https://lnkd.in/gcPjHpeg Here's the full podcast: https://lnkd.in/gHAquZpD

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I recently shared that one of my favorite parts about working at Amplitude is the fact that despite being a public company, it still feels like a startup. And I think one of the key reasons for that is the high sense of ownership that everyone here has. Ownership is one of our core company values, and I love being able to see it in action every day. I share more below:

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Last week Vivun unveiled Technical Sales Insights, the industry’s first set of benchmarks based on aggregated and anonymized data. Goodbye surveys, hello reality. Let’s talk about what I see in AE:SE ratio… Currently, AE:SE ratios are an average of 2.9:1 This ratio decreased 31% YoY 🤯 Takeaway? A “flatter” ratio (ie. closer to 1:1) highlights the continued importance of technical sellers to driving revenue. As much rhetoric exists around “doing more with less” and “replacing your SEs with full stack AEs” the data clearly tells a very different story. 🧐 It’s also why we launched Technical Sales Insights. To use real data to cut through noise in the market or false narratives based on how the best companies in the world actually operate. This trend was also supported by the conversations I had at the Salesforce Ventures CEO summit in April, where CROs were looking to add PreSales firepower to their teams due to the need to bring more technical expertise to their sales motions – and do it earlier. 💪 More broadly, we also see this ratio flatten as Sales Engineers are pulled into new work streams such as post-sale support to retain and expand customers while also helping with early stage pipeline qualification and acceleration. Every month Vivun will update these critical KPI’s and I’ll share a synopsis of the most salient points. Bookmark the Technical Sales Insights page to never miss a beat: 👇 https://lnkd.in/dAX8rZRy If you’re a CRO working with your CFO on headcount planning, or a PreSales leader saddled with an out of whack AE:SE ratio, now you can rely on these insights to guide your strategy and planning. #b2b #sales #presales

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Congrats to the team at Rockset on their acquisition by OpenAI! 🚀 We’re excited to see how their tech and expertise will boost OpenAI’s real-time capabilities. Unfortunately, this means the Rockset commercial product will be discontinued. If you're a Rockset customer looking for alternatives, we'd love to help you. Seamlessly migrate your Rockset data to Imply Polaris, a fully managed, cloud-native analytics database built from #ApacheDruid. 🧙🏼‍♂️ https://lnkd.in/ejQqPbw2 #Rockset #OpenAI #ChatGPT #AI #LLM #realtimeanalytics #streamingdata #Apache

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Excellent study from Damian Rollison and the SOCi, Inc. team which seems to confirm my longtime hypothesis around citations (https://lnkd.in/gxZQwdx3): the only citations that are valuable are the ones that rank in Google for the keywords you want to be known for. https://lnkd.in/g49DMSeN

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TollBit just landed $24M in Series A funding, and this is the kind of deal that doesn’t just raise eyebrows—it rewrites the playbook. Lightspeed led the charge, but they weren’t rolling solo. Heavyweights like Section 32, Sunflower Capital, AIX Ventures, and more piled in to back this rocket ship. Massive congrats to Olivia Joslin and Toshit Panigrahi for pulling off what every AI startup dreams of: solving a huge problem and locking down the resources to scale it like nobody’s business. Now let’s talk impact. AI companies have been cruising through the internet, gobbling up content like a buffet, while publishers have been left holding the bill. TollBit is here to fix that. They’ve built a platform that tracks AI bot traffic, charges for every content use, and does it all automatically. For publishers, it’s not just a payday—it’s getting paid on time without the hassle of chasing down AI companies. And for AI businesses? They finally get a legitimate, scalable solution for content licensing—without stepping into legal quicksand. That’s a win-win. But that $24M isn’t just for show. It’s getting funneled straight into infrastructure. We’re talking faster payment protocols, even smoother integrations, and the muscle to handle the tidal wave of new publishers and AI companies jumping into the mix. And with Time Inc., Penske Media Corporation, and ADWEEK already signed up, this isn’t just a proof-of-concept. TollBit is the real deal, sitting at the intersection of AI innovation and media monetization. Olivia Joslin doesn’t mince words: “This market’s moving at light speed, and neither publishers nor AI companies can afford to be bogged down by manual deals. We’ve made it frictionless for both sides to come out on top.” Toshit Panigrahi sees the bigger picture: “What we’re building isn’t just a platform. We’re creating a future where publishers get paid for the content that powers AI, and AI companies keep innovating without stepping on any toes.” Here’s the real takeaway: TollBit just carved out their place as the go-to for AI content licensing. They’ve figured out how to make sure the creators behind the content get their fair share while helping AI companies keep the innovation engine running. This $24M Series A? It’s just the beginning. Everyone else in the space? Better start taking notes. #Content #Publishing #Publishers #ContentLicensing #Technology #Innovation #AI #MediaMonetization #Technology #Innovation #ContentPayment #AIInnovation

Let’s say you run a lift test for TV every six months and those results change over time. Here’s what you want (and don’t want) to hear back from your MMM vendor when you ask about incorporating the results of lift tests: A bad answer in that situation would be: The modeler just takes the average between the two tests. If they’re doing something like that, it might be because their modeling framework isn’t compatible with marketing channel performance changing over time. A good answer in this situation would be: Yes, we can use the lift tests to help train the model and inform the model’s results even if the results change over time. You really want a model that can naturally incorporate the results of various different types of lift tests over time naturally and consistently, and model that new information in a statistically rigorous way. I think it’s obvious that lift tests and experiments go hand-in-hand with good marketing mix modeling and can be used to reinforce each other. But I also know that actually integrating them isn’t as straightforward as it seems. I’m biased but we’ve done some very interesting stuff on this at Recast and Michael Kaminsky and I would love to share what we’ve learned over the last few years. We’ll be doing a live Office Hours + Q&A where we’ll get technical on how we do this and the best practices we recommend – so, if you’re a marketing-focused data scientist, we’d love to see you there! I’ll put the link to join us in the comments.

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If you want to hear two guys geeking out about latest fraud trends, then this is for you!! This is one of the best podcasts I have done. Thanks again Jordan for having me on www.fraudboxer.com

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When we first met Jon + Pål a year ago, they were deep in figuring out how to unlock the next phase of growth: building their outbound sales motions. With 500K+ devs using the platform, Sanity had built a strong PLG motion and they were looking to see if they could spot enterprise/upsell opps from all this activity. They took a bet on Koala — that they now call a gamechanger. I'm particularly proud of how we've iterated together – working with Jon to deliver quick early value during the POC and then working with both Jon and Pal to layer in lots of new data & use cases together. Two weeks in:  - Sellers now have self-serve access to website, docs, and product data.  - We’re already getting pinged with meeting highlights sourced with Koala! Fast-forward one year:  - Koala has become the go-to daily tool for sellers. - They integrated G2 intent and PLS data to further identify top accounts in-market.  - Outbound has become their main lead source! What’s next? (We're not done yet!) - Migrating from their legacy ABM platform — with Koala’s intent data, they’re unlocking use cases like ad retargeting, segmentation, and more. I think what made it all really click for me was when Michael, a top SDR @ Sanity shared how with Koala: 😍“Outbounding is fun again!!” 😍 For us, that’s what it’s all about: building a platform that sellers actually want to use, and we can’t wait to watch more magic happen at Sanity! Check out the case study here 👇 https://lnkd.in/gg-d_uW5

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Want to run #llama models on #arm devices? Check out the latest in llm’s on arm devices. #xnnpack #android #chatbot #meta #exucutorch #pytorch

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MAJOR news to share – announcing Totango Unison, the only enterprise-grade AI built to tackle churn with real precision, not just the typical “out-of-the-box” solutions you’ve all seen lately –👇 1. Here's how Unison is different: Everyone’s throwing around “AI” like it’s a magic wand. But most of these so-called solutions are surface-level, one-size-fits-all models that claim to predict churn or growth by pulling just sentiments from emails or call transcripts on Gong / Zoom. For enterprises, they need AI that’s as tailored and complex as their own operations—not some generic plug-and-play model. Unison's Custom AI is built from your data, your customers, and your specific business model. We dig deep into your "historical" customer data—relationships, contracts, usage, feedback, sentiments, and even market signals. We then analyze that information to build customized models that deliver insights unique to your business. This allows you to predict churn and spot expansion opportunities months or even years in advance with UNMATCHED accuracy. 2. Security and compliance you can trust: This is where Unison’s custom models really shine. In enterprise environments—especially those with thousands of employees and rigorous security protocols—deploying AI solutions often hits roadblocks during security reviews. Out-of-the-box solutions, while marketed as “security-ready,” usually fail to pass real-world security audits. Why? Because they often pool and process data across multiple customers in shared environments, which poses significant risks to data privacy, data residency, and access control. With Unison, all models are isolated within your controlled environment. This means you’re not sharing model inputs, outputs, or training data with other businesses. P.S. For smaller teams, we also have something for you 😃. Unison offers a standard AI model that will deliver immediate insights by analyzing your customer data in days! Wait there’s more: Catalyst Software has unified under the Totango brand, along with Unison, creating one unified, comprehensive suite of products. This lets us deliver the most advanced, enterprise-grade solutions, ensuring that both fast-growing businesses and global enterprises have the tools they need to drive customer-led growth. And ONE MORE piece of news: we’ve also brought on Keith Frankel as our Chief Product Officer. His team at Parative built the tech behind all of this, achieving over 99% accuracy in churn prediction for some of the world’s largest enterprises! This is just the beginning. Unison launches in December, and we’ve already got more AI models and features coming in 2025. The future of customer-led growth is here, and Totango is leading the charge. See comments for a special event to join us to learn more: #CustomerSuccess #AI #ChurnPrediction #RevenueGrowth #EnterpriseAI #CLG #AI #Revenue #GTM

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PSA: Stop using the original OpenAI CLIP weights. I see a lot of people using CLIP for multi-modal retrieval. What many people may not know is that there are 100’s of CLIP models now since OpenAI’s original release. These newer models offer significantly better performance across many benchmarks. For product search related tasks we recommend the following. + Fastest inference: ViT-B-32 [laion2b_s34b_b79k] + Best balanced: ViT-L-14 [laion2b_s32b_b82k] + Best-all round: xlm-roberta-large-ViT-H-14 [frozen_laion5b_s13b_b90k] You might notice these are not the ones that perform the best on the benchmarks. Why? Because the benchmark only test for very specific tasks which are often quite different from search and recommendations. These are ones we have found to be the best for product related search and retrieval tasks and generally very good first choices if you are unsure where to start (even for non-product related search). Here is a hugging face space with all the data: https://lnkd.in/gxu5UYkt Read more: https://lnkd.in/g5_D4ccs

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Great post by Andrew Petcash on the rise of wearable tech in sports. From performance tracking to data-driven decision-making during games and practices, there is now - literally - too much information available for any one person to act on in real time. And that’s where AI has been transforming the playing field (pun intended) by turning a flood of data into guidance and insights. Not to mention leveling the landscape: this kind of individualized insights and bespoke planning isn’t just for elite athletes anymore, it’s there for anyone who can strap on a wearable. And this $120bn market is wide open for future innovation. It’s an exciting time to be part of it, with BlueSkyAI helping Fieldhouse Holdings integrate all the wearable tech. #ai #wearables #blueskyai #genai https://lnkd.in/eafhpbmC

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$2 H100s: How the GPU Bubble Burst Last year, H100s were $8/hr if you could get them. Today, there's 7 different resale markets selling them under $2. What happened? The market has flipped from shortage ($8/hr) to oversupplied ($2/hr), because of reserved compute resales, open model finetuning, and decline in new foundation model co’s. Rent instead. https://lnkd.in/gpQEidmC

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It feels impossible to plan more than 6 months ahead in GTM right now. Ok, sure, we’re still thinking about financials and growth in multi-year plans.  But AI is changing the landscape of GTM so quickly that making a detailed roadmap more than a few months long is a waste of time. Here’s how I’ve made my planning more flexible: 1/ Go back to first principles thinking. What are the core problems we’re trying to solve? Who are we trying to help? The way we get there might change, but the answers to these questions don’t. 2/ Think Legos, not bricks. Build our core tools and features to solve multiple problems vs. creating point solutions. This helps ensure our new releases aren’t obsolete in a few months. 3/ De-risk with design partners. Getting customer feedback early and often helps us see around corners and avoid betting on the wrong solutions. We got sooo much valuable feedback from our AI Strategy design partners that played a huge role in what the final product looks like! Curious what other founders and leaders in my network are doing to stay flexible? 

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🚀 Zetta just released a new feature for automated anomaly detection to tell you when your data isn't looking right. Data quality is one of the biggest challenges data teams face and Zetta is solving this so you can trust your data and make confident decisions. Zetta is an all-in-one AI data platform designed to give you instant data clarity and insights—no data team, infrastructure, or building required. Company wide automated analytics and insights can be setup as quickly as one day with Zetta. Here’s what you get with Zetta: Ready-to-use dashboards that track your key metrics Automated insights that tell you what’s driving your numbers Predictive forecasts to help you plan ahead A conversational AI Analyst so you can explore your data with simple questions A secure, reliable, and scalable data warehouse that’s fully managed for you Let's unlock the full potential of your data, together! https://zettadata.ai/ #AI #Data #Analytics #Businessintelligence #Startups #Growth

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👉 Analytics and Product trailblazers tell their real-life stories of insights that led to massive growth. Shoutout to Lenny Rachitsky / Lenny's Newsletter, Phil Carter, Maria Pergolino, Casey Winters, Japna Sethi, Ken Rudin, Darius Contractor, Jenna Lemonias, Andreas Gross, Elena Dyachkova for contributing 🌟 - your insights are driving innovation and shaping the future of data analytics. 🙏 I’m thrilled to share our collection of "Outta Sight" Insights from these leaders. 🚀. We asked our partners to share their geeky, cocktail party-worthy stories 🍸 - incredible journeys and impactful insights! The stories they shared are incredible. 🍻 🔥These trailblazers, who helped build Airbnb, Google, Grubhub, Quizlet, Zynga, Adobe Marketo, Dropbox, Otter.ai, Life360, Peloton Interactive, The Atlantic, Loops, and other companies, have seen firsthand the transformative impact that robust insights can deliver. Enjoy reading about their bold, analytics-driven moves in this Outta Sight Insights collection. 🔥 Here’s why we built this collection: At Loops, we know there's too much "data theater" out there, distracting from the robust insights that truly drive growth. 🎭 We wanted to inspire data analysts and product growth teams by showcasing these powerful wins. It’s easy to forget the thrill of a big win, especially when grappling with daily growth challenges. These stories remind us of the profound impact that data-driven decisions can have on a company's trajectory.🚀 👉 Together, let's move beyond data theater and focus on what really matters: driving actionable insights that fuel success! 💡📈 https://lnkd.in/d4QsxKgf #DataDriven #ProductAnalytics #GrowthInnovation #CausalML #DataDrivenDecisions

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How to transform your reactive QBRs into a churn forecasting powerhouse PART I ⬇⬇ Let’s face it: The way most orgs run QBRs turn out to be a waste of time (for everyone). They’re time-consuming to prepare, executives rarely show up, and when they do, they’re bored out of their minds. Why? Because most QBRs focus on cherry-picked usage stats and roadmap slides that have nothing to do with your customer's key needs. But here’s the thing: the problem isn’t the QBR itself—it’s how it’s delivered. Executives don’t care about your internal metrics. They care about Outcomes. They want to know "how is your solution helping us achieve our goals" and if not "what are we doing about it?" Here’s how to fix it: ➡ Don’t call it a QBR. The new world is called a Value Review — a session to review the value your customer is (or isn’t) realizing and identify ways to unlock more. ➡ Be a partner consultant. Your job is to guide your customer on their value journey, not just review the past. Position yourself as the expert who can help them achieve their priorities. ➡ Center the agenda around outcomes. Engage stakeholders beforehand to understand their priorities and perceived gaps. Build the meeting around actionable recommendations tied to their goals. When we’ve helped teams make these shifts, the results have been game-changing: ✳ 50%+ increase in executive engagement. Simply reframing the invitation as a Value Review can get decision-makers in the room. ✳ 33% of Value Reviews lead to upsell opportunities. By identifying unmet needs and offering targeted solutions, teams can turn reviews into growth moments. Stop wasting your time with internal metric-driven QBRs. By focusing on customer-value driven reviews, your Exec Buyers (and revenue) will thank you. Want to dive deeper on how this works? Check out our latest article in the comments below detailing this critical mindset shift. We've got two more pieces coming out this week so stay tuned for more! #CustomerSuccess #ValueRealization #QBRs

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