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The answer is probably Yes, if we look at the actual, hard SaaStr data from SaaStr Annual 2024: What you can see above is: As the booth gets bigger, the number, almost linearly Activations are a big deal. So if you are doing them, the data suggest lean is as much as you can. The Latest SaaStr Data appeared first on SaaStr.
Vendr’s latest data suggest overal l, net net, ACVs aren’t going up from AI. Vendr’s Data Says … Maybe Not appeared first on SaaStr. Salesforce is going big here with AgentForce at $2 per usage. Others are more cautious. Not yet at least: Where this will all go, I think it’s too early to know.
The Real Truth About AI Data Privacy in 2025: What Every SaaS Company Needs to Know The explosion of AI adoption has created massive new privacy risks for SaaS companies. The Dirty Secret of AI Training Data Let’s get real: Everyone’s rushing to build AI features into their SaaS products.
So they see 40% of all mobile subscriptions — and a ton of data from it. But the RevenueCat data suggests even longer may work well. But if they are using it and getting value, the RevenueCat data is a reminder to be patient here. The data across ~40% of all paid mobile subscription apps in U.S. Dont play games.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.
Our platform unifies core financial and broader operational data and processes within a single platform, with solutions that maintain the integrity of corporate reporting standards for Finance while providing operationally significant insights for business users.
If the Modern Data Stack is dead, what replaces it? The Postmodern Data Stack. This idea is taking hold in data. Two juggernauts, Snowflake & Databricks, are challenging the dominance of Google, Microsoft, & Amazon at the biggest scales of data. We will discuss the Postmodern Data Stack at TC Disrupt in October.
Organic Search remains our #1 source of SaaStr readers for 13 years straight: But, I can also tell you what our data says. Our Data Says “Yes But” appeared first on SaaStr. That’s a gift. At a high level, as big a gift in 2024 as 2011. Which is that we have literally 100x more quality content than 2011-2014.
On Monday, at TC Disrupt Colin Zima CEO of Omni , Jordan Tigani CEO of Motherduck , Daniel Svnova CEO of Superlinked & Toby Mao CTO of Tobiko Data who are leading the evolution of the Post Modern Data Stack discussed the trends they are seeing. Most data workloads are quite small, about 100MB. Vectors power AI systems.
Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage
He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use. . 💡 This new webinar featuring Maher Hanafi, VP of Engineering at Betterworks, will explore a practical framework to transform Generative AI prototypes into impactful products!
.’ The twist this time is the data is very hard for startups to acquire or accumulate. So it’s not just the innovation piece, but you need a proprietary data set to do something more meaningful with AI. Brian believes incumbent players like HubSpot and Salesforce have some key advantages: 1.
So it’s interesting how folks craft headlines from data. Carta release its latest funding data the other data for 2024 Year-To-Date here : What you can see is that large, hot AI later stage deals overall are indeed driving venture capital deployments up ~ +17% the year. But that’s for late-stage capital.
They recently did an Analyst day though that had some more great data that I thought was super interesting, and worth a deep dive. Starter Customers More Important Than Ever, Now 47% of Customer Base This data is super interesting, and a challenge to some conventional thinking in terms of how to go upmarket. But helpful to see.
The findings arent much different from what weve discussed before at SaaStr, but still very helpful to see, given how much data they are pulling from: On average, $100k deals take about 70 days to close. That ties to our overall rough sales cycle data here: Dear SaaStr: What’s a Good Benchmark for B2B Sales Cycles?
Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage
This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. When developing a Gen AI application, one of the most significant challenges is improving accuracy. The number of use cases/corner cases that the system is expected to handle essentially explodes.
So the latest data from Carta is helpful. But still very helpful to see the actual data. Of 1,302 seed-stage rounds on Carta this year. The bar in SaaS has gone up, and VCs really want to see AI-fueled growth.
But as Brian Halligan, chairman of HubSpot put it, the downturn where there was one, ended in Q3’24: And as one small data point, that’s what we are seeing in early SaaStr Annual attendance data as well: Overall early 2025 attendance is up +156% of 2024 December 2025 ticket sales were +181% of 2024.
” – Alex Rosenblatt The Channel-First Approach That Actually Works Instead of the everything-at-once approach, Rosenblatt advocates for a methodical, data-driven methodology: 1. . “When companies try to do a little bit of everything in marketing, it becomes nearly impossible to become amazing at any single thing.”
All unstructured data to be instantly searchable. You’ll fall behind if your data can’t be found or it’s in painful dashboards. People expect to extract data from your product with a plain English prompt. Build a workflow to connect data, and the CEO will get a dashboard. Settling is worse than hiring no one.
Greg Loughnane and Chris Alexiuk in this exciting webinar to learn all about: How to design and implement production-ready systems with guardrails, active monitoring of key evaluation metrics beyond latency and token count, managing prompts, and understanding the process for continuous improvement Best practices for setting up the proper mix of open- (..)
It’s also data that suggests that for those that have seen a downturn, the bottom is behind us. So it was fun and perhaps not surprising to see the latest SaaStr poll data. It’s not just AI, although that’s part of it. Holy cow are some top SaaS stocks on a run.
There was a real initial problem Samsara identified early on a ton of food (and money) being wasted due to old infrastructure and lack of data among the delivery fleets. There are all these like leading indicators of risks and if you have all that data, you can actually get out in front of the risk and coach the driver.”
It starts with silicon chips, GPU, and data centers. After that, you have infrastructure around the models, helping you pick the right models or managing the data to be fed into the models. Previously, we had enough data centers to power a lot of CPU computing needs. Then, there are models like GPT4 and Claude from Anthropic.
There’s a lot of great data in the report, but one analysis helped answered a question I’ve been wondering the past 12-18 months: Are start-up actually more overvalued today than at the peak of Cloud mania in 2021? So Battery Ventures has a new detailed report on The Open Cloud you can dig into here. in 2021 to 23.4x
Contact and company intent data both have their advantages. This infographic unpacks the advantages of both contact and company data and gives details about how B2B marketers can benefit from both. This infographic unpacks the advantages of both contact and company data and gives details about how B2B marketers can benefit from both.
Improving Rev Ops for Data-Driven Decision Making One of Lindsey’s first priorities was diving deep into the company’s existing data to identify trends and leverage these findings for growth. Lindsey found a way to track this data, then brought it into every meeting. Better RevOps uncovered excessive discounting.
Far more than ever, per Redpoint’s latest data. So we’ve all watched the mega VC rounds for OpenAI, Databricks, Anthropic etc as VCs deploy billions into the AI winners at scale. But just how much of venture capital overall is going to … the top names? In 2024, 31% of all VC capital went into just 20 deals.
The Three Types of Context Your AI Needs Your AI agent needs access to: Team documents & engineering specs (the “how it works” knowledge) Systems of record like CRM (the structured data) Unstructured info from Slack, Zoom, etc. the tribal knowledge) Without all three, you’re flying blind.
Data quality requirements User experience impact 4. Data Architecture Makes or Breaks AI Success The unsexy truth? Your AI is only as good as your data infrastructure. This underscored the importance of a unified data architecture and comprehensive training approach. Cost structure (can we sustainably deliver this?)
More on that data here. Canva, Stripe, Databricks, ServiceTitan, Gusto, Wiz should all have epic IPOs in 2025 or maybe 2026. But one thing is clear: it’s taking longer to IPO: It used to take 10.4 years on average to IPO in SaaS. The last 3 SaaS IPOs took 11.3 years Now look, who knows. These aren’t the only IPO candidates.
A few data points: At $800m ARR, Qualtrics was still getting 25% of revenue from professional services. Dear SaaStr: How Much Should a SaaS Company Invest in Professional Services? A rough yardstick is that most enterprise-focused SaaS companies tend to get about 8%-10% of their revenues from professional services.
Joselyn Goldfein , Managing Director at Zeta Venture Partners, which invests in AI and data infrastructure-focused startups from inception through seed stage And see everyone at 2025 SaaStr Annual, May 13-15 in SF Bay!! ” Weavi Founded in 2020, they anticipated the growing importance of unstructured data and embeddings.
Per Carta’s latest data, only 18% of tech sales execs are women, and 26% of reps overall: I’m slightly heartened that overall it’s at 26% women outside of the executive suite. So have we made any real progress in diversity in SaaS the past 5 years? The past 10? In some ways, for sure yes. A long way.
Sales and marketing leaders have reached a tipping point when it comes to using intent data — and they’re not looking back. More than half of all B2B marketers are already using intent data to increase sales, and Gartner predicts this figure will grow to 70 percent. Intent data can be overwhelming if you don’t know how to use it.
However, when Theory layered in the actual data to see how much more efficient they were year-over-year, the numbers were between -4% and 4%. There is a dominant belief that AI makes processes more efficient, but this belief has not yet been confirmed by the data. #8:
Rather than committing to single solutions, successful companies are: Testing multiple AI models continuously Using self-served platforms for rapid experimentation Empowering engineers to make model-selection decisions Avoiding long-term vendor lock-in Data Management Data quality and integration have emerged as critical factors in successful AI implementation. (..)
MailChimp) Document search and analysis Form filling and data entry This is particularly impactful in vertical SaaS, where industry-specific manual processes can be automated to reduce staffing needs while maintaining quality. Success stories include: Transcription and note-taking (e.g., Zoom summaries) Email copy generation (e.g.,
Usage data feeds product-led growth (PLG) lead scores, enabling account executives to outbound to the most promising users. Longer sales cycles : Recent data shows usage-based pricing models experienced 29% longer sales cycles in 2023 compared to 21% for seat-based companies.
Data normalization. However, if lead generation, reporting, and measuring ROI is important to your marketing team, then data normalization matters - a lot. At its core, data normalization is the process of creating context within your marketing database by grouping similar values into one common value. Why is this so essential?
Just had a check-in call with a $50k a year vendor we use for SaaStr They sent a new sales rep to call who didn't know the account They just walked through a deck with data they didn't collect or know That we'd already read What a waste of everyone's time The Death of the… — Jason ✨????SaaStr
So they’re always going to have a pretty large stack of existing data to bring over. Instead, they use data from the product trials to observe customer behavior and leverage that user data to personalize their sales outreach. Make these things easier.”
. #4: What Really Works When Hiring VPs, and How M&A Really Works, with HubSpot Co-Founder and Chair Brian Halligan + Jason Lemkin #5: Whats Better, X/Twitter or LinkedIn?
Enterprise data is the differentiator: While public data dominates foundation models, enterprise-specific data represents less than 1% but delivers exponentially more value when properly leveraged. cost savings, 35% time savings The Partner Ecosystem Play IBM isn’t going it alone.
In this webinar, Joe Apfelbaum, CEO of Ajax Union and business strategist, will take you through the ABCs of intent data. You'll learn how to effectively use it to drive business results, with practical tips on how to leverage both company and contact intent data to maximize your marketing efforts.
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