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Based on internal analysis of industry data, we estimate the customers of trades businesses, which we refer to as “end customers,” spend approximately $1.5 ”” Benchmark Data The data shown below depicts how the ServiceTitan data compares to the operating metrics of current public SaaS businesses.
Here’s some additional data on their products I mentioned above (all copy / pasted from the S1) FigJam : “FigJam is our product tailored for ideation, brainstorming, and rapid communication of ideas. .” ” [ My note: Their products here are Figma Sites and Figma Buzz.]
This isn’t your typical “here’s what public companies are doing” report – this is real data from real private companies dealing with real challenges in 2025. Learning #4: The Equity vs. Bootstrap Reality – Paying for Growth Has Hidden Costs The data reveals a fascinating paradox in B2B funding strategies.
In “ Data Rules Everything Around Me ,” Matt Slotnick wrote about the difference between SaaS & AI apps. A typical SaaS app has a workflow layer, a middleware/connectivity layer, & a data layer/database. AI makes writing frontends trivial, so in the three-layer cake of workflow software the data matters much more.
As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. With the 3.0
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 Foundation: 18 Million Words of Training Data First, the obvious part. We launched it without any further training and QA — and with that amount of data to underpin it, it was often good. Harvey didn’t just dump data into a model and call it good. Scale AI’s Data Engine Scale AI gets this better than anyone.
Billion PostgreSQL Battle for AI Agent Supremacy Brief Overview : Two data giants are making strategic moves to dominate the AI agent infrastructure market through major PostgreSQL acquisitions. Databricks: The $1.25 With this news, we will be introducing Snowflake Postgres: enterprise-grade, AI-ready, and fully managed.
When you’re expanding your software business into new regions, industry benchmarking data can help you make better strategic decisions by answering important questions about business in the region. and EU customer data to set “one-size-fits-all” global pricing. The post Breaking Into Asia?
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0
The Data : Analysis of 14,000 executives across companies in Pave’s real-time compensation database, measuring annual turnover rates and implied median tenures. Thanks to new data from Pave analyzing 14,000 executives across their compensation database, we finally have the numbers. Yes — probably. And honestly?
.’ 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 SVB still is the #1 bank for tech companies in Silicon Valley and they have a ton of data. The data shows down rounds comprised 30-50% of IPOs even during “good” periods. They have 24% of all U.S. VC deals now come from accelerators and incubators — and that's going way up.
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.
And now we have enough data to add Threads to our roster, too! And now we have the data to prove it. Posts published then had the highest median engagement — that is likes, replies, and reposts — across all the data we analyzed. on Wednesday. Other high-performing times were 7 a.m. – 9 a.m.
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.
Here are some best practices that I’d recommend: Centralize Data and Metrics RevOps thrives on data. Use tools like Salesforce or HubSpot to centralize this data and ensure everyone is working off the same numbers. Without this, you’ll constantly be fighting over whose data is “right”.
” This philosophy has driven Rubrik’s expansion from a core data protection platform to multiple product pillars spanning data protection, data security, and AI enablement. The Problem: Rubrik’s platform handles data across data centers, cloud VMs, databases, object storage, and SaaS applications.
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.
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?
Delivering great AI customer service depends on more than just sophisticated AI models – it relies on real-time data and access to business systems like CRMs, billing platforms, and past conversations. MCP integrations rely on two key roles: Clients (like Fin) that request and use external data.
commercial revenue surged 71% as Artificial Intelligence Platform (AIP) gains enterprise traction Deep government/defense roots benefit from heightened geopolitical tensions worldwide Clear AI differentiation has made it indispensable for data-driven decision making How AI Led Palantir From Slow Growth (13%) to Hypergrowth (49%!)
The data, compiled by Stanford’s Venture Capital Initiative, shows IPO share of unicorn exits dropped from 83% in 2010 to just 11% in 2024—a fundamental restructuring of the exit landscape that has permanent implications for SaaS founders. The data is stark: IPO share of unicorn exits dropped from 83% to 11% between 2010 and 2024.
The Numbers Don’t Lie: A Market Overall in Retreat The latest data from Jamin Ball’s Clouded Judgement analysis paints a sobering picture of the SaaS landscape. Billion in ARR The data presented reflects aggregate performance across the cloud software universe as tracked by Clouded Judgement. billion in Q1 2024.
Your Customer Data Is Your Biggest Competitive Advantage The Key Insight: The companies with the richest, most proprietary datasets will win in the AI agent era. “What data are you sitting on that is proprietary to you?” ” This transforms static data into dynamic business intelligence. ” Levie asked.
The Problem Was Real: Apple and Google’s app stores weren’t giving developers the data they needed. They were two developers who had lived through the nightmare of subscription infrastructure while working at Elevate. Basic questions like “What’s our churn rate?”
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.
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.
You Need ALL Your Data (And It’s Probably Messier Than You Think) Your AI needs to be trained on everything: CRM data, marketing automation platforms, website content, past event attendees, sponsorship history — all of it. But here’s the reality check: your data probably isn’t as clean as you think.
Amplitude Amplitude’s onboarding process focuses on defining what success looks like for your product and encourages you to start implementing event tracking as soon as possible (with or without demo data). This approach makes sense for a data-driven product like Amplitude. What can you learn from Grammarly?
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.
Our audience is sophisticated — they want real insights, backed by real data. We use Claude to dive deep into complex topics, analyze data patterns, and uncover insights that would take our team hours to research manually. The dynamic charts it creates from our SaaStr data? That’s where Claude comes in. ” 9.
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?
Companies with strong PLG foundations have rich product usage data and customer engagement signals that can be leveraged for enterprise sales. The data infrastructure is already there—it just needs to be redirected toward sales motions. While this data wasn’t initially configured for sales use, the foundation was solid.
The data also shows Figma’s Research & Development spend nearly equals Sales & Marketing spend. Both of these are removed in the non-GAAP data cited above. Non-GAAP Op Margin 17.0% Sales Efficiency 1.00 Adjusted FCF Margin 1 24.2% The gross margins are identical. Figma’s 132% Net Dollar Retention is top decile.
.” What Revenue Operations Actually Means : “The operations team that supports the revenue function, which can support sales, design the quota, design commissions, and design go-to-market strategies and leverage data to make good strategies for the team.” We should make decisions, leverage data.
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.
Bootcamps With 1,025+ Organizations Are a Key Marketing Strategy Customers want to solve their big data problems with AI, but aren’t 100% sure how. NRR of 114% I would have expected even higher given the huge deal sizes, but no matter, still top tier at scale. And a true engine of growth. #9. Maybe copy this. Wow, Palantir.
” – 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.”
Here are a few compelling reasons why you should consider joining Bluesky: Greater control over your data : Unlike traditional social media platforms, where a central entity controls your data, Bluesky gives you ownership of your information. This means greater privacy and security, allowing you to manage your data on your terms.
In this post, I’ll take a data-driven approach in evaluating the overall group’s performance, and highlight individual standouts along the way. Let’s get into some high level data. Of course, the above chart shows historical (ie lagging) data. The median beat was ~2.2%
In the rapidly evolving healthcare industry, delivering data insights to end users or customers can be a significant challenge for product managers, product owners, and application team developers. The complexity of healthcare data, the need for real-time analytics, and the demand for user-friendly interfaces can often seem overwhelming.
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