Scaling Related Topics 
SaaStr
NOVEMBER 20, 2024
At this year’s SaaStr AI Summit, GitHub CRO Elizabeth Pemmerl shared how to bring AI products to market at scale successfully. With 90% of the Fortune 100 on GitHub and 40% of its $2B revenue coming from AI products, these real-world examples will also help you launch an AI-powered product at scale. The next horizon is scaling.
SaaStr
NOVEMBER 19, 2024
Drata VP of Customer Success Management, Wen Yao, and VP of Customer Experience, Ashley Hyman, share how to scale customer success from 0 to 5,000 customers. When Drata took this approach, it helped them scale to 5,000 customers, all within 3.5 Every touch point matters, but you can’t scale like this.
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SaaStr
AUGUST 15, 2024
There’s always someone a few years ahead of you on the scaling journey who can share their lessons learned. Robbie O’Connor, the GM EMEA at Notion and the first European hire at Asana and Dropbox takes the stage at SaaStr Europa to talk about the building blocks required to scale GTM teams and operations. Timing is also essential.
SaaStr
AUGUST 9, 2024
From startup to $500M CARR, Spencer Burke, SVP of Growth at Braze, shares how Braze scaled a growth and customer success team. In this AI moment we’re in, there are still many moments in scaling a company where intuition still matters. You don’t have to leave as the organization scales. This isn’t new.
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Download the eBook to learn about Best Practices for Deploying & Scaling Embedded Analytics. Packaging and Deployment Workflow: Embedded analytics solution should work seamlessly with your current application.
SaaStr
JULY 12, 2024
Scaling to $150M ARR and beyond is no simple task. Will this change as Grafana Labs scales? But they’re still one of the last remaining open-source companies at scale. The post Scaling to $150M ARR and Beyond with Grafana Labs and Lightspeed appeared first on SaaStr. How did it come to be? Probably not.
SaaStr
JUNE 25, 2024
Scaling a company from 0 to 100 employees is no small feat. While these top ten tips may seem obvious, many founders overlook the importance of some of them as they scale their businesses. In the chart above, you can see that pre-seed starts at -2 on the scale. If we’re being honest, scaling a company is hard.
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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources.
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As with all things related to customers, there’s an art and science to effectively dealing with each of their ideas. With so many voices competing for attention, you need a means of effectively dealing with all of the data that will come your way.
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Key topics will include: Getting started: the "crawl" phase of ABM and its boundaries Unlocking the potential of the "run" phase and using the right strategy for scaling your efforts The essential components of an effective ABM tech stack within each stage Real-world insights from NetSPI's ABM journey with MarketingOS
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Unfortunately, even the most sophisticated organizations often lack clear strategies and processes for collecting and managing the voice of the customer at scale—or worse, they simply ignore this valuable source of data. What better source of insight into what those are and how they’re changing than your customers themselves?
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And for those who are already underway, scaling their results across their organizations is completely uncharted waters. Many organizations are dipping their toes into machine learning and artificial intelligence (AI). However, for most organizations embarking on this transformational journey, the results remain to be seen.
Speaker: M.K. Palmore, VP Field CSO (Americas), Palo Alto Networks
In most cases, the COVID-19 crisis has sped up the desire to engage in digital transformation for medium-to-large scale enterprises. Roadmaps are rarely implemented without challenges.
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In 2023, companies are looking to improve their revenue and drive sustainable growth by scaling their subscription offerings, to increase the rate of growth and resilience by moving from one-time sales to recurring revenue.
Advertiser: Data Robot
In our eBook, Building Trustworthy AI with MLOps, we look at how machine learning operations (MLOps) helps companies deliver machine learning applications in production at scale. For businesses that are AI-driven, this trust hinges on the confidence that their AI solution can help them make their most critical decisions.
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