A strong start to consumption transformation for Confluent as “higher propensity” customers sign up

(Jay Kreps)


A strong start to fiscal 2024 for Confluent with 45% year-on-year growth for Confluent Cloud, now making up the majority of the firm’s subscription revenue. 

For Q1, the firm reported  total revenue up 25% year-on-year to hit $217.2 million, while subscription revenue was up 29% to $206.9 million. Meanwhile GAAP operating losses fell from $166.1 million this time last year to $111.4 million this year. Other stats of note from the post-earnings release analyst call:

  • 160 new customers were added during the quarter, the largest sequential growth since Q1 2023.

  • Total customer count is now 5,120.

  • Customers with Annual Recurring Revenue (ARR) of $100,000+ now number 1,260, up 17% year-on-year.

  • Customers with ARR of $1 million+ now number 168, up 24% year-on-year.

  • Confluent Platform revenue was up 15% year-on-year to $100.1 million.

CEO Jay Kreps pointed out that Q1 was the first in which the company’s shift to consumption pricing was in action:

We oriented our sales compensation for cloud towards incremental consumption and new logo acquisition. We rolled out new systems, metrics and measures and made pricing adjustments to reduce friction in landing new customers. It remains early days but we are encouraged by the strong promising signals of our consumption orientation, particularly around new customer acquisition and stabilization of consumption trends.

He added:

I think that’s gone really well. We had to execute really a large number of changes in a pretty short period of time and I think we really significantly de-risked the set of changes by rolling out a bunch of system changes. They were well adopted by our field team. They’ve actually proven themselves out with customers. And I think that’s shown up in the higher rate of new customer acquisition. I think one of the nice things is, in addition to just getting more customers, we’re actually targeting and getting higher propensity customers. So, it’s kind of more volume and higher quality. 

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As for the generative AI angle, Kreps said that Confluent is seeing “particularly strong traction” in the digital native space:

One such customer is an AI-powered customer intelligence platform to manage contact centers and customer engagements. A powerful communications AI is central to its platform and is used for a variety of use cases, including surfacing real-time insights for call center managers and identifying when agents need immediate assistance or intervention in handling problematic situations.

Their existing architecture was unable to handle the demands of real-time with latency sometimes exceeding a minute. This sluggishness was unacceptable for an AI application that requires access to fresh and continuously updated data. So this customer turned to Confluent Cloud for fast and scalable data streaming.

By integrating Confluent with other components of its architecture, the customer was able to significantly reduce latency for response times from over a minute down to as low as 10 milliseconds. With faster, fresher data and more real-time insights available, the customer is better equipped to meet the needs of its customers and provide them with valuable tools and analytics for managing their contact centers and customer engagements.

But it’s not just digital-native firms that are tapping into gen AI, he added, citing supply chain and procurement specialist GEP Worldwide as a case in point:

This billion-dollar revenue company provides software, consultancy and managed services to some of the world’s biggest multinationals. Its software offerings are infused by gen AI to support chatbots and decision support tools.

Previously, the team was an open source Kafka shop, but operating and maintaining open source became too burdensome to maintain, ultimately stifling their ability to iterate and innovate quickly. So they turned to Confluent.

With Confluent serving as the central nervous system of its software, the company is able to more quickly connect data across hundreds of applications, including both custom apps and the operational and analytical estates, to provide contextual, relevant and real-time insight into its AI platform.

My take

A solid performance in which the metrics are all going in the right direction and the shift to the consumption model bodes well for continued growth.



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