Snowflake tumbles on slower quarterly product revenue growth forecast
The company logo for Snowflake Inc. is displayed on a banner to celebrate the company's IPO at the New York Stock Exchange (NYSE) in New York, U.S., September 16, 2020. REUTERS/Brendan McDermid
Dec 4 (Reuters) - Snowflake shares slumped 11% on Thursday after the cloud data analytics company forecast slower product revenue growth for the fourth quarter, partly hit by discounts offered on the large, long-term deals.
If losses hold, around $10 billion of the company's market value could be wiped out. The stock has risen more than 70% this year, through last close.
CEO Sridhar Ramaswamy said on Wednesday the company provides more favorable pricing for larger volumes or longer-term contracts, noting that these "don't tend to have an immediate impact on revenue."
Snowflake's third-quarter product revenue was "undoubtedly decent" but was slightly below investor expectations after peers like Datadog, Confluent and MongoDB showed impressive performance, analysts at Scotiabank said in a note.
Products offered by companies like Snowflake help clients to store, process and consolidate data that helps in producing business insights.
Still, at least 16 brokerages raised their price targets on the company's stock, encouraged by the pace of adoption of its AI offerings.
"While it remains very early innings, we are encouraged by the signs of traction SNOW is showing with AI use cases," said analysts at Truist Securities.
Snowflake said more than 7,300 businesses engage with its AI features weekly, while its agentic AI solution, Snowflake Intelligence, has attracted around 1,200 customers within a month of launch.
The company also announced a $200 million multi-year agreement with Anthropic to bring Claude models to its platform.
Snowflake expects fourth-quarter product revenue growth of 27%, while it reported a 29% growth for the October quarter.
The company trades at 165.11 times the estimates of its earnings for the next 12 months, compared with 65.86 times for Datadog and 76.07 times for MongoDB.
(Reporting by Jaspreet Singh in Bengaluru; Editing by Vijay Kishore and Sriraj Kalluvila)
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