Cloud Stocks: Splunk On The Right Track With Cloud And PaaS

Big data player Splunk (Nasdaq: SPLK) recently announced its fourth quarter results that surpassed market expectations. But as the company transitions from an on-premise to a subscription-based cloud software provider, its financials have suffered in the short-term. Revenues have declined year on year and margins remain elusive. But it has made the right moves so far by remaining focused on cloud, subscriptions, and PaaS.

Splunk’s Financials

Revenues for the fourth quarter fell 6% over the year to $745 million, surpassing the market’s expectations by 10.6%. Adjusted loss of $0.38 per share was higher than the market’s estimate of a loss of $0.39 per share.

By segment, license revenues fell 21.6% to $406 million. Maintenance and service revenues fell 3.7% to $167.7 million. Cloud revenues grew 72% over the year to $171 million.

Among other metrics, Annual Recurring Revenue (ARR) grew 41% over the year to $2.36 billion, ahead of the market’s estimate of $2.33 billion. ARR from cloud computing software improved 83% over the year to $810 million.

For the full year, revenues declined 5% to $2.23 billion and loss per share was $0.55.

Splunk forecast revenues of $480-$500 million for the first quarter, compared with the market’s forecast of $512.88 million. Splunk did not provide a forecast for the current fiscal.

Splunk’s Product Upgrades

During the quarter, Splunk announced enhancements to Splunk Cloud and Splunk Enterprise to strengthen its Data-to-Everything Platform offering. For the Data-to-Everything Platform, it announced an integrated solution that provides a single and consistent user experience across metrics, logs, and traces. It provides seamless monitoring, troubleshooting, and investigation capabilities. The comprehensive and powerful solutions will allow both IT and developer operation teams to tackle monitoring and observability challenges that other tools are unable to address. It also added a Splunk Machine Learning Environment that will allow companies to build and operationalize machine learning models by bringing data from several sources into one platform.

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Disclosure: All investors should make their own assessments based on their own research, informed interpretations and risk appetite. This article expresses my own opinions based on my own ...

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