Investors Can Benefit From Revenue Fluctuations In The Big Data Market

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Includes: DATA, INFA, MSTR, QLIK, SPLK, TDC, VEEV, VRNS
by: Lior Ronen

Summary

Revenues of big data companies fluctuate in a particular way that can be used to create a basic high level revenue forecast.

Revenues of big data companies increase during the year and reach their peak in the fourth quarter according to the growing number of deals closed throughout the year.

Understanding revenues trend and the correlation with number of deals closed can help investors to create a revenue forecast with a possible variance of only 2% from reported in actual.

In an earlier article, I presented a theory about the high correlation among stock prices of big data software vendors; however, the high correlation is much deeper in that market and can help estimate future revenue or earning trends according to the big data market fundamentals.

In order to highlight any trends in the big data market, I will use eight big data companies that have big data visualizations or analytics in their core business: MicroStrategy (NASDAQ:MSTR), Qlik Technologies (NASDAQ:QLIK), Informatica (NASDAQ:INFA), Splunk (NASDAQ:SPLK), Tableau (NYSE:DATA), Veeva Systems (NYSE:VEEV), Teradata (NYSE:TDC), and Varonis (NASDAQ:VRNS).

As shown in chart 1 below, revenues of all eight companies vary similarly between the quarters, with the greatest increase in the fourth quarter and the most significant decline in the first quarter. The black highlighted lines represent the high and low bars of the range of the quarter-over-quarter revenues increase.

The fluctuations in the quarterly revenue within a year is driven mainly by the timing of deals closed, when the number of deals increase from quarter to quarter and reach its peak in the fourth quarter. That trend within the year creates sharp declines between the maximum revenues / deals closed of the fourth quarter to the little revenues / deals closed of the first quarter. Chart number 2, below, best illustrates the sharp increases and declines in the fourth and first quarters.

In chart 2, quarterly revenues of big data firms are divided by the annual revenues to present the portion that each quarter contributes to the annual revenues of the year. As mentioned above, it is easy to spot that every company captured generated most of its revenues in the fourth quarter and the least of its revenues in the first quarter. On average, a big data company generates between 27% and 35% of its annual revenues in the fourth quarter, while it generates only 16% to 25% in the first quarter.

As described above, revenue fluctuations are driven by a change in the number of deals closed every quarter. Most of the deals are closed in the fourth quarter, driving the revenue to its annual peak in that quarter. To illustrate that link, chart number 3, below, presents the portion of Informatica's quarterly revenues and number of deals closed in every quarter as a percentage of the annual figure.

As shown in chart 3, quarterly revenues and the number of deals as a percentage of the annual totals are highly correlated and provide evidence for the strong connection between the number of deals closed to revenues and the primary driver behind the revenues fluctuation. The correlation between the number of deals above $1M and the revenues is 0.92, and that between the number of deals above $300K and the revenues is 0.91.

Given that I have thoroughly presented above that the revenue of big-data firms is seasonal and is highly correlated with the number of deals closed, now it is time to translate that information into actionable data. I prefer to use that information to create a revenue forecast that could later be used in the valuation, but it can also be used to understand other trends in that market.

In order to demonstrate how to build the revenue forecast, I will use revenue data from the last eight quarters of Qlik Technologies and MicroStrategy to create a forecast for the following quarters and compare it to the actual quarterly revenues these companies reported. To compile the revenue forecast, I will multiply each quarter with the compounded average of the same quarter in the previous year and previous two years (even that in the previous three years if that information is available). The previous year will have an 80% weight, and the quarter two years ago will have a 20% weight (I will change that to 75%-15%-10% if you have third-year information).

The outcome of that calculation is presented in chart 4 below. The EST line for QLIK and MSTR represent the estimated forecast for the quarters if we use the above technique compared with the actual revenue figures the companies reported in their earnings release.

While the revenue forecast can never accurately predict the quarterly revenue a company will generate, it can provide a good estimation for the number. This method yielded revenue forecast of only a 2%, far from the actual revenues reported, making it a pretty good mechanism to high-level estimate the following quarters' revenues for a big data company, thanks to the revenues trend. In addition, any change to the number of deals closed should have an impact on the revenues trend and should be embedded into the forecast.

This high-level forecast is the first stage in estimating revenues for a big data company, and on top of that, you should add significant business developments that may have an impact on revenues such as acquisitions, spin-off, weather conditions, and currency losses to receive a better and refined forecast.

Conclusions

Big data market is seasonal by its nature and experiences a seasonal revenues trend that can be used to estimate better the future revenues of the big data company. As most of the deals are closed towards the end of the year, historically, the fourth quarter of every year has the highest level of quarterly revenue that year. Using that trend, an investor can create a basic high-level revenue forecast easily that can be used as a basis for more in-depth forecast. Major changes and development could later be implemented into the model to improve it and make it more accurate.

Disclosure: The author is long QLIK, SPLK. Information provided in this article is for informational purposes only and should not be regarded as investment advice or a recommendation regarding any particular security or course of action. This information is the writer's opinion about the companies mentioned in the article. Investors should conduct their own due diligence and consult with a registered financial adviser before making any investment decision. Lior Ronen and Finro Financial Consulting and Analysis are not registered financial advisers and shall not have any liability for any damages of any kind whatsoever relating to this material. By accepting this material, you acknowledge, understand and accept the foregoing.

The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it (other than from Seeking Alpha). The author has no business relationship with any company whose stock is mentioned in this article.

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