Enterprise Artificial Intelligence Could Make Gridsum Holding The Swing Trade Of 2018

Artificial intelligence (AI) continues to potentially be one of the most dynamic growth opportunities for stock investors. However, there are not many publicly-traded pure-play artificial intelligence companies that actually have substantial revenues. That creates a scarcity factor, and often a scarcity premium for the “real deal” public companies. We saw this scarcity factor in 2017 when Veritone, Inc. (NASDAQ:VERI), an AI company with trailing twelve month revenues of $13 million, saw its stock go from $10 to over $70 and still currently trades 22 times this fiscal year revenues. Gridsum Holding, Inc. (Pending:GSUM) is one of the only public pure play artificial intelligence companies with substantial revenue growth, margins, and is at the cusp of profitability. However, this story has yet to be discovered as it currently only trades at 3.8 times this year’s fiscal revenues.

Consumer AI versus Enterprise AI

GSUM explained the difference between consumer AI and enterprise AI in a May press release.

Consumer-focused AI differs substantially from enterprise-focused AI:

Broadly speaking, consumer AI often leverages the ubiquitous consumer Internet ecosystems across e-commerce, social, mobile, entertainment and others and focuses to quickly solve or facilitate relatively simple (from a mathematical standpoint) but often time-consuming, “painful” or distracting consumer challenges. Consumer AI is incredibly broad in scope, having the potential to revolutionize and facilitate the way people live and, within the next 10 years, will likely touch the lives of most of the people on the planet.

Enterprise AI is different in focus, structure, development, management, application and goals. It requires a very different and focused “organizational DNA” which is particularly rare in China (and elsewhere in Asia). It is focused on creating immediate and quantifiable value for companies with an immediate KPI impact and evolving and increasing that value-add over time.

Enterprise AI hence requires deep domain expertise and knowledge of the target industry and its ecosystem. This allows Gridsum to understand the challenges and opportunities where AI technologies can be effectively applied to delivering immediate and quantifiable value to an enterprise customer whether it is improving efficiency, reducing cost, or allocating marketing budget for optimized ROI. These are often industry-specific drivers and dynamics, typically requiring more focus and depth in a narrower area than consumer AI. To accomplish this, enterprise AI tends to heavily leverage supervised learning techniques to infuse human expertise into the resulting intelligent solutions.

Additionally, Ravi Sarathy, Co-CFO of GSUM, explained the difference between consumer and enterprise AI in this CNBC interview:

Link to GSUM CNBC interview titled “What’s next for AI”

To date, much of the AI we have heard about has been consumer focused, such as the voice intelligence Siri by Apple, Inc. (NASDAQ:AAPL) and Alexa by Amazon, Inc. (NASDAQ:AMZN). However, a report this morning out of the World Economic Forum in Davos, Switzerland, states that enterprise AI has moved beyond experimentation and is now in widespread use in enterprises and showing tangible return on investments:

According to the survey, 73% of respondents agreed or strongly agreed that their AI deployments have already transformed the way they do business, and 90% of c-level executives reported measurable benefits from AI within their organisation.

AI deployments are no longer imminent but are becoming pervasive, as 86% of organizations surveyed have middle- or late-stage AI deployments and view AI as a major facilitator of future business operations.

80% who said they’ve seen at least some measurable benefits from AI agreed or strongly agreed that their organization had a defined strategy for deployment, while 53% of all respondents said that their industry has already experienced disruption due to artificial intelligence technologies.

However this appears to be the ground floor/first inning of this large market opportunity. Whit Andrews, vice president and distinguished analyst at Gartner, was able to put some hard numbers to the trend:

“We are in the very earliest stages of enterprise adoption of artificial intelligence,” he said. “Specifically, in our most recent CIO survey from 2017, one in 25 CIOs described themselves as having artificial intelligence in action in their organizations.”

As you can see from the chart below, revenues from enterprise AI applications market worldwide are forecasted to go from $360 million in 2016 to $31 billion in 2025.

Source

GSUM is a pioneer and leader in enterprise AI in China:

Since this early inception, the Company has continued to stay at the forefront through focus and investment, hiring and training extraordinary engineers and architects and, importantly, playing an active and leading role in the AI academic and developer communities. The Company believes it is currently the technology market leader in the China enterprise-focused AI space with a global best-of-breed Enterprise-AI engine.

As of March 31, 2017, Gridsum has filed 1,653 patent applications in China, of which 583 are big-data focused and 148 are explicitly Enterprise-AI focused. The Company believes this is the largest number of Enterprise-AI focused patents for a company in China today.

I contend that enterprise AI will be the top investment theme of 2018.

Risks

Risks could include slow product development, slow adoption of software as a service in China, as well as execution risks by management.

Financials

GSUM had cash and cash equivalents of $38.8 million as of September 30, 2017. Management believes the company has a healthy balance sheet and that it is fully funded on the path to profitability. There are approximately 30 million shares outstanding. Executive officers and directors owned 42% of GSUM per the most recent filing.

From 2013 to 2016, GSUM had a revenue CAGR of 86%. For the full year of 2017, the company increased its guidance. Net revenues are now expected to be in the range of RMB628 million to RMB640 million, representing approximately 58% year-over-year growth at the midpoint.

Gross margin was 83% for the quarter ending June 30, 2017, and Gridsum has stated an expectation for a long-term gross margin of 85%.

For more historical growth information, see the GSUM presentation.

Analysts forecast GSUM to be profitable in the fourth quarter of 2017. GSUM confirmed this on the third-quarter conference call. It is also forecasted to grow revenues from $95 million in 2017 to $150 million in 2018 while having earnings per share swing from a loss in 2017 to overall profitability in 2018.

Very impressive revenues and growth.

Valuation

AI company Veritone has a price to sales ratio of 22 based on this year’s forecasted sales. Data analytics intelligence company Alteryx has a price to sales ratio of 12 based on this year’s forecasted sales.

To apply a 12-22 price to sales ratio to GSUM’s 30 million shares outstanding for the forecasted 2017 sales of $95 million equates to a share price of $38-70.

GSUM closed at $12.28 on January 24, 2018.

Why Is GSUM So Undervalued Compared To Competitors?

It is my goal as a 20-year veteran swing trader to find undiscovered stories that are not properly valued compared to competitors or like companies. In this long bull market, it is getting harder and harder to find such cases. When I research GSUM and compare the current price to sales ratios of companies in similar technology, I am both baffled as to why and excited about the opportunity that potentially exists as an investor. If you told me this week that a homegrown software company in China that is a pioneer and leader in big data and enterprise artificial intelligence, which is growing revenues at 50% with over 80% margins, is going public, I would think the interest and demand would be off the charts. Here are some of the reasons why I believe such a great opportunity exists in the price to sales ratio valuation gap GSUM has with big data/AI/cloud software companies:

1) GSUM is an undiscovered gem. Although it was taken public by Goldman Sachs (NYSE:GS) Asia, it recently did its first public road show in the United States over a year after it went public. It had little exposure outside China until recently.

2) I believe when GSUM went public in October 2016, it was a bad time for both technology and Chinese stocks. Again, there was not much exposure for the company.

3) GSUM hit all of its revenue target by analysts in 2017, but missed some of their bottom-line estimates due to increased investment in research and development for future growth. As a newly public company, this may have hurt the stock price in 2017 (It is interesting to note that both VERI and AYX are still losing money, while GSUM is forecasted to be profitable for Q4 2017 and for the 2018 fiscal year in aggregate).

4) The funds that bought the IPO at $13 were underwater most of 2017, and I think we saw massive tax loss selling at the end of 2017. That trend has started to reverse sharply in 2018. VERI and AYX did not have this problem at all.

5) Until the fourth quarter 2017, it is my opinion GSUM had done a very poor job of getting its story out to investors outside of China and in general. Press releases were few and far between. As already mentioned, the company did not even do a US road show for its entire first year being public (Gridsum has recently hired an international investor/public relations firm, Christensen, which I believe will be the start of getting this great story out beyond China to US and global investors. The company recently did its first conference in the United States at Needham).

6) Volume in GSUM for most of 2017 was under 100k daily and too small for institutions to take positions in. This has changed radically in 2018, and GSUM’s average daily volume is now comparable to AYX.

7) As media and investor appetite for big data and artificial intelligence has grown enormously, VERI and AYX both had some great exposure in places like Barron’s and CNBC, where GSUM has yet to receive mainstream exposure.

Conclusion

  • I contend that enterprise AI will be the top investment theme of 2018.
  • I contend GSUM is undiscovered given the massive discrepancy in multiples it trades to its peers.
  • This could make GSUM the swing trade of 2018.

Disclosure: I am/we are long GSUM.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.


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