Cheetah Mobile Inc (CMCM) 2024 Q3 法說會逐字稿

Good day and welcome to the Cheetah Mobile third quarter, 2024 earnings conference call. (Operator Instructions) Please note today's event is being recorded.

I would now like to turn the conference over to Helen Jing Zhu, IR for Cheetah Mobile. Please go ahead.

Thank you operator. Welcome to Cheetah Mobile's third quarter, 2024 earnings conference call. With us today are our company's Chairman and CEO, Mr. Fu Sheng; and our Director and CFO, Mr. Thomas Ren.

Following management prepared remarks. We will conduct the Q&A section. Please note that the CEO script will be presented by an AI agent. Before we begin, I refer you to the safe harbor statement in our earnings release, which also applies to our earnings conference call today as we will make forward-looking statements.

At this time, I will now turn the call over to our CEO, Mr. Fu Sheng. Please go ahead, Fu Sheng.

(spoken by AI) Hello everyone. Thank you for joining us today, Cheetah Mobile, once again achieved accelerated revenue growth in Q3, driven by our service robotics and internet businesses. This consistent growth results from our strategies to expand the use cases of our wheeled service robotics and expand into overseas markets, as well as the resilience of our legacy internet business.

Industry demand for service robots continues to rise, especially in the overseas markets and in restaurants, hotels, factories and offices. Business owners are using robots more often to help their staff and improve efficiency. In the past weeks, I visited many customers and partners in Europe and Southeast Asia. In fact, I am still in Europe today, meeting with our local partners to further strengthen our presence, building a strong local distribution network is very important for our global expansion as it will set us apart from our peers. That's why I have spent significant effort on this initiative.

During my conversations with local partners, l learned that our robots are helping them solve labor shortages. Some customers in Europe shared that using Cheetah's robots have reduced employee absences and turnover. Meanwhile, some Japanese customers told us our robots are much more reliable than other players' offerings and are switching to our products.

In September, we launched a new robot for factory and fulfillment center use. This robot can autonomously deliver goods to move low payload items from transit warehouses to assembly lines. We are currently optimizing the product to better meet the needs of customers in overseas markets. This highlights the importance of receiving feedback and input from local partners. We believe this product will become an important part of our service robotics business in 2025.

While the robotics industry is still in its early stages, it will be $1 trillion market. Robots are becoming essential helpers for humans and will happen in the belt markets first, and LMs will speed up this growth by enabling robots to do more tasks and making them easier to deploy than ever before. Through our conversations with investors, we've noticed a lot of interest in how LMs are making our robots smarter and driving steady revenue growth.

Today, I will share what we've achieved so far with LMs in our products and what's coming next. First, we are using LMs to improve the way our service robots interact through voice, thanks to our strong far-field voice recognition. Robots already hear user as well. Now with LMs, they understand users' questions more clearly and respond better, making the overall experience much smoother.

For instance, in restaurants, our robots don't just deliver food doing busy hours. It can also help attract customers, boosting the return on investments for restaurant owners by taking on more roles and because LMs breakdown language barriers, we are expanding these voice-enabled robots to international markets.

We also working on edge a system that lets customers set up tasks for robots using voice prompts. For example, you can tell a robot to check each table at 2: 00 PM to see if anyone wants to order more food before the kitchen closes at 2:30 PM. The robot will go to each table, skip the ones without customers and even allow people to place orders if the ordering system is late. Without LMs, this kind of functionality would be nearly impossible or would require writing a lot of complicated code.

Secondly, we are using more multimodality models to improve our robot into our autonomous driving. One area that we are working is to enable our robots to map out a large factory as they move and look around once the map is ready. R&D team can mark key locations and the robot can then navigate the factory on their own to deliver goods.

Factory owners want to change these key locations. They can easily update the map whenever needed. Based on our initial testing, thanks to LMs, the time it takes to set up our robots can dramatically reduce from about two days to just two hours. We are already using vision-based autonomous driving technology, in some cases, and plan to expand it further.

For instance, our robots user vision-based autonomous driving technology to avoid people and obstacles and understand their surroundings. Over time, we aim to achieve an end-to-end navigation system. This will allow our robots to handle more complex environments entirely on their own.

Third, we're adding robotic arms to our robots to help them do specific jobs. Some of these arms can press buttons, which is useful for delivering things between floors. In particular, in the overseas markets, where business owners are reluctant to adjust their elevator access control systems due to security concerns.

Others can pick up and sort items, which is great for use in factories. These arms are powered by on device, multimodality models, making it easier to automate routine tasks. When it comes to LMs, we use advanced models through API calls to support some of the features we have discussed. At the same time, we are also developing our own models.

In November, we launched an 8 billion by 7 billion mixture of experts model covering many languages, including Chinese, English, Korean and Japanese. We have made it open source and use it to power our robots, especially the agent OS features. Additionally, we have trained smaller on device models to support indoor autonomous driving and control robotic arms turning to LM based applications. We recently introduced AirDS, an AI based data service platform, consisting enterprises in data and building prompts for their LM based applications.

AirDS was built on top of our insights into developing LMs and LM based apps. So far, we have received positive customer feedback on our LM based applications and we will continue to enrich our portfolio. Our goal is to offer relatively standardized SaaS products, allowing businesses to use LMs gain efficiency.

Before handing the call over to Thomas for the financial highlights, I want to stress this, Cheetah Mobile is in a good position to tap into the growing market for service robots and LM based apps. We bring years of experience from the PC and mobile phone areas as well as expanding into international markets and we have strong LM expertise. We've already made solid progress in growing our revenue and cutting losses. This is just the beginning of Cheetah Mobile's turnaround.

Thank you, Fu Sheng. Hello, everyone on the call. Please note that unless stated otherwise, all money amounts are in RMB terms. In Q3 2024, our financial results demonstrated solid execution on two strategic objectives. Number one, accelerating revenue growth driven by both the sales of robots and the legacy internet business; and number two, enhancing our operational efficiency to reduce our operating losses sequentially.

In the third quarter of 2024, our total revenues increased 16.6% year-over-year, marking the second consecutive quarter of accelerating revenue growth compared to 11.6% in Q1 and 12.3% in Q2. Our real-based service robots continue to be a key driver of growth. Additionally, our legacy internet business remained resilient achieving solid revenue growth and margin expansion.

Our profitability, we made further progress. Non-GAAP gross profit rose 14% year-over-year and 7% quarter-over-quarter to RMB131 million with non-GAAP gross margin expanding to 68% in the third quarter from 65% in Q2 and 63% in Q1. Non-GAAP operating loss was RMB61 million in the quarter, reduced from RMB63 million in Q2 and RMB66 million in Q1.

We continue to focus on managing costs and expenses. Notably, we consolidated the teams of Cheetah and Beijing OrionStar, streamlining staff and services with overlapping functions. For example, in Q3, we reduced bandwidth cost and cloud costs, professional and legal service fees and certain labor costs related to G&A and operations.

We are also decisively investing in AI, using AI to enhance our service robotics business. For example, our non-GAAP R&D expenses increased 25% quarter-over-quarter in Q3, and now about 60% of our revenues are invested in R&D.

As Fu Sheng has said in the past, while we focus on developing products that generate immediate revenue and profits, we remain attentive to the latest technological advancements. Another highlight is the continued trend of our legacy Internet business, which grew 26% year-over-year and 18% quarter-over-quarter.

Operating margin, excluding share-based compensation expense for this segment improved to 10% from 6% last year. As of September 30, 2024, we maintained a strong balance sheet with cash and cash equivalents of RMB1,831 million or USD218 million, long-term investments of about RMB886 million or USD126 million.

In closing, we are making solid progress in expanding revenues and narrowing losses. We are confident in our investments in AI because we see a significant market potential of integrating LLMs into our service robotics business. At the same time, we remain disciplined in reducing losses and driving efficiency in our AI operations.

Thank you. This concludes our prepared remarks. Operator, we are now ready for the Q&A session.

(Operator Instructions)

Everyone for today's call, management will answer questions in Chinese and then AI Asian will translate management's comments into English in another line. (Event Instructions)

Operator, we are now ready to take questions. Thank you so much.

Thomas Chong, Jefferies.

(spoken in Chinese)

Thanks management for taking my question and a good evening. My question is about Cheeta Mobile business. In 2025, what are our goals in terms of volume, revenue growth and revenue contribution from the robot business? Thank you.

(spoken in Chinese)

Thank you operator. Can we move to the next question, please?

Vicky Wei, Citi.

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Thank you.

Thank you. Please move to the next question, please.

[Yan Long Chen] CITIC Securities.

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Operator, please move to the next question, please.

Yani Su, High Tide Securities.

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Operator. Please move to the next question, please.

[Surin], Sealand Securities.

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Operator, please move to the next question. Thank you.

[Yi Zu], Founder Securities.

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Operator, please move to the next question, please. Thank you.

Ben Shan Wu, TF Global.

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Thank you operator. Please move to the next question.

Alex Wong, SDICS International.

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Thank you, please move to the next question, please.

[Young Ping Dio], Guotai Junan Securities.

Hi management. Thanks for taking my question. And we have noticed that you have a large amount of net cash on your balance sheet. So do you have any plan of dividend distribution or share repurchase in the future? Thank you. (spoken in Chinese)

(spoken in Chinese)

Thank you. Operator, please move to the next question.

[Gigi Zao], GF securities.

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Thank you operator, please move to the next question.

Joanna Ma, CMBI.

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Operator, please move to the next question.

[Chen Gu lee] with [Wyong] Securities.

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Thank you and operator, if we have no further questions, we can just conclude the Q&A section.

We have no further questions, ma'am.

Okay. So thank you so much for joining Cheetah Mobile third quarter of 2024 earnings conference call. If you have any further questions, please do not hesitate to reach out our IR our team. Thank you so much, Bye, bye.

Thank you. This concludes today's conference call. We thank you all for attending today's presentation. You may not have scratched your lines and have a wonderful day.