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Samsara (NYSE:IOT) is a cloud-based company that monitors and manages fleet operations and has recently expanded to managing physical assets. Despite being a nascent company, Samsara has grown impressively, with an ARR of over $795M, and has a small market share. However, the market potential is significant, but competition is expected to increase as larger tech companies enter the market. The company's transition to focus on larger accounts may result in longer sales cycles and delays in achieving profitability. Hence, I currently maintain a hold rating on Samsara's stock.

Samsara has identified a Total Addressable Market (TAM) of $55 billion for 2021, expected to grow at a 21% CAGR to $96.9 billion by 2024. The majority of this market opportunity lies within the Connected Fleets segment, which has a TAM of $32.9 billion, while Connected Equipment and Connected Sites represent an additional $21.7 billion. The company expects the expansion of the Internet of Things (IoT) and artificial intelligence (AI), as well as increased regulation and environmental standards, to drive TAM growth.

Gartner estimates the Telematics market size to be around $33 billion in 2021, with a projected CAGR of 25% to reach nearly $64 billion in 2024, fueled by a greater emphasis on fuel efficiency and IoT growth. IDC predicts the number of connected devices will increase from 33 billion in 2021 to 55.7 billion devices by 2025, with over 400 million vehicles having embedded connected devices by that year. By tapping into existing sensors, Samsara can reduce hardware costs and expand margins while capturing more data.

Samsara

Samsara offers software solutions for physical operations across various sectors, including construction, field services, utilities and energy, wholesale, government, and more. These sectors represent almost half of the GDP, and no single industry dominates the company's customer base. Samsara began in 2015 with fleet management, providing customers with in-vehicle technology that connected to their cloud platform for compliance, safety, fuel management, navigation, and routing. The company's "plug-and-play" solutions are easy to install, with a customer success team available to assist with any issues. The company operates on a highly visible subscription model, with ~98% of revenue coming from 3-5 year average contracts.

Samsara's cloud platform is built on AI and machine learning and is capable of processing the six trillion data points collected by the company in FY23. The value lies in the analytics that Samsara provides to its customers. Despite being a relatively young company, Samsara has become one of the market leaders in the space, with over $795M in ARR reported in the last quarter. This implies only a fraction of the market share and high market fragmentation.

Samsara's solutions are designed to benefit industries that rely on transportation and logistics, making them highly relevant in today's world. Additionally, the company's broad customer base across multiple industries means that it is well-diversified and less likely to be impacted by any single sector's economic downturn. Overall, Samsara's success is based on its ability to offer easy-to-use, data-driven solutions that can help customers increase their operational efficiency and safety.

Samsara's "core" customers, who spend over $5K per year, account for around 95% of its ARR. When Samsara was a new player, it focused on acquiring non-core customers, with a large number of them currently paying less than $5K per year. However, as Samsara has grown, it is now prioritizing up-selling to its higher paying clients, resulting in natural churn of non-core customers through anticipated price increases during the contract renewal process. This natural churn is necessary to better utilize the time of the existing sales team. Currently, over 1K customers generate over $100K per year for Samsara, with at least 51 customers spending over $1M per year. The company is transitioning its focus towards bigger accounts, which may result in longer sales cycles (up to three years) and require up-front investment in hiring and training a capable sales team. Overall, Samsara's success relies on its ability to up-sell to existing customers while also attracting new high-paying clients.

Samsara

As Samsara's ARR has reached $795M in 4QF23, I believe the company's focus has shifted from customer acquisition to cross-selling products. Non-core customers, who contribute less than $5K of ARR per year, are expected to approach 0% of Samsara's customer base. In the early years, Samsara may have focused on acquiring small, non-core customers to establish credibility and receive feedback. However, as customer contracts typically span 3-5 years, low-paying non-core customers are expected to naturally churn out as Samsara proposes higher price points. Meanwhile, the number of high-paying customers is expected to continue expanding. As Samsara goes up-market, its sales cycle may also extend. Overall, Samsara's growth trajectory will depend on its ability to cross-sell to existing customers while attracting new high-paying clients.

The competition in the operations monitoring and management space is increasing, with established players shifting from hardware to a software-focused sales model. Major tech giants like Amazon are also showing interest in this space through their subsidiary AWS IoT FleetWise. Telecom companies like Verizon (through Verizon Connect) and AT&T (through AT&T Fleet Management) are also participating. Despite the competition, Samsara offers a superior solution with a wider range of services on a single platform, which is reflected in the higher starting price for its monthly fees.

The secular growth opportunity to modernize existing fleet management solutions and expand into equipment, sites, and more physical operations should provide a backdrop that allows Samsara to expand at a high pace. The company has been able to grow its topline at a CAGR of 52.6% over the past two years, driven by growth in customer count and adoption of additional modules by existing customers.

Samsara

I believe the company will be able to grow at a high double-digit rate over the next five years. I expect that will come with improving operating leverage, especially following fiscal 2023 after the company absorbs a full year of public market costs.

Samsara has been able to rapidly expand its gross margin in recent years, with a non-GAAP gross margin of 72.1% in 4Q22, up from 69.8% in FY21 and 59.7% in FY20. The cost of revenue is mainly due to hardware costs, including the amortization of IoT devices, cellular-related expenses, cloud hosting, and personnel costs. As the company expands into Sites and OEMs embed more sensors into their models, there will be a decreasing reliance on hardware. Samsara is also expected to renegotiate its cloud hosting fees with AWS as it becomes a larger customer. However, despite these factors, I anticipate a more tapered gross margin expansion in the future, with a projected gross margin of 73% by FY26. Overall, the expansion of Samsara's gross margin has been impressive, but its future growth may be impacted by changes in the market and its ability to adapt to those changes.

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Samsara is currently experiencing losses, but its potential for extensive top-line growth is attractive, given the low penetration of the industry. However, there are concerns about increasing competition and interest from major tech companies in establishing a greater IoT presence in the physical world. This may require Samsara to invest more heavily in its salesforce, increasing S&M expenses and potentially delaying profitability targets. Additionally, economic headwinds may lead to customers becoming more price sensitive, potentially driving down the average pricing for fleet management services. Overall, while Samsara has significant growth potential, it also faces challenges in a competitive and rapidly evolving market.

Samsara is a cloud-based operations monitoring and management company that specializes in fleet management. The company has recently expanded its offerings to include software solutions for the management of physical operations assets such as equipment and sites. Despite being a relatively young company, Samsara has achieved remarkable growth, with an ARR of over $795M, making it the fastest-growing company in the space. While Samsara's impressive results make it one of the largest players in the industry, the potential addressable market is huge, indicating that the company's market share is only 2%. However, competition in the industry is likely to intensify, as tech majors show increasing interest in the space, and there are only moderate barriers to entry. Samsara's business model may experience delays in turning profitable due to the competitive landscape and longer sales cycles resulting from the company's transition of focus towards bigger accounts. I maintain a hold rating on IOT and currently don't have a price target on the stock.

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Samsara Stock: Impressive Growth Trajectory But Limited Upside ... - Seeking Alpha

You may have heard a lot about artificial intelligence (AI) and machine learning (ML) in recent years as the demand for each continues to rise. These two terms in the technology world have been tossed around for one specific reason: They take our device hardware beyond its physical limitations. For example, the best Android phones rely on an AI or ML model to achieve even more impressive photos from the camera. This guide discusses Google ML Kit and how it uses efficient on-device processing to improve our smartphone experience.

Google has been one of the biggest supporters of using on-device processing in smartphones, dating back to the original Google Pixel. One of the earliest uses of leveraging AI and ML models was for the camera, which is still one of the leading forces in today's mobile devices. Google's dedication and power in AI and ML are still evolving on their most recent Google Pixel 7 and Google Pixel 7 Pro smartphones. And with the Google Tensor chip powering these devices now, they continue to surpass many hardware limitations, as they emphasized all those years ago.

With Google's massive success and leadership in AI and ML, Google also wanted to offer the same power they use to all mobile app developers. Because without third-party app developers constantly creating new experiences, our smartphones would have less character and uniqueness about them. Since these developers are crucial to the success of the mobile industry, giving them everything they need to succeed makes perfect sense. That's where Google ML Kit comes into play. No longer are these powerful on-device AI and ML features exclusive to Google.

Announced at Google I/O 2018, Google ML Kit was designed with ease of use to bring on-device machine learning features to mobile devices. Google wanted the process to be as seamless as possible, allowing developers to focus more on what they do best innovate. As such, developers on Android and iOS can take the free software development kit (SDK) that Google provides and apply it to their apps. This enables a new world of possibilities with machine learning features, pipelines, and APIs powered by Google's advanced in-house ML models.

The ML Kit SDK is optimized for mobile devices, ensuring you get the best experience. In previous methods, machine learning models required a separate dedicated server, relying on the cloud to communicate with a mobile device. Tapping into the power of on-device functionality using Google ML Kit allows app developers to run these complex operations offline. With hardware acceleration on supported devices, you get more performance gains and less latency. Using the easy-to-implement APIs for Google ML Kit also removes the complicated step of gathering data and training a machine learning model.

Privacy also comes to mind when discussing on-device versus cloud-based computing and machine learning models. By offering these features in a local setting, the data never leaves the mobile device. It works without a network connection. When using Google ML Kit for these computations, an app uses less mobile data since it doesn't rely on a cloud server. Still, ML models and datasets are typically large in scope or size when stored in the cloud. Reducing the overall footprint to run on a mobile device lessens their capabilities, but they're still quite powerful.

As of this writing, mobile developers can effortlessly implement 12 primary turn-key APIs into their apps using Google ML Kit. Combining this with Flutter, another open source framework from Google, further simplifies the app-building process. Google continues to enhance and optimize these pre-trained models and will add new ones when ready for public use. There is also an interactive tutorial to help you design your first on-device ML app, regardless of your machine-learning expertise.

Here's the list of available Google ML Kit on-device APIs:

Google also offers additional tools for developers that need more than the default turn-key solutions. Regarding specific use cases beyond the standard APIs or ML models, Google gives developers complete control over their datasets. They can retrain current ML models entirely or train one of their own using a preferred method of choice. For example, you can prepare a custom ML model on TensorFlow and bring it into ML Kit. Then you can import the model to your app with Gradle via Android Studio and configure it for production using the Firebase console.

TensorFlow is another helpful open source tool by Google that developers can use to manage their machine learning models and data dependencies. As a platform designed for deep learning, TensorFlow can process and deploy any ML model, including the default pre-trained ones or custom user models. When ready for deployment, they can be used on-device, in a web browser, or in the cloud, giving developers as much flexibility as they need for their ML model needs.

Natural language processing is how these ML models can do advanced things like recognizing items, people, or text in a photo. Mimicking the human brain, a neural network uses specific algorithms from datasets to train the models via patterns. And they are typically trained using massive data dependencies of billions of images, words, or other sets of information. It can take days, weeks, or even months to finish this process, depending on the size and scope of the project. TensorFlow allows developers to train their ML models without negatively affecting the speed and performance of the process.

If a developer wants to move beyond the default pre-trained ML models, there are custom TensorFlow Lite models. Like Google ML Kit, TensorFlow Lite focuses on bringing these types of on-device experiences to mobile devices on Android and iOS. Another option is to use TensorFlow Cloud with Google Cloud, allowing for a complete online-based ML model environment. Hosting, training, and managing ML models in the cloud is becoming more popular with many developers. Relying exclusively on cloud computing allows for greater flexibility without dedicated hardware, reduced overall costs, and a more user-friendly experience.

Aside from everything Google ML Kit offers to mobile developers, there are open source app samples that anyone can download and try from GitHub. For each of the 12 turn-key APIs mentioned above, Google has a sample app showcasing what they can do. These quickstart samples are available on Android and iOS, letting you try them on any device you own. Visit the ML Kit samples website, find one you want to demo, then click Try it on Android or Try it on iOS. It takes you to that GitHub listing with instructions on how to use it.

We covered what Google ML Kit is and how app developers can easily tap into its features, but how about some real-world examples of it in action? What might be possible when third-party app developers use on-device ML models for mobile devices? You still benefit equally if you're not an app developer using Google ML Kit. As the user and consumer, many of your favorite third-party apps can offer new on-device ML features that weren't possible until now. We'll mention just a few examples below to paint a better picture of what's possible.

We touched on the camera a bit earlier. However, there's so much more that ML models can offer other than taking better photos. One example Google highlights is how Adidas leverages on-device features using Google ML Kit. According to the case study, Adidas is tapping into the object detection and tracking API to create a new shopping experience. Powered by augmented reality, customers can open the Adidas app on their smartphone and scan shoes in real time with the camera. This allows them to find more information about a product at leisure without other interactions.

Another case study mentions how a top-rated Android app called Lose It! enhances the experience of its users by utilizing Google ML Kit. It is a health-based app with a calorie counter and food diary to better track food intake and weight management. The company is tapping into ML Kit to replace its own ML models in the cloud with the new ones that work on-device. This allows them to add an instant offline text scanning feature to let users fill in food nutrition information by scanning a label in real time.

For years Google has excelled and exceeded expectations regarding its success and leadership in AI and ML. Their secrets were initially locked down and only accessible by Google until 2018, when Google ML Kit officially arrived. Creating, training, and deploying a unique ML model is tedious and costly, but that's no longer the case. Google ML Kit removes the guesswork and gives developers everything they need to deploy ML models on mobile devices. Since on-device machine learning opens the doors for the future, we expect to see more possibilities in the coming years.

With how popular AI-powered chatbots have become, you should learn what Google Bard is and what it can do. We cover all the details you need to know, including where it got its name and how you can try it out today.

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Google ML Kit: Everything you need to know - Android Police

The demand for estate apps that provide user-friendly features to navigate the complex market has significantly increased. In the early months of 2022, the real estate sector experienced a dip. However, as September rolled around, there was a significant surge in demand. Data indicates that home ownership in the United States alone increased by 66% during this period. This trend is not exclusive to the U.S., as other countries have also witnessed a similar increase. As we move towards 2023, it is safe to assume that real estate is once again becoming a sought-after investment opportunity.

Developing a real property app can be a profitable venture due to several reasons. Users can search for properties from the comfort of their own homes, making it a convenient and accessible way to find the perfect property. What is more important, estate apps generate substantial revenue through advertising and commission fees, making it an attractive business opportunity for entrepreneurs seeking to capitalize on the growing demand for digital real estate services. In this article, we will delve into the topic of real estate app development.

The target audience for real estate goes beyond just buyers and sellers. Consequently, there are numerous types of estate apps, each with its unique features and functions. In the following list, we will outline the most popular and in-demand real property apps on the market:

To ensure the success of your real estate app, its important to choose the right type of app that aligns with your concept and target audience. Depending on your specific needs and goals, one type of app may be more suitable than others.

Moreover, its important to note that you can also create a hybrid app that combines features from multiple types of real property apps. For example, a mix of property listing and virtual tour apps could offer users the ability to search for properties and take virtual tours of them, all within the same app. This can provide added value to users and set your app apart from competitors.

One of the most profitable ways is by charging a commission fee for successful transactions completed through the app. Typically, this fee ranges from 1% to 10% of the transaction value.

The most popular method of generating revenue is in-app advertising. In 2021, real estate was the 10th highest-grossing category for in-app advertising, generating around $2.2 billion in revenue, according to a survey by Data.ai.

Another way is by offering premium features for a subscription fee, which can include access to exclusive property listings, advanced search options, or additional virtual tours.

Real estate apps can also create referral programs to encourage users to invite friends and family to use the app. For every successful referral, users can earn rewards or commissions.

Finally, estate apps are able to sell their data to third-party businesses, such as market research firms or real estate agents. In 2020, the global data market was valued at $138.9 billion, according to a report by IDC, and is expected to reach $229.4 billion by 2025.

While there are many more categories of estate app users than buyer and seller, the latter 2 are the main target audience. Therefore, we will look at the most important features for them.

For Buyers:

For Sellers:

To ensure a smooth and efficient development process for your estate app, its important to gather the necessary tools and resources beforehand. Here are some essential tools that will simplify your app development journey:

SDKs and APIs: SDKs (Software Development Kits) and APIs (Application Programming Interfaces) are tools that enable developers to integrate third-party features and functionalities into their apps. Real property apps may need to integrate with mapping APIs, MLS (Multiple Listing Service) data, and payment gateway SDKs.

Database management system: This is a software tool that allows developers to manage, store, and retrieve data. To store property data and user information implement databases like MySQL and MongoDB.

Virtual tour software: Virtual tour software like Matterport and TourWizard create immersive 3D virtual tours of properties.

Analytics tools: Analytics tools like Google Analytics provide insights into user behavior and app usage, which can be used to improve the apps performance and user experience.

Cloud hosting services: Cloud hosting services like Amazon Web Services (AWS) and Microsoft Azure offer scalable and secure hosting solutions.

Collaboration and project management tools: Collaboration and project management tools, for example Jira and Trello help teams stay organized during the development process.

Testing tools: Testing tools like Appium and TestFlight help developers test the apps performance, functionality, and compatibility across different devices and operating systems.

With all the necessary tools at your disposal, youre now ready to start the development process for your estate app. Here are the key steps which you will need to go through:

The increasing popularity of AI is no secret, and incorporating artificial intelligence in real estate search applications can give your app a significant advantage over the competition. While it may be more expensive to implement initially, in the long run, AI-powered apps will always be in higher demand. In addition to AI, there are several other emerging technologies worth considering. Lets explore these technologies in more detail:

Augmented Reality (AR) and Virtual Reality (VR): AR and VR technologies provide potential buyers with a virtual tour of a property, allowing them to experience the property as if they were there. This technology enables buyers to visualize a propertys layout and features more clearly, leading to better decision-making.

Artificial Intelligence (AI): AI can provide personalized recommendations to users based on their search history, preferences, and behavior. It can also automate tasks such as lead generation, customer support, and predictive analytics.

Big Data Analytics: Big data analytics can gather insights about the real estate market and trends, aiding buyers in making informed decisions. It can also analyze user behavior and preferences, providing valuable data to improve the user experience.

Blockchain: Blockchain technology provides secure, transparent, and tamper-proof transactions. It can also be utilized for identity verification, property title management, and smart contracts.

By incorporating at least one of the cutting-edge technologies discussed above, you can enhance the functionality of your app and attract a wider user base.

Developing an estate app is an excellent choice for a startup. With the continuous rise in real estate prices, there is a significant opportunity to generate revenue through such apps in the long term. Dont miss out on the chance to leverage the potential of the real estate market and make a name for yourself in the real estate app development industry.

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What Features to Include in Your Real Estate App: A ... - London Post