Data in motion and real-time AI may propel this stock much higher

Three pieces of background

(1) There are three states of data to categorize structured and unstructured data: (a) data at rest, (b) data in motion, and (c) data in use. Data at rest is all data in computer storage that is not currently being accessed or transferred. Data in motion is data that is moving or being transferred between locations within or between computer systems. Data in use is data that is currently being updated, processed, accessed, and read by a system.

(2) Apache Kafka is an open-source stream-processing software platform developed by Linkedin and donated to the Apache Software Foundation. It is designed to handle real-time data feeds and operate as a distributed event streaming platform. Kafka provides fault tolerance, scalability, and a unique publish-subscribe model, which can be used for stream processing and message queuing. It's particularly well-suited for handling large volumes of real-time data, making it popular among enterprises for tasks like log aggregation, data warehousing, real-time analytics, and mission-critical applications.

(3) Apache Kafka was originally developed at LinkedIn by Jay Kreps, Neha Narkhede, and Jun Rao. After its creation, LinkedIn later donated Kafka to the Apache Software Foundation, where it became an open-source project. The three co-creators of Apache Kafka founded Confluent in 2014. Confluent (CFLT) went public in 2021 during the hypergrowth stock craze on NASDAQ.

Confluent in a nutshell

Confluent is a platform dedicated to data in motion, specifically to stream processing and real-time data. There are two key product offerings:

Confluent Platform: A more complete distribution of Apache Kafka with additional tools and capabilities to enhance the streaming experience for large enterprises.

Confluent Cloud: A fully-managed event streaming service based on Apache Kafka, allowing businesses to harness the power of real-time data without the operational burden.

Market Position, competitive landscape, and use cases across industries

With data becoming increasingly crucial in real-time decision-making processes for businesses, the demand for platforms like Confluent's is rapidly growing.

While other players are in the real-time data streaming market, Confluent's deep ties to Apache Kafka give it a unique edge.

The demand for real-time, low latency data streams from AI, IoT, Adtech, Autos, and more will increase meaningfully in the future, and Confluent has the best cloud platform to constantly stream such data at scale, enhance, maintain, and provide analytics for it.

Heavyweight competitors, mainly Amazon's AWS, Microsoft's Azure, and Alphabet's Google Cloud, have their own managed data streaming products, as do some legacy on-premise providers such as Tibco Streaming, Red Hat, part of IBM, and Oracle.

Open-source Kafka remains the free alternative for small and medium-sized enterprises and selected use cases.

However, none have the focus, scale, rich features, implementation, integration, support, and cost savings that Confluent's agnostic platform can offer.

Confluent serves all markets - cloud, hybrid, and on-prem with a business focus on their cloud native subscription platform, which has been growing at an annual rate of 83%, providing sustainable and recurring revenue streams.

What makes Confluent stand out?

Best in Class Product: Even though AWS, Azure, and Google Cloud are strong competitors, they don't have the core functionality or rich features, integration, and breadth that Confluent does. A Comparison of the products (Amazon MSK vs. Azure Event Hubs vs. Confluent vs. Google Cloud Dataflow) showed 26 integrations for Confluent versus 9 and 10 for the rest, much wider deployment, and more robust support and training.

Integrated Data Streaming Platform: CFLT management outlined several new aspects and growth drivers during the latest earnings call, and the build-out of a comprehensive DSP (Data Streaming Platform) stood out.

With Kafka as the foundation, the DSP does much more than stream data. It uses five integrated streaming processes: connecting, governing, processing, and sharing. All these components, including the non-Kafka ones, can be monetized, and Confluent has started Freemium licensing/subscriptions to increase engagement and revenue.

Using Apache Flink (a recent acquisition) also increases engagement and monetization for stream processing, governance, and sharing from customers like Netflix and Instacart. Revenue from the Stream Governance Advanced Offering was their fastest product grower ever. The stream processing offering should be available in 2024. The stream-sharing offering will be valuable to the finance, insurance, and travel insurance industries, which must share data with providers and customers.

Confluent use cases - two examples

Here are two examples from C.E.O. and Co-Founder Jay Kreps from the Q2, 2023 conference call, highlighting how best to use Confluent:

Confluent and AI

The Confluent platform is exceptionally well-suited for various applications in the AI domain, including generative artificial intelligence (Gen AI) and other specialized AI applications. Use cases of how the Confluent platform can be integrated into AI workflows entail:

1. Real-time Data Ingestion: AI models, especially those deployed in real-world scenarios, often require real-time data for prediction or analysis. Confluent can stream large volumes of real-time data to AI models, ensuring they operate on the most recent information.

2. Training Data Collection: For training AI models, vast amounts of data are typically needed. Confluent can facilitate the collection, organization, and streaming of training data sets to machine learning environments.

3. Anomaly Detection: In cases where AI is used for real-time anomaly detection (like fraud detection or network security), the Confluent platform can stream transactional data or network logs, enabling the AI model to detect anomalies in real-time.

4. Feedback Loops for Model Improvement: As AI models make predictions or analyses, the results can be streamed back through the Confluent platform to create feedback loops. This continuous feedback can be used for model refinement and retraining.

5. IoT and Edge Computing: Many AI applications today are deployed on the edge (like in IoT devices). Confluent can manage the vast data streams from these devices, making real-time AI analyses on the edge more efficient.

6. Chatbots and Virtual Assistants: For AI-powered chatbots or virtual assistants requiring real-time data (like customer transaction history or inventory data), Confluent can stream this data to ensure the chatbot provides timely and accurate responses.

7. Integration with AI Tools and Platforms: Confluent can be integrated with popular AI tools and platforms, such as TensorFlow, PyTorch, or cloud-based AI services, facilitating seamless data flow between data sources and AI processing units.

8. Real-time Personalization: In applications like recommendation engines or personalized marketing, where AI models need to respond to real-time user interactions, Confluent can stream user activity data, enabling AI models to provide real-time personalized experiences.

The Confluent platform is the backbone for many AI applications, ensuring that data is reliably and timely available for AI models to function effectively in real-world scenarios.

Example of using Confluent for a real-time travel guide

Integrating the Confluent platform into a real-time travel guide can significantly enhance the user experience by providing timely, relevant, and context-aware information.

Real-time Travel Guide with Confluent Platform:

1. Live Location Data: Confluent can stream the traveler's real-time location data from their smartphone or wearable device. This live tracking can be used to provide instant recommendations and alerts based on their current position.

2. Dynamic Points of Interest (POI) Recommendations: The system can recommend nearby points of interest based on the live location and user preferences. New recommendations can be streamed in real-time as users move, ensuring relevancy.

3. Traffic and Transit Updates: Confluent can integrate with traffic management systems or public transit data sources to provide real-time traffic alerts or public transportation schedules. This can help travelers adjust their routes or timings.

4. Real-time Event Notifications: If there are live events, festivals, or spontaneous happenings in the vicinity, the travel guide can notify users instantly, allowing them to partake if interested.

5. Instant Weather Updates: Integration with weather services can allow Confluent to stream real-time weather updates, helping travelers prepare for sudden weather changes.

6. Social Integration: The travel guide can show real-time reviews, ratings, or comments from other travelers about a particular place, restaurant, or attraction, providing fresh, user-generated insights.

7. Emergency Alerts: In case of emergencies, like natural disasters or security threats, real-time alerts can be sent to travelers, guiding them on immediate steps to take or safe zones to head to.

8. Dynamic Pricing and Availability: For those looking to book accommodations, tickets to attractions, or transportation on the go, Confluent can stream real-time availability and pricing data, ensuring travelers have the most up-to-date information.

9. Interaction with Other Travelers: If users opt-in, they can be notified of friends or fellow travelers in the vicinity, facilitating spontaneous meetups.

10. Feedback and Reviews:

• Travelers can provide real-time feedback after visiting a location or utilizing a service. This data can be instantly streamed to businesses or service providers, allowing for immediate response or service improvement.

By leveraging the Confluent platform, a real-time travel guide can offer travelers a highly responsive, context-aware, and enriched experience. Such dynamic information delivery can transform how travelers interact with their surroundings, making their journey more engaging, safe, and informed.

Financials

Confluent showed excellent growth in the first half of 2023. All the essential metrics were solid.

Revenues grew 37% YoY, aided by massive growth in cloud platform revenue of 83%. The cloud platform is the backbone of the business, getting sustainable and recurring subscription revenue.

Confluent employs a great SaaS land and expand strategy with 130% Net Revenue Retention (NRR) and a gross retention rate of about 90%. The cloud platform has an NRR of 140%.

On-premise license revenues also rose positively, with growth north of 16%.

The other big plus was the margin improvement, with single-digit improvements in gross GAAP and adjusted margins, resulting in a 50% improvement in adjusted operating losses.

Currently, the adjusted OPM is down to -16%, and Confluent has guided to -10% for 2024, promising to break even on an adjusted basis in Q4. In a choppy and challenging environment, focusing on reducing costs is crucial, and for the first half, the most significant improvements came through lower sales and marketing expenses.

Confluent is far from GAAP positive with a humongous $171Mn of Stock-Based Compensation in 1H2023, nor is it generating operating cash - but it is a step in the right direction.

Large clients over $100,000 in ARR also grew well with a 33% YoY increase, as did the 147, over 1 $Mn clients with an even more impressive 48% growth.

Buying the dip

As of writing (October 18, 2023), the stock is around $30 after briefly reaching $40-$41 during the AI hype in July. Confluent's first-half performance confirmed the positives of the company. It's worth buying for the long run.

Valuation: The forward P/S ratio is over 9 (quite average for a hypergrowth SaaS company), with high growth of 33%, bringing it down to 7 and 5. Patience will pay off, and given the Fed's higher-for-longer stance and with 10-year treasury yields over 4.9%, valuations are stretched across the board.

I would buy between $27-29. There may be a 10-15% drop from current levels. Confluent is well suited for a covered call strategy. Option premiums are high. Buying below $30 and selling calls - strike $35 or $40 - makes much sense.

The following earnings report is just around the corner. Initial guidance for 2024 may have a significant influence on the stock. We suggest waiting after the earnings are out and acting according to the guidance.