Developers building for AR glasses and wearable devices face an infrastructure gap. The hardware is ready, but creating AI experiences requires integrating live camera and microphone streams, multimodal AI models, enterprise data, tool use, deployment infrastructure, and device-specific runtimes. NVIDIA XR AI is designed to address this challenge by providing a reusable foundation for…

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Every swipe, transfer, and payment on a modern financial network encodes a pattern of human behavior. Transaction data is one of the richest signals an enterprise owns. Yet most production use cases for such tabular data still depend on hand-engineered features and rule sets that are brittle, expensive to maintain, and blind to the sequential structure inside a customer history.

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NVIDIA RTX technologies are deeply integrated into Unreal Engine 5 through the NVIDIA RTX Branch of Unreal Engine and the NVIDIA DLSS Unreal Engine plugin. This provides developers with direct access to advanced rendering, frame generation, and ray-traced lighting. NVIDIA is expanding this integration with new tools for building on-device AI characters and gameplay, as announced at Unreal Fest…

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Transformer architectures are the backbone of many modern large language and generative AI models. As these models grow in size, training runs consume more GPU hours and more engineering iteration time. Accelerating transformers is therefore not just a performance optimization, but directly affects how quickly teams can experiment and how large a model they can afford to train.

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NVIDIA delivered a clean sweep in MLPerf Training v6.0, the latest edition of industry-standard AI training benchmarks developed by the MLCommons consortium. NVIDIA achieved the fastest time to train at scale, and also delivered the highest performance when normalized on a per-accelerator basis on every benchmark. It was also the only platform to submit on every test.

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Foundation models are reshaping computational biology. Pretrained on massive corpora of protein or genomic sequences, models such as ESM2 (a protein language model) and Evo 2 (a DNA language model) capture statistical regularities of biological sequences. These transfer well to a wide range of downstream tasks, including structure prediction, variant effect, and functional annotation.

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Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable substantially larger model capacity while activating only a subset of parameters for each token, offering an unparalleled approach for scaling performance within a practical compute budget. As model scales continue to grow…

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Quick glossary for readers new to VLA/WAM terminology VLA Vision-Language-Action model: a robot policy that starts from a pretrained VLM backbone and adapts it to generate actions from visual observations and language instructions. Large-scale VLM pretraining is a core part of the recipe. See Pi-0 and GR00T N1. WAM World-Action Model: a policy that starts from a pretrained world-model or video…

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AI agents have fundamentally changed the complexity of inference workloads. Until now, the industry has struggled to define a standard for measuring how inference systems perform under these conditions. Artificial Analysis AgentPerf (AA-AgentPerf) offers the industry’s first multi-vendor open benchmarks profiling trajectories that are representative of real-world AI agent coding tasks.

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As enterprise AI adoption scales, developers are increasingly forced to stitch together fragmented pipelines—separate models for text, vision, and code—leading to added complexity, higher costs, and slower iteration. MiniMax M3—available on NVIDIA accelerated infrastructure including NVIDIA Blackwell—changes this by enabling a single multimodal system capable of long-context reasoning…

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NVIDIA Quantum InfiniBand now offers intent-based security profiles in Unified Fabric Manager (UFM) that enable multi-tenant fabric security in a single click. NVIDIA Quantum InfiniBand supports three profiles: General, Bare Metal Cloud, and Secured Bare Metal Cloud. Network administrators can now auto-configure: This cuts deployment time to minutes from hours or days…

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Developers building real-time AI—such as chat assistants, copilots, and agentic workflows—are often constrained by token-by-token generation speed. This limits responsiveness, increases serving costs, and makes fluid, interactive experiences difficult to achieve. DiffusionGemma, created by Google DeepMind and optimized to run efficiently across NVIDIA platforms, introduces a new approach to…

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Developers building real-time AI—such as chat assistants, copilots, and agentic workflows—are often constrained by token-by-token generation speed. This limits responsiveness, increases serving costs, and makes fluid, interactive experiences difficult to achieve. DiffusionGemma, created by Google DeepMind and optimized to run efficiently across NVIDIA platforms, introduces a new approach to…

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AI factories are changing what data-center infrastructure must do. Unlike traditional data centers, AI factories are built to manufacture intelligence at scale. They run power-dense training and inference workloads, increasingly support agentic and reasoning models, and must deliver predictable performance even as compute demand shifts rapidly. In this environment…

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As AI infrastructure scales, enterprise expectations for operational maturity are increasing. Organizations expect these systems to be provisionable, observable, secure, and manageable at scale—the same standard applied to all critical infrastructure. The moment an AI system moves from development into enterprise deployment, that operational foundation is essential. NVIDIA DGX Spark and…

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Converting a quantized checkpoint into an NVIDIA TensorRT engine bridges the gap between model optimization and production deployment, enabling faster inference, higher throughput, and more efficient GPU utilization at scale. In a previous post, we produced a high-quality FP8-quantized Contrastive Language-Image Pretraining (CLIP) checkpoint with NVIDIA TensorRT Model Optimizer.

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Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a server optimizer setting, a SCAFFOLD variant, or a model architecture tweak may all look promising before an experiment starts. After the run finishes, the harder questions begin: Did the change actually improve the metric?

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Training a speech AI model to correctly recognize or synthesize clinical terminology is surprisingly difficult. Drug names like Acetaminophen, Amlodipine, Cefazolin, and Biktarvy are not part of everyday vocabulary. Procedure names, anatomy terms, and specialty-specific diagnoses introduce the same problem in a different form. Off-the-shelf speech systems can sound fluent and still miss the words…

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Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step time can add up to days of training and substantial compute costs. Numerical precision is one of the highest-leverage knobs available, but low- bit mixed-precision pretraining is hard to get right. To address this…

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Single-turn chatbots are evolving into long-running agents that can reason, maintain context, use tools, and run efficiently across many turns to complete complex workflows. However, these multi-agent workflows cause token counts to grow quickly. Agents plan, call tools, invoke sub-agents, receive information, and then pass history, outputs, and reasoning steps back into the model…

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AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with day-to-day tasks such as coding, video editing, and content management. NVIDIA and Microsoft are teaming up to enable the next generation of developers to build on-device agents on the Windows platform, with easier setup, native security…

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AI agents are a powerful tool for synthesizing data to accelerate research, summarize information, and help teams make decisions faster. But combining internal data with public sources poses security challenges. This post shares an open source example using Hermes Agent with NVIDIA NemoClaw for product research across Outlook, Slack, and GitHub. NVIDIA OpenShell enforces a security-approved…

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As AI agents move from the digital world to the physical environment, they can readily use NVIDIA Jetson to accelerate real-world deployment with optimized memory and performance. NVIDIA JetPack 7.2 directly supports one-command deployment of NVIDIA NemoClaw, an open source stack that adds privacy and security controls to OpenClaw. It introduces NVIDIA agent skills for Jetson—Jetson device…

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The rise of autonomous, long-running AI agents has introduced a new class of compute demand, namely tasks that maintain large context windows, spawn concurrent subagents, and iterate continuously without cloud dependency. Security and privacy concerns are also accelerating the shift toward local agents. Developers, by running autonomous agents on hardware they own with NVIDIA NemoClaw…

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Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can reason over more complex driving scenes and produce richer intermediate reasoning are predominantly trained in open-loop, where model outputs are directly compared to ground-truth behaviors without considering their effect on the environment.

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Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what’s happening in their world, predict what’s likely to happen next, and generate actions for specific environments, embodiments, and tasks. NVIDIA Cosmos 3 is a frontier foundation model for physical AI that combines physical reasoning…

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The AI era is driving a new class of infrastructure: AI factories that transform data into intelligence for autonomous AI agents operating at unprecedented scale. Powered by accelerated computing, AI factories enable enterprises to train, fine-tune, and deploy AI with greater speed and efficiency. This new class of infrastructure also introduces a fundamentally new attack surface spanning…

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Each wave of AI has created a new scaling law. Pretraining scaled intelligence through larger datasets, more parameters, and massively parallel GPU systems. Post-training scaled usefulness through instruction tuning, and re-balancing GPUs for generative inference. Test-time scaling improved reasoning by giving models more generated tokens for thinking. Now, agentic AI and reinforcement…

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AI is now essential infrastructure, powered by AI factories that generate intelligence in the form of tokens. As demand grows, these factories must scale faster, operate more efficiently, and lower the cost of intelligence across the five-layer stack: energy, chips, infrastructure, models, and applications. NVIDIA DSX platform provides the complete playbook for designing, simulating, building…

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Modern LLM serving is hard to tune because each deployment is a stack of interacting choices: model backend, tensor-parallel shape, prefill/decode split, worker counts, scheduler settings, routing policy, KV cache behavior, autoscaling thresholds, and topology. Those choices interact across layers, and a local improvement can shift the bottleneck somewhere else. For larger models…

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As AI models grow in complexity and regulatory scrutiny intensifies under frameworks including California’s AB-2013 and the EU AI Act, software teams face a challenge beyond delivering great code: They need to produce comprehensive, auditable model documentation before the models are released. Model cards describe how a model works, its intended use and license, training data, performance…

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AI applications are moving beyond text generation to multimodal systems that can perceive, search, and reason across images, documents, video, and language in real time—turning fragmented information into actionable insights. Step 3.7 Flash, the latest from StepFun, brings these capabilities to production and enterprise-scale, available on NVIDIA-accelerated infrastructure. It is a 198B…

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In production inference deployments, demand fluctuates over time, requiring inference replicas to scale elastically. However, cold-starting inference workloads on Kubernetes can take several minutes. During that time, GPUs are allocated but idle, generating no tokens and serving no requests. This delay increases the risk of service level agreement (SLA) violations during traffic spikes…

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NVIDIA RTX provides game developers with direct paths to AI-driven characters, frame generation, and ray-traced rendering. This post walks through a meaningful set of recent updates across the RTX ecosystem. NVIDIA ACE expands its multilingual AI character capabilities, making it easier to ship conversational NPCs. NVIDIA DLSS 4.5 arrives as an Unreal Engine (UE) plugin…

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NVIDIA CUDA 13.3 brings new capabilities and performance optimizations to developers across the CUDA ecosystem. The launch of NVIDIA CUDA Tile programming in C++, enables high-level, tile-based kernel development that automatically manages complex low-level GPU details for optimal performance and portability. Additionally, CUDA Tile programming is now supported on Compute Capability 9.0…

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NVIDIA CompileIQ tackles one of the hardest problems in performance engineering: finding the compiler options that unlock the best performance for a specific workload. Consider a team that has spent weeks optimizing an LLM inference pipeline on GPUs, tuning batch sizes, quantizing to FP8, adopting flash attention, fusing every kernel they can. The profiler says there’s nothing left to squeeze.

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Precision medicine depends on two fundamental capabilities: understanding disease at the genomic level and identifying treatments at the molecular level. NVIDIA’s contributions to precision medicine extend far beyond accelerated computing, delivering a full-stack platform that translates hardware and software advancements directly into healthcare outcomes. Sequencing the human genome…

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High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by data scarcity, privacy restrictions, and the high cost of expert annotation. As a result, training reliable 3D medical imaging models is frequently bottlenecked by small, narrow, and hard‑to‑share datasets, limiting model robustness and generalization. To help teams overcome…

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In quantitative finance, researchers build algorithms to trade assets, derivatives, and other financial instruments. A key part of that work is finding signals: patterns in messy market data that may help predict future returns. These signals can come from price and volume data, economic indicators, fundamentals, or alternative sources like news sentiment. For years…

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As AI models grow in scale and complexity, realizing the full performance of modern accelerated infrastructure depends as much on how workloads are placed as on the hardware itself. NVIDIA GB200 NVL72 delivers exascale compute in a single rack, unlocking real-time trillion-parameter models. Yet capturing that performance in a shared cluster requires schedulers that understand the system…

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Maximizing the value of AI infrastructure demands deep visibility into GPU utilization. Yet many platform teams running AI workloads on Kubernetes operate with limited visibility into how their GPUs are used. Most don’t know who’s consuming them, how much memory is in use, and whether Kubernetes pods are pending or silently idle. Without a signal, GPU fleets are routinely underutilized and slow to…

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Telcos around the world are building sovereign AI factories based on the NVIDIA Cloud Partner (NCP) reference architecture, giving governments, enterprises, and startups access to in‑country AI infrastructure with the right controls, trust, and performance. But infrastructure alone doesn’t get you to high-margin, production-ready enterprise AI services. Model sizes and reasoning workloads…

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Autonomous AI agents are taking on all types of work for businesses: routing logistics fleets, triaging support tickets, generating code, and orchestrating multistep workflows. How do you take a general-purpose model and make it excel at your specific task? Customization provides an agent with the right capabilities. This post explains nine techniques for customizing AI agents…

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Agent harnesses like Claude Code, Codex, and LangChain Deep Agents are excellent orchestrators. They manage sessions, chain tools, execute code, and respond to developer intent. But when these harnesses need to do deep research, such as multi-document synthesis, decision briefs backed by enterprise data, and long-horizon analysis with source attribution, the complexity of deep research shifts back…

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Autonomous AI agents are becoming more capable. Open models, Model Context Protocol (MCP)-connected tools, and portable skills are also making agents easier to extend. But scaling agent use with structural transparency and operational integrity requires more than runtime guardrails. Organizations and teams need to understand and trust the skills, or instructions, an agent is using.

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Evaluating an AI model and evaluating an AI agent are related—but they answer fundamentally different questions. A model benchmark tests the capability of a foundation model (how well it understands language, follows instructions, or solves problems on static tasks). An agent evaluation tests the behavior of a system operating end-to-end—planning, calling tools, handling uncertainty…

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Agentic inference has fundamentally changed the runtime dynamics of inference workloads by introducing non-deterministic trajectories—actions, observations, and decisions that an AI agent produces while working through a task. These trajectories compound end-to-end latency across hundreds of inference requests per session. NVIDIA Vera Rubin NVL72 handles the bulk of that inference load as…

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A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors, batteries, and catalysis. It produces ultrashort X-ray pulses that can record the movements of atoms and electrons. These instruments can detect the smallest change in material structure caused by defects and other influences.

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In today’s data-driven world, organizations increasingly rely on video to capture critical information, yet extracting meaningful, real-time insights from massive amounts of footage remains a challenge. NVIDIA Metropolis Blueprint for video search and summarization (VSS) overcomes this hurdle by transforming millions of live video streams or hours of recorded video into instantly searchable…

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The path from a trained AI model to production should be smooth, but rarely is. Many teams invest weeks fine-tuning models, only to discover that exporting to a deployment format breaks layers, input shapes cause runtime failures, or version mismatches silently degrade performance. These issues are collectively known as pipeline friction, and they cost organizations time, money…

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The compute capability of large GPU fleets presents unprecedented opportunities to innovate and provide value to customers in record time. Yet these advancements come with a variety of challenges. At scale, teams are juggling heterogeneous hardware, fast‑moving software stacks, tight power envelopes, and spiky, multitenant workloads. A single hotspot, misconfigured driver, or subtle hardware fault…

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Bash is one of the most flexible and powerful interfaces exposed to AI agents. In the right system, a model that emits , , , or a shell pipeline is producing an executable action that can read files, mutate a workspace, open network connections, and chain tools together. For the NVIDIA AI Red Team, this makes command generation a useful research target. If smaller language models can be guided…

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An agentic exchange must preserve a structured interaction: assistant turns interleave reasoning with one or more tool calls, and subsequent user turns return the corresponding tool results to the model context. Reasoning replay is model- and turn-dependent: some reasoning should be retained, while some should be dropped. The inference engine is responsible for supporting this more expressive…

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Model quantization is an effective method to reduce VRAM usage and improve inference performance on consumer devices such as NVIDIA GeForce RTX GPUs. By lowering computational and memory requirements while preserving model quality, quantization helps AI models run more efficiently in resource-constrained environments. This post walks through how to use NVIDIA Model Optimizer to quantize a…

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NVIDIA GB200 NVL72 introduces a fundamentally new way to build GPU clusters by extending NVIDIA NVLink coherence across an entire rack. This design enables exascale performance, but it also changes the assumptions that many scheduling systems were built on. As a result, “rack-scale locality” becomes a hard constraint. When workloads cross domain boundaries, performance drops sharply…

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Distributed deep learning depends on fast, reliable GPU-to-GPU communication using the NVIDIA Collective Communication Library (NCCL). When training slows down, it becomes challenging to determine why and what to do next. A problem can span computation, communication, a specific rank, or underlying hardware. NVIDIA NCCL Inspector accelerates triaging by providing a lightweight and continuous…

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The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and acting. In most vehicles on the road today, in-vehicle assistants still rely on fixed command-response patterns: interpret a phrase, trigger an action, reset. While effective for well-defined tasks, this approach doesn’t scale to modern…

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Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different. Agents don’t follow a pre-determined sequence of actions. They call tools, spawn sub-agents with different tasks and models, retain information in memory, manage their own context window, and decide for themselves when they’re finished. In doing so…

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Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, and interdependent decision-making. Traditionally, specialized operations research (OR) teams solved these problems by translating business questions into mathematical models. This process can take weeks and often produces fragile solutions that struggle to adapt when conditions…

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Today, game developers can begin integrating NVIDIA DLSS 4.5 with Dynamic Multi Frame Generation, Multi Frame Generation 6X, and the second-generation transformer model for NVIDIA Super Resolution. In this post, we’ll go over new technologies and resources to share with our game-developer community, including: At CES 2026, we introduced DLSS 4.5, extending its AI-driven…

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Neural network techniques are increasingly used in computer graphics to boost image quality, improve performance, and streamline content creation. Approaches like super resolution, denoising, and neural rendering help real-time engines work more efficiently, offering new creative possibilities while keeping performance in mind. Unreal Engine 5 (UE5) has taken several steps in this direction…

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Creative and visualization teams today produce more assets, in more formats, with leaner teams. Generative AI can accelerate that work – compressing tasks that once took hours of manual effort into automated, repeatable pipelines. ComfyUI is an open-source, node-based creative tool that runs locally on NVIDIA RTX GPUs. It connects image generation, video synthesis, and language models into…

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NVIDIA CUDA Tile (cuTile) is a tile-based programming model that enables developers to write GPU kernels in terms of tile-level operations—loads, stores, and matrix multiply-accumulate—rather than manually coordinating threads, warps, and shared memory. cuTile.jl brings the same tile-based approach to the dynamic programming language Julia. Users can write custom GPU kernels without dropping…

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The next wave of enterprise productivity is being built on AI factories. As organizations deploy agentic AI systems capable of reasoning, automation, and real-time decision-making at scale, competitive advantage increasingly depends on the infrastructure that supports them. Success requires more than raw compute. It demands a scalable, predictable foundation that can orchestrate intelligent…

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For decades, computational biology has operated under a reductionist compromise. To fit complex biological systems into the limited memory of a single GPU, researchers have had to deconstruct them into isolated fragments—single proteins or small domains. This created a context gap, where larger proteins or complexes could not be folded zero-shot due to GPU hardware memory constraints. Now…

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Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on fragmented model chains—separate stacks for vision, audio, and text. This increases inference hops and orchestration complexity, driving up inference costs while weakening cross-modal context consistency. NVIDIA Nemotron 3 Nano Omni…

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The subsurface industry is at a critical point in its digital evolution. For decades, unlocking reservoir potential has relied on experts performing essential and time-intensive manual workflows. As data complexity grows, the gap between machine speed and human bandwidth has become a primary bottleneck. On-demand simulation workflows are currently hampered by both manual data overhead…

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DeepSeek just launched its fourth generation of flagship models with DeepSeek-V4-Pro and DeepSeek-V4-Flash, both targeted at enabling highly efficient million-token context inference. DeepSeek-V4-Pro is the largest model in the family, with 1.6T total parameters and 49B active parameters. DeepSeek-V4-Flash is a smaller 284B-parameter model with 13B active parameters, designed for higher-speed…

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Federated learning (FL) is no longer a research curiosity—it’s a practical response to a hard constraint: the most valuable data is often the least movable. Regulatory boundaries, data sovereignty rules, and organizational risk tolerance routinely prevent centralized aggregation. Meanwhile, sheer data gravity makes even permitted transfers slow, expensive, and fragile at scale.

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In March 2026, three LLM agents generated over 600,000 lines of code, ran 850 experiments, and helped secure a first-place finish in a Kaggle playground competition. Success in modern machine learning competitions is increasingly defined by how quickly you can generate, test, and iterate on ideas. LLM agents, combined with GPU acceleration, dramatically compress this loop. Historically…

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In a previous post, we introduced the Universal Sparse Tensor (UST), enabling developers to decouple a tensor’s sparsity from its memory layout for greater flexibility and performance. We’re excited to announce the integration of the UST into nvmath-python v0.9.0 to accelerate sparse scientific and deep learning applications. This post provides a walkthrough of key UST features…

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AI integration is redefining mainstream enterprise applications, from productivity software like Microsoft Office to more complex design and engineering tools. This shift requires the modern data center to move beyond single-purpose silos. For developers, gaining access to dedicated GPU compute can often be a bottleneck. Virtual machines (VMs) solve part of this challenge by providing secure…

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Higher-order optimization algorithms such as Shampoo have been effectively applied in neural network training for at least a decade. These methods have achieved significant success more recently when applied to leading LLMs. In particular, Muon (MomentUm Orthogonalized by Newton-Schulz) was used to train some of today’s best open source models, including Kimi K2 and GLM-5.

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The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical world. Developers are eager to deploy these models at the edge, enabling physical AI agents and autonomous robots to automate heavy-duty tasks. A key challenge is efficiently running multi-billion-parameter models on edge devices with limited memory. With ongoing constraints on…

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As LLMs transition from simple text generation to complex reasoning, reinforcement learning (RL) plays a central role. Algorithms like Group Relative Policy Optimization (GRPO) power this transition, enabling reasoning-grade models to continuously improve through iterative feedback. Unlike standard supervised fine-tuning, RL training loops are bifurcated into two distinct, high-intensity phases: a…

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AI tools are significantly accelerating software development and changing how developers work with code. These tools serve as real-time copilots, automating repetitive tasks, executing tasks, writing documentation, and more. OpenAI Codex, for example, is a coding agent designed to assist developers through tasks like code generation, debugging, and automated pull request (PR) creation.

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XINKER Cards are your all-in-one, customizable digital hub. Perfect for entrepreneurs, creators, and professionals, XINKER Cards let you build your personal or business profile in minutes. Whether you want to showcase your portfolio, share your social links, or direct your audience to your latest projects, XINKER Cards make it easy.

With a sleek and intuitive design, XINKER Cards empower you to:

• Create a professional online presence: Build a personalized, mobile-friendly page to showcase your brand.
• Simplify link sharing: Share all your important links in one place, from social media accounts to websites and products.
• Boost engagement: Direct your audience to your most important content with ease.
• Track performance: Use built-in analytics to see how your audience interacts with your page.

This is more than just a BioLink tool — it’s a versatile platform designed to help you stand out and achieve your goals.

Link example:

Business: https://xinker.org/cards/business

Artist: https://xinker.org/cards/artist 

What’s Included in XINKER PRO?

With this new addition, XINKER PRO now offers even more value without increasing the price. Here’s the full package:

1. 2 Podcasts on the XINKER Site and App
   Gain access to exclusive podcast episodes where industry leaders and entrepreneurs share insights and strategies to help you grow.

2. 1 Still in XINKER Power Profiles
   Get featured in the XINKER Power Profiles, a curated section showcasing top thinkers and creators on the platform.

3. 1 CEO Story Article on the XINKER App
   Share your entrepreneurial journey in a unique CEO Story article, giving you credibility and exposure to the XINKER community.

4. 1 Company Story Article on the XINKER App
   Highlight your business and its mission with a dedicated article to attract new customers and partners.

5. XINKER APP Author Access
   Publish articles and share your knowledge with the XINKER audience, building authority in your niche.

6. Exposure on 30+ Platforms
   Expand your reach with exposure across XINKER’s extensive network of platforms.

7. NEW: XINKER Cards
   Build your own BioLink-style page to showcase your brand, links, and content, all in one place.

Affordable Pricing

We’re proud to keep XINKER PRO accessible to everyone. Despite adding the powerful XINKER Cards feature, our pricing remains the same:

• HKD 88/year
• USD 11.99/year
• GBP 9.99/year

No hidden fees. No extra costs. Just more value for the same price.

Why Choose XINKER PRO?

XINKER is more than just a platform — it’s a community. It’s your ultimate destination for mastering business strategies, discovering passive income opportunities, and learning the principles of success. With XINKER PRO, you gain access to tools and resources that empower you to:

• Build your brand.
• Expand your audience.
• Achieve financial freedom and entrepreneurial excellence.

Join XINKER PRO Today

Don’t miss out on the opportunity to elevate your personal or business presence. With the addition of XINKER Cards, XINKER PRO gives you everything you need to stand out in today’s competitive landscape.

Join the XINKER PRO community today and experience the benefits for yourself via here: https://xinker.org/cards/pay/1 

Explore XINKER. Build your future.

Complexity and Precision

Beef Wellington is renowned for its complexity and the precision required to perfect it. Similarly, mastering business strategies involves intricate planning and execution. The dish represents:

• Attention to Detail: Just as each step in making Beef Wellington must be carefully executed, successful business strategies require meticulous planning and attention to detail.
• Layered Excellence: The layers of flavors in Beef Wellington mirror the layers of skills and knowledge needed in business.

Craftsmanship and Mastery

Creating Beef Wellington demands culinary craftsmanship, akin to the expertise needed to excel in the business world. Both require:

• Skill and Patience: Achieving financial success is a journey that demands skill, patience, and dedication—qualities reflected in crafting this gourmet dish.
• Innovation and Tradition: The fusion of traditional and innovative elements in Beef Wellington parallels how XINKER blends time-tested principles with cutting-edge strategies.

The Song's Design

The song is crafted to inspire and motivate, using the metaphor of Beef Wellington to convey:

• The Pursuit of Excellence: Encouraging users to strive for excellence in their entrepreneurial endeavors, just as a chef aims for perfection with each Wellington.
• Strategic Layering: Highlighting the importance of building a solid foundation and layering skills and knowledge to achieve success.

Bridging Culinary Art and Business

The connection between Beef Wellington and business is a celebration of mastery, whether in the kitchen or the boardroom. Both require:

• Dedication: A commitment to continuous improvement and learning.
• Community and Sharing: Just as a delicious Beef Wellington is shared among friends, XINKER fosters a community where ideas and successes are shared.

Full Lyrics:

Start with the beef, a delicious quest,

Salt and pepper, for the very best.

A culinary art we're set to unfold,

With rich flavors, bold and gold.

Beef tenderloin, seared to gold,

Olive oil in, let the story unfold.

Season well, then let it cool,

This is our first flavor rule.

Rich in taste, with a crust divine,

Capturing flavors, oh so fine.

Precise in measure, cooking's grace,

The heart of the dish, setting the pace.

Mushrooms chopped, four hundred grams,

Garlic, thyme, in the pan.

Sauté them until they're dry,

Rich in flavor, let them lie.

Savory blend, oh what a treat,

Melded together, a flavor feat.

A bouquet of aromas fills the air,

A symphony of taste, beyond compare.

Lay out bacon, eight slices flat,

Puff pastry ready for the wrap.

Beat one egg, for the glaze,

Prepare it all for baking's blaze.

Tender bacon, a smoky note,

Wrapped around like a loving coat.

Golden layers, hold secrets tight,

Creating a dish of pure delight.

Spread the mushroom, bacon's embrace,

Wrap it tight, no time to waste.

Into the fridge for a little rest,

While pastry waits for its test.

Chill it well, let flavors blend,

Awaiting the warmth of the oven's send.

Patience here is virtue true,

In every step, perfection pursue.

Preheat the oven, two hundred strong,

Bake till the color is golden and long.

Rest it briefly, slice and see,

A perfect dish for you and me.

Crusty magic, a golden glow,

Inside the layers, flavor flows.

A masterpiece served with pride,

Taste sensations, side by side.

Careful slicing reveals the art,

Layer by layer, a work of heart.

Accompanied by sides, fresh and bright,

An orchestra of tastes, pure delight.

Wine to pair, enhance the taste,

A dining experience, none to waste.

Sharing moments, joy and cheer,

This culinary treasure, brings us near.

The Wellington's ready, a meal divine,

A classic flavor, for every time.

Slice it gently, serve with grace,

A culinary journey you'll embrace.

Hearts content, with every bite,

A feast of memories in the night.

Conclusion

XINKER’s theme song uses the art of cooking Beef Wellington as a metaphor for the intricate and rewarding journey of mastering business strategies. It’s a creative way to illustrate the dedication, precision, and layered approach necessary for achieving entrepreneurial excellence.

That’s right, this brand is called “Chagee” and it is the hottest tea brand in the catering industry recently.

And it took only 6 years to open more than 3,500 stores. What does this mean? It took Heytea 12 years to break through 3,000 stores.

Although some brands can open stores at this speed, this is in the tea beverage market. Tea beverages are the most competitive market in the catering industry . In 2017, it was difficult for tea beverages to have a chance to become a phenomenal brand. However, Chagee was able to carve out a path when it was founded in 2017. Moreover, it opened overseas less than two years after its establishment, and currently has more than 100 stores in Southeast Asia and other countries.

The Overlord Tea Princess was like holding a big knife and riding a dark horse, charging into this fiercely competitive battlefield and quickly seizing one territory after another.

But generally, when opening stores quickly, the profitability of a single store is ignored. It is said online that the monthly turnover of a single store of Chagee can exceed one million. I also asked my friends who opened Chagee directly and indirectly, and they said that there are indeed million-dollar stores, but it also depends on the location of the store in the business district. Not all stores are like this.

Even so, what is even more surprising is that most of Chagee's store menus only have a dozen products , and they are basically a simple combination of tea and milk. This product efficiency is very enviable. You know, most milk tea shops can't achieve a monthly turnover of 200,000 with 100 products.

(Screenshot of the mini program menu at the Chagee store in Guangzhou)

In recent years, we have been getting deeper and deeper into the catering industry. I understand that the success of a brand is caused by many factors , and it is difficult to copy and learn directly. But when I learned about the Chagee brand, I found that there are many underlying logics and many brand strategies behind it, which are worth learning from for catering people.

Moreover, the brand strategy and development of Bawang is also a classic case of a restaurant brand combining capitalization, digitalization and branding, and it also reflects many current consumption trends . The business logic of the restaurant industry is no longer the same as it was in the past, and many things have undergone earth-shaking changes.

When we study a brand, we don’t learn its superficial actions, but the brand and business logic behind it, and think about how to apply effective practices to our own brand.

So today I plan to use an article to carefully analyze the brand strategy of Chagee, hoping to give some inspiration to friends who are in the catering industry (a few days ago, I also shared the brand strategy of Chagee in the form of a short video on my video account, but short videos are difficult to show a lot of thoughts and more content in detail, so it is more convenient for everyone to think about it by writing articles. Next, I will share whatever I think of, the full text without deletions).

This article will analyze and share the logic of Chagee from the perspective of the brand, and will also include some of my personal thoughts. It will mainly share from 0 to 1, 1 to 10, 10 to 1000 to 10000:

1. Strategic positioning: brand positioning that starts from the end

2. Model design: From 0 to 1, learn from the proven successful single-store profit model

3. Brand leverage: From 1 to 10, leverage and leverage to quickly increase brand power

4. Fission expansion: from 10 to 10,000 nationwide and global layout

(Special note: I have no vested interest in Overlord Tea Princess. This article is just for sharing and does not provide any investment or other reference advice. If there is anything wrong, please correct me.)

1. Strategic positioning: brand positioning that starts from the end

We have been focusing on brand consulting in the catering industry for many years and found that there are generally two ways to do catering branding in China:

One is that for some reason, you open a store with your family, and then you find it is quite profitable, so you open branches. As you have more stores, you start to build teams, supply chains and other standardized operations. The Mixue Ice City and Wallace that you see belong to this method. I summarize this method as the "from low to high" method.

Another approach is to go from high to low - first select products and tracks, then formulate appropriate strategic positioning to compete with differentiation. Then, it is necessary to build and optimize the profit model of a single store, build a team, attract investment, build a store opening rhythm and supply chain, introduce capital and other professional operations . This is also called the brand approach of starting from the end . Chagee belongs to this approach, and so does Luckin Coffee.

With the development of the catering industry, there will be more and more catering brands that have top-level design and an end-to-end approach from the very beginning.

1.

Okay, let’s first take a look at why Zhang Junjie, the founder of Chagee, chose the tea beverage track.

We often say that choosing products is like choosing your life . If you don’t choose the right products, even the best people can only make a little money. If you choose the right track, a little effort may bring you dozens or even hundreds of times the return of others.

If you ask what kind of stores are the most common on the street, they must be tea shops, which are opened next to each other and are basically chain stores.

Mixue Bingcheng is the representative of brands with a price of less than 10 yuan, and brands with a price of 10-20 yuan include Cha Baidao, Shuyi Shaoxiancao, Guming, Yidiandian, etc., all of which have more than 1,000 stores. Heytea, which represents the mid-to-high-end price range, also opened for franchising last year, and has blossomed all over the country, with more than 3,000 stores.

Tea beverages are the most competitive sector in the catering industry, but they are also large in scale, having reached a market value of 300 billion by 2023.

However, the tea beverage industry is a track where the head effect is relatively obvious, and it is basically the same few who are fighting each other. What they compete for is the supply chain and limited business district resources . The rest can only be the boss in the region, or open stores in some places where competition is not fierce to avoid competition from big brands.

It can be said that before Chagee was established in 2017, the tea beverage market seemed to have little chance of producing another phenomenal brand.

Then why did founder Zhang Junjie choose the tea beverage sector when he founded Chagee in 2017?

From public information and personal analysis, I think there are two main aspects:

1) Familiar with and good at the tea industry

Zhang Junjie had been working in the tea industry before. He started working in a milk tea shop when he was 17 years old, and later became the regional director of a milk tea brand. Before founding Bawang, he had been working in this industry for nearly 10 years and had rich experience and knowledge in the tea industry.

I think this is the main reason. Most people who start a business will generally choose something they are good at and have an advantage in as their entry point, and the success rate will be higher.

2) Tea drinks are in line with the goal of becoming a global brand

Before establishing Chagee, Zhang Junjie went abroad for inspection and exchange. Seeing that Starbucks had opened stores all over the world, he thought to himself: Why can Starbucks, which sells coffee, open stores all over the world, but our Chinese brands can’t open stores all over the world?

So from the very beginning, our brother Jie had a strategic goal of globalization.

But if a category wants to be accepted by people all over the country or the world, it must have two basics:

First, the category itself must be addictive and acceptable to any human being.

Starbucks sells coffee, and coffee is addictive. And if you look at the top five brands in China with 10,000 stores, Mixue Ice City's milk tea, Wallace's fried chicken, Zhengxin Chicken's fried chicken, Juewei Duck Neck's spicy marinated food, and Luckin's coffee, they are all basically addictive products, and no matter where you are from, you can accept them.

Many regional specialties are difficult to sell across the country because their taste is not universal and addictive.

The second is the output of cultural potential.

When McDonald's and KFC opened in China, people didn't even know what a hamburger was. People didn't come to McDonald's for the hamburgers, but because they admired European and American culture at the time. This behavior of consuming because of admiration or pursuit of a certain culture is very common.

Behind the brand is the category, behind the category is the culture, and behind the culture is the economy. Only when the economy is good will people admire and consume your culture.

Sometimes I think some so-called fashion shows abroad are ugly, but the public still likes this kind of aesthetic. Aesthetics are transmitted from high to low, and this "high" is economic development. For example, more than ten or twenty years ago, Hong Kong's economy was developed, and Hong Kong movies were popular in the mainland. Although the taste of Hong Kong-style tea restaurants was not very addictive, they were also popular throughout the country.

If you open an African restaurant in China, even Africans in China may feel embarrassed to go in and consume.

With the rise of China's economy, Chinese catering brands have already possessed this cultural potential to go global . Tea is an element that can best represent Chinese culture, and tea is also addictive. Therefore, tea brands have the opportunity to open all over the world.

Bawang’s current brand slogan, “Meet friends from all over the world with Oriental tea”, directly states the purpose of the global brand.

2.

Okay, here comes the second question. The competition in the tea beverage market is so fierce, what brand direction should we take in order to stand out?

When we look at a brand, we should not only look at its current success, but also look at how it got started, what problems it encountered, how it made decisions, why it did so, and what inspiration can we get from it?

In 2017, tea drinks were divided into two categories: Chinese tea drinks and modern tea drinks. Modern tea drinks are divided into three categories:

The first category is "pearl milk tea" . Many brands originally came from Taiwan, such as COCO, Yidiandian, etc.

The second category is called "drinks turned into desserts ", and the representative brand is Shuyi Grass Jelly.

The third category is "fruit tea" , which was also the hottest direction at the time. Because fruits have the advantages of "health" and high value, many brands that are not specialized in fruit tea have also launched fruit tea related products. Among them, Gu Ming and Cha Baidao are the representatives of the 10-20 yuan price range, and Heytea and Nayuki are priced at 30 yuan. Some social space scenes are also added.

There is also a category called new Chinese tea drinks, which is fresh milk tea , which is mainly milk and tea. Then some traditional Chinese elements are added, which is also called traditional Chinese fresh milk tea. In 2017, the most representative one was Cha Yan Yue Se, which has been opening stores in Changsha. With the trend of the traditional Chinese concept and Changsha's status as an internet celebrity city, it quickly became popular on social media, but it only opened stores in Changsha.

(Data source: DT Business Observation "Number and proportion of milk tea SKUs of different categories in 2023")

Seeing this, if it were you, which direction would you choose?

We all know now that Bawang has chosen the direction of traditional Chinese style fresh milk tea .

Many people on the Internet are saying that Chagee imitates the Chinese style fresh milk tea positioning of Cha Yan Yue Se. But when Bawang was first established in 2017, the most popular was the fruit tea represented by Heytea and Nayuki.

Why doesn’t Chagee make fruit tea but instead imitate the Chinese style fresh milk tea which has a relatively small market share?

Here we can see the ambition and strategic analysis ability of Zhang Junjie, the founder of Chagee.

First of all, the fruit tea market has already become a leader, and it will be difficult for you to surpass them if you try to compete.

There is a very important principle in our brand positioning.

Rather than being better, it’s better to be different.

Only by making a differentiated positioning can you have a chance to stand out.

Cha Yan Yue Se was founded just two years ago and has been in Changsha. So at that time, there was no national leading brand in the field of traditional Chinese milk tea. This was a potential market that had not been fully occupied, and there was an opportunity for a leading brand of traditional Chinese milk tea to emerge.

But I don’t think this is the most important reason. I think the more important reason is that as mentioned earlier, Ba Wang Cha Ji had the goal of becoming a global brand from the beginning.

Zhang Junjie hopes that Bawang can be opened all over the world like Starbucks. But for restaurants to be opened all over the world, there is another very critical issue, which is the standardization and stability of the supply chain.

Look at Starbucks, it is mainly coffee beans, and the supply chain is very simple, stable and standardized. The same is true for McDonald's chicken.

One of the important reasons why the Yu Ni Zai Yi Qi restaurant I worked for was able to open more than 2,000 stores in just a few years was the standardization of the Basa fish supply chain and the popularization of cold chain logistics, which ensured a stable supply of food ingredients to stores across the country and even the world.

Let's go back to the several directions of tea drinks. First of all, pearl milk tea does not conform to the overall changes in consumption trends. The raw materials of tea dessert products are relatively complex, and the standardized production efficiency and supply chain efficiency at the store end are not high.

Let's look at fruit tea. It is difficult to standardize the fruit supply chain, and the management cost is high. Although fruit is healthy and valuable, it has to take into account seasonality, logistics and transportation losses, and storage conditions. If it is not done well, there will be no profit, and it is not stable enough.

Just think about it, a box of mangoes is shipped from Yunnan to Singapore, and the whole process needs to be cold-chained and delivered, and then peeled and processed in various ways when it arrives at the store. How high is the cost and loss? This is also why fruit tea brands dare not expand blindly and quickly, because the fruit supply chain cannot keep up.

The core of the fresh milk tea supply chain is tea and milk. If you ship a box of tea to different parts of the world, the cost is relatively low and there won’t be much loss. Moreover, China’s tea supply chain is very mature and stable. Not to mention milk. This provides the supply chain foundation for Chagee’s global layout.

This is also why the hot pot market is so large. Everyone likes to make hot pot instead of stir-frying because the hot pot supply chain is simple and stable, and the store does not need a chef to ensure a stable taste.

So, if you want to become a national chain brand, you cannot just consider whether your product is popular, you also have to consider the back-end supply chain issues.

Some categories achieve relatively stable store-side products by solving the problem of kitchen staff and business model. For example, Xiaocaiyuan, a stir-fry restaurant, has opened 500 to 600 stores. It does not rely on the standardization of the supply chain, but relies on talent organization to solve the problem of stable products. However, it is difficult to quickly replicate the opening of stores. However, it is already very impressive for such a Chinese restaurant to open hundreds of stores.

3.

After solving the general problem, we need to solve another problem:

There are so many tea brands on the street now, why should customers choose Chagee?

You have to give customers reasons to buy from you.

Whether this reason for purchase is strong or not depends on the difference between customer value and price.

I think there is no such thing as expensive or cheap products in this world, only whether they are worth it or not. The more valuable a product is, the easier it is to impress customers and make repeat purchases.

Either you provide greater customer value than other competitors at the same price. Or if your meat filling is fresher and larger, I will choose you first.

Or you can offer a lower price for the same value. If you sell a cup of fresh fruit tea for 12 yuan and Mixue Ice City sells it for 6 yuan, many customers will naturally choose Mixue Ice City, which has a better price-performance ratio.

Ba Wang Cha Ji’s strategy is to set price first and then determine value .

After all, positioning comes before pricing, and pricing determines the world.

The higher the price, the narrower the customer base. The lower the price, the wider the customer base .

Bawang's price range is 15-20 yuan, which is a mid-range price. Although the customer base is not as large as that of the 15 yuan and below, the base of this price range is not small , which is enough to open 10,000 stores in China . This pricing also provides a certain gross profit to create a Starbucks-style social space experience.

Okay, now that the pricing is determined, let’s take a look at what customer value Chagee provides and see if it’s worth it.

The size of customer value depends on whether customer needs are better met.

Value that does not satisfy customer needs is not called value, it can only be called "self-satisfaction".

This has to do with Ba Wang Cha Ji’s accurate grasp of consumer demand.

What do today’s consumers fear most when drinking milk tea?

"China's New Tea Drinks Big Data Research and Consumer Behavior Survey Data" shows that 49.4% of consumers are worried that consuming new tea drinks is bad for their health, and 42.2% of consumers are afraid of gaining weight.

Concerns about gaining weight, too much sugar, high calories, etc. Behind this is the " healthification " that everyone has been discussing recently.

Consumers' health concerns are also forcing the tea industry to make a healthy transformation. This is not only true for milk tea, but many other categories are now pursuing this health trend.

But in fact, customers’ real demands are often to have more and more!

Drinking a cup of milk tea should be healthy, delicious, and satisfy the need to take photos and share, show off, and have emotional value.

Why has Dongfang Leaf not been popular for several years? It is healthy but not tasty. However, Yuanqi Forest has become popular all of a sudden. It has 0 sugar and 0 fat, is healthy and tasty. Of course, the word "healthy" is in quotation marks.

Therefore, whoever can better meet consumers' multiple demands for milk tea that must be healthy, delicious, and pretentious, consumers will choose you first.

It is not difficult to say that it is delicious, and the sweetness of milk tea itself is addictive. The key is who can make the health concept more perfect and more attractive to customers.

Let’s see how Ba Wang Cha Ji has taken health awareness to the extreme.

First of all, Chagee’s initial product development direction was: tea + milk, refreshing and non-greasy, which is in line with the health characteristics of the product.

At the same time, this healthy awareness is constantly strengthened.

For example, while others are still using creamer-like ingredients, Bawang has taken the lead in using a new base product, "Ice Brown Non-Hydrogenated Base Milk", which achieves 0 creamer, 0 non-dairy creamer, and 0 hydrogenated vegetable oil, allowing customers to drink healthily without burden.

The company also launched a tea product ID card . Starting from the ingredients of tea drinks, the company launched the first "tea product ID card" to disclose the product formula, nutritional information, and product flavor list in detail, directly addressing consumers' health concerns and allowing consumers to understand what they are drinking.

(Picture screenshot from Chagee official mini program)

Later, other brands also followed suit and launched product ID cards, such as Heytea and other brands.

We often say that first-class brands set standards, and second-class brands follow the standards. Bawang Tea Princess is on its way to becoming a first-class brand, taking the lead in proposing and setting standards for the industry.

Later, a calorie calculator was added to let customers know exactly how many calories a cup of milk tea contains.

(Picture screenshot from Chagee official mini program)

You see, it is delicious and healthy. You don’t have to worry about getting fat after drinking it. You can control your calories at any time, and you don’t have to feel guilty about drinking milk tea.

You say, if customers give priority to Ba Wang Cha Ji when selling milk tea for the same price of more than ten yuan?

At this point the product value seems to be almost there, but it’s not enough!

Because customers always want more and more, we also need to understand what other needs of consumers are not met. The more you can meet the needs of customers, the more you will be chosen by customers.

When we build brands, we need to pay attention to and understand the changes in consumer demand at any time. If your brand products cannot meet consumer demand, how can you make money? (Even if you are doing TO B business, you must pay attention to the changes in B-side demand. The nature of the demand is different, but the essence is the same)

What changes have taken place in consumers’ demand for milk tea?

Nowadays, consumers view a cup of milk tea not only as a drink, but also as a kind of social currency, which they use to take photos and share on WeChat Moments to show off, as well as to satisfy certain emotional values.

Therefore, your milk tea is not only delicious and healthy, but also needs to be packaged beautifully and convey a brand concept that can meet some emotional needs. For example, you can have a cup of Bawang Tea to kill time while slacking off at work, or have a cup of milk tea as a companion after get off work to watch TV series.

The packaging design and store design of Bawang Tea Princess combine elements of traditional Chinese culture with elements of international luxury goods, allowing customers to take photos and share the feeling of being tasteful and high-end.

It also collaborates with various brands representing young people and trends and launches various social activities, making consumers feel that Chagee understands young people and creates an emotional resonance.

Selling emotions and social value is one of the secrets to selling a brand at a high price.

The reason to buy is not a single point, but a combination of values.

This combination of measures can basically differentiate itself from most tea brands and give customers a strong enough reason to buy.

This is also the logic behind why we often say that "cost-effectiveness" is not as good as "quality-price ratio".

After reading this, you may also want to think about this question: With so many choices now available to customers, why should they choose your brand? Are the reasons you provide for purchase strong?

If you are running a social restaurant with a dine-in model, you cannot just think about your reasons for purchase based on the product value. You also have to design your differentiated positioning and customer value from multiple dimensions such as your service experience, environmental experience, business district location, store model, etc.

Just like when we do brand positioning for a skewers hotpot brand, we not only say that the product is fresh and delicious, but also how is the service and scene experience. After all, customers come to a hotpot restaurant to eat not only to eat hotpot, but also to socialize.

Different product categories have different customer demands.

OK, after solving the positioning direction and customer value problems, if you want to replicate the store and build a chain brand, the next step is to create a replicable chain single store profit model.

2. Model design: creating a replicable chain store profit model from 0 to 1

Why is it that many people become successful after opening one store, but start to lose money after opening the second or third store?

Why is it okay to open a business in a shopping mall, but you can’t make money in a community?

Why do others need 500,000 to open a store, but you need 1 million?

Why can others make back their investment in half a year, but it takes you three years?

……

This is mainly due to the different profit models of single stores.

No matter how big your restaurant business is, a single store is always the smallest competitive unit. No matter how big a brand is, if your single store cannot compete with the stores next to it within a few kilometers of the nearby business district, it will die.

If a single store is not profitable, the chain will also go bankrupt.

This is why it is difficult for the catering industry to be monopolized by the leading brands . The catering industry is a relatively fair industry with low barriers to entry among many industries. It does not matter your background and resources. As long as you have the ability, you can make money in the catering industry and realize your dreams!

Therefore, if you want to be a chain brand, you need to create a replicable chain single-store profit model.

Polishing a single store's profit model usually takes time and effort. Is there a faster and more efficient way?

Here we have to talk about the strategy of the Overlord.

The single-store model directly borrowed from the then proven successful Chinese-style fresh milk tea brand Cha Yan Yue Se, and quickly completed the chain single-store profit model from 0 to 1. No trial and error, just one step.

But I think Bawang's goal is to become the Starbucks of the East, and the global brand is just a direct reference to Cha Yan's many models that just happened to match at the current stage. If you look at it, you will find that Cha Yan is just a passer-by in Bawang's goal of achieving a global brand. After all, before they grow up, the eagle and the dove look similar.

Now Bawang’s single-store profit model is different from Cha Yan’s.

So what is a good chain single-store profit model?

Everyone has different perspectives and standards. From the perspective of chain stores, I will look at it more from the perspective of customer value and investment efficiency - whether your single-store profit model can better meet customer needs and provide better value to achieve a higher return on investment. To put it bluntly, both C-end consumers and B-end partners benefit more, and that is a good profit model.

The single-store profit model involves a lot of content, including product structure, business district location, pricing, cost structure, customer selection, investment cost, etc. I will have the opportunity to write an article to explain it in detail in the future.

Here I will first talk about the product strategy of Chagee.

The catering industry is generally divided into two product strategy directions: fast fashion and classic.

Have you noticed that McDonald's, KFC, Starbucks, Nanchengxiang and other brands have not changed their core products for decades. This is the product strategy of choosing classic models. New products are only used to stimulate and awaken old customers or for marketing promotion to tell them that there are new products to try, but most of the profits come from classic models.

Recently, I visited Chengdu to inspect a hotpot brand called "Wuliguan", which also has this kind of product strategy.

Among them, the most representative brand is Uniqlo in the clothing industry. They sell the same type of clothes for several years, but their profit margin is higher than other fast fashion brands that sell trendy items.

The advantage of the " fast fashion" product strategy is that there is a constant influx of new traffic, but the disadvantage is that there is no supply chain accumulation and it requires continuous new product development capabilities. Once it fails to keep up with customers' demand for new products, it will be eliminated.

For example, the hot pot brand "Zhu Guangyu" which has a relatively high potential online recently adopts this product strategy, which requires launching various new products to attract customers to check in and try them out. Once customers have nothing new to attract them, they will no longer choose you.

According to statistics, the profit margin generated by the classic strategy is almost twice that of innovative products.

The "big single product" strategy chosen by Bawang Tea Princess from the beginning is this classic strategy - focus on quality rather than quantity, and make classic products based on common tea and milk bases.

Most of Bawang's store menus mainly have three series: original leaf fresh milk tea, snow top series, original leaf fresh brewed tea,

There are about a dozen flavors in total (Yunnan stores have some fruit teas and other products).

(Screenshot from Chagee Guangzhou store mini program)

However, the top three products account for about 70% of the total sales. The sales of the signature product Boya Juexian account for about 20-30% of the total sales.

After all, the more concentrated the products, the more streamlined the supply chain , which improves the efficiency of front-end stores and back-end supply chains.

So does it mean that the fewer products you make, the better?

uncertain.

The product structure design should match your single-store profit model and strategic positioning.

The key is that the store can make money and improve overall efficiency.

I have to say a little more here: in the end, when it comes to branding, we all hope to achieve economies of scale - the more stores we have, the lower the total cost.

But we are not afraid of not reducing costs, but we are most afraid of increasing costs . This is also what Zhang Junjie said about anti-scale gravity - the bigger the scale, the higher the cost.

The three major costs of catering are: ingredients, labor and rent.

As the scale increases, the cost of ingredients will indeed decrease. As the brand power increases, the rent may be a little lower, but not much.

The key point is that labor costs will not necessarily decrease as the number of stores increases, but may increase instead.

Because the labor cost of a single store cannot be reduced, if you need 10 people for a store, you will still need 10 people for the 1,000th store. However, the personnel management cost of each store will increase, and the human factor is the most uncertain, which will also affect the stability of the overall customer experience.

So what can you do? Either improve labor efficiency through organizational management, or minimize reliance on manpower in your single-store profit model design to reduce uncertainty and management costs.

Therefore, Chagee simplifies its product structure by making classic models, reduces the difficulty of store operations by standardizing the upstream supply chain, and uses intelligent milk tea making machinery and equipment, etc. These are all aimed at reducing dependence on people and reducing store management costs.

You see, the chain brand that has done the most extreme in this regard is Juewei Duck Neck. The store employees do almost the same job as cashiers. The store does not require any processing. Products are delivered to the store and put on display for sale, which greatly reduces the cost of human management.

The opposite example is Chinese food. Restaurant products rely heavily on chefs and manpower, which is why few Chinese cooking brands can quickly open thousands of stores. Many Chinese food brands are working on product standardization and robot cooking to solve this standardization and labor cost problem.

What we need to note here is that in the single-store profit model, if you do less in one aspect, you will have to do more in other aspects.

So where should we do more? We should do more and invest more in areas that can increase customer value.

There is another important strategy in Chagee's single-store profit model: opening large stores

Most mid-range and lower-end milk tea shops are less than 50 square meters, but Chagee stores were between 50 and 100 square meters at the beginning. Although this will increase costs to a certain extent, the tea brand in the price range of 15-20 yuan has a better experience , and the price is lower than Starbucks and it also enjoys a certain "third space" scene experience.

By opening large stores in this price range, Bawang Tea Princess has been able to quickly differentiate itself from other brands in unfamiliar markets, which is also very helpful in enhancing its brand power.

(Pictured is the overseas concept store of Chagee)

Just like we ask some of our fast food chain brand clients to provide fruit buffets in their stores. Because fruit brings high value to customers, the cost has to be increased, and then the corresponding cost is offset by reducing and cutting out areas that do not generate much value.

Moreover, the single-store profit model is not static and needs to be adjusted according to the brand development needs and market changes.

At the current stage, an extreme single-store profit model like Bawang can better meet the needs of rapid brand development.

However, this model will encounter problems in the future. For example, as Bawang has more and more stores, there will be a diversion between stores. In addition, the single product will also lead to a single demand being met, resulting in a decrease in the repurchase rate.

As competition becomes increasingly fierce in the future, the Overlord team needs to continuously iterate and optimize to balance efficiency and market demand.

I believe that friends who can see this really want to build their own brand.

When you have no problems with brand positioning and single-store profit model, then everything is ready except the east wind, which is brand leverage.

3. Brand Leverage: From 1 to 10, use brand leverage to increase brand potential

The higher the brand potential, not only will customers and franchisees be more willing to choose you, but when you go to select a location and get a shop space, those property investment people will see you as if you were the God of Wealth and may even waive rent for you.

If you don’t have brand power, you won’t be able to command a premium and you won’t get a good store location.

What brand leverage does Bawang use? Some people say it is because it has money and capital. That’s right, capital is a kind of leverage .

Today's catering brands are increasingly combining capital to accelerate brand development.

Whether or not to join capital depends on the individual. After all, capital has its pros and cons, and everyone’s goals are different.

The addition of capital also shows that the competition in the catering industry is no longer limited to single-store competition and brand competition. Catering has become an industry with comprehensive competition . The capitalization of brands such as Chagee, Luckin Coffee and Kudi has shown that the catering industry has fully entered an era of professional, branded and data-based competition. In the future, more and more catering companies will be listed.

The era when you could just rent a shop, hire a chef and open a restaurant to make money is over.

In addition, there are cultural elements, such as celebrity endorsements, various brand collaborations and other levers to quickly increase brand awareness and enhance brand power.

You see, the brand name Chagee has been leveraging from the very beginning - leveraging the potential energy of the name of the classic Chinese story "Farewell My Concubine". Then, in the design of the brand symbol, we refer to cultural elements such as Chinese opera characters, the charm of Buddha statues and Western geometric lines, leveraging the leverage of the cultural matrix.

There are also collaborations with Grave Robbers' Chronicles and Super Monkey Fitness Platform, and the organization of Frisbee activities, all of which are constantly strengthening the healthy and youthful brand tone of Chagee.

This series of operations has made Chagee's brand potential increasingly stronger, and its premium rights in franchisee screening and business district site selection have also become increasingly higher.

What many people are talking about now as the cultural matrix is ​​actually leveraging the collective subconscious and elements that are already known to everyone to leverage the brand.

But it should be noted that it depends on who your target customers are. Some so-called cultural matrices are not sensitive to them. Just like if you want to do business with post-00s, and your decoration style is like Hong Kong style or 90s style, these post-00s will at most try it out and check in, but will not repurchase, so it is difficult to resonate with them.

Well, in the end, it is a rhythmic and rapid fission expansion. From 10 to 10,000 global layout

4. Fission expansion: from 10 to 10,000 nationwide and global layout

I looked at the brand store opening path of Chagee. Its store expansion rhythm is mainly:

First, he created the world in Yunnan.

Then Chengdu stood tall and proud.

Then the whole country was flooded with

Finally, the world launched a comprehensive effort.

The most critical moment was that the company acquired a shop with an annual rent of 7 million yuan in Chunxi Road, the most prosperous business district in Chengdu . It became famous overnight and had the brand potential to go nationwide.

Later, the entire headquarters was moved to Chengdu. After all, Chengdu is a city with high catering potential in the country. Occupying Chengdu means occupying the national potential, which further increases the brand's premium and attracts many franchisees and cooperation resources.

But it costs money to build brand potential. So Bawang Tea Princess’ strategy is to build the brand in core cities and make profits by expanding the scale in other cities.

The same logic applies to opening stores in a city. Flagship stores are opened in core business districts to build brand potential, while other business districts quickly expand scale and reap profits through franchising and joint ventures.

In 2024, Bawang's focus may be on opening stores overseas quickly. The Bawang team may be selecting locations in Europe and the United States.

(A side note: The globalization of Chagee has also given many domestic catering brands great confidence to go overseas. So I also wish that Chagee's global development will get better and better, and I hope to see Chagee's stores and products in different countries)

The Overlord strategy is a high-profile strategy, which involves first penetrating the region, then increasing the potential, and finally rolling out across the board . If you have money, resources, and strength, you can adopt this strategy.

But if the resources are not enough, you can try another strategy.

For example, the region is king, and resources are concentrated on a certain region, and it is steady and cautious . For example, Chengdu's Kaojiang Grilled Fish and Fujian's Xiaojiaotian brand in Fuzhou use this approach.

Or they can use the strategy of surrounding the cities from the countryside, starting with the surrounding cities or counties where the competition is not fierce, and then slowly penetrate into the core market . Mixue Bingcheng and Wallace used this strategy in their early days.

Let me summarize at last.

Looking at the six-year brand development history of Chagee, we can find that building a chain brand is not as simple as giving it a name, thinking of a slogan, and creating a super symbol.

Instead, it includes various aspects such as strategic positioning, creation of chain store profit model, brand building and store opening rhythm.

Either optimize and upgrade on the existing basis, or learn to think about your development path from the end to the beginning, and then optimize as you go.

No matter what you do, some things remain constant.

First, you need to understand consumer needs and develop or optimize your brand strategic positioning

Consider the competition in the market, whether it is a stock market or an incremental market, what is the market trend, what are my own advantages, and how can I differentiate myself from my competitors?

How can my core advantages be prevented from being imitated by competitors, such as your brand power, supply chain and organizational management.

Then create a replicable chain single-store profit model, and then use various levers to enhance your own brand value, increase brand premium, and avoid getting involved in price wars.

The brand is the result of this series of complex operations. There is still a lot of room for development in China's restaurant chain, which is also an opportunity for many restaurant people.

Of course, the process of brand development is ever-changing and requires specific analysis based on specific circumstances.

But what never changes is that we keep advancing with the times——

Only by keeping up with the changes can you avoid being eliminated.

Only by adapting to changes better and improving yourself can you be welcomed by the market and gain more rewards.