Why stores without salespeople are becoming the new norm
A store with no cashiers, consultants, or queues, yet where every customer receives personal service and leaves satisfied,mdash;not so long ago it sounded like a futuristic fantasy. Today, this is already a working model: the combination of computer vision, sensors, analytics, and neural networks allows for the launch of "store-as-a-service" formats, 24-hour stores in office centers, and fully autonomous points in residential areas. The question is no longer whether this is technically possible, but what business task you want to solve with such a format.
For the owner of a chain or a single "without salesperson" store,mdash;this is not only savings on labor costs. It is a way to flexibly manage the shelf, test assortment, increase turnover and get a deep understanding of customer behavior without endless surveys and focus groups. Let's figure out how neural networks "see" the customer, what decisions are made in such a point, and what is important for a business that is thinking about launching such a project.
From "smart checkout" to store without a salesperson: the evolution of retail
The path to a store without personnel began with simple things: barcode scanners, self-checkout cash registers, loyalty programs. Each of these solutions removed a piece of routine from salespeople and gradually accustomed the customer to the fact that they can take part of the shopping process themselves. The next logical step is to completely remove the checkout and salesperson, replacing them with neural network models that record who entered, what goods were taken, and what the customer ultimately left with.
Unlike a classic store where the main digital entity is a receipt, an autonomous store is built around a continuous flow of data. Cameras track the movement of people and goods, weight sensors react to changes on the shelf, the identification system understands which specific customer is performing actions. This is added to data from a mobile application, purchase history, CRM, and external sources. The neural network becomes the "brain" of the store: it learns to connect these events into a single picture and predict what the customer needs right now.
How a neural network understands the buyer: from event to insight
At the heart of a shop without a salesperson lies several key AI models that work together. They can beсловно divided into three levels: perception, understanding and action. At the perception level, computer vision models and signal processing from sensors work: they recognize faces or anonymous silhouettes, identify goods by shape and label, and record the removal and return of products to the shelf. At the understanding level, the neural network analyzes the sequence of actions: how a person moves through the hall, which shelves they approach most often, what they combine in the basket, how they react to promotions and dynamic pricing.
At the action level, the system makes specific decisions: whether to show the customer a personal discount in the app, offer cross-selling (e.g., coffee with dessert), send a push notification with an offer when a person approaches "their" shelf, or, conversely, not touch them to avoid disrupting the usual scenario. It is important that modern models do this in real time: a delay of several seconds can already cost a lost sale.
If you simplify, the key tasks of a neural network in a shop without a salesperson can be described as follows:
- Buyer identification — via app, card, image recognition or token at the entrance.
- Product tracking — understanding exactly which product was taken, moved or returned to the shelf.
- Intent interpretation — distinguishing "just looking" from real interest in a purchase.
- Dynamic pricing and promotions — offers here and now for a specific person.
- Final receipt assembly — automatic debiting without a cashier and queues.
As data accumulates, the neural network begins to see not individual people, but behavior patterns: how office employees behave at lunchtime, how residents of the area shop in the evening, what differs between weekday and weekend scenarios. This turns an autonomous store into a powerful source of analytics for category management and marketing.
Practical cases: where a shop without a salesperson is already working and why
The «store without a seller» format is not yet mass-market, but even now several working scenarios can be identified where it shows convincing results. Below — examples of real-world application of AI technologies in retail.
Case #1: Amazon Go (USA)
→ a network of cashier-less stores from Amazon. The stores use the «Just Walk Out» system, which combines computer vision, sensors on shelves and machine learning algorithms. A customer enters, scans an app, takes the desired goods and simply leaves — the check is formed automatically, without a cashier or queues. The first public Amazon Go store was opened in Seattle in 2018, and since then the format is expanding in the form of both individual points and integration of technologies into other retail formats.
Case #2: Zippin (USA, Japan, Brazil and others)
→ a platform for stores in the «enter — take — leave» format, which is used in airports, stadiums and universities.
One of the illustrative examples is Tiger Express store on the campus of Towson University: an autonomous point operates 24/7, serving students without cashiers or queues, and more than 20% of sales fall on nighttime hours. According to the operator, after implementing the Zippin solution, monthly sales increased by about a third, and the customer experience became noticeably more convenient.
Case #3: VkusVill (Russia)
→ one of the first real cases of an autonomous store in the CIS. The network launched a non-personal point of entry where entry is made via an application, and a system of cameras and AI algorithms tracks which goods the customer takes from the shelf. Payment occurs automatically after exit. The format was launched in a pilot mode and showed, that such technologies can work in Russian conditions with existing infrastructure.
In all these cases, the neural network acts as a personal analyst, which monitors customer behavior and shelf conditions around the clock, proposes hypotheses and automatically tests them in practice. The role of a person shifts towards strategic decisions: what formats to launch, which metrics to consider key, how to adapt the concept to different locations.
Money and project launch
How much does it cost to launch a shop without a seller based on a neural network?
There is no single price here: the final budget depends on the area of the store, the number and type of sensors, the complexity of integration with your current IT system, SLA and data security requirements. The order of costs is also affected by how unique your model should be (from using ready-made components to developing a target architecture for a network of hundreds of points). To avoid guessing and quickly assess the range of investments, it is convenient to collect key project parameters and test several scenarios through → EasyByte neural network cost calculator. This approach helps to understand the order of figures even before detailed technical discussions.
What to do if we don't have our own machine learning team?
To launch a store without a salesperson, it is not necessary to build an internal R&D center. It is often more effective to combine your own retail expertise with the capabilities of external AI partners. At the start, it is useful to conduct a strategic session: analyze the current operational model, define goals (cost reduction, revenue growth, new niche) and choose an optimal technological stack. This can be done in the form of a consultation with profile specialists. For example, you can → register for a free consultation with an expert EasyByte, to discuss the concept of an autonomous store specifically for your business case, without immediately diving into technical details.
What happens "under the hood": architecture of a store without a salesperson
For a store without a salesperson to operate reliably, it is not enough to hang cameras and write a few scripts. Under the hood of such a solution is a complex multi-layered architecture, where each component must be robust and scalable. Simplified, it can be described as: devices at the point (cameras, scales, RFID gates, access terminals), "boundary" computing (edge servers that process the flow in real time), cloud part with neural network models and analytics, as well as integrations with ERP, CRM, acquiring systems and security systems.
Cameras form a video stream, which is processed by computer vision models: movement trajectories, hand positions, and shelf interaction are tracked. Signals from weighing or other sensors help to clarify whether the goods were actually taken, and data from the application "link" actions to a specific user. All events are combined into a single timeline, on which the "digital footprint" of the customer inside the store is built. Based on this footprint, a receipt is formed and recommendation algorithms are launched.
For business, it is important that the architecture supports not only the current scenario, but also possible expansions: connection of new formats (kiosks, vending machines), work with multiple countries and currencies, a single central management center for the entire network. Therefore, at the design stage, it is worth thinking not only about the first pilot store, but also about how you will scale success if the pilot shows good results.
If you look at the benefits from a business perspective, launching a store without a salesperson gives the business at least three strategic effects:
- Reduction in operating costs due to optimization of personnel numbers and reduction in losses.
- Revenue growth through personalized recommendations, dynamic pricing and extended operating hours.
- Deep analytics on customer behavior and assortment effectiveness at the specific location level.
Risks, limitations and the human factor
Any technology that affects money and customers should be considered not only from the point of view of opportunities, but also risks. A store without a salesperson has several sensitive areas. Firstly, this is recognition quality: errors in product or customer identification can lead to discrepancies and conflicts. Secondly, questions of confidentiality: how video is stored and processed, what data is personalized, where the boundaries of anonymization lie.
It is important to think in advance which processes you are ready to fully entrust to a neural network and where human control is needed. For example, automatic check deduction can go without operator participation, but resolving disputed situations, managing exceptions, and handling security incidents remain with people. In addition, when implementing autonomous stores, it is important to maintain open communication with customers: explain how the technology works, what data is used, and what security measures are applied. Transparency here directly affects the level of trust and willingness of people to use the new format.
How businesses can prepare for launching an autonomous store
A successful shop without a salesperson doesn't start with buying cameras, but with a clear understanding of why it's needed by your company. Before discussing technologies, it's worth answering a few practical questions: what problem does it solve, how will it be integrated into the current network, how will it change the supply chain and customer experience.
A practical approach is to break down the path into several steps:
- Define the business goal. This could be reducing costs, entering new locations (e.g., smaller formats where a classic store wouldn't be profitable), increasing loyalty, or collecting data for subsequent transformation of the entire network.
- Assess infrastructure readiness. Communication, power supply, integration with accounting systems, availability of a mobile application or loyalty program.
- Develop a pilot scenario. Identify one or two locations where the effect of the autonomous format will be most noticeable, and describe success metrics: revenue, margin, NPS, loss reduction.
- Choose partners and the technology stack. Here, it's important to combine experience in AI, an understanding of retail, and the ability to support the solution after launch.
- Launch a pilot and scale. After a period of testing and "debugging" successful cases can be replicated, gradually adding new features and points.
With such an approach, a shop without a salesperson ceases to be an experiment for the sake of experimentation. It becomes a managed investment project with a clear logic, payback periods, and strategic impact on the business.
📌FAQ: frequently asked questions about retail automation
Question: Is a shop without a salesperson suitable for a small local retailer?
Answer: Yes, but in a simplified form. For local networks, hybrid solutions are often used: part of the processes are automated (shelf accounting, demand analytics, self-checkout), and fully autonomous points are opened in places with a guaranteed flow - for example, near offices or transport hubs. It is important to consider not only savings on personnel, but also revenue growth due to extended working hours and increased customer convenience.
Question: How difficult is the integration with an existing accounting system and cash registers?
Answer: The more "closed" the current infrastructure, the more effort will be required for integration. Ideally, systems for inventory management, loyalty, and acquiring should be accessible via API. In practice, many projects start with a separate pilot loop and then gradually integrate it into the main IT architecture. The main thing is to lay down requirements for data exchange and security in advance.
Question: How quickly does a store without a seller pay for itself?
Answer: Payback periods depend on rent, customer flow, equipment costs, and the degree of automation. In some cases, the project becomes profitable within 1.5-2 years, in others - it becomes part of a long-term strategy for presence in new locations. To avoid making calculations "by eye", it is useful to model financial scenarios in advance, taking into account the assortment, margin, and projected traffic.
Question: Can you transition to a store without a seller gradually, without closing existing locations?
Answer: Yes, a phased approach is one of the safest. You can first implement individual elements: computer vision for shelf control, dynamic pricing, automatic stock recalculation. Then - open a pilot autonomous format on a new site without rebuilding the existing store. This will allow you to compare indicators and make a balanced decision.
Question: What competencies are needed within the company to manage such a store?
Answer: On the retailer's side, key competencies remain assortment management, logistics, and marketing. Plus, there is a need for basic digital literacy of the team: understanding what data the system collects, how to read reports, and how to use insights to adjust the strategy. Deep expertise in machine learning can be with external partners, but it is important for the company to have a "product owner" format who understands how technology affects the business.
Question: How to understand which level of automation is suitable for my store format?
Answer: There is no universal recipe here: it all depends on the area, assortment, traffic and willingness to invest in equipment and analytics. It is optimal to start with an assessment of key parameters — this can be done through → EasyByte neural network cost calculator, and then discuss your conclusions with a specialist.
If you need help choosing the right level of automation and implementation scenario, you can → sign up for a free consultation with an EasyByte expert, to assess several development options and choose the most rational.