eLeader Shelf Recognition AI – unbeatable functionality


We call eLeader Shelf Recognition AI the most functional solution of its class in the world. Below we will try to prove this bold thesis. Let’s have a closer look at the display parameters and reports which can be obtained from these parameters and the business areas where the tool can deliver tangible benefits.



Information on meeting simple and advanced merchandising standards

Out of stock
Shelf number
Product / category block
Products / categories relations
SKU share in category
Availability level
Share of shelf
Display height
Strike zone
Prices and price tags
Second placement

Information on competitors

Reports do not only serve the purpose of providing information about the condition of one’s own products, but can also say a lot about the competition. Through recognition of the brands of the competitors, we will receive information on the share of shelves and trends.


Adjustment of the display to standards

Regardless of how merchandising standards are formulated, eLeader Shelf Recognition AI will be able to read them and analyze where the display needs to be improved. Sample parameters include the number of faces, the immediate vicinity or the shelf number.

Adjustment of the display to the planogram

If a planogram has been developed for a given store (e.g. created with JDA Space Planning), eLeader Shelf Recognition AI is able to compare the display with the valid template and indicate which products on the shelf need to be added or rearranged in order to achieve full compliance.

Identification of competitors’ products

Your own expositors are an ideal place to sell products and promote your brand. It is no good when competitors’ products can be found in them. Our solution can detect it and inform the user that e.g. in the refrigerator there is an unwanted product.

Automated surveys

The reasons for the lack of products on the shelf or other concerning (or just interesting) situations can be diagnosed through triggering a survey at the right moment of a visit. This fully automated functionality facilitates better understanding of the situation on the market.

Perfect Store&Visit

The requirements for a specific display in a specific store can be expressed as KPIs. Using Perfect Store methodology to fulfil them increases the effectiveness of visits. eLeader Shelf Recognition AI accelerates and automates the stage of gathering display-related data for the purpose of measuring and improving the KPIs of the store, employee or visit.



The absence of a product from the shelf is usually a signal to place an order for it. Identification of such a lack may be a designed condition for adding the product to the shopping cart automatically (also in a pre-defined form calculated based on the history of orders).


An example of applying eLeader Shelf Recognition AI in a broader context is the functionality of checking the possibility of rewarding a shop as part of an internal promotional campaign or a loyalty program. All you need to do is to supply the module with the relevant data on display, while the system, based on the photo taken, will calculate the products and determine the possibility of rewarding the store manager.

Automated control

Good news for auditors and managers! eLeader Shelf Recognition AI practically eliminates the need to conduct audits in stores. Thanks to the photographic documentation and flow of tasks necessitating such checks and corrective measures like surveys or negotiations, inspections can be limited only to special occurrences, often without the need to visit the store.

Audit of promotions

The monitoring of promotional campaigns can be automated. The system verifies the products covered by a special offer valid for a given shop. In addition, the application will check the compliance of POS (POSM) materials – also by means of image analysis.


Route planning

Scheduling visits to particular points is a game where the winner is the one who best assesses the sales potential in their area. eLeader Shelf Recognition AI provides information about the availability of products, share of the shelf, the number of faces or meeting merchandising standards. This information is an excellent, relevant input for creating a company ranking of points that should be visited as frequently as possible, because they generate the most profit.

Retail Activity Optimization (RAO)

A good salesman should always know how to react to irregularities during a visit or further improve good sales results. eLeader Shelf Recognition AI provides quantitative data for comparison, e.g. with three previous visits, detects non-execution of contracts or product shortages. The solution allows for planning visit scenarios in such a way that suggested corrective actions and relevant contextual materials are suggested to the salesman at the moment they are needed.

For example: the lack of a product on a particular shelf during the third visit means that the shop manager should be interviewed in order to ensure that contractual obligations are fulfilled or to change the terms of the contract so that the store can meet them.

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