Retail execution depends on speed, accuracy, and visibility. Yet many companies still rely on manual audits, delayed reporting, and inconsistent shelf checks that make it difficult to control what is really happening in stores. ShelfMatch^TM helps solve this problem by using image recognition to automate retail audit, improve shelf visibility, and turn store photos into actionable data.

Photo credit: Unsplash | Pipe Gil

For brands, distributors, and retail teams, the challenge is familiar: products can be listed in the system but missing from the shelf, planograms can be broken, price tags can be wrong, and promotional displays can be incomplete. These issues are often discovered too late, after sales opportunities have already been lost. ShelfMatch^TM addresses this gap by giving teams a faster and more reliable way to monitor execution in real time.

Why manual retail audits are not enough

Traditional retail audits are often slow and resource-intensive. Field teams spend time checking shelves, counting facings, recording compliance issues, and filling out reports by hand. Managers then receive this information later, which delays corrective action and reduces the overall impact of the audit process.

Manual methods also create inconsistency. Different employees may evaluate the same shelf differently, and human error can affect the quality of the data. As store networks grow and assortments become more complex, these limitations become even more visible. Brands need a better way to track shelf conditions at scale without increasing the burden on their teams.

How ShelfMatch^TM works

ShelfMatch^TM is a retail audit solution based on image recognition and computer vision. A sales representative or field employee takes a photo or video of the shelf using a mobile device. The system then recognizes products automatically, analyzes shelf conditions, and generates structured audit data.

This approach makes shelf analysis faster and more objective. Instead of relying only on manual entry, ShelfMatch^TM transforms visual information into measurable results. That allows teams to identify execution problems immediately and react while they are still in the store.

Key retail problems ShelfMatch^TM helps solve

ShelfMatch^TM is designed to address the most common challenges in retail execution. These include:

• Out-of-stock detection
• Shelf presence and SKU recognition
• Planogram compliance checks
• Price tag verification
• Void and gap detection
• Share of shelf analysis
• Competitor monitoring
• POSM and promotional display control

These are the areas where shelf execution directly affects visibility and sales. If a product is present in the store but not visible on the shelf, the brand loses opportunity. If a promotion is not executed correctly, the campaign may fail to deliver results. ShelfMatch^TM helps identify these issues earlier and more accurately.

Benefits of automated retail audit

One of the main benefits of ShelfMatch^TM is speed. Teams can collect shelf data quickly and receive results almost immediately, instead of waiting for manual reports to be processed. This shortens the time between detection and correction, which is critical in retail environments where shelf conditions change rapidly.

Another major benefit is consistency. ShelfMatch^TM applies the same recognition logic across all locations, which improves data quality and makes results easier to compare across stores, regions, and retail chains. This creates a stronger foundation for reporting, performance analysis, and operational planning.

ShelfMatch^TM also reduces the administrative burden on field teams. Sales representatives spend less time on manual documentation and more time on actions that improve execution. That makes retail visits more productive and more valuable for the business.

Why image recognition matters in retail execution

Image recognition has become an important tool for modern retail because it converts shelf photos into useful business data. Instead of relying on incomplete notes or delayed inspections, brands can see what is actually happening in the store and act faster.

This is especially important for companies that manage large assortments, multiple store formats, or frequent promotions. The more complex the execution process becomes, the harder it is to control manually. ShelfMatch^TM provides a scalable way to monitor shelf conditions, standardize audits, and support better decision-making.

ShelfMatch^TM for FMCG and retail brands

ShelfMatch^TM is particularly relevant for FMCG companies, distributors, and retail teams that need to maintain strong shelf standards across many locations. It helps track product availability, improve planogram compliance, monitor competitors, and ensure that promotional activity is executed as planned.

For brands, this means better control over execution and fewer missed sales opportunities. For field teams, it means a simpler audit process and clearer priorities. For managers, it means more reliable data and better visibility into store performance.

Conclusion

Retail audit is no longer just about collecting information. It is about making store execution visible, measurable, and actionable. ShelfMatch^TM helps companies do that by using image recognition to automate shelf analysis and improve the quality of retail audits.

If your goal is to reduce manual work, improve compliance, and react faster to shelf issues, ShelfMatch^TM offers a practical solution. It turns store photos into structured data and helps retail teams move from observation to action with much greater speed and confidence.

Contact us to see how ShelfMatch^TM can boost your business.

Retail audits are a critical part of in-store execution, but traditional audit methods often struggle to deliver the speed, consistency, and visibility that modern retail operations require. Image recognition introduces a more scalable approach by turning shelf photos into structured, actionable data. For brands and retail teams, that means better control over compliance, fewer manual bottlenecks, and faster response to execution issues.

Photo credit: Unsplash | Allef Vinicius

The value is not limited to automation. Image recognition supports a more disciplined audit process, where detection, correction, and verification are connected in a single operational flow.

The operational challenge

Retail execution depends on timely information. If a product is missing, a price is incorrect, or a promotion is not implemented properly, the problem needs to be identified quickly. In a manual audit model, however, teams often spend valuable time collecting data, checking shelves, documenting findings, and later interpreting results.

That approach can create delays and inconsistencies. Different auditors may assess the same shelf differently, and managers may receive information too late to act effectively. Over time, these gaps reduce the impact of field activity and limit the organization’s ability to maintain standards at scale.

Image recognition addresses these challenges by standardizing visual audit analysis. It helps organizations move from subjective checks to objective shelf intelligence.

How the process works

The workflow is simple. A field user captures an image of the store shelf, display, or merchandising area. The image recognition engine analyzes the photo and identifies products, shelf positions, facings, price tags, and other execution elements.

The system then compares what it sees against the expected standard, such as a planogram or merchandising rule set. If it finds a mismatch, it flags the issue automatically. This can include missing items, incorrect placement, poor visibility, or non-compliant promotional execution.

When connected to task management tools, the audit outcome can move directly into corrective action. This is where image recognition becomes especially powerful: it does not just report a problem; it helps operational teams address it immediately.

Areas of audit coverage

A strong retail audit solution with image recognition can support many core execution metrics, including:

• Out-of-stock identification
• Product presence on shelf
• Planogram compliance
• Pricing accuracy
• Promotional compliance
• Share of shelf
• Shelf placement quality
• Competitor monitoring

These measures are fundamental to in-store performance. A product may be available in the store, but if it is not displayed correctly, it may still fail to generate sales. Image recognition helps ensure that execution standards are visible, measurable, and consistently monitored.

Why objective analysis matters

One of the strongest advantages of image recognition is the ability to create a consistent analytical standard across locations. Manual audits are influenced by experience, interpretation, and time pressure. Image-based analysis reduces that variability and gives teams more dependable data.

This matters for organizations that need to compare performance across regions, stores, or categories. Reliable data improves decision-making, strengthens reporting, and makes compliance tracking more meaningful. It also gives leadership a clearer view of where operational issues are recurring and where support is needed most.

From audit insight to execution control

The real value of an audit process comes from what happens after the issue is identified. Image recognition is most effective when it is integrated into a closed-loop workflow that supports action and verification.

A typical process follows three steps: identify the issue, assign the fix, and confirm the result. This “see it, fix it, prove it” model helps retail organizations reduce delays and improve accountability in the field. It also ensures that audit findings translate into actual operational improvements rather than remaining as isolated data points.

For retail execution leaders, this creates better control over store standards and a more transparent view of what is happening in the field.

Strategic impact for retail organizations

Retail audit image recognition is more than a technology upgrade. It is a practical way to strengthen store execution, reduce manual effort, and improve the reliability of field data. For large-scale retail operations, the impact can be significant: faster audits, better compliance, more accurate reporting, and quicker corrective action.

It also supports stronger collaboration between field teams, managers, and headquarters. When everyone works from the same visual evidence and the same execution standards, it becomes easier to prioritize actions and maintain consistency across the network.

Closing perspective

Retail audits should not simply document store conditions. They should help organizations improve them. Image recognition makes that possible by giving teams a faster and more dependable way to detect shelf issues, verify compliance, and act on what they find.

For companies focused on retail execution, this approach offers a clear advantage: less manual work, better visibility, and a stronger connection between store observations and business results.

Cases of fraud in digital merchandising are frequent and, alas, inevitable. Field personnel – probably due to the nature of without regular supervision of a manager – are very good at creating new and original tricks to work less and earn more. Therefore, for companies that have merchandisers on staff, the issue of auditing and checking their employees is always relevant.

In this article we will talk about the types of fraud in merchandising and our experience in how to technologically overcome the pitfalls of careless employees. In the 1^st and 2^nd, practical parts, we will dwell in more detail on how we work with certain types of fraud. And in the 3^d part, the theoretical one, to complete the picture we will talk about other popular methods of deception on the part of field personnel.

Fraud in digital merchandising: goals

Fraud is an attempt to cheat the merchandising system in order to influence the result. Since field specialists engaged in taking photos of shelves and creating reports are often paid by the piece, manipulating photographs allows them to create the appearance of a larger number of points of sale visited and shelves captured. Hence, fraud occurs when a field specialist cheats the system to make more money.

Another case is when a specialist’s fee depends not on the number of photographs taken, but on the quality of the work – that is, the KPIs achieved. In this case, the more high-quality photographs a person provides to the customer in terms of ideal shelf layout, the greater reward the person will receive.

In this post we will tell how ShelfMatch^TM works with fake photos, namely:

1. Identifies and searches for duplicate photos
2. Distinguishes and rejects poor quality photographs from the entire stream
3. Identifies photographs taken from smartphone/tablet screen or print media

1) Detecting duplicate photos

Photo duplication is the most common type of fraud. The merchandiser uploads the same photo into the system with an interval of several days or weeks. This photo can be a real photo from a POS (point of sale), or it can be downloaded from a special database: merchandisers all over the world have secret chat groups for exchanging high-quality photographs of shelves, which contain comprehensive gallery of images which could be used in almost any situation. The point of this trick is not to pay a visit to the POS, but to send the supervisor an old, out-of-date photo of shelves with goods – as if the employee had visited the outlet. Consequently, all the merchandisers had amazing results at the end of the day.

How ShelfMatch^TM works with this type of fraud:

On the one hand, finding duplicate images is a fairly well-known, understandable and solved problem for Machine Learning. An image processing service creates a digital fingerprint of the photo, adds it to the database, creates such a virtual fingerprint for each incoming image and compares each new fingerprint with the previous ones, thereby finding duplicate images.

On the other hand, in our context this task is not as straightforward as it might seem at first glance. There are several difficulties that may make work difficult.

First, what is considered a duplicate? It would seem that the answer is simple: exactly the same image of a shelf. But with this understanding of the duplicate, the system will be quite easy to deceive. For example, you can slightly crop the original image, even partially sacrifice some SKUs in the photo. And then, for computer vision, it will no longer be an absolute duplicate – although will be a duplicate, in terms of content. And this is the first difficulty that has to be taken into account when creating a system for finding duplicate images.

Second, for what period should we search through the database of previous visits? This complexity lies in the plane of server capacity. It’s one thing when there is a certain image database of a fixed size. But it’s another matter when this database grows daily by several tens of thousands of pictures or more. As a result, you need to solve the issues of: a) physical space for storing fingerprints and b) speed of comparing each fingerprint with the entire database. You can periodically clear the database of old entries, but sooner or later merchandisers will get a chance to upload older images.

Despite these nuances, ShelfMatch^TM can protect you from persistent and obvious attempts to forge a photo.

2) Detecting bad quality images

It also happens that employees send poor quality photographs: overexposed, blurry, unclear images, photos taken at a random angle, or in which the entire rack is not visible, and other unusable files. All such photos make the automated image recognition impossible. The reason why employees take such photos is because of amount of data. It’s easier to take a lot of low-quality photos and send them to the supervisor than to try to take high-quality photos. And, of course, human factors always play a vital role: everyone can make mistakes and accidentally take a bad photo.

How do we deal with such fraud?

In ShelfMatch^TM a special separate module is responsible for assessing the quality of photographs. Image processing occurs in 2 stages:

1. pre-processing (before SKU recognition) and
2. post-processing (after recognizing the SKU and price tags in the photo).

1. Pre-processing

In the preprocessing stage, the incoming shelf image passes through several neural networks that evaluate following parameters:

• Shooting angle. In particular, the neural network looks for the angle of inclination of the shelves in the photograph. This is a fairly important parameter, since the quality of SKU recognition depends on the shooting angle. The more frontally the photo is taken, the more accurate the recognition results will be.
• Presence of goods in the photo. Another neural network determines whether the photo contains images of goods or has other content. “Other” can mean a lot of options: in practice, we come across photographs from a car, photos of the entrance area of a store, images of the floor/ceiling sent by mistake, and similar irrelevant images.
• Image quality, overall. A separate neural network determines the parameters in combination (“image clarity degree” + “exposure”) and counts a specific value: whether it is an image of poor or good quality.
• Screen photo or real photo. We will talk about this version of fraud in more detail below.

The results of preprocessing allow us to weed out poor quality photos and not to take them into recognition phase, and the merchandiser, in turn, can receive a quick request for a new, high-quality photo (if the work takes place online).

2. Post-processing

It often happens that not the whole photo is bad, but some part of it. ShelfMatch^TM can evaluate such cases using post-processing.

During the post-processing process, the quality of not the entire photo is measured, but separately each small piece of the image containing the SKU or price tag.

During post-processing you can:

• track which merchandisers take perfect or close to perfect photos, and which ones don’t try very hard
• receive additional information about the accuracy/doubtfulness of the SKU and/or price tag recognition results in the specific location of the image.

One way or another, in the field of automated product recognition there is a constant struggle for recognition accuracy. But the recognition accuracy often suffers for reasons beyond the control of neural networks: a large glare crept into the photo, or the hand trembled when taking a photo and part of the image was blurred, etc. In such cases, assessing the quality of the photo, in general, will not give anything. But post-processing and evaluation of individual areas will show that the results from such a photograph are not very accurate and they need to be at least partially omitted. Thus, we have another tool for identifying low-quality photographs, allowing us to increase the accuracy of the analysis.

Besides the question “How to grab the attention of customers and make them want to buy from me rather than from my rivals”, every day, managers, marketers, merchandisers and sales representatives are faced with a large number of other questions – regarding the display of goods on store shelves:

• Are my products placed on shelves uniformly and coherently across multiple stores in accordance with the planogram?
• Are products placed in the order demonstrated in the planogram?
• Are all product items (SKUs) present on the shelf?
• Are product labels facing the consumer?
• Are products surrounded by complimentary items to drive cross selling?
• What is my competitive environment?
• Are all my goods affixes with price tags?
• Has the retailer leveraged promotional shelf labels?
• Are signs, banners, and shelf dividers adequately visible?

And so on.

Timely and accurate answers on the questions above are extremely important to improve the inventory turnover, increase sales volume, skillfully manage items with irregular demand (e.g. seasonal products), and effectively manage contract obligations compliance.

Will you please count the number of SKUs in the photo above in a couple of seconds by eye? How about determining the share of shelf for each product category and comparing the data with a planogram – yep, with your own eyes, but accurately and quickly?

Here’s where Artificial Intelligence will help you out!

In 2,5 seconds AI will recognize and count the number of SKUs, share of shelf, conduct planogram compliance and detect inconsistencies of product layouts. An automated inventory management system can also help businesses keep track of their stock levels automatically with minimal human intervention. How long would it take your sales rep to manually calculate these metrics?

Computer vision in AI is the development of automated systems to interpret images in the same manner as people do. Computer vision technologies are extremely sophisticated and developing quickly. They are capable of coping with merchandising audit (retail execution) tasks not only much more quickly, than a human eye, but also more accurately than the human brain.

Artificial Intelligence and Retail image recognition are coming to store shelves, delivering almost instant access to data captured by field representatives and enabling stakeholders gain smart vital insights into what’s on their shelves and make better evidence-based decisions almost in the twinkling of an eye. 

To be continued.