How does image recognition works for in-store retail execution

The quantity of items on the shelf, their positioning, and their prices can all be shown in one picture, which gives merchandisers the ability to plan placement, optimise the planogram, and verify pricing compliance. With the use of new picture recognition technology, appropriate pricing can be managed quickly and out-of-stock situations are reduced. The capacity of a system to properly receive and interpret material from any visual source, such as an image or a video, using IoT and AI, is known as image recognition. This is made feasible by technology like computer vision, which can decode every image down to its pixel level, identify every item, person, and location, and turn that information into data that is intelligent and understandable. 

A digital camera or mobile camera may be used to record photographs or movies anywhere. Image recognition is also known as AI-based object detection when it comes to decoding everything present on a digital picture. In this article, we will see how image recognition works for in-store retail execution in detail.

 

Detailed working of image recognition in retail

The human brain can grasp visuals in less than 13 milliseconds, according to studies and subconsciously, our brains have been conditioned to acquire this capacity for quick processing and seamless interpretation of the real world around us. Machines do not yet have the same remarkable processing speeds that humans have. 

In order to decode digital images into their constituent pixels and understand their meaning, a computer system still sees them as a collection of numerical arrays and interprets them based on patterns. Image recognition may be divided into three primary methods for simplicity: "Digital sight" through camera vision, image recognition algorithms, and intelligent but understandable insights. The algorithm used for comparison must be trained on a variety of automotive brands, their variations, types, and other essential characteristics.

Imagine this situation taking place in a store with hundreds of products displayed on the shelves. Here, an algorithm must not only recognise a particular item or Stock-Keeping-Unit (SKU), but also recognise its variation, kind, price, and other characteristics. To recognise not only the SKU assortments of your brand but also the assortments in other product categories belonging to rival companies, considerable algorithm training is needed.

 

How is image recognition being used in retail

Today's retail customers are more concerned with product accessibility than selection diversity. According to a research, over 32% of shoppers often run into out-of-stock situations at businesses. Consumers often take four actions in these situations. They choose the same product from a competing brand instead of yours, they choose another item from the same brand, they buy the identical item elsewhere, such as online, or they wait a few days until they are back in stock. In all the 4 cases, retailers see a decline in sales and leave their customers dissatisfied which hampers their business in the long run.

Stockouts may be a nightmare for retailers with strong sales and category leaders. They may appear as a result of inadequate ordering brought on by erroneous demand predictions. It is difficult for companies to understand how their items are stocked, presented, and positioned for their customers when they buy at these stores since there is a continual battle for the optimum amount of shelf space per category. Retail executives want to know how quickly their items run out of stock and how easy customers can find them when they purchase in shops. Lack of in-store visibility can cause execution problems to go uncorrected for a long period of time, costing money and having a poor return on investment for any promotions that are run.

 

Process of manual auditing and how will it not sustain in future

Retail manufacturers resolve the issue of in-store visibility by hiring market research companies to carry out thorough shop audits and by sending out a vast network of merchandisers or field agents to manually gather on-shelf data by visiting a number of locations. 

Without a doubt, manual store audits help to partially fix the in-store visibility issue. However, given the expansion of product categories and unexpected spikes in demand, retail manufacturers will need to implement a tech stack that makes their supply chain more flexible and effective in order to Adapt quickly to changes in demand and optimally replenish stores to prevent stock-outs and boost On-Shelf-Availability, Improve store distribution to more effectively control inventory waste. Also, manual auditing provides very little visibility, is time-consuming and expensive, and will not meet your company's future needs. The moment to make a change is now.

The reasons why manual auditing will not survive in future are below

A merchandiser or field agent must spend at least 40 minutes manually counting and recording in the SKUs on the shelves within a shop. You only collect data from a small percentage of retailers, thus your data is inconsistent and incomplete. Manual audits have a narrow scope and increase in cost as your business grows. ‍Agents must manually collect data when they visit each store, which is a time-consuming operation. It is hard and time-consuming to manually count SKUs and their variations that fall under various categories. Merchandisers frequently enter inaccurate data with an error margin of over 20% as weariness sets in.

 

Image recognition powered by Artificial Intelligence

Considering the lack of tools and technological platforms available to give correct in-store data, manual audits eventually became the standard operating procedure. However, IoT and AI technology have advanced significantly and now provide retail manufacturers compelling and reliable value. Retail stakeholders may receive real-time visibility into their stores by taking just a few pictures of the retail shelves thanks to in-store mobile retail execution and image recognition. Retail stakeholders may now monitor every SKU on a shelf across hundreds of locations in all time zones and geolocations.

By allowing your merchandisers to use their mobile devices to take pictures in-store and by installing IoT-based low-form-factor cameras on shop shelves to take regular photos of the shelves throughout the day are the two ways to automate in-store operations.