How Retailors Are Using AI And Image Recognition For Product Monitoring And Analysis

With the e-commerce boom, today’s entrepreneurs have discovered that conservative methods of sales promotions or visual merchandising won’t withstand profits in the aggressive CPG industry. A lot of retailers have already implemented image recognition and Artificial Intelligence (AI) to deliver the next-level customer experience, starting a new era in the retail industry. As per Gartner, by the year 2020, 85% of the customer interactions in the retail sector will be organized by AI. Product recommendations, Product discovery, and trend analysis are a few areas for implementing image recognition and computer vision. This blog discusses how CPG and retail companies implement image recognition.

Customers make important buying decisions at stores, and companies need to utilize different technologies to stay ahead of the competition. Collecting key consumer data assists companies in better understanding their requirements. Shelf recognition with computer vision digitizes store checking and is important in collecting important consumer data with AI.

Computer vision with a profound neural network finds objects in images of shelves and classifies them according to brand, category, and items. It is useful in streamlining time-consuming, manual, and error-prone store audits of SKUs to help direct manual efforts toward the real job of product selling.

The product placement in the store could either make products sell like hotcakes or stay persistently on a shelf. Companies like Tesco have already started the usage of image recognition software for better product placements in the stores. Image recognition utilizes photographs of hotspots or shelves and immediately gives insights into preferences, consumer behavior, and product placements on display. As AI can interpret enormous amounts of data as well as offer important insights, which is not possible for humans to achieve with accuracy, planners can discover the product movements, how they get consumed, or how well the product placement works towards having optimum ROI from placements as well as advertising messages. AI also gives insights into how any brand’s product is used across social media and allows marketers to make efforts.

The majority of companies rely on sales reputation to audit company products’ placements, brand compliance, and brand presence at different outlets. It could be a tedious job. Image recognition could be used for tracking compliance with merchandising standards for every outlet as well as having superior trade program versions. Machine vision can also be used for tracking competitor brands, brand presence, placements, and policies using a competitor’s distribution, shelf strategies, and channels, as well as offering important business ideas.

Whenever you visit any supermarket, you are expected to get a product from P&G if you want something in the pharmacy or beauty category. It is a result of the company staying watchful of customer trends as well as competitor behavior about the product categories. Using APIs, which quickly organize images into categories and recognize the stocks of competitors in these categories, as well as the competitor provides, allows P&G to create new marketing scenarios that result in more shelf share.