Artificial Intelligence for Retail Shelf Monitoring

 Retail shelf monitoring

The latest advancement in AI technology and deep learning algorithms are changing the retail industry. With a large number of data sets comprising thousands of shelf images, companies can now leverage AI to better monitor their retail shelf presence. Retail shelf monitoring will help in recognizing product conditions on shelves such as availability, assortments, space, pricing, promotions and many more. It will empower companies to take immediate corrective. AI algorithms can definitely improve planogram compliance by providing accurate stock visibility insights. Companies will be able to monitor and benchmark the duration of stock instances, which will lead to better in-store product placement.

How retail shelf monitoring works

Not much will change in the daily routine of the field agents apart from the fact that they will have more flexibility in terms of the quality of pictures that they have to share with the analysis team. The current industry has a lot of bottlenecks that affect final insights in which failure to analyze unclear images is a major issue. This leads to an increase in time and costs to the company to retrieve new images for fresh analysis. Field agents will just have to click pictures of all the relevant shelves and feed it to the Retail shelf monitoring system. Obstruction while the field agents click shelf pictures is another damper in the retail audit process. This too is taken care of by retail shelf monitoring as the system becomes highly scalable and loss of pictures due to obstruction while photography can be ignored.

retail shelf monitoring

The AI algorithm will analyze all types of inputs and deliver insights. Its capability to analyze poor quality images will enhance the credibility of the final results. Traditional systems have a hard time analyzing unclear/low light images which are not the case with AI. Confusion between similar looking products is another contentious issue that is resolved by using AI.

ParallelDots has leveraged the power of AI to create ShelfWatch, an AI shelf analysis service that empowers field agents with flexibility and companies with scalability. Shelf Watch will eliminate all gridlocks in the traditional retail audit process that is currently eating into the revenue of the consumer goods organizations. The extent of its advantages can be fully understood by analyzing each stakeholder in the retail audit process.

Field Agents

The reps face major challenges while collecting data in the form of pictures and videos. There is a lack of uniformity in stacking patterns across retailers which leads to different kinds of pictures in terms of stock orientation, lighting, and positioning. Field agents struggle with maintaining consistency with the data they collect because such non-standard pictures take longer to analyze. In the pursuit of standard images, field agents fall prey to other types of human perception biases.

ShelfWatch helps the field agents by giving them the flexibility to take all possible pictures in any orientation, lighting or positioning. Such flexibility is allowed because shelf watch is not dependent on standard uniform images to give accurate output. Using state-of-the-art AI algorithms, Shelf Watch is able to analyze even the most distorted images because it uses AI packs recognition technology.


Compliance audits are tough tasks for retailers as well. To comply with the pre-set planogram is part of the service agreement between the retailer and the brands. If in the final assessment the retailers are found to be violating the agreement by displaying too few products, or by not positioning the products correctly, it can attract penalties and even termination of contracts ( in extreme cases ).

Since retail ShelfWatch allows field reps to be flexible while collecting data, it will also help retailers comply with the service agreements because all the images collected by the agents are analyzed irrespective of the light, positioning, and orientation of the products on the shelf. This saves retailers from false audit reports because even if their shelf is not well stacked in terms of positioning and lighting, Shelf Watch will detect all the objects on the shelf, thus reducing incidences of non-compliance due to poor data collection.


Finally, it will be the Consumer Goods companies that will benefit the most from our AI-powered solution. They will be able to analyze all types of pictures from retail audits by using SmartGaze. SmartGaze will help cut the time lag between input data and final insights. This abets the company to take on-time corrective action, if necessary.

Liked the blog? Read this other blog to understand how AI is winning the retail strategy.

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