Welcome to a complete guide on how image recognition helps retail brands optimize their retail store execution. Learn how accurate and real time retail store analytics derived by complex AI system can help you improve overall store operations and improve per-store sales, at scale.
Image recognition is the ability of a system, combining IoT and AI, to accurately process and interpret content from any visual media, say an image or a video. This is made possible through technologies such as Computer Vision and trained Deep Learning algorithms to decode every image down to its pixels, and identify all objects, people, places and convert them into intelligent and comprehensible data. The images or videos can be captured through a digital camera or mobile camera at any location. Image recognition is also called AI-based object detection for decoding every object present on a digital image.
Studies have found that the human brain can comprehend images in less than 13 milliseconds! Our brains have been trained subconsciously to develop this rapid processing capability and interpret the real world around us effortlessly.
Contrary to humans, machines are yet to possess such an extraordinary processing speeds. A computer system still views digital images as a series of numerical arrays and interprets them based on patterns using AI algorithms to decode images into their pixels and comprehend their content.
For simplicity, Image recognition can be broken down into three main systems; "Digital sight" through camera vision, image recognition algorithms, and intelligent yet comprehensible insights.
For example, algorithms can be trained to identify certain objects in an image say, a car. But what if you want your algorithm to not only identify a car but, the make, model, and type?
Comparison requires the algorithm to be trained on various automobile brands, the variants, the type, and other crucial attributes required for classifying them.
Now, imagine this scenario in a retail store with hundreds of items located on the store shelf., Here an algorithm has to not only identify a specific product or a Stock-Keeping-Unit (SKU) but also identify their variant, type, price, and more. This requires extensive algorithm training to identify not only the SKU assortments of your brand but assortments in other product categories belonging to other competing brands!
Retail consumers today care less about the variety in assortments and more about product availability. In a study, almost 32% of consumers frequently encounter out-of-stock scenarios at stores. In such cases, consumers typically do four things:
The result is always the same - Lost sales and dissatisfied consumers.
For retail sales and category leaders, stockouts can be a nightmare. They can manifest out of insufficient ordering due to inaccurate demand forecasting. With a constant war being waged for maximum shelf space per category, brands find it challenging to understand how their products are stocked, displayed, and positioned to their consumers when they shop at these stores. Retail leaders want to understand how easily do consumers discover their products when they shop at stores and how often do their products go out of stock. Lack of in-store visibility can lead to a significant delay in fixing execution errors thus resulting in lost sales and poor RoI on any promotions being executed.
According to Retail Drive, Retail brands see almost 70% deviation in the strategy that was planned Vs what is being executed in stores.
When you sit out of an office, it is impossible to gain visibility into every store and every shelf. So, without the liberty to see things in action, how do you accurately monitor the performance of your display and promotional strategies at any given moment, when they hit the road?
Retail manufacturers solve the problem of in-store visibility by relying on market research firms to conduct extensive store audits by deploying a large network of merchandisers or field agents to visit a set of stores and manually collect on-shelf data.
Manual store audits partially resolve the in-store visibility problem, no doubt. But, considering the increase in product categories and sudden unforeseen peaks in demand, retail manufacturers will have to adopt a tech stack that transforms their supply chain to be more agile and efficient to:
Manual auditing offers very limited visibility which will become time-consuming and expensive and will not suit your business demands going forward. The time to switch is now!
In retrospect, manual audits became the MO considering the dearth of tools and technology platforms available to deliver accurate in-store data. But, AI technology and IoT, have come a long way and offer compelling and credible value to retail manufacturers.
In-store automation combined with image recognition helps retail stakeholders to gain real-time visibility into their stores by capturing just a few images iof retail shelves! Retail stakeholders can now track every SKU on a shelf across thousands of stores in any geo-location and time zones, at any given moment.
In-store automation can be achieved with two methods:
There are important factors you need to consider before you invest in image recognition and understand how they can deliver high value to your business. Some important considerations are as follows:
Brick and Mortar stores do not always offer an ideal scenario to capture in-store images. This is especially relevant in General trade stores or small-scale high-frequency mom and pop or Kirana shops.
Image recognition platforms can be an indispensable tool for any retail sector ranging from consumer goods to fashion to grocery to consumer electronics. For example, FMCG and CPG manufacturers are constantly challenged to track in real-time hundreds of SKU assortments per category per segment across thousands of stores which is not so simple.
With rising competition and an increase in different product categories to address niche consumer demands, CPG manufacturers are actively adopting AI, ML, and IoT technologies inside stores. This enables them to better understand their store's performance, understand consumer pulse and take necessary steps to improve product sales, and incentivize stores to ensure better RoI on in-store promotions.
It is safe to say that, Image recognition can overcome several roadblocks posed by manual-store audit processes. While image recognition can deliver instant store analytics that enables you to not only gain complete visibility but add tangible value to your business. Going forward image recognition system should adopt with your growing business needs and offer a more pro-active recommendations and uncover the unseen. Some of them are as below:
Want to explore use cases on in-store retail and predictive analytics can scale your retail business in the future? Read more here - How Advanced Retail Store Analytics Can Elevate Your In-Store Sales Performance
For a demo on how image recognition can help you optimize your retail execution, visit our INFIVIZ product page or click on the "Book Demo" link above.