AI in Retail Industry
Consumer retail is one such industry where AI is going to have a huge impact
Artificial Intelligence is impacting each and every aspect of life and business. With the rapid development of work and research AI is more a technology that is limited to only research work. More and more solutions are built in AI which are impacting the existing processes and systems in a great way.
Consumer retail is one such industry where AI is going to have a huge impact. Its impacts in the future are going to be felt from small retail shops to big shopping complexes, warehouses, and the consumer products manufacturing industry. So, the impact of AI would be there across the retail segment.
Customer Analytics and Insights
Product Detection and Classification
Personalized Customer Experience
Chatbots and Virtual Assistants
Our Expertise
Image Annotation
In image annotation an object which you want to detect in an image is marked using a polygon or line around the object using different annotation tools. It is a time-consuming process and requires a lot of manual effort. However, this process is mandatory as for object detection models like Yolov5, Yolov8 these annotated images are used as input i.e., used as input data to train a Yolo object detection, segmentation, and classification model. We at Futureai Tech have a team of experts who are experts in performing image annotation in an efficient manner for large to small object detection models.
Object Detection Using CNN models
Futureai Tech Pvt. Ltd. is having its own models for object detection and classifications in different domains. We have our own trained models for various solutions like object detection for FMCG i.e. Consumer Retail industry.
Our Yolo model process, techniques, and algorithms are efficient in nature which makes us one of the best ai, compute vision companies in Gurgaon, Delhi, India, USA, UK, and Dubai. We follow industry standards for the training of yolov8 models and predictions using yolov8 models.
Challenges
Inventory Out of Sync
The majority of retailers as of now do not have any system/process where they can effectively manage their product inventory. Because through traditional methods you can not forecast or evaluate store inventory. For a product that is out of sight from the customer is of no use, a similar way out of the stock board in any retail store chain kills its business for the long term.
AI can help retailers i.e., store chains in managing their stock using Computer Vision AI techniques. Not only its help them in managing their product inventory in real-time it also removes manual intervention in managing the inventory system.
Effective Marketing
In the absence of meaningful and relevant data marketing cannot be effective though retailers can try any marketing technique. AI can solve this problem of data to a great extent. Using AI algorithms and NLP techniques you can extract meaningful data from your raw data and find the hidden relations between different parameters of data.
With the emergence of Deep Learning in AI you do not have to be dependent on supervised learning techniques of Machine Learning. Using Deep Learning unsupervised techniques AI programs find hidden relations between different parameters of raw data.
Solutions
Making Unused Data an Asset
Most retailers have a large chunk of raw data that they collect daily based on product transactions and customer behavior. However, they do not have any method to extract meaningful information from this data. So, for them, this huge amount of data is of no use.
AI, particularly Deep Learning Unsupervised technique can solve this problem to a great extent. Currently, Deep Learning models which are based on CNN are available which you can use to train your AI Model to extract relevant information and relations between different paraments of this data. Later retailers can use this product transaction data and customer behavior and demography data to design effective and efficient marketing strategies.
Demand Prediction
Customer product purchase demand prediction can happen in two ways i.e., Current Demand prediction and future demand prediction. In current demand prediction retailer do not have too much time to act however in future demand prediction retailer has enough time that they can plan their purchase.
Different AI techniques are used in both prediction scenarios. In current demand, prediction retailers can deploy Computer Vision Model to check current customers in retail stores by counting the number of customers and their demography. This customer behavior and demography are clubbed with customer purchase patterns for that moment or you can say on that particular day.
In the case of Future demand prediction retailers have to analyse customer product purchase behaviors and history along with demography. AI Models for price predictions can be used to price and demand predictions. Deep Learning is a suitable technique to predict future demand predictions.
Surveillance and Security Systems
Using AI Deep Learning Computer Vision techniques retailers can effectively build secure and effective Surveillance and Security Systems. Customer and Staff authorized and unauthorized areas can be identified and then the Face Recognition AI solution can be used to effectively manage authorized and unauthorized are.
To implement Face Recognition using AI, first, we have to choose a face detection technique. There is a larger no of options there however you should choose the latest and most effective technique for that like RetinaNetResNet50 and RetinaNetMobileNetV1 etc.