The retail industry is being transformed by artificial intelligence (AI). AI is tremendously changing the retail shopping experience. Retailers can use AI to interact with their consumers and run more efficiently, from utilizing computer vision to customize promotions in real time to using machine learning for inventory management.
Over 28% of retailers have already implemented Artificial Intelligence/Machine Learning solutions, a sevenfold increase from 2016 when only 4% had done so.
Let’s understand the use cases below to understand how AI is going to enhance and scale retail businesses:
AI Chatbots for customer service:They boost consumer service by improving search, sending notifications about new collections, and suggesting related products.
If a buyer has already purchased a black hoodie, a chatbot can offer a pair of jeans to complete the appearance. Eighty percent of brands around the world are now utilizing AI Chatbots or plan to do so in the near future.
AI to increase customer experience:The data collected by the POS system is utilized to produce fresh product recommendations for a certain consumer. Digital signage collects information on the types of people that shop and when they shop so that merchandising may make better product promotion decisions.
All of this leads to more precise segmentation and customized experiences based on a customer’s tendencies and preferences.
AI for automated inventory management:Smart shelves are used in AI inventory management to swiftly discover out-of-stock items and pricing problems. For more up-to-date inventories, inventory robots can warn personnel about low stock or misplaced products. In addition, computer vision enabled checkout systems can aid in real-time product loss more time to focus on improving the customer experience.
We at smartData have team of AI/ML developers, having expertise in developing/maintaining complex algorithms based on Computer Vision, OpenCV, Recommendation Engines, STT, TTS, NLP, NLTK, Spacy, Baysian, BERT, Elmo, Gaussian processes, PyTorch, Scikit, Tensorflow, regression models, SVC, SVM, classifier models, Predictive analytics, Visualizations (Matplotlib, Seaborn, Google DataStudio, Plotly, Dash etc.), Deep Learning with neural networks (R-CNN, Mask-RCNN, SSD, YOLO), NumPy, Pandas,etc.
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