Abstract
The lines are blurring between online (eCommerce), and offline retail. But, eCommerce has the advantage that the sellers can see the customers' feedback easily because many of them post reviews on most of the products' website like amazon.com or ebay.com making the whole process faster. The purpose of the article is to analyze several technologies which can be implemented for indoor shopping and for improving customers' experience. Therefore, we aim to explore several methods which can help reduce the time that customers spend to pay for the products and to customize their shopping experience by analyzing their feelings towards shops and products. We propose an online platform which identifies the customer's opinion. The retail organizations must correctly understand their customers' wishes and expectations through NLP (Natural Language Processing), a form of Artificial Intelligence (AI), namely analyzing an encoded sentiment data based on feedbacks considering the context and the environment. One of the methods approached to obtain the customer's related to the Speech-to-Text technologies so that the client will spend so much time writing full sentences, products reviews, etc. This Speech-to-Text application is developed based on a vocabulary composed of words which express and highlight strong emotions such as bad, horrible but also analyze if the sentence is affirmative or negative, and if the sentence is negative to notice if the customer said not good, not bad and so on. By implementing smart shop solutions, we can manage some of the issues mentioned above, such as monitoring the shop facility hot areas, update product prices and locate products easily, all with the objective to improve the customers' shopping experience.
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