Artificial Intelligence and Personal Preference in the Fashion Industry « RetailFuse

Artificial Intelligence and Personal Preference in the Fashion Industry Written by RetailFuse Contributor on July 9, 2020

Retailers around the world are naturally interested in artificial intelligence and are trying to harness the power of AI technology to gain business advantages. Accordingly, machine learning in retail helps retailers optimize pricing structures, collect customer information and make efficient logistics decisions, reduce costs and build long-term customer relationships.

It’s fairly well recognized that data intelligence had been rapidly becoming the new currency of brick-and-mortar retail even prior to the current health crisis, as brands and retailers measure sales and operations at a speed and agility that was previously impossible. Of course with massive shifts in retail, this trend has shown no signs of slowing down.  Artificial intelligence solutions in retail have been developed and offered by companies such as Amazon, Google, Microsoft, Apple, IBM and IBM Research. They strive to transform manual business approaches to become fully digital and automated. There is a great opportunity to integrate artificial intelligence and digitally driven automation into brick-and-mortar retail.

Risk mitigation achieved through Artificial Intelligence approaches have lead to more informed and confident decisions to achieve the desired results. The right data pipelines can be delivered, supported by defined variables such as sales, customer satisfaction, sales volume and customer loyalty, and customer experience.

Perceptions about Artificial Intelligence According to an article published by MIT Sloan Management Review, 84% of executive respondents say that artificial intelligence will enable them to gain and maintain a competitive advantage, and 83% of companies now consider it a strategic priority (as of 2017).

The global retail artificial intelligence market was expected to grow to by $23 billion USD globally between early 2020 and 2026, and demand for it is being fueled by the rise of AI – driven data analysis, artificial intelligence (AI), and machine learning.  While certain portions of that projection may now be revised, it is still certainly a high growth area. In addition, the growing awareness of the need for rapid decision-making and improved customer experience will strengthen AI as a key component of retail business in the near future, according to various report.

Retail AI enables companies to use advanced data to improve their retail operations and find new business opportunities. Machine learning – based solutions such as artificial intelligence and machine learning can help retailers grow, and this will lead to higher revenues and a better customer experience for their customers.

While brands compete to remain relevant, understanding why AI is becoming a bestseller – the solution – boils down to key factors. The key to understanding AI and why it is the best solution for retail.

For example, in the fashion industry, , VUE AI offers traditional retailers the opportunity to make the most of their brick-and-mortar business. VUE AI is retail’s artificial intelligence platform that puts the human experience at the center, using an AI powered visual style recommendations enable shoppers to explore relevant products. The maturity of the retail AI market is considerable, with analytics technology moving from pure theory to smart applications that can demonstrate and demonstrate their ROI and impact on the customer experience.

Using Image Recognition and Data Science, Vue AI analyzes retail catalog data to help retailers make better and faster decisions, automate retail processes from catalog management to merchandising, save resources, significantly reduce time to market and personalize the customer experience across all channels, enabling brands to differentiate and grow. The growing need to improve the customer experience, maintain inventory accuracy of retailers and improve productivity means that AI is increasingly being used in the retail market.

By adding artificial intelligence (AI), retail brick and mortar stores it will revolutionise the fashion industry and other industries which require a high degree of personal preference by equipping fashion brands with greater intelligence. With the power of AI, fashion companies can gain access to more information about their customers, products and customer base. AI overloads the pillow industry with the ability to scale business activities by bringing more intelligence to decision-making – and making processes along its value chain.

Major retailers are investing in artificial intelligence to improve ROI and optimize the supply chain. AI uses trend analysis based on historical data to speed up business processes, shorten the time to come up with ideas and sell, and improve trading decisions. Robots and automated conveyor systems within a distribution center or warehouse can also play a crucial role in logistics and supply chains, for example in the food and beverage supply chain. In logistics, robots can play a crucial role in food processing, distribution and distribution of goods and services.

Intelligent robots transform the operation of distribution centers with speed and accuracy as key capabilities. While retail robots are still a novelty, the proliferation of smart robots and the sophistication with which they evolve will make them ubiquitous in the near future. According to a recent report by the Institute for Robotics and Automation (IRA), this cleverness is driving the growing acceptance of intelligent robots and will be available in abundance in the future.

Retailers are harnessing the rise of robots to deliver improved productivity, lower costs and improve the customer experience. For customers, the increased presence of helpful robots in stores will cause trouble-free, personalized shopping experiences. With a combination of people, artificial intelligence and chatbots, private brands can gain insight into fluctuating market dynamics such as sales growth, customer demand, price changes, sales volume and sales trends, and customer preferences.