- Artificial Intelligence
AutoML Current Uses and Approaches
- Artificial Intelligence
More and more fashion companies tend to incorporate Artificial Intelligence (AI). The technology has been known for years, but it went viral only several years ago.
AI contributes to multiple processes in fashion: search personalization, stock management, supply chain management, and others. But demand forecasting using AI and trend prediction are considered the essential use cases for machine learning (ML) algorithms application.
Basically, there are multiple ways AI and ML benefit all aspects of the fashion industry. Let’s review the substantial ones.
Smart technologies monitor consumers` preferences and behavioral patterns. They also better analyze retail success compared to conventional statistical tools. AI-driven software also excels in comparing a company’s performance metrics in real-time.
Basically, in the trends prediction context, AI in fashion industry greatly facilitates decision-making.
Customers enjoy a unique experience ensured by AI-driven software in fashion. It includes the following possibilities:
These are only a few ways to make shopping more individual and interactive with the help of AI.
It is well-known that multiple minor clothing defects are difficult for the human eye to see. AI is of great assistance for Quality Assurance, as it can help detect such imperfections.
AI-driven optical systems are widespread in the production of fashion items. Basically, they review fabricated products using cameras. The system has sets of particular metrics to evaluate products. Sub-quality items are spotted and filtered out.
So, defective clothing does not deteriorate customers’ experience and a brand image thanks to AI.
Customers usually fail to recognize counterfeit products while buying online. That’s because images that are shown on the websites do not reflect the quality and originality of goods. Consequently, retailers are to detect fake goods themselves.
Similar to the process of imperfections detecting, AI-featured optical systems can spot counterfeit fashion items. This way, retailers may prevent fake goods from entering their warehouses and store shelves.
Postindustria, for example, widely incorporates ML regularization techniques in the projects to achieve better results. Learn more about our services by following the link.
AI can even boost a company’s sales and marketing processes. Thanks to production optimization, for example. Here are a few aspects of fashion companies’ operating processes that AI-based solutions may empower.
AI fashion trends prediction is one of the multiple ways to incorporate ML in systems for forecasting in the fashion industry. What’s also essential is that AI allows making accurate demand forecasts and detecting customer buying behavior patterns.
The principle of demand and sales forecasting is basically the use of data for previous interactions of customers with the company’s business environment — searches and purchases. Then, AI makes conclusions regarding the demand changes and seasonality of sales, for example.
Consequently, retailers may use prediction in AI to forecast the demand for their particular products at a specific time. It helps to manage stock more effectively and increase sales overall.
AI goes further than just predicting sales. It directly enhances the production of fashion items by participating in manufacturing, operation, and service automation. The ways AI-based solutions can improve processes are the following:
Another way to increase the business revenue is to replace unproductive labor with AI-driven software and robots. Experts claim that companies that have already integrated AI in their processes increased their sales by more than 50%. At the same time, they reduced operation costs by almost 60%.
That’s why AI-powered systems are highly promising investments. Nearly every company will likely employ one shortly.
As mentioned previously, large companies are already using AI for demand forecasting. It helps them to keep the lead in the industry. ML is an integral part of smart algorithms as it basically ensures that AI improves over time based on changing factors.
Let’s see how two well-known companies, H&M and ASOS, are using modern ML techniques:
ASOS implements ML to predict the consumer lifetime value better. Even the minor changes in this indicator may evidence global changes in behavioral patterns. This way, the company always knows how much or how little their customers are willing to pay on an annual basis.
Such information helps the marketing sector to make better decisions regarding the sales strategy overall.
H&M utilizes ML to analyze their customers’ behavior locally. Financial profits of distinct stores, numbers and types of goods sold, and numerous other factors. These are carefully standardized and taken into consideration.
While ASOS performs better in predicting global changes in trends, H&M forecasts their local sales more accurately.
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