Machine Learning is Changing the Beauty Industry: Explore How?

Machine Learning
Machine Learning

Digitalization and technological advancement have a significant impact on the beauty industry. Artificial Intelligence and Machine Learning are reshaping the way beauty vendors operate and run their businesses. The beauty industry was valued at around USD 532 billion in 2017. It follows a rapid upward trajectory; the estimated worth is estimated to reach approximately USD 805.61 billion by the end of the year 2023. You can get an idea about how huge the industry, and how the AI beauty industry has witnessed a revolutionary change over the last few years.

Owning a curling iron was an extravagance, and it was one of the high-tech beauty products. But now people use AI and ML-based devices and apps that offer customized beauty treatments and product recommendations depending on your beautiful skin. Some numerous apps and devices provide personalized skincare advice for your skin.

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Machine Learning: How it is Benefiting the Beauty Industry

The global cosmetic market experienced a massive jump between 2004 and 2019. In 2018, the global cosmetics market grew by 5.5% compared to the previous year. Since the twentieth century, cosmetic production has been controlled by a handful of multinational corporations. The global beauty and cosmetic industry are broken down into primary categories, and skincare is the largest one among all other types as it covered around 36.4% of the worldwide market in 2016.

Undoubtedly, machine learning (ML) can help the beauty sector in numerous ways. It merely starts by assisting people in looking more attractive to provide a statistical basis for attractiveness and develop products customers’  tackle the specific needs. 

The beauty and cosmetic industry are one of the rapidly growing markets. In the US alone, the beauty industry employs around  670,000 people, and job growth is relatively faster than the average. The beauty sector is worth $532.43 billion in 2017 and predicted to grow and reach around $805.61 billion by the end of 2023.

With the advent of smart technology, modern technology such as artificial intelligence (AI) and machine learning (ML) use has increased dramatically. It is embracing personalization and knowledge-based beauty routines to the customers. It’s estimated that global cosmetic products will grow and reach around USD 806 billion by the end of 2023.

ML (machine learning) is a subsidiary of AI (artificial intelligence). It leverages a variety of learning models based on a particular business use case. Similarly, machine learning (ML) apps and devices are automatically improving through experience. Music streaming giants Spotify, Netflix, and many more opted for machine learning for providing a more personalized experience to customers on their demand.

During the past few years, both technology (AI and ML) have penetrated the cosmetics and beauty sector. In a report, experts predict that spending on AI and ML in the beauty industry is expected to increase by $7.3 billion by 2022. There are mainly five primary ML apps and devices which beauty brands adopted; these include:

  • ML and AI-based apps beauty sector used to try makeup
  • Dynamic and personalized content
  • Voice assistant for recommendation product
  • ML-powered beauty product searches
  • Supply and demand forecasting tools

ML innovations are essential to gain a competitive edge in the beauty and cosmetic sector. There are several ways in which beauty brands can successfully use machine learning (ML) to win over customers. Many brands are leveraging AI and machine learning technology to adapt to the new normal and keep every customer engaged. Keep on reading to know how machine learning (ML) is affecting the beauty sector.

Increase of Personalized Products

Machine learning-based apps in the beauty sector can offer customers insight into the market. It helps brands have deep insight into customers’ requirements and provide them with unique products that help them to satisfy their needs. Additionally, it helps provide a range of generic products to purchase off the supermarket; beauty brands can provide beauty lovers with personalized products to cater to customers’ specific requirements.

Proffering Personalised Skincare Consultation

The thought of seizing skincare advice from a mobile app using chatbots might seem gimmicky and impersonal at first glance, moreover, due to the vast volume of data available for analysis. AI, DL, and machine learning (ML) often provide real-time and more realistic based insights compared to traditional market research.

Many brands provide customers with a skin advisor service by requesting beauty lovers to share their selfies. After analysis, customers are asked to give an answer to some of the questions based on which they are recommended with specific needs and advice on their skincare regimen. It makes it easier for beauty brands to instruct customers about using a laser hair removal machine, how long they need to use it, what time it will require to get a fantastic result, etc.

Presenting Immersive Experiences

Customer engagement is significant for building loyalty and ensuring that every customer purchases the beauty product or service. A brand makes the purchase process exciting and appealing, which helps attract a large audience in a short period. 

Combo of Machine learning (ML) and Augmented Reality (AR) allows you to create various looks, make beauty product recommendations based on past purchases, and provide suggestions based on data available on the customer’s skin tone or type.

Growth in Beauty Innovation: The Cosmetic Sector Has Long Way to Go!

Predictions indicate that the global beauty and cosmetic market is estimated to grow at 5.9%. It’s estimated to increase and reach around US$ 758.45 billion by the end of 2025 according to a ResearchAndMarkets report. Global players are using machine learning to disrupt the market; it is evident that beauty brands need to keep pace with the latest trends and ML and AI innovation to stay in the game.

With customers expecting personalized skin care advice and recommendations, brands need to gather customer data, purchase patterns, online behavior trends, and host other data points. It can be analyzed to make recommendations or develop products that focus on satisfying specific customers’ needs.

The scenario shows that manually analyzed data developing strategies are next to impossible for the consumer market and marketing teams. There are various types, and with the sheer volume of data, beauty brands are capitalizing on existing digital touchpoints where clients interact with them and integrate them using AI and ML-based tools such as deep learning, chatbot technology, etc.

Use of algorithms to simplify the process of analysis to provide insights based on efficient strategies and development. The goal is to develop a relationship with consumers to keep them interested in the brand by recommending a daily skincare regimen and making product recommendations for providing price comparisons online.