Suyash Katyayani

Co-founder & CTO,

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Leveraging Big Data To Make A Big Impact In The Beauty Industry

Building and sustaining a competitive product portfolio in today’s constantly evolving market largely depends on how effectively brands can capture and capitalize on shifting consumer trends in a timely manner.

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Leveraging Big Data To Make A Big Impact In The Beauty Industry
Leveraging Big Data To Make A Big Impact In The Beauty Industry

In recent times, the beauty and personal care industry has witnessed tremendous growth, attributed to the growing D2C business and the investor confidence in the sector. According to a report by Avendus, the beauty and personal care market is estimated to grow 1.7 times since 2020, it is projected to reach $27Bn by 2025. As the per capita consumption of beauty products increases in the country, the need to experiment, indulge, and willingness to try new brands is a compelling need to arrive at the perfect product for women.

Amidst globalized beauty industry trends, brands must constantly innovate to meet the evolving desires and needs of customers looking for new products and services. As leaders in the space of beauty e-commerce, addressing consumer needs and demands is relatively easier thanks to analytics. The one area that has benefited immensely from this is new product development.

Machine learning to invest in the future of product development

Big data and analytics is essential for a variety of processes ranging from packaging and formulation to sampling and overall marketing strategy. A diverse range of customers can be tapped to obtain unbiased and accurate information. This can help fasten the process of new product development, target audience mapping, and opportunity analysis to ensure a diverse range of products with optimized margins.

The magic lies in leveraging artificial intelligence and machine learning based clustering, an algorithm that combines search keywords and data mining to create the perfect concoction of rational thinking almost like the human mind. With the mining of millions of data points across categories like search volume and current products, prediction is also easier.

This is half the journey completed, from here we move into developing ideas that can be put through a fitness test and rate them in order of the likeness for them becoming a successful product. Apart from mapping trends and ensuring new products are backed by data, measuring the success rate they can achieve is imperative. Without which measuring impact is likely a farfetched dream.

Purplle spent considerable time in building the ‘Beauty Intelligence Suite’ akin to the human mind, the tool gathers copious amounts of data publicly available from the internet. This data is then segregated like a person would into, skin type, preference, ingredient list, and so on. The segregated data assists brand managers and teams to build products that are the need and want of the market at any given point. The tool also helps in predicting future trends.

The beauty intelligence suite has a knowledge map that gathers data, and is capable of covering over 4500 Brands, 120 Sub Categories, 1000 Product Types, 1300 Active Ingredients, 500 skin and hair type related benefits and concern areas, and over 200 product claims. Currently the tool maps over 10MM+ Purplle search queries and can tag and classify information to build them into relevant data pointers.

With India being a country where per capita consumption of beauty products changes as per region, it is imperative to map data across the country. Today, brands can personalise the customer journey and needs by expanding their data mapping to smaller pockets of the country.

Beyond building products, AI and ML together help brands to ride the wave of trends correctly, at the right time and right place. If brands learn to leverage technology and act quickly to read and asses a trend, future predictions is possible thereby innovate today to develop products for tomorrow.

Riding this wave includes mapping publicly available data earlier to ensure you launch the product when it is a trend in the country. For beauty, prominent trends are predicted via user consumption and behavioural patterns in the West and popular Asian countries like Korea.

The best example to quote will be the rise of Hallyu in India, which led to the ingredient-based K beauty fad. Along with this came an interesting product called Snail Mucin, a humectant that preserves moisture. The ingredient is perfect for Indian skin types based on the current cold wave in the country. So marrying data trends across the globe and then customising them for India is a tough choice for brand managers. Technology simplifies the decision.

With machine learning comes in the ability to fine tune and analyse data for a human -like understanding of products and consumers. Machine learning combined with human intervention via product recommendations and advisory is the future of the new beauty order in a country like India. As the per person consumption of beauty products increases, the importance of personalisation grows and the need to adapt to machine learning based solutions is imperative.

Building and sustaining a competitive product portfolio in today’s constantly evolving market largely depends on how effectively brands can capture and capitalize on shifting consumer trends in a timely manner. If brands want to thrive and appeal to the new-age consumers, they need to understand the consumers’ evolving tastes and preferences and keep reinventing their offerings to bridge this gap. Artificial intelligence and big data can be used together to develop alternative or completely new product offerings for consumers. Data analysis can help win newer customers by developing targeted pricing strategies to accommodate emerging demographic segments. Furthermore, one must leverage data analytics not only to understand the target customers better, but also to increase customer engagement, social sharing, and customer interaction.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house

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