You are unique. That’s the message luxury cosmetic brands want to convey through AI. This technology is revolutionising customer experience but also requires a digital, and managerial transformation… How can companies seize these new opportunities while addressing ethical and regulatory challenges?
The holy grail in today’s luxury market is to offer a 100% personalised beauty routine. To achieve this, beauty brands in this segment are embracing innovation, especially digital innovation, to surprise their customers and offer them unique experiences that take personalisation to new heights.
One example is L’Oréal, which has been using augmented reality (AR) for several years to allow consumers to virtually try on its foundations or hair colours. And in 2021, for the Yves Saint Laurent brand, the company developed a revolutionary connected device that uses artificial intelligence (IA) to design 100% personalised skincare, foundations and lipsticks perfectly tailored to the consumer’s needs.
Enhancing customer experience
As brands face fierce competition and increasingly discerning consumers, data and AI are proving to be powerful levers for improving the customer experience and strengthening trust in this era of digital transformation.
Research carried out in this area shows that by collecting and analysing behavioural, transactional and self-reported data, it is possible to adapt offers in real time, fine-tune marketing strategies, and deliver seamless and cohesive customer journeys.
According to Carlson (2023), “unstructured data makes up 80 to 90% of all data in the world today”. To extract all the value contained in this information, advanced technologies such as natural language processing (NLP) are needed.
As an example, NLP makes it possible to understand and interpret textual content (comments, online reviews) in order to better identify consumer preferences and expectations.
AI, a driver of transformation
Today, AI plays a central role in this transformation. It facilitates the deployment of chat and recommendation tools supported by algorithms capable of anticipating the needs of consumers.
Among these algorithms, machine learning allows a program to evolve by analysing large amounts of data, and deep learning, a subset of machine learning, uses artificial neural networks to recognise complex patterns, much like the human brain.
These approaches automate complex tasks such as predicting buying behaviour or personalising offers.
Smart chatbots
AI is also making chatbots smarter. These conversational user interfaces are increasingly being used online to provide instant and personalised customer service. In fact, Gartner predicts that by 2025, 80% of customer service and support organisations will be applying generative AI technology in some form to improve agent productivity and customer experience.
In the luxury cosmetics sector, these technologies take the form of functionalities such as retargeting, push notifications, or product recommendations based on past purchases and expressed preferences. Sephora is doing this and is ahead of the curve in this segment.
Insights from experts
To better understand the conditions for success and the limitations of these approaches, we carried out a qualitative study by conducting semi-structured interviews with ten luxury cosmetics professionals who work with data and AI on a daily basis.
Our conversations highlighted the importance of centralising and classifying data in order to create coherent and reliable customer profiles. The participants also emphasised the need to comply with regulations, such as GDPR, and to ensure that data is kept secure and confidential: rigorous governance is seen as a key factor in maintaining consumer trust (Hakobyan, 2022).
Read also: Forecasting the present: The role of Google data in assessing real-time economic conditions
Deploying predictive models and chatbots appears to be a catalyst for customer satisfaction, as real-time analytics make it possible to reduce sales pressure and suggest offers or content that are truly tailored to each individual’s preferences.
Keys to a succesful transformation
In terms of management, their recommendations focus on four main areas:
- First, data centralisation and quality require the use of robust ETL (Extract, Transform, Load) tools to aggregate and ensure the reliability of information coming from different digital channels (websites, mobile applications, marketing campaigns).
- Second, it is important to establish clear compliance policies, while ensuring that sensitive information is protected and employees are trained in best practices.
- The professionals interviewed also stressed the importance of analysing data in more detail using specialised tools: “Using these analytics to create detailed profiles and segment the database makes it easier to personalise interactions and marketing campaigns” (Dahirel, 2023).
- Finally, there are ethical and environmental considerations to take into account when integrating AI: as data processing activities increase and algorithms become more powerful, the digital carbon footprint can grow significantly.
The dark side of AI
Given the challenges of collecting quality data, the reliance on third-party cookies, which are now being phased out, and increasing regulatory requirements, companies need to be very agile.
The professionals we interviewed also mentioned the technical complexity and costs associated with implementing AI: they stressed that effective AI deployment requires sustained investment in infrastructure, training and talent recruitment.
Read also: Are we ready to let AI make purchasing decisions for us?
However, it would be worth expanding our study: the lack of diversity in the profiles interviewed (for example, there were no women in the sample) reduces the range of findings, and the lack of quantitative data makes it impossible to statistically confirm the trends observed.
Retain hard-to-please customers
In any case, the use of data and AI is radically reshaping customer experience in the luxury cosmetics sector, by enabling interactions that are more personalised and engaging. Chatbots, recommendation engines and predictive analytics solutions are all tools that can increase conversion rates and help retain hard-to-please customers.
However, these technological advances also present significant challenges in terms of security, confidentiality and environmental responsibility that need to be addressed. A holistic approach and exemplary data governance are therefore necessary if innovation is to remain a differentiating asset and customer trust and satisfaction is to be maintained over the long term.