Bellesa basada en dades: com The Estée Lauder Companies obté informació amb ChatGPT

The Estée Lauder Companies⁠(s'obre en una finestra nova) (ELC) és un líder global en bellesa de prestigi, amb una cartera de més de 20 marques icòniques, com ara Clinique, La Mer, Bobbi Brown Cosmetics i Aveda. L’empresa i les seves marques són conegudes per productes de bellesa capdavanters combinats amb experiències personalitzades i d’alt contacte per als seus consumidors globals.

A mesura que el món de la bellesa tendeix cap a la hiperpersonalització, també ho ha fet l’enfocament de l’empresa sobre la investigació i la tecnologia. L’adopció generalitzada de ChatGPT Enterprise a ELC està ajudant l’empresa a potenciar la creativitat dels empleats i obtenir informació per servir millor els consumidors d’avui.

La indústria global de la bellesa es mou ràpidament, i la cadena de productes d’ELC està influïda per grans quantitats de dades de consumidors procedents d’enquestes, assaigs clínics i ús de productes.

The ELC team adopted ChatGPT for data processing and analysis at scale. Today, ChatGPT is an integral part of ELC’s workflows, with more than 240 custom GPTs that give their employees greater time and insights to do what they do best: develop new and market existing products with precision, aligned to emerging skincare and beauty trends.

Central to ELC’s rapid adoption of AI was the company’s core belief that creativity lies in every chair. “When OpenAI came out with ChatGPT, we asked employees to tell us how they would use it,” says Raheel Khan, SVP of Foresight and Growth Intelligence at ELC. “Over one thousand people submitted ideas.”

This interest led ELC to establish their GPT Lab: A cross-functional group empowered to experiment and develop business solutions—“custom GPTs”—with ChatGPT. 

“Our job is to uncover high-impact use cases that drive value to our different brands and regions,” said Charmaine Pek, ELC’s Director of ChatGPT Enterprise Adoption. “The GPT Lab aims to identify patterns across meaningful use cases and amplify these successes to scale to more brands and regions.”

En només 10 setmanes, els membres del Lab van desenvolupar diversos GPT personalitzats, cadascun amb el seu propi propòsit i abast.

Una de les aplicacions més destacades creades pel GPT Lab és el Fragrance Insights GPT. Desenvolupat per ajudar l’equip de Fragrance Foresight a extreure informació de les enquestes de consumidors, aquest GPT analitza grans conjunts de dades d’enquestes per descobrir tendències i preferències, cosa que permet a ELC dissenyar productes que connectin amb diferents grups demogràfics.

«Abans passàvem hores netejant i organitzant dades manualment per obtenir informació», diu Yuan Zhan, directora de l’equip Fragrance Foresight d’ELC. «Amb el Fragrance GPT, podem fer preguntes complexes en anglès planer, i revisa les dades a l’instant.»

Els membres del Lab també van llançar el Clinical Trial Data GPT, que extreu ràpidament informació sobre l’eficàcia dels productes per a la cura de la pell —per exemple, determinar el percentatge de millora d’hidratació immediata d’un producte com el sèrum Advanced Night Repair d’Estée Lauder— a partir de milers d’informes d’assaigs clínics amb una consulta senzilla.                 

Altres GPT inclouen un Copywriting GPT, un assistent de redacció personalitzat per a diverses marques, dissenyat per crear contingut detallat, rellevant i alineat amb la marca en diferents plataformes, i un Vendor Snapshot Creator GPT, que sintetitza informació essencial sobre cada proveïdor, inclòs el seu perfil, l’historial de compres d’ELC i altres detalls rellevants.

The Estée Lauder Companies structures its GPT creation process like a sprint, focusing on quick prototyping and testing to identify success predictors and build for scale.

“We look at the value the GPT would bring to the organization, and we also look at the effort that is needed,” explains Kingsuk Chakrabarty, Director of Enterprise Architecture, AI and R&D at ELC. “Then we prioritize the GPTs which have high value and can be built quickly.”

Within the GPT Lab, “teams” of people—a business user, a subject matter expert (SME), and a technical lead—are responsible for ensuring each idea is based on impact and feasibility:

1. Design: the business user defines the purpose, scope, and audience for the GPT in a two-page Use Case Brief to gain clarity before building.
2. Prepare: the SME shapes the use case by gathering and preparing relevant data, ensuring best practices for GPT development.
3. Build & Test: the tech lead builds the GPT with sets of data and rigorously tests to assess accuracy and consistency.
4. Launch: the full team deploys the GPT and a user guide for teams to use.
5. Pivot & Scale: the full team uses feedback loops to iterate and optimize the outputs based on GPT performance.

“Designing the right use cases means asking the right questions,” shares Pek. “Why do we want to build this GPT? What is the problem that we're trying to solve? What impact will it have?”

By working in iterative cycles and sharing insights, the GPT Lab was able to develop AI-powered prototypes in a number of weeks.

Not only has ChatGPT helped employees work faster, it’s expanded their creative capacity by reducing manual tasks:

• Time savings: Across R&D and marketing teams, ChatGPT has improved response time by more than 90%. What once took teams several hours to research—like finding claims about product efficacy—now takes minutes. 
• Speed to market: By accelerating data analysis, ELC can launch products faster, ensuring they remain responsive to fast-changing consumer trends. 
• Internal adoption: There’s been enthusiastic adoption of GPT across the organization, with more teams asking to integrate AI into their workflows for creative solutions.

“Using ChatGPT Enterprise has done two things,” says Khan. “One, it allows our incredible human talent to spend more time on tasks that require creativity. And two, it actually increases capability to deliver on what matters most to our consumers.” 

ELC plans to apply top GPTs across its portfolio of brands, empowering more teams to take advantage of AI-driven insights and creativity. 

“AI enables us to deliver market-leading products on a larger scale, and better. With OpenAI, we’re reducing low-value work for our employees and giving our teams the opportunity to create on a whole new scale,” says Lauder.