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Can Generative AI Power Best-in-Class Personalization for Estée Lauder?
The Estée Lauder Companies is seeking to enhance its operations through a strategic partnership with Google Cloud that will focus on finding new uses for generative AI across its brand sites. The retailer is aiming to utilize AI to better understand consumer sentiment, enhance its R&D efforts, and create high-touch digital experiences to elevate its prestige beauty brands.
Generative AI will enable Estée Lauder to monitor consumer sentiment and feedback in real time in order to proactively address customer concerns and respond to external trends, with the goal of ultimately creating more personalized experiences.
Additionally, Estée Lauder is developing new generative AI business applications on Vertex, Google Cloud’s AI platform, that could help it streamline and simplify operations to reduce costs. The retailer is also using PaLM 2, Google’s large language model, to gain greater insights into consumer sentiment on channels including social media and call center operations.
The new efforts with generative AI build on a multi-year partnership between Estée Lauder and Google, as well as development partner Eviden. Estée Lauder has been using Google Cloud technology to consolidate its data into a single environment to help it better understand consumer intent and tailor personalization for more than 20 brands.
“The beauty market is undergoing a significant transition, with heightened consumer expectations, ever-changing trends, and a shift to personalization,” said Thomas Kurian, CEO of Google Cloud, in a press release. “Our work with The Estée Lauder Companies to build a foundational data platform is now helping drive new generative AI use cases that will transform the consumer experience and the beauty industry overall.”
Greater investment in generative AI puts Estée Lauder in good company when it comes to building a better personalization engine. A recent survey by BCG found that 67% of major GenAI users harness the technology for personalization, including retailers creating “hyper-personalized recommendations that entice shoppers to buy more.”
While personalization is beneficial for all companies across retail, it’s particularly important for the beauty industry. A survey by Bolt found that 75% of shoppers would pay more for beauty and skincare products if they got personalized online shopping experiences. This opportunity is especially large among Gen Z, where 72% are willing to pay a more than 10% premium for personalization, while 57% would pay 11% to 20% more for purchases that include custom and curated online shopping journeys.
Making the most of this opportunity will be essential to Estée Lauder’s near-term success. The company is expecting sluggish sales growth in the second half of 2023 due to a slower-than-anticipated recovery in its travel retail business, particularly in Asia, and weakening demand in the U.S. Standing out with personalized recommendations and cost-efficient processes could help the company offset this weakness and build momentum for the future.
Discussion Questions
Is personalization the key ingredient Estée Lauder needs to help it overcome the difficulties it is facing? Will advances in generative AI be the technology that fuels the next evolution in retail personalization?
Poll
Loading …BrainTrust
John Lietsch
Chief Operating Officer, Bloo Kanoo
Lisa Taylor
Director of Retail Consulting U.S., Thought Provoking Consulting (TPC)
Estée Lauder is applying AI in sensible ways that allow it to make better sense of data and feedback for the purposes of improving product development and marketing. However, this is nothing particularly new and virtually every beauty brand – big and small – selling on the Amazon marketplace has had these tools for quite some time. Of course, gathering the data is one thing – making sense of it to actually formulate new products is quite another and this is where Estée Lauder has often been weaker. On top of this, AI does nothing to solve the distribution problem where Estée Lauder is overly-reliant on weaker channels like department stores for revenue.
As fast as the Generative AI train is moving, Estee Lauder is essentially jumping on for the ride. The question just around the corner is, how will brands differentiate when everyone is using Gen AI? Personalization would seem to be the answer yet branded communications will need to be sharp as a tack. This is particularly true for portfolio companies like Estee Lauder that will be challenged to differentiate content and customer communications between brands while standing out in a sea of Gen AI capabilities overall.
Technology does not alone solve problems with a business’ strategy, execution or business model. It can be part of the solution (one of the many “hows”) but simply using AI and saying that it will cure a fundamental business problem is unfair to AI, to any tech. However, fundamental issues aside, it sounds like Estee Lauder will leverage the power of GAI by training it on data that could definitely improve its analytical capabilities and could translate to better information with which to make better decisions. The challenge is that personalization as a strategy has been around for some time. Provided people and companies see beyond the hype and don’t abandon GAI because they implemented the hype, not the business case (whatever happened to the metaverse), I believe GAI could fuel the next evolution in retail personalization by inspiring innovation that will capitalize on GAI’s strengths.
Estée Lauder has a lot to gain with AI, but I think this is also a cautionary tale for retailers everywhere who are starting to leverage the many powers that AI promises. In one word, the cautionary tale is, well, Google. In a few more words: Don’t put the strategy fox in the henhouse. New and best-of-class AI tools are becoming available all the time, and Google certainly won’t own or partner with all of them. It’s only natural that Google will look to its own vast portfolio and moonshots before searching elsewhere. And, being Google, the search shouldn’t be that difficult.
As an example, there’s a solution available already that leverages user generated content (UGC) to personalize makeup and skincare product matching. It’s not a Google product, either. Come to think of it, maybe that’s a good test case to see if Google as a strategist makes the best strategy choices for Estée Lauder. We shall see.
There’s still a lot of white space in beauty when it comes to personalized recommendations, and innovation in general considering how trend-driven the category is, so this is kind of table stakes. I’m glad it’s a focus though.
Every company is sitting on mountains of data and when additional 3rd party sources are infused to make it even more rich, the mountain gets exponentially taller. The key is leveraging the tools available that make sense for each company’s use cases. Certainly, for Estee Lauder or any beauty company, personalization efforts are table stakes. It’s what’s being done with the data that makes a difference. Google has an array of generative AI tools but so do many other companies. Estee Lauder ought to test/trial a number of technologies to find the combination that suits them best.
Right now this collaboration is stronger for Google (Alphabet? I forget) than it is for Estee Lauder. They will learn. Estee Lauder will learn also, but they will learn that AI is not really ready for commercialization, at least not in this way. EL is acting as a pathfinder here for everyone else, and I hope they benefit from the experience. From my perspective the search feature on most websites are worthless, so good luck!!
The real story here is Estée Lauder using AI to understand consumer sentiment. The University of North Carolina has been using AI for healthcare diagnosis for several years with great success. The computers are fed thousands of pieces of information on an ongoing basis. A patient’s symptoms are then fed into the system, and a diagnosis is spit out, no matter how rare. Accuracy and timeliness far outstrip those of human professionals.
Now imagine if consumer sentiment were fed to such a dynamic database. If Estée Lauder can harness this tool, it will give them a considerable advantage. The better the company knows its customers and feeds that knowledge into their decision-making, the better it will execute the product, promotion, and price vis-a-vis the customer.
Personalization could be pivotal for Estée Lauder’s success. With consumer expectations for tailored experiences in the beauty industry, using generative AI to offer personalized recommendations and streamline operations can help mitigate sluggish sales and cater to evolving customer demands.
Generative AI has the potential to drive the next evolution in retail personalization, offering tailored experiences, personalized product recommendation, virtual try-on, augmented reality, dynamic price optimization and efficient operations to meet consumer demands.
Utilizing GAI to interpret consumer sentiment is complex, but has potential. Sentiment as a concept is far more nuanced and subtle than other typical cases of AI usage (price optimization, sales forecasting, etc.). Measuring from a data standpoint would ideally require a very large spectrum of data points – everything from basic sales data to product interaction, mentions in social channels and any additional surveys or direct communication with consumers.
Additionally, very close collaboration between data science and marketing would be key to this type of application of GAI. A formula for sentiment is not common usage, so a data science team would benefit greatly from heavy involvement in understanding consumer behavior to curate the most effective model.
Leveraging generative AI to provide personalized experiences is a great approach by Estée Lauder to address the difficulties they’re facing. The data already shows that customers are looking for personalized experiences more and more. And, as the article notes, beauty in particular is a strong marketplace for personalized shopping experiences with customers looking to purchase products that match both their personal preferences and physical characteristics.
As far as the evolution of retail, AI is already fueling change for early adopters. Brands are using it to provide more personalized and targeted marketing materials and search and recommendations experiences. Brands not currently evaluating their strategies and implementing solutions into their tech stack will quickly fall behind if they don’t do so soon.