Levi’s Katia Walsh on AI: You can do a lot with 168 years of data
Levi’s Katia Walsh on AI: You can do a lot with 168 years of data
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Managing AI and ML in the Enterprise
The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build.
Levi’s is best known for its denim, but the company’s master plan is to redefine itself with artificial intelligence, machine learning and data science. And the apparel company has 168 years of data to become more efficient, predict and create trends and improve the customer experience.
ZDNet caught up with Dr. Katia Walsh, Chief Strategy and AI Officer at Levi’s, to talk about implementing AI, machine learning and data science at a 168-year-old company during a pandemic. Here are a few highlights. The full conversation is in the video.
Where an AI and data science team sits in an organization. Walsh said that AI is a department that covers multiple units and is a horizontal function much like finance, technology and human resources.
She said:
This team is very new to the company. I barely got from London to San Francisco myself as the leader and founder of the capability, and then COVID happened. So we started the year with 12 people, including me and my assistant. We were just learning to educate the entire enterprise on what this combination of digital data and AI is and what it can do for the company, and then March 16th, the lockdown in San Francisco happened. And we all know the challenges that we have been encountering for the last year. But the last year was a great opportunity to really show what digital data and AI can do for a company.
The COVID-19 crash course. Like other technologies, the COVID-19 pandemic accelerated plans. Walsh said from March to August 2020, Levi’s saw a sprint where AI and data had to be used for “anything from improving the customer experience to delivering internal operating and operational efficiencies, and also possibly looking into new revenue models and business models for the company.”
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E-commerce and shipping. Walsh said Levi’s saw a surge in e-commerce sales and the company moved to ship from the stores closest to the consumer. She said:
Using AI, we devised a machine learning engine that optimized a number of different variables, including what items each store had in its inventory, how far or close it was from the specific consumer placing the order, how much it would cost to ship, whether the item that was ordered was going to have to be discounted later if it went out today, et cetera…
We were able to use data on climate and weather and epidemiology models and financial and market outlooks. So you know how I talked about the three parts of this flywheel; digital, data, and AI. What makes this particularly useful is that it uses more data than before, which gives us more points, more perspectives, more variables, and then we’re able to apply machine learning, which then makes the model even smarter and to deliver even better.
Using 168 years of data. Levi’s has 168 years of data and the company considers itself one of San Francisco’s original startups. Walsh said that rich history and data set can inform what products will thrive in the future.
In the case of Levi’s all the products that the company has created and manufactured in the past 160 years are data. The Bing Crosby jacket that he wore in Canada is data. Once it’s photographed, and that photograph is digitized, that’s data. The Einstein jacket that he was photographed as man of the year by Times Magazine in 1939 that we created a replica of in the past year, that’s data. So we are now using images of products to predict demand for new products based on using computer vision that can tell us based on similarity between certain products that have been sold in the past new products that have never been sold, what the demand for new products would be. So the opportunities are absolutely endless when it comes to data and Levi’s.
We are absolutely predicting right now what demand for products will be like. The further you go into time, the less accurate the model will be because there are just so many unknowns that happen to accumulate as time goes by. And we are looking to predict demand in the next half of the year, in the next month, in the next three months.
The role of algorithms in product design. Walsh said:
Well, product design is a very creative process. I’ve worked in financial services, I’ve worked in telecommunications, I’ve worked in technology. This is my first time leading AI and helping drive digital transformation in the creative company, in a fashion company. It is incredibly creative. It is a highly imaginative process. What we are doing is partner with designers, partner with planners, planning is the original data science function in a company like Levi’s and retail and apparel, and bringing the latest tools and this combination, this flywheel of digital data and AI to be able to drive demand, to predict demand, to optimize costs, and to also really deepen the connection with consumers.
People, processes, privacy matter as much as the tech stack. Walsh said that building out AI capabilities has four building blocks.
I always start with the people. Yes, tech stacks are very important, but if we don’t have the right people and the right number, and I’m not talking about an army of people, but people who have the technical skillset, but are also entrepreneurial, good communicators, focused on business priorities, able to partner and think about the future. So people are very important. Processes are quite important to. Not to be bureaucratic, but we are incorporating agile ways of working in the company. We are driving a great deal of attention on privacy.
Privacy is always important. It’s particularly important when you’re dealing with data and AI. We talk about responsible and ethical AI. So we have a code of conduct when it comes to data and AI in the company. Data of course, itself, very important. We now have more data than ever before, certainly internal data from our own operational systems, but also external data from partnerships or from mobility patterns or from social media, always with permissions in place. And of course technology is the fourth building block, also important. Yes, we use open source tools. We also partner with cloud providers, from AWS to Google Cloud Platform to make sure that we the most advanced tools that we can find.
Build vs. buy. Walsh said Levi’s primarily builds its own algorithms and data science approaches. “It’s not that we don’t like to start with something that has been done, but when it comes to retail with the exception of the Amazons of the world, this is a very new field. We are now cultivating a new kind of professional data scientist or machine learning engineer that knows retail and apparel,” said Walsh. “So, for that reason, we’re starting from scratch and we’re creating our own custom algorithms.”
Published at Fri, 26 Mar 2021 10:52:30 +0000
Technology is undoubtedly ruling over us lately. Owing to the COVID-19 pandemic, technology is our best bet today. At present, no industries are left untouched from adopting cutting-edge technologies like Artificial Intelligence (AI) and Machine Learning (ML). Some industries that adopted AI and ML include automotive industries, health, logistics, etc. The hospitality sector in India has also not been exempted from adopting such world-class technology to ensure profit maximization in the competitive market.
The hospitality industry encompasses a broad category of fields like tourism, lodging, transport, event planning, theme parks, and others. It is undoubtedly a wide sector, but notably, the leaders in the field seem to be the hotels and accommodation along with restaurants and bars. The hospitality sector in India strongly requires the much-needed application of AI and ML. It is not only for surviving in the pandemic scenario but also for maximizing their profit margins. This industry mostly caters to the wants of the customers more than their needs. Therefore, companies in the hospitality sector must adopt AI and ML for ensuring continuous delivery of their best services at a competitive price to their customers. Here’s how AI and ML help the hospitality sector in ensuring survival and profits.
Artificial intelligence enhances hotel operations
Answering customer calls, attending front desk guests while responding to online guest queries simultaneously is no more a troublesome issue that existed for so long. With Artificial Intelligence (AI), staff does not need to work under high-quality performance pressure and perform like robots which leads to poor service quality. Chatbots emerged as a saviour and a boon to assist a multitude of guests effortlessly. Chatbots take care of all the online queries from potential customers while the front desk staff can serve the guests in a hassle-free manner, thereby enhancing hotel operations without compromising on any customer.
AI boosts personalized services
AI personalization synthesizes diverse customer data sets to extract relevant insights. By leveraging the priceless insights, the hotels can target great marketing efforts and deliver outstanding services. This helps in offering a seamless customer experience. With AI, hotels can properly understand the preferences of each customer. AI chatbots also offer a personalized conversation that effectively addresses the individual needs of every customer more conveniently and helpfully. Customers prefer a fast response to all their queries, especially online. AI chatbots complement it by facilitating immediate response and managing multiple customer needs seamlessly.
Competitive pricing via ML
What’s important for every business to survive in the marketplace is to use a competitive pricing strategy. The hospitality sector leverages competitive pricing strategies that are considered to be one of the most significant strategies to attract customers and enhance sales without compromising on profits. What helps in achieving this is Machine Learning. It helps to achieve the best possible pricing for services based on hotel history, seasonability, local competition, external real-time events, local events, third-party promotions, etc. Basis such factors, the data is run via predictive models and can be analyzed to offer competitive pricing for companies that help them achieve an edge in the broad marketplace.
ML improves customer service and satisfaction
Customers belong from varied backgrounds so their expectations, needs, and demands differ from each other. A process known as Market Segmentation is employed in every sector, including the hospitality sector, to ensure that each customer’s wish is fulfilled according to their tastes and preferences. The entire customer spectrum is segregated into various segments with more or less similar expectations and demands. With Machine Learning (ML), the customer spectrum becomes more diverse into extremely smaller groups and sub-groups that were not witnessed ever before. This process improves the quality of customer service and satisfaction by ensuring a more profitable business.
Summing up
Both Artificial Intelligence and Machine Learning are leading the way to innovation in every sector. They continue to be explored and applied to new sectors every day. The hospitality sector should also leverage these technologies to tackle the unseen problems and challenges to satisfy the customers. Therefore, from enhancing a hotel’s operation, offering personalized services & competitive pricing to improving customer satisfaction via phenomenal service, the role of AI and ML to maximize profits in the hospitality sector is unparalleled. It shows us the untapped potential of the technologies that own the ability to change the face of the hospitality sector.
By Siddharth Goenka, Founder, Aiosell Technologies
Published at Fri, 26 Mar 2021 09:56:15 +0000
