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Consumer goods companies preparing for climate change impact

FOX News

BERLIN – Companies behind some of the best-known consumer products -- from soaps to sodas -- are beginning to factor climate change into their business equation, according to a report published Monday. The survey of 16 major corporations by non-profit group CDP found that many are working to lower their carbon emissions, prepare for the effects of global warming on their supply chain and respond to growing environmental consciousness among customers. Examples include brewer AB InBev's efforts to develop a variety of barley that needs less water and Unilever adjusting its detergent formulas so they work at the lower "eco" temperature settings on modern washing machines, the London-based group said. "We were surprised how much these companies were aligning themselves with changes in consumer preferences," said Carole Ferguson, the report's lead author. This includes chasing trends such as veganism, a small but growing factor in the market that's driven by people who shun animal products for ethical or health reasons, but also because they have larger carbon footprints.


Reckitt Benckiser to acquire Mead Johnson for $16.6 billion

U.S. News

Reckitt Benckiser, which makes products ranging from condoms to Lysol, offered $90 for each Mead Johnson share, or about $16.6 billion. That's a 29 percent premium from Mead Johnson's closing stock price on Feb. 1 -- before word of the deal began to be discussed publicly.


Robot room service is coming to US hotels

AITopics Original Links

The next time you call room service for extra towels, your order may be delivered by a robot. It might not be able to change your sheets, but Savioke's Relay hospitality robot can bring everything from toothpaste to Starbucks, and it uses Wi-Fi and 3D cameras to navigate. The robot is already being used by some hotels in the US, and with recent funding of $15 million, autonomous butlers could soon become a lot more popular. The next time you call room service for a new tube of toothpaste, your order may be delivered by a robot. It might not be able to change your sheets, but Savioke's Relay hospitality robot can bring everything from clean towels to Starbucks, and it uses Wi-Fi and 3D cameras to navigate Each of the Relay robots stands roughly three feet tall.


CES for Marketers: Alexa Wows, Virtual Reality Underwhelms

#artificialintelligence

Over the past few years the CES trade show has become a familiar post-holidays pilgrimage for many of the country's biggest marketers. They see the event as a way to get a sneak peek at the latest tech gadgets and technologies that can help them engage with their customers. This year marketing executives from companies such as Coca-Cola, Unilever, Johnson & Johnson, Campbell Soup and PepsiCo Inc. made their way to Las Vegas for the gathering. The convention was jam-packed with everything from self-driving cars to robots that play chess to Procter & Gamble's air-freshener spray that can connect with Alphabet Inc.'s Nest home to automatically release pleasant scents in the home. But there was one category that seemed to especially win over marketers: virtual assistants.


Scalable bundling via dense product embeddings

arXiv.org Machine Learning

Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well examined concept in academia. Historically, the focus has been on theoretical studies in the context of monopolistic firms and assumed product relationships, e.g., complementarity in usage. We develop a new machine-learning-driven methodology for designing bundles in a large-scale, cross-category retail setting. We leverage historical purchases and consideration sets created from clickstream data to generate dense continuous representations of products called embeddings. We then put minimal structure on these embeddings and develop heuristics for complementarity and substitutability among products. Subsequently, we use the heuristics to create multiple bundles for each product and test their performance using a field experiment with a large retailer. We combine the results from the experiment with product embeddings using a hierarchical model that maps bundle features to their purchase likelihood, as measured by the add-to-cart rate. We find that our embeddings-based heuristics are strong predictors of bundle success, robust across product categories, and generalize well to the retailer's entire assortment.