Consumer Products & Services

There are many ways to order a pipin' hot Domino's pizza. To get started, you'll first need an Easy Order account, which is set up through the Domino's website. Next, you'll have to enable the Domino's skill through the Alexa app and link your Domino's and Alexa accounts. Absolutely, but then so is ordering pizza.

The European hair care market clocked revenues worth USD 18 billion in 2013; it is anticipated to generate revenues worth $24 billion in 2018. Recently L'Oréal presented its flagship connected beauty innovations at Viva Technology Paris show held at Porte de Versailles in Paris from 15–17th June 2017.Five of it's Group's brands -- Lancome, Kérastase, L'Oréal Paris, La Roche-Posay and L'Oréal Professionnel -- showcased how they leverage advanced digital technologies to create personalized services for consumers. L'Oréal also discovered the new version of sun care innovation My UV Patch by La Roche-Posay, L'Oréal Group's dermatological skincare brand, designed as a wearable,the first stretchable skin sensor designed to monitor exposure to UV radiation(sun rays) minimizing the frequency of sun burns and to select the right sun protection based on user's skin type. The wearer simply scans the patch with his or her smartphone to determine the wearer's daily sun exposure.Thanks to the specific algorithm, the application uses graphs and statistics to provide advice on or optimal sun protection.It also takes account of hair and skin color into consideration and offers personalized UV protection recommendations.The app alerts the user when UV protection becomes insufficient.This ultra-thin self-adhesive patch comes fitted with an electronic sensor and analyses how much UV radiation the body receives.

Mezi added to its consumer travel assistant app by launching a corporate "Travel-as-a-service" application at MobileBeat. Mezi for Business is designed for travel management companies, corporate and travel agents and now has customers including American Express and several travel agencies. Amazon, and later Netflix, popularized recommendation engines that offer consumer suggestions as to things people might like to purchase based on recent purchases; for example, "People who bought a Schwinn bicycle also bought a Kryptonite lock." Later, as the database of customer profiles grows, Square can offer users things like automated loyalty programs.

The report investigated how many travel brands have used Facebook Messenger to deliver customer service, and how many bookings were secured. Nearly two-thirds of airline brands (64.1%) are responding to customers within 24 hours, ahead of hotels, airlines and car rental companies in that order. Just under half of online travel agencies (OTAs) provided assistance for booking through a Messenger chatbot, compared to 18.8% of car rentals, 15.2% of hotels, and 8.7% of airlines. So as Facebook reports earnings this week, travel brands should be looking to read between the lines to understand where one of the world's three most valuable internet companies is headed next.

Earth Fare has reported solid improvements in year-on-year top-line sales, a year after adopting artificial-intelligence (AI) technology to help optimize promotions, determining which items to promote and how often to do so. The Asheville, N.C.-based natural and organic grocer, which partnered with Toronto-based AI software company Daisy Intelligence, operates 41 stores and is currently working to grow store units by more than 25 percent annually. Before deploying the solution, Earth Fare's merchandising and marketing teams had to earmark numerous hours each week for determining which products to promote, and the demands for time to drive innovation for new store activations were challenged as the company began to activate its robust pipeline. Since the deployment, Earth Fare's category managers have been using Daisy's weekly promotional recommendations to improve associated sales growth and guide decision-making.

To that end, retailers are increasingly turning to technologies such as artificial intelligence algorithms, messenger bots, and even robots, to gather data and improve the in-store experience for shoppers. First, there's the fact that different technologies measure different things: a beacon can track a customer's movement, but a sensor placed on a shelf might be able to see which item a customer picks up and measure how long they hold it for. Amazon's new grocery stores and bookstores show how technology can be seamlessly integrated into a retail space, improving the customer experience and also facilitating the collection of data on consumers. By requiring each shopper to set up an account with Amazon and equipping each store with technology that is expressly designed to track their movements, Amazon has the ability to collect mountains of data on each individual's shopping habits and behavior outside of online settings.

For that reason, marketers are now turning to messaging platforms to improve communication channels for sales and customer service conversations. Companies like United Airlines, Pizza Hut, Denny's Diner, Focus Features, and Patrón, just to name a few, have implemented bots on social media to field customer service issues or help consumers seek information more quickly.

For example, our ML Infra team built a general feature repository that allows users to leverage high quality, vetted, reusable features in their models. Additionally, ML infra created a new framework that will automatically translate Jupyter notebooks into Airflow pipelines. Data scientists are typically accustomed to machine learning related tasks such as feature engineering, prototyping, and model selection. At Airbnb, we built a framework called ML Automator that automagically translates a Jupyter notebook into an Airflow machine learning pipeline.

MIT researchers developed Recipe1M, a database of recipes annotated with information about the ingredients in a wide range of dishes. The closest match Pic2Recipe came up with for a McDonald's Big Mac was a White Castle cheeseburger, a competing brand popular in the US. Images of the latest culinary masterpieces on social media may soon serve a practical purpose, if the creators of a new AI system have their way. We also tested the app using some of the most recognisable food items from a number of popular restaurants and fast food chains, including a McDonald's Big Mac (left) which was matched to a White Castle cheeseburger (right), a competing brand popular in the US This was a match of 88 per cent.

But will AI-driven customer service truly provide superior customer service? To test the hypothesis that AI delivers superior customer service, we have to define use cases, or situations, that can be evaluated. Simply providing data-driven answers to easy questions is not a demonstration of AI's potential to provide superior service. There's another thing that makes me question whether AI-driven customer service will truly deliver superior results.