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Summary artificial intelligence in practice- part-2

  1. 1. Some Impressionistic Take away from the Book of Bernard Marr & Matt Ward Artificial Intelligence in Practice ( Part – 2) ( How 50 Successful companies used AI & Machine Learning to Solve problems) Ramki ramaddster@gmail.com
  2. 2. The Summary of this book is made in 4 parts due to large coverage of the book . This is Part – 2 ( Read this after Part-1)
  3. 3. Part – 2 Retail, Consumer goods & Food & Beverage Companies
  4. 4. Using AI to Sell Luxury
  5. 5. Burberry  British fashion brand Burberry is one of the most recognized luxury clothes labels in the world. Starting in 2006, the company aimed to reinvent itself as an “end to end” digital enterprise. Its strategy was to use Big Data & AI) to boost sales and customer satisfaction  It does this by asking customers to voluntarily share data through a number of loyalty and reward programs.  This information is used to offer personalized recommendations, online and in store.  When an identified customer enters a store, sales assistants use tablets to offer buying suggestions based on their customers’ purchase history as well as their social media activity.  If Burberry knows that a customer has recently bought a particular coat, for example, then assistants may be encouraged by the app to show them a handbag which is popular with other buyers of the coat.  Products in their 500 stores spread across 50 countries are also fitted with RFID tags which can communicate with shoppers’ mobiles, giving information about how items were produced or recommendations on how they can be worn or used.
  6. 6. Burberry  It does this by asking customers to voluntarily share data through a number of loyalty and reward programs.  This information is used to offer personalized recommendations, online and in store.  When an identified customer enters a store, sales assistants use tablets to offer buying suggestions based on their customers’ purchase history as well as their social media activity.  If Burberry knows that a customer has recently bought a particular coat, for example, then assistants may be encouraged by the app to show them a handbag which is popular with other buyers of the coat.  Products in their 500 stores spread across 50 countries are also fitted with RFID tags which can communicate with shoppers’ mobiles, giving information about how items were produced or recommendations on how they can be worn or used.  This usage of technology and tactics usually confined to online retail in a “bricks and mortar” setting prompted then CEO Angela Ahrendts (now SVP of retail at Apple) to state that “walking through our doors is just like walking into our website”, in 2014.
  7. 7. What Technology, Tools & Data were used?  Buying habits across the group’s online and physical stores are tracked.  The generated data was shared with shop assistants via tablet terminal.  Enabled assistants to make suggestion and show customers products which they are interested.  Burberry also uses radio-frequency identification tags on in-store products to give customers insights into how they were created & how to wear them. Results  Burberry built a picture of who the visitors to its physical stores are, and approach them with the same convenience of online.  Sales assistants at stores was able to address every customers by name while interactions  Data & AI enabled to understand why particular items may be selling well in stores but not so well online.  Insight was gained the importance of product image
  8. 8. Key Challenges , Learning Points & Takeaways  Understanding , Tracking & Modelling customer behaviour has been a feature of offline shopping for longer than it has with online, thanks to loyalty programs.  Ability to bring advanced AI solutions to the data- technologies developed to power the e-commerce revolution.  High end fashion retailers need to keep a physical storefront for their discerning customers- AI can help replicate the convenience of shopping online in the real world .
  9. 9. Using AI to Stay at the top of the Soft Drinks Market
  10. 10. Coca Cola  The Coca Cola Company is the world’s largest beverage company selling more than 500 brands of soft drink to customers in over 200 countries.  Every single day the world consumes more that 1.9 billion servings of their drinks including brands like Coca Cola (including Diet and Zero) as well as Fanta, Sprite, Dasani, Powerade, Schweppes, Minute Maid and others.  Of course, this also means that it generates mountains of data – from production and distribution to sales and customer feedback, the company relies of a solid data-driven strategy to inform business decisions at a strategic level.  In fact, Coca Cola was one of the first globally-recognized brands outside of the IT market to speak about Big Data, when in 2012 their Chief big data officer, Esat Sezer, said “Social media, mobile applications, cloud computing and e-commerce are combining to give companies like Coca- Cola an unprecedented toolset to change the way they approach IT. Behind all this, big data gives you the intelligence to cap it all off.”  More recently, Greg Chambers, global director of digital innovation, has said “AI is the foundation for everything we do. We create intelligent experiences. AI is the kernel that powers that experience.”
  11. 11. Product Development  Coca Cola is known to have plowed extensive research and development resources into artificial intelligence (AI) to ensure it is squeezing every drop of insight it can from the data it collects.  Fruits of this research were unveiled earlier this year when it was announced that the decision to launch Cherry Sprite as a new flavor was based on monitoring data collected from the latest generation of self-service soft drinks fountains, which allow customers to mix their own drinks.  As the machines allow customers to add their own choice from a range of flavor “shots” to their drinks while they are mixed, this meant they were able to pick the most popular combinations and launch it as a ready-made, canned drink.  Coca Cola is also looking to follow the lead of tech giants by developing something similar to their “virtual assistant” AI bots such as Alexa and Siri.  The AI will reside in vending machines ,allowing greater personalization – for example, users will be able to order their favorite blend from any vending machine, with the machine mixing it to their individual preference.  The AI will also adapt the machines’ behavior depending on its location. This could mean more lively and excitable vending machines in malls or entertainment complexes, and more somber, functional behavior in a hospital.
  12. 12. Healthy Options  As sales of sugary, fizzy drink products have declined in recent years Coca Cola has also hooked into data to help produce and market some of its healthier options, such as orange juice, which the company sells under a number of brands around the world (including Minute Maid and Simply Orange).  The company combines weather data, satellite images, information on crop yields, pricing factors and acidity and sweetness ratings, to ensure that orange crops are grown in an optimum way, and maintain a consistent taste.  The algorithm then finds the best combination of variables in order to match products to local consumer tastes in the 200-plus countries around the world where its products are sold. Augmented Reality  Augmented reality (AR) where computer graphics are overlaid on the user’s view of the real world, using glasses or a headset, is being trialed in a number of the company’s bottling plants around the world.  This allows technicians to receive information about equipment they are servicing, and get backup from experts at remote locations who can see what they are seeing and help to diagnose and solve technical problems. It is also used to inspect problems with vending machines and dispensers in remote or difficult-to- reach locations, including cruise ships while they are at sea.
  13. 13. Social Data Mining  With 105 million Facebook fans and 35 million Twitter followers, social media is another hugely important source of data for the company.  Coca Cola closely tracks how its products are represented across social media, and in 2015 was able to calculate that its products were mentioned somewhere in the world an average of just over once every two seconds.  Knowing this gives insight into who is consuming their drinks, where their customers are, and what situations prompt them to talk about their brand.  The company has used AI-driven image recognition technology to spot when photos of the products , or those of competitors, are uploaded to the internet, and uses algorithms to determine the best way to serve them advertisements.  Ads targeted in this way have a four times greater chance of being clicked on than other methods of targeted advertising, the company has said.  Looking further ahead, the company is also interested in the idea of using AI to create adverts.
  14. 14. Key Challenges , Learning Points & Takeaways  Perceptions and customer behaviour vary greatly from Market to market.  Understanding the differences helps tailor specific messages for different markets, rather than one-size-fits all.  AI provides a viable method of structuring the data and the insights when we are dealing with global brands, user data from Social media or data generated from vending machines.  Computer vision technology – image recognition tools helps to analyse millions of social media images to understand the customer and brand more.  Helps in designing new products & services
  15. 15. Using AI to Serve Up
  16. 16. Domino’s  Domino’s is the world’s largest pizza delivery chain with over 48000 outlets in 85 markets serving up millions of pizzas each year- selling 0.3 million pizzas a day in 2017.  Pizza delivery might not seem, on first glance, the most high-tech of industries. But Domino’s has consistently pushed its brand onto new and developing tech, and as such it is now possible to order pizzas on Twitter TWTR +0%, smart watches and TVs, in car entertainment systems such as Ford’s Synch, and social media platforms like FB.  The drive to keep a Domino’s order button at the fingertips of their customers at all times is referred to currently as Domino’s AnyWare.  Of course this multi-channel approach to interfacing with customers gives rise to the potential to generate and capture a lot of data – which Domino’s capitalizes on by using it to improve the efficiency of their marketing.  “Domino’s AnyWare literally translates to data everywhere,”  Data captured through all its channels – text message, Twitter, Pebble, Android, Amazon Echo – to name just a fraction – is fed into the Domino’s Information Management Framework. There it’s combined with enrichment data from a large number of third party sources such as the United States Postal Service as well as geocode information, demographic and competitor data, to allow in depth customer segmentation.
  17. 17. Domino’s  Domino’s is the world’s largest pizza delivery chain with over 48000 outlets in 85 markets serving up millions of pizzas each year- selling 0.3 million pizzas a day in 2017.  Pizza delivery might not seem, on first glance, the most high-tech of industries. But Domino’s has consistently pushed its brand onto new and developing tech, and as such it is now possible to order pizzas on Twitter TWTR +0%, smart watches and TVs, in car entertainment systems such as Ford’s Synch, and social media platforms like FB.  The drive to keep a Domino’s order button at the fingertips of their customers at all times is referred to currently as Domino’s AnyWare.  Of course this multi-channel approach to interfacing with customers gives rise to the potential to generate and capture a lot of data – which Domino’s capitalizes on by using it to improve the efficiency of their marketing.  “Domino’s AnyWare literally translates to data everywhere,”  Data captured through all its channels – text message, Twitter, Pebble, Android, Amazon Echo – to name just a fraction – is fed into the Domino’s Information Management Framework. There it’s combined with enrichment data from a large number of third party sources such as the United States Postal Service as well as geocode information, demographic and competitor data, to allow in depth customer segmentation.
  18. 18. Domino’s  Information collected through the group’s point of sales systems and enrichment data add up to 85,000 data sources, both structured and unstructured, pouring into the system every day.  The family or household is fundamental to Domino’s tactics for segmenting customers. “Pizza ordering is a household exercise,”  “We have the ability to not only look at a consumer as an individual and assess their buying patterns, but also look at the multiple consumers residing within a household, understand who is the dominant buyer, who reacts to our coupons, and, foremost, understand how they react to the channel that they’re coming to us on.”  This means that individual customers – or households – can be presented with totally different presentation layers than others – different coupons and product offers – based on statistical modelling of customers fitting their profile.  As well as customer segmentation data is used to assess performance and drive growth at individual stores and franchise groups. “We can work with a store to tailor their coupons, and tailor what we think their upselling capabilities might be in terms of adding more revenue.  “But what we can also do is tell them what is not working for them – considering not only their market but comparing their market to other markets, and also to our competitive landscape.”
  19. 19. Pizza Checker Domino’s Pizza Checker – Checked by DRU AI is all about working our team members and busy stores to ensure our customers receive pizzas as they should be every time. The new cut bench quality camera unit is an electronic eye that sits above the cut bench and photographs pizzas to check them for quality control. It identifies pizza type, correct toppings, topping distribution, crust type (base) and temperature and reports back in seconds. Launching in stores in 2018, Pizza Checker is almost like a virtual trainer helping our team members make better pizza for our customers. The transparent technology also provides customers with an image of their pizza on the cut bench, which will appear in real time on the Pizza Tracker page of each customer’s order. As part of this process, customers are notified if their pizza has failed the strong quality testing, resulting in a remake. Pizza Checker will help to give our customers increased satisfaction and the reassurance their order is in safe hands. System developed by Dragontail & uses google map recognition algorithm to identify the type & placement of toppings & the temp at which the pizza was cooked. Installed in 2000 Domino’s kitchen in 7 countries Delivery delivered through Robots self guided.
  20. 20. Key Challenges , Learning Points & Takeaways  Level consistency in quality when there are huge numbers of outlets serving million of customers . AI has helped to maintain this.  Natural language technology is at a stage where it can provide the same level of customer service as human telephone operator.  Autonomous vehicles for delivery saves operators’ money – leads to lower cost – passed on to customers.  Environmental friendly
  21. 21. Integrating AI to make Sense of Customer Data
  22. 22. Kimberly Clark  Kimberly-Clark is a Fortune 500 company. It's personal care product brands, including Huggies, Kleenex, and Scott, touch nearly 1 of every 4 people each day in 175 countries.  Through the company offers products & solutions to create healthier, safer and more productive workplaces in a variety of industries including food services, healthcare, manufacturing, office buildings and more.  As an industry leader, Kimberly-Clark is committed to driving digital innovations to improve operations and customer experiences in the fast-moving consumer goods category.  Here are a few ways Kimberly-Clark is using big data, the Internet of Things (IoT) and artificial intelligence (AI) in their operations….
  23. 23. Kimberly Clark K-Challenge Brings Innovation to the Consumer Packaged Goods Industry  Kimberly-Clark sponsors the annual K- Challenge that invites entrepreneurs, start- ups and other inventors to develop innovations for the consumer packaged goods category via the Kimberly-Clark Digital Innovation Lab (D’Lab).  There are six categories the competition focuses on including data and predictive analytics, cyber security, omnichannel shopper experiences, IoT / wearables/connected devices, supply chain/operations solutions and content and media experiences. This investment in technology innovation helps Kimberly-Clark adapt and apply some of today's best ideas into its operations. Self-Service Retail Analytics  Kimberly-Clark generates a lot of data from several internal sources such as sales and marketing spend, external sources such as Nielsen, web applications, store performance and purchasing information.  Until they adopted a platform powered by Tableau, Amazon Redshift, and Panoply, their complex data resided in inflexible systems from multiple data sources that made it difficult to use.  By consolidating the data is now accessible to more professionals within the organization and they are saving time (eight hours weekly) and money ($250,000 over the course of two years) because they are spending less time collecting and sorting through the data and more time interpreting it. They are now able to access and use the data efficiently.
  24. 24. Kimberly Clark Internet of Things App for Facilities Managers  To help facilities managers monitor & manage the condition of restrooms remotely, KC Professional introduced an Intelligent Restroom App. The state of a building's restrooms is critical in how tenants & customers perceive a building.  An unhygienic bathroom can cause customers & tenants to have a lower opinion of the facility.  Sensors on soap dispensers, air fresheners, entrance doors and more collect data that is then sent to the cloud-based app.  Facilities managers can access the data from mobile or desktop to monitor the condition of the property's restrooms, and they don't have to be on-site. A pilot study of the Intelligent Restroom app showed the number of supplies used decreased by up to 20 percent when the app was deployed. Simplifying a Complex Supply Chain with Data  The global supply chain KC manages to produce its diverse line-up of products is massive and complex.  They have adopted a more networked approach to supply chain since each party is typically involved in multiple stages of the process.  Data-driven analytics throughout its supply chain - from planning, manufacturing, partner management and delivery - help KC simplify and sort out the complexities inherent in their supply chain as well as find value throughout the process. data is vital in helping them meet changing customer demands from transparency in the supply chain to customer expectations about the product, price, service, and quality. They also adopt a more open approach with suppliers and focus on co-innovation.
  25. 25. Key Challenges , Learning Points & Takeaways  Company moved towards advanced analytics with webtrends in increased signup rates of 17%.  Increase in conversions by 24%.  AI Driven analytics is far more powerful than traditional business intelligence solutions for customer segmentation & targeting when dealing with truly big data.  Businesses must earn their reputations as tech champions & pioneers to attract the necessary human talent.
  26. 26. Using Robots & AI To automate process
  27. 27. McDonald  Largest fast-food establishment, operating in 188 countries employ 400,000 staff across 36,000 restaurants and serving more than 69 million people each day.  McDonald’s creates volumes of data, but it’s what they do with it that will yield powerful results.  Here are just a few ways McDonald’s is getting ready for the 4th industrial revolution and using AI, big data and robotics. Personalized and improved Customer experience  Customers order & pay through the McDonald’s mobile app and get access to exclusive deals, but when customers use the app, McDonald’s gets vital customer intelligence about where and when they go to the restaurant, how often, if they use the drive thru or go into the restaurant, and what they purchase.  The company can recommend complementary products and promote deals to help increase sales when customers use the app.  In Japan, customers who use the app spend an average 35% more thanks in part to the recommendations they are provided at the time they place an order. Favorite orders are then saved by the app and offer a way to encourage repeat visits. App users can avoid the lines at the drive thru and at the counters, reason enough for many to share their buying data in exchange for convenience and perceived perks.
  28. 28. McDonald Digital menus that use data  McDonald’s continues to roll out new digital menus.  These aren’t just fancier versions of the old menus, these menus can change based on the real-time analysis of data.  The digital menus will change out the options based on time of day and even the current weather. For example, on a cold, blustery day, the menu might promote comfort foods while refreshing beverages might be highlighted on a record heat day.  They’ve been used in Canada and resulted in a 3% to 3.5% increase in sales.. Trends analytics  Embracing a data-driven culture – performance of each individual restaurant & uncover best practices and sharing with all other stores.  Since McDonald’s uses a franchise business model, consistency of food and experience is important across the franchise.  It’s important from the customer’s perspective to experience the same food and offerings from one restaurant to another no matter where they are located or who it’s owned by.  The company looks at multiple data points in the customer experience. For example, when they look at the drive-thru experience they not only assess the design of the drive- thru, but they review the information provided to the customer and what’s happening for customers waiting in line to order. They analyze the patterns in an effort to make predictions and alter design, information and people practices if necessary.
  29. 29. Kiosks and interactive terminals As one solution to the increasing costs of labor, McDonald’s is replacing cashiers in some locations with kiosks where customers can place their order on a digital screen. Not only are labor costs reduced, but the error rates go down. By the end of two years, you can expect an ordering kiosk to be available at a McDonald’s near you. McDonald’s France is also testing out interactive terminals. Once a customer places an order they take a connected RFID card associated with the order to their table. When the order is ready, a McDonald’s staff person locates the customers through the RFID card and then delivers their meal to them. As McDonald’s continues to embrace its data-driven culture, expect to see the company improve performance based on the insights and efficiencies realized from artificial intelligence, big data and robots. .
  30. 30. Results ,Key Challenges , Learning Points & Takeaways  Customers get to skip lines for ordering – less time  Company collects detailed information about the customer  Sustainable reduction in cost  Businesses that are engaging in automation & AI are keen to imply that technology will assist workers rather than replace them.  Spread of intelligent automation is an impact on long-term .
  31. 31. The Home & Workplace with AI
  32. 32. Samsung  Korean company Samsung was said to behind its competitors in terms of researching and developing artificial intelligence (AI) technology, but the company’s recent strategy suggests that it’s committed to closing the gap and even competing for the top spot.  Since 70 percent of the world’s data is produced and stored on Samsung’s products, the company is the leading provider of data storage products in the world.  By revenue, Samsung is the largest consumer electronics company in the world .  It has even overtaken Apple and sells 500 million connected devices a year.  From industry events to setting goals with AI at the forefront to updating products to use artificial intelligence, Samsung seems to have gone full throttle in preparing for the 4th industrial revolution.  Samsung started 2018 with intention to be an artificial intelligence leader by organizing the AI Summit and brought together 300 university students, technical experts and leading academics to explore ways to accelerate AI research and to develop the best commercial applications of AI.
  33. 33. Samsung Bixby: Samsung’s AI Assistant  Bixby-Samsung’s artificial intelligence system designed to make device interaction easier, debuted with the Samsung Galaxy S8.  The latest version, 2.0, is a “fundamental leap forward for digital assistants.” Bixby 2.0 allows the AI system to be available on all devices including TVs, refrigerators, washers, smartphones and other connected devices. It’s also open to developers so that it will be more likely to integrate with other products and services.  Bixby is contextually aware and understands natural language to help users interact with increasingly complex devices. Samsung plans to introduce a Bixby speaker to compete with Google Home and Amazon Alexa. Samsung to add AI to all devices by 2020  Samsung has announced that artificial intelligence capabilities would be a part of every device it manufacturers by 2020.  As part of this strategy, it combined all smart programs into a new Smart Things app that makes it easier to connect and control all devices.  Not only will all Samsung devices be Internet of Things ready, they will also have AI by 2020. “AI and machine learning are major strategic initiatives for Samsung”.
  34. 34. Samsung AI technology based on machine learning to upscale images  Samsung Electronics was the first to unveil 8K AI tech for television.  The technology can analyze content and can automatically upscale low- resolution images to 8K picture quality.  This innovation solves the current problem with the availability of high- resolution content to use on super-high resolutions screens. Now, all pictures can be transformed to 8K, which is currently the highest resolution capable in digital television. Samsung’s AI robot  Another Innovation-is Saram, Korean for human, a humanoid robot now out of development that’s packed with AI.  So far, Samsung has used the technology in its own factory with an AI robot arm, but experts expect to see the company commercialize its robots, although that hasn’t happened yet.  Sources confirm that Samsung has completed vertical walking robot technology which would allow Saram to be stable and walk across a variety of surfaces.
  35. 35.  Investments in other robot companies and the fact that robotics has been a long- term research project for Samsung seem to point to Samsung introducing its own commercial robot in the very near future.  With its reputation and success with Android, appliances and home electronics and its earlier lessons with AI and a resolute strategy toward AI growth and excellence, the future looks promising for Samsung to close the gap between itself and competitors in the race to be prepared for the 4thindustrial revolution and innovations from big data, artificial intelligence and robotics.
  36. 36.  Bixby has enabled Samsung to compete with Amazon, Apple and Microsoft- Natural language virtual assistant.  6.2% of market for AI enabled voice assistant devices sold in 2018- Siri-Apple- 45.6% , Google assistant- 28.7%  Samsung firmly committed to idea that AI will dominate consumer electronics with it 2020 promise.  Betting on autonomous, mobile robots.  Services will let us use AI to tie together all of the data we gather through disparate “ Smart” devices & present it to us in way we can use to take action. Results ,Key Challenges , Learning Points & Takeaways
  37. 37. Using AI to sell millions of Coffee everyday
  38. 38. Starbucks  Not only does Starbucks go through mounds of coffee beans to satiate its raving fans, but they also have mounds of data that they leverage in many ways to improve the customer experience and their business.  Close 30000 outlets globally , 4 billion cups of coffee , 90 million transactions a week , 87000 combinations of menu , the coffee giant is in many ways on the cutting edge of using big data and artificial intelligence to help direct marketing, sales and business decisions.  Biggest non-domestic market is China – 12.5% of outlets are located. Starbucks Rewards and Mobile App  When Starbucks launched its rewards program and mobile app, they dramatically increased the data they collected and could use to get to know their customers and extract info about purchasing habits.  The mobile app has more than 17 Million and the reward program has 13 million active users.  These users alone create an overwhelming amount of data about what, where and when they buy coffee and complementary products that can be overlaid on other data including weather, holidays and special promotions. Here are just some of the ways that Starbucks uses the data it collects.
  39. 39. Starbucks  Here are just some of the ways that Starbucks uses the data it collects. Personalizing the Starbucks experience  Members of the rewards program and mobile app authorize Starbucks to gather a lot of info about their coffee-buying habits from their preferred drinks to what time of day they’re usually ordering.  So, even when people visit a “new” Starbucks location, that store’s point-of-sale system is able to identify the customer through their smartphone and give the barista their preferred order.  In addition, based on ordering preferences, the app will suggest new products (and treats) customers might be interested in trying. This intel is driven by the company’s digital flywheel program, a cloud-based artificial intelligence engine that’s able to recommend food and drink items to customers who didn’t even know, yet, they wanted to try something new.  It’s so sophisticated that the recommendations will change based on what makes the most sense according to the day’s weather, if it’s a holiday or a weekday, and what location you’re at.
  40. 40. Starbucks Targeted and personalized marketing  The same intel that helps Starbucks suggest new products for to try also helps the company send personalized offers and discounts that go far beyond a special birthday discount. Additionally, a customized email goes out to any customer who hasn’t visited a Starbucks recently with enticing offers—built from that individual’s purchase history—to re-engage them. Virtual barista  My Starbucks Barista through the Starbucks mobile app, allows you to place an order through voice command or messaging to a virtual barista using artificial intelligence algorithms behind the scenes. Since there are so many nuances to an individual order, it’s quite an accomplishment for an artificial intelligence engine to provide a seamless customer experience.
  41. 41. Starbucks Determine new store locations  Right location is essential to succeed in retail.  The Starbucks market planning team doesn’t rely on their gut feelings to determine where stores should be located, but taps into the power of data intelligence through Atlas , a mapping & business Intelligence tool developed by Esri.  This tool evaluates massive amounts of data, such as proximity to other Starbucks locations, demographics, traffic patterns and more, before recommending a new store location.  This system even predicts impact to other Starbucks locations in the area if a new store were to open. Even though it feels like there’s a Starbucks on every corner (and some so close to each other you might imagine that they would cannibalize sales from one another) rest assured the data told them to build it. Expansion of products into grocery stores  When the company decided to expand & offer Starbucks products customers could purchase at grocery stores and enjoy at home, they turned to data to determine what products they should offer .  It combined data it had from its stores about how customers ordered their beverages and combined that intelligence with other industry reports about at-home consumption to create their grocery store product lines.  From pumpkin spice caffe latte K-cups to iced coffee without milk or added flavors, Starbucks’ data-driven approach to production expansion is smart business
  42. 42. Results ,Key Challenges , Learning Points & Takeaways  Better understanding of customer habits- Starbucks is able to build brand loyalty through right products at right time & personalized promotional offers.  Target marketing strategies , using localized datasets.  Working globally across an enormous number of markets makes getting a thorough overview of customer base challenging, but today machine learning technology means it can be done.  Order ahead and skip queues.  Online conveniences- interactivity between mobile phones and in-store- systems
  43. 43. Combining The power of AI & Humans to disrupt Fashion Retail
  44. 44. Stitch Fix  Stitch Fix established in 2011 in San Francisco, has disrupted the fashion retail industry.  With input from the customer and collaboration between artificial intelligence (AI) and human stylists, the online styling subscription service eliminates the need for their customers to go out and shop for clothing or even browse online, because they deliver personalized recommendations right to their door on a regular schedule.  The customers can keep all of the products or return what they don’t like or need.  That feedback gets input into the company’s data vaults to make the algorithms even better at determining the preferred style for each person and even identify trends.  In 2017, the company had US $ 1 B in revenue and 2.2 million active customers, but competitors such as Amazon and Trunk Club are lining up to mimic its style of retail.  Here are just a few of the most intriguing ways Stitch Fix uses artificial intelligence in combination with human stylists to propel its business….
  45. 45. How they used AI? Personalization of Clothing & Accessories  Stitch Fix has combined the expertise of personal stylists with the insight and efficiency of artificial intelligence to analyze data on style trends, body measurements, customer feedback and preferences to arm the human stylists with a culled down version of possible recommendations.  This helps the company provide its customers with personalized style recommendations that fit their lifestyle and budgets. Improve satisfaction rate and lower return rate  The better the Stitch Fix stylists—human and machines—are at providing their customers with products they will love, the better their business runs.  As they invest in merchandise they know their customers will love, the less they waste on warehouse space, return costs and donating items that weren’t sold. As Eric Colson, Chief Algorithm Officer of the company said, “Our business is getting relevant things into the hands of our customers.”
  46. 46. How they used AI? Develop new styles  Back in 2012, Stitch Fix had one machine learning algorithm, today they have hundreds.  Using the data it collects, the company is designing its own styles known as Hybrid Designs.  They think of each style as a collection of attributes such as color, arm length and neckline.  Then, they look at the feedback that’s available for each of these attributes.  By recombining attributes and even mutating them slightly, Stitch Fix is able to create new designs to share with its human designers to vet the final styles that make it into their inventory.  Then, the styling algorithm will get the new products into the hands of customers and when they share their feedback, the cycle of evolution continues. Know the client  Stitch Fix not only asks customers to fill out a style profile to determine style, size and prize preferences but records every touch point it has with clients and considers the “state” of each customer at any given time (starting a new job, have a special event, going through a life transition such as divorce). As the algorithms identify a shift in “state,” it can help deliver the most relevant items and ultimately provide info to inform system level impacts.
  47. 47. How they used AI? Streamline operations  Artificial intelligence is at work through all aspects of the warehouse and delivery system of Stitch Fix.  When a shipment is requested, an algorithm determines and assigns it to a warehouse based on the location of the client and the inventory of the warehouse and its match to a customer’s style among other considerations.  Once items are selected for shipment, algorithms optimize the pick route to fill the box and look at possible combinations that would allow shipments to be picked at the same time. Inventory management  Stitch Fix is just as concerned with inventory management as traditional brick-and-mortar stores.  As clients receive and keep merchandise, they need to restock their inventory to give stylists a large enough inventory to meet demand.  They need to figure out how many of each style to purchase so that it meets demand, but there’s no extras they can’t sell. The company uses algorithms to help optimize these and other complex inventory management issues.
  48. 48. Results ,Key Challenges , Learning Points & Takeaways  Understanding customer requirements & preferences has meant that Stich Fix is able to automatically despatch items that, according to its data, its customers are more likely to love- means wasted warehouse space, shipping cost, return expenses and end of season overstock.  Machine learning has allowed it to increase revenue & customer satisfaction, while decreasing overall cost.  AI can give a better understanding of the customers – less chance of disappointment to them with products & services.  AI poses a real risk to human jobs- similar to industrial revolution.  Designing intelligent systems that augment the capabilities of human workers rather than make them redundant is a key challenge across all industries.
  49. 49. Using AI to Streamline Recruiting & On boarding
  50. 50. Unilever  It’s hard to live a day in the developed world without using a Unilever product.  The multinational manufactures and distributes over 400 consumer goods brands covering food and beverages, domestic cleaning products and personal hygiene- 190 countries.  With so many processes to coordinate and manage, artificial intelligence is quickly becoming essential for organizations of its scale.  This applies to both research and development as well as the huge support infrastructure needed for a business with 170,000 employees.  Recently it announced that it had developed machine learning algorithms capable of sniffing your armpit and telling you whether you are suffering from body odors.  While this may seem like "using a sledgehammer to crack a walnut," the technology which has been developed could well go on to be used to monitor food for freshness, helping to solve the problem of food overproduction and waste endemic in society.
  51. 51. AI-enhanced recruiting  Unilever recruits more than 30,000 people a year and processes around 1.8 million job applications.  This takes a tremendous amount of time and resources.  As a multinational brand operating in 190 countries, applicants are based all around the world.  Finding the right people is an essential ingredient for success, and Unilever can't afford to overlook talent just because it is buried at the bottom of a pile of CVs.  To tackle this problem, Unilever partnered with Pymetrics, a specialist in AI recruitment, to create an online platform, which means candidates can be initially assessed from their own homes, in front of a computer or mobile phone screen.  First, they are asked to play a selection of games that test their aptitude, logic, and reasoning, and appetite for risk. Machine learning algorithms are then used to assess their suitability for whatever role they have applied for, by matching their profiles against those of previously successful employees.  The second stage of the process involves submitting a video interview. Again, the assessor is not a human being but a machine learning algorithm.  The algorithm examines the videos of candidates who answering questions for around 30 minutes, and through a mixture of natural language processing and body language analysis, determines who is likely to be a good fit.
  52. 52. AI-enhanced recruiting  Unilever's Chief of HR, Leena Nair, has mentioned that around 70,000 person- hours of interviewing and assessing candidates had been cut, thanks to the automated screening system.  She said, "We look for people with a sense of purpose – systemic thinking, resilience, business acumen.  Based on that profile, the games and the video interview are all programmed to look for cues in their behavior that will help us understand who will fit in at Unilever."  Referring to the video interview analytics for their future leaders program, she mentioned -“Every screenshot gives us many data points about the person, so we work with a number of partners and use a lot of proprietary technology with those partners, and then we select 3,500 or so people to go through to our discovery center.”  After spending a day with real leaders and recruiters, Unilever selects about 800 people who will be offered a job.  The system is also designed to give feedback to all applicants, even those who aren’t successful.  “What I like about the process is that each and every person who applies to us gets some feedback,” Nair says.  “Normally when people send an application to a large company it can go into a ‘black hole’ – thank you very much for your CV, we’ll get back to you – and you never hear from them again.
  53. 53. Robots helps employees settle into jobs  After making the grade, another machine-learning-driven initiative is helping new employees get started in their new roles – adapting to the day-to-day routines as well as the corporate culture at the business.  Unabot is a natural language processing (NLP) bot built on Microsoft’s Bot framework, designed to understand what employees need to know and fetch information for them when it is asked.  “We joke about the fact we don’t know whether it’s a man or a woman – it’s Unabot,”  “Unabot doesn’t only answer HR questions, questions about anything that affects employees should be answered by Unabot, and it is now the front face for any employee question – they might ask it about IT systems, or about their allowances – so we are learning about what matters to employees in real time.”  Through interacting with employees, Unabot has learned to answer questions such as where parking is available, the timing of shuttle buses, and when annual salary reviews are due to take place.  Unlike, for example, Alexa or consumer-facing, customer-service corporate chatbots, Unabot must also be able to filter and apply information based on who it is speaking to. It is capable of differentiating the information it passes on based on both the user's geographical location and their level of seniority within the company.
  54. 54. Robots helps employees settle into jobs  Unabot was first rolled out for employees based in the Philippines and is now operating in 36 countries. It has been selected as the next AI initiative that will be rolled out globally in all of Unilever’s 190 markets.  “It’s a new way of working,” , “We never go in and say it's perfect so let’s roll it out in all countries,’ we learn what we can in one country and roll it out in the next one.” – the company says.  Currently, all of its data comes from internal sources, such as company guidelines, schedules, policy documents and questions asked by the employees themselves. In the future, this could be expanded to include external data such as learning materials.  And although it’s early days, the initial analysis seems to show that the initiative is popular with staff – with 36% of those in areas where it is deployed having used it at least once, and around 80% going on to use it again.  Machine learning can overcome this due to its ability to detect which questions are repeatedly asked, even if they are asked in different ways, and present the right information.
  55. 55.  With 1.8 million applications for employment every year – employee screening process has saved around 70000 man hours of interviewing time.  Automated feedback to applicants.  AI allowing us to more human.  Capability to assess applications from hundreds of thousands of people means more applicants can be considered for the role.  Who are likely to be successful are less likely to slip through the net than when the process depends on a human recruiter  Ease at which initial screening and analysis .  Chatbots provide hassle-free interfaces where employees & new hires can quickly get answers to common questions and Results ,Key Challenges , Learning Points & Takeaways
  56. 56. Using AI To keep Shelves stacked & customers happy
  57. 57. Walmart  Walmart was founded in 1962, it’s on the cutting edge when it comes to transforming retail operations and customer experience by using Machine Learning , IOT & Big Data.  In recent years, its patent applications, position as the second largest online retailer & investment in retail tech and innovation are just a few reasons they are among the retail leaders evolving to take advantage of tech to build their business and provide better service to their customers.  11000 retail stores worldwide- biggest company by revenue- largest private employer – 2.3 million employees.  Creating a bridge & enhancing the shopping experience through machine learning.  Seamless experience between what customers do online and what they do in their stores.”  While its arch nemesis in business may be Amazon.com, Walmart has the advantage of using the best of both worlds—Walmart was an early adopter of RFID to track inventory & has a tech incubator called Store No.8 in Silicon Valley to “incubate, invest in, and work with other startups, venture capitalists and academics to develop its own proprietary robotics, virtual and augmented reality, machine learning and artificial intelligence technology.”
  58. 58. Walmart  Recently, Walmart launched Pick-up Towers in some of its stores that are 16 x 8-foot self-service kiosks conveniently located at the entrance to the store that retrieves online orders for customers.  Customers can just scan a barcode on their online receipt and within 45 seconds the products they purchased will appear on a conveyor belt. So far, customers give these Pick-up Towers positive reviews as an improvement over the store’s traditional pickup process.  Another way Walmart hopes to improve the customer experience with new retail tech is through Scan and Go Shopping.  Customers in the pharmacy & money services areas will be able to use the Walmart app for some aspects of the checkout process instead of waiting until they reach the counter and then will be able to bypass the main queue to get in and out of the store more quickly.  This is a step in the direction of being able to bypass the checkout process entirely with the use of computer vision, sensors and machine learning as used at the Amazon Go concept store.  Walmart already uses machine learning to optimize the delivery routes of their associate home deliveries.
  59. 59. Walmart Unhappy waiting in line?  One of the new ways Walmart might impact its operations is by using facial recognition technology to identify unhappy or frustrated shoppers.  As the machines learn to identify different levels of frustration through the facial expressions of those in line, it could trigger additional associates to run the checkouts and eventually could analyze trends over time in a shoppers’ purchase behavior.  In 2015, Walmart did also test out this technology to try to detect and prevent theft. Tags to monitor product consumption  What’s next? According to a Patent application Walmart filed, it seems like its next step is integrating Io tags to products in order to monitor product usage, auto replace products as necessary and monitor expiration dates or product recalls.  These sensors would rely on a variety of technology such as Bluetooth, barcodes, radio frequencies and RFID tags and would provide Walmart with an incredible amount of data including the time of day products are used to where the products are kept in the house.  This data could help create personalized advertising and expand cross-selling opportunities.  If you had a tag reader installed on your fridge, it could scan everything you place inside and alert you when you need to restock or when items are expired.  In another example, a RFID system could monitor how many times you pick up your laundry detergent and predict how much is left. This info could be added to your shopping list and fed to Walmart data vaults to illustrate consumer behavior.
  60. 60. Results ,Key Challenges , Learning Points & Takeaways  Customer benefit from the added convenience that there is a higher likelihood the product will be stocked & the on the right shelf for them to find when they need it .  Reduction in wastage expenditure, shelf space on obsolete items.  AI is not a choice- it is necessity for survival.  Big traders tread the path between minimizing costs & maximizing customer convenience. AI & data can do both.  Robots are not designed to replace human workers but they are deployed for repetitive jobs.
  61. 61. Mail your comments to ramaddster@gmail.com End of Part -2 Will continue the summary in Part - 3