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.
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.
Part – 2
Retail, Consumer goods & Food &
Beverage Companies

4.
Using AI to Sell Luxury

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.
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.
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.
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.
Using AI to Stay at the top of the Soft Drinks Market

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.
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.
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.
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.
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.
Using AI to Serve Up

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.
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.
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.
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.
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.
Integrating AI to make Sense of Customer Data

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.
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.
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.
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.
Using Robots & AI To automate process

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.
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.
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.
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.
The Home & Workplace with AI

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.
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.
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.
 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.
 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.
Using AI to sell millions of Coffee everyday

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.
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.
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.
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.
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.
Combining The
power of AI &
Humans to disrupt
Fashion Retail

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.
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.
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.
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.
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.
Using AI to Streamline Recruiting & On boarding

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.
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.
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.
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.
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.
 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.
Using AI To keep Shelves stacked & customers happy

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.
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.
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.
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.
Mail your comments to
ramaddster@gmail.com
End of Part -2
Will continue the summary in Part - 3