How is AI improving customer experience in Retail?

December 11th, 2018
How is AI improving the customer experience in Retail?
Helena SOUZA
Juliana VECTORE
HEC mentor: Professor Gachoucha KRETZ
MBA Project
FINAL REPORT

MBA Project: Final Report – Helena SOUZA and Juliana VECTORE
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Content
Introduction 3
1. AI definition and implications for this project 5
2. AI applications mapping for customer experience in Retail 6
Learning Algorithms 6
Smart Vision 8
Virtual Reality & Augmented Reality 10
Natural Language Processing 13
Robotics 14
3. AI applications assessment for customer experience in Retail 17
4. Retailers benchmark 20
Alibaba 20
Walmart 23
Sephora 25
Conclusions 29
Bibliography 34
Appendix 36

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Introduction
The retail industry is facing unparalleled change, the wide-scale embrace of technology coupled with
fast-changing consumer habits and expectations has produced an ever-changing marketplace for
companies to compete in.
Customers increasingly want retailers to offer convenient, responsive and personalized services.
They now demand retailers to deep dive in their customer’s journey to really understand and deliver
what is valuable to them.
In 2018, this seismic revolution in retail market place, aligned with an unpredictable macroeconomic
environment and increasing competition for customers will continue. Key trends to watch out are
(Source: Deloitte and student’s analysis):
1) Customer experience will reach unprecedented level with Artificial Intelligence's (AI)
support:
● Augmented Reality (AR) and Virtual Reality (VR) are set to transform retail
experience online and offline;
● AI solutions are supporting personalization at scale;
● Smart vision is being used to facilitate product search and shopping experience.
2) Physical stores need to be reimagined: stores still have a key role to play, but high
competition and economic uncertainty is leading to fewer of them to remain open.
Consequently, retailers need to make sure their stores remain relevant and create value to
their customers, providing them a unique experience so they want to come back. Another
imperative is that digital is now a core part of the customer in-store experience and online
and offline channels need fully integration.
One of the consequences of this increasing competition, unpredictable macroeconomic
environment and industry trends is that the gap between the industry leaders and laggards will
probably widen. Organizations leading the customer experience transformation, with a clear
strategy and agile execution will experience major growth and succeed.
Consequently, we believe AI provides the essential tools to revolutionize the retail experience and
allows companies to meet customers’ new expectations. Given this context, our MBA project focus
on the following business question: How is AI improving the customer experience in Retail?
In order to answer this question, a lot of research, information and insights were needed. As a result,
key data input was mainly gathered from:
● Secondary sources:
o Research of related articles, reports, papers (e.g.: Nielsen, Deloitte, McKinsey, CoreInsight,
HBR, etc) to understand customer journey, AI solutions, trends in Retail, etc
o Benchmarks: clipping and companies’ website
● Primary sources: we carried out 5 interviews in total with market experts, retailers and start-
ups that provided key inputs for our insights and findings. List of interviewees:
o Consultant at Deloitte Digital

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o Head of AI at La Redoute
o Product Manager at Amazon
o Digital Marketing Director at FNAC Darty
o CEO and co-founder of Diakse
In this report, we will address our key business question on “How is AI improving customer
experience in Retail” in 4 main chapters:
1. AI definition and implications for this project
2. AI applications mapping for customer experience in Retail
3. AI applications assessment for customer experience in Retail
4. Retailers benchmark
Chapter 1 aims to define AI as well as its implications to this project.
During our initial research we found out that there are several definitions for AI. Some of them are
more focused, others are broader. In this chapter we will present our understanding of what AI
stands in today’s environment and the restrictions it imposes to our analysis.
Chapter 2 is the foundation of our project. Here we mapped all AI key technologies and their
applications currently impacting customer experience in Retail.
In this chapter we define and characterize each technology, further detailed in applications.
Applications are later described for their Retail usage and impact in the customer journey. Finally,
we provide examples of players currently supplying solutions for retailers.
In total, we mapped 5 key technologies and 15 applications impacting customer experience in Retail.
Many examples are provided and at least one successful supplier/start-up example is detailed for
each technology.
Chapter 3 is the core of this project. After mapping all AI applications currently available, the
following questions arouse to understand how retailers prioritize their investments in AI:
‒ Why do retailers use this application? What is this key benefit?
‒ What is this application level of maturity? How much personalization can it bring to
retailers?
This chapter answers these 3 main questions to each of the 15 initial mapped applications, resulting
in a matrix of level of maturity vs value creation potential with identification of application’s benefit.
This matrix is broken down for the physical store and e-commerce world since level of maturity for
applications in those scenarios differs.
Chapter 4 aims to test our analyses and applications’ assessment – resulting in the matrix detailed
in Chapter 3 – in real case examples.
In order to do so, we selected 3 retailers in different segments, recognized by the media to be key
players in AI deployment to its business success. Those retailers are: Alibaba, Walmart and Sephora.
We concluded our findings from Chapter 3 hold true for the analyzed benchmarks. We also further
discovered that applications primary benefits appear to be a key decision-driver for retailers, linked

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to their purpose to focus on either providing unique experience or value-for-money products for
customers.
We close our project with the Conclusion chapter. This last chapter really crowns our work with our
reflections and key learnings from all the project.
Briefly, we found out that: AI is indeed a focus investment area for retailers; E-commerce is still
more advanced than physical stores in the usage of AI; physical stores will not disappear and AI can
be used as a driver to different stores achieving their goals; and AI's pace of development depends
on applications’ investments and level of maturity, a result from their value creation and usage
breadth.
Finally, in light of all these findings, we developed a 3-step approach to support retailers to
successfully select and invest in AI applications that meet their business goals.
This project was completed in over 6 months of hard work and immersion in AI, customer experience
and Retail words. We believe our findings are interesting and many insights can also be extrapolated
to other industries.
We hope you enjoy this reading as much as we enjoyed doing this project.
1. AI definition and implications for this project
Artificial intelligence is one of the biggest and hottest technological developments nowadays,
however it is not a new concept and it has been present in computer programs and services for
decades. The first discussions about AI appeared in a paper published in 1950 by Alan Turing –
“Computer Machinery and Intelligence”. Alan Turin defines Ai as: “Artificial intelligence is the
science and engineering of making intelligent machines, especially intelligent computer programs”.
In this project, we add to Alan Turin’s definition of AI, including one more requirement in order to
consider a technology truly artificial intelligence. We define AI as “the ability of computers to mimic
human thinking and logic, meaning that machines can simulate "cognitive” human functions, such
as learning and problem solving”. For us, truly AI solutions should be able to gather data (observe),
generate insights from it (reason), drive useful recommendations (take action) and learn from the
results, continuously improving the reasoning phase. See Exhibit 1 below for the AI value chain.
Exhibit 1: the AI value chain

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As a result, in the scope of this project solutions will only be classified as AI if they can close the loop
between the reasoning and the learning phase, in the sense that the solution will be useful to drive
recommendations and actions. As we see further in the research, some solutions may be used
conjointly to gather data and take action, but they will only be classified as AI if each of them is AI
on a stand-alone analysis.
2. AI applications mapping for customer experience in Retail
In order to better understand what artificial intelligence is bringing to retail businesses and, more
specifically, how they it is impacting the customer experience, we need to first have a clear view of
how the technology works.
Therefore, we mapped five key AI technologies, all of which differ on complexity, on the way they
collect and learn from data, and on how the data is going to be used afterwards. The categories are:
Learning Algorithms, Smart Vision (SV), Virtual Reality (VR) & Augmented Reality (AR) and Natural
Language Processing (NLP).
On the following pages, we are going to provide, for each technology:
● Explanation on its overall concept
● Specific use cases in retail businesses
● Analysis on how they are impacting the customer journey
Learning Algorithms
These algorithms collect data from various sources and use machine learning to continuously and
automatically learn from it, providing cognitive insights to companies. Learning algorithms are
extremely useful for companies, as they are able to detect patterns and relationships on huge
amounts of data, deriving deep and actionable insights, both predictive and prescriptive.
There are two different applications within learning algorithms technology:
1. Reinforcement learning models
These are algorithms that run exploratory tests and learn by analyzing the results of interacting with
the environment, based on a reward and punishment system. They are used when companies need
to explore ways of maximizing returns and want to test different campaigns, search keywords,
segmentation and prices, for example.
2. Predictive models
These are algorithms that analyze historical data from multiple sources, being able to detect
patterns and predict future outcomes. The main use case is companies willing to understand future
customer needs and to be able to fulfill them in an early stage of the journey.
Reinforcement learning and predictive models have three main applications for retail businesses:

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A. Online advertising optimization
By using reinforcement learning, companies are able to test several advertisement strategies, in
order to maximize whichever indicator is important for them (conversions into sales, clicks,
impressions etc). The algorithm then suggests optimal parameters for advertisement campaigns,
such as which search engine keywords are more effective and which website layout, pictures or
texts leads to more sales or clicks.
This application helps companies increase the visibility and relevance of their products to potential
customers, making sure they will have the right product advertised to them when they need it.
Translating this to the customer journey, this solution helps retailers improve awareness and
consideration.
B. Demand forecast and product recommendation
This algorithm uses predictive models to analyze company and customers’ data, such as past sales,
in-store information and search history, in order to detect opportunities for product
recommendations. Companies like the American startup Personali are able to capture shoppers’
emotional responses when browsing the website and send them personalized incentives, creating
engagement and encouraging them to spend more.
Product recommendation solutions can affect customers on different phases of the journey. They
may create awareness if the algorithm is extremely predictive and recommends products before
customers even realize they need them, or consideration if customers are already searching for the
product and the recommendation makes him/her starts considering a specific retailer. In the case
of Personali, that creates incentives to encourage customers to purchase when they already
browsing on the website, it may facilitate and accelerate the purchase phase (specifically the
product search).
C. Assortment optimization
Also using predictive models, this algorithm analyzes different data sources, such as historical sales,
fashion blogs, articles, social media images and in-store shopping habits, and is able to suggest
optimal product assortment in terms of price, product category and even how the product should
be placed in-store. Companies like the Canadian Daisy Intelligence are now able to predict retailers’
optimal product mix, inventory level and pricing, allowing them to be much more assertive and
efficient with their production and sales operation.
This application is not directly affecting and improving customers experience in-store or online,
however it uses artificial intelligence to predict what customers will want to purchase in some weeks
(or even months), at what price they will want to purchase and where they are most likely to find it.
Therefore, we believe it helps retailers provide more relevant products and make sure customers
find what they are looking for.
The following exhibit sums up the three applications powered by learning algorithms technology
impacting customer journey in Retail.

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Exhibit 2: Applications powered by learning algorithms impacting customer journey in retail.
Additionally, in the APPENDIX we detailed startups that are working on each application presented:
Sentient Ascend, an American company for Online Advertising Optimization; Personali, also
American, for Demand Forecast; and Daisy Intelligence, Canadian company, for Assortment
Optimization.
Smart Vision
Smart vision (SV), sometimes also called computer or machine vision, enables the extraction of
meaningful and actionable information by analyzing digital images and videos. This technology
allows computers to learn from images, recognize patterns and use this to execute a specific
outcome that will vary according to each application.
There are three business applications for smart vision:
1. Object and facial recognition & categorization
By using 2D and 3D modeling aligned with machine learning, computers can learn how to recognize
objects and even people. This can be used to categorize products and consumers’ profiling,
supporting great levels of personalization in-store.
2. Tracking and visual surveillance
Also using 2D and 3D modeling combined with machine learning, computers are able to detect and
follow the position and movement of objects and people in an environment.
3. Human behavior understanding and prediction
Using more complex techniques than the previous two applications, this technology can recognize
human activities and even emotions and feelings, being able to identify patterns and predict
behaviors.

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Relying on smart vision technologies, there are four solutions applied to retail businesses:
A. Image diagnosis and product recommendation
Smart vision powered softwares can analyze images of customers and recognize certain
characteristics, such as body measures, hair color and skin type. With these analysis, it can provide
actionable insights and accurate product recommendation.
Makeup retailers, for example, have very good opportunity to scale their online sales by using this
technology, as it allows companies to provide a similar service customers would receive in-store.
Fashion retailers are also taking advantage of it, as it can provide customers’ true size by analyzing
pictures or webcam images.
This solution helps customers find products that are suitable and relevant to their needs when they
are still on the consideration phase, additionally it can reduce one of the biggest concerns
consumers have about online shopping, which is not being able to test and evaluate products on
their body before purchasing.
B. Automated store check-out
Smart vision cameras, usually combined with sensors and RFID, are able to identify articles bagged
by shoppers in-store. This allows customers to check-out and leave the store without queuing in line
and going through the cashier, just paying through credit card or online account. The SV cameras
are usually located in moving robots, so this solution combines smart vision and robotics
technologies.
Despite the very famous example of Amazon Go using automated store check-out, the technology
is still not scalable, being used only in test phase by other retailers such as Alibaba, who is also
including a facial payment to it.
C. In-store shelf auditing
SV cameras, usually applied on moving robots, audit store shelves looking for misplaced items, price
errors and out-of-stock products. This solution is extremely relevant for grocery retailers that have
a higher frequency of price changes and faster inventory turnover, and therefore have the possibility
of automating what before was a highly operational job.
This solution guarantees that consumers will find the desired products exactly where it should be in
the store, affecting the purchase phase of the customer journey (specifically product search).
D. In-store analytics and assortment optimization
SV cameras gather anonymous information about customers, such as gender, age, emotions and
how they move inside of the store. With this data, the algorithm is able to deliver insights such as:
which parts of the store shoppers spend more time; which products shoppers are seeing the most
or paying less attention to; what emotions shoppers are having in specific locations or at specific
products. The possibilities are endless.

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With this level of detailed information, retailers are able to optimize the assortment and product
placement, affecting customers on the purchase phase of the journey. RetailNext, an American
startup, is applying this technology on over 400 retailers in 75 different countries, being able to
increase sales, eliminate unnecessary costs and reduce theft.
The following exhibit sums up the four applications powered by smart vision technology impacting
customer journey in Retail.
Exhibit 3: Applications powered by smart vision impacting customer journey in retail.
StrechSense, a New Zealand for image diagnosis and product recommendation; Trail, a Japanese
company for automated store checkout; Qopius, a French startup for In-store shelf auditing; and
RetailNext, an American company for in-store analytics and assortment optimization.
Virtual Reality & Augmented Reality
Virtual Reality (VR) and Augmented Reality (AR) are computed-generated simulations of experiences
in a virtual or in the real world. They differ in the level of immersion provided to users: VR makes
the user feel completely immersed in a virtual world, usually wearing a head-mounted display
(HMD); and AR provides users with layers of extra reality that supplement the real world, mixing
virtual and reality.
VR technology has two main business applications:
1. Telepresence
Sensors and robots are controlled and operated remotely by a user immersed with HMD or a
desktop. The idea of telepresence is to put the user in a real but distant environment that may be

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difficult to reach, such as undersea exploration or bomb disposal robots. It is not relevant for retail
businesses.
2. Immersive System
An equipment provided with HDM uses video and audio to provide the user with a totally immersive
experience in a virtual world. The immersive system puts the user in a virtual environment that is
totally different from the real one. One of the main use cases is videogames, but it is also heavily
used in retail as we will see below.
AR technology has also two main business applications:
3. Window on World
An equipment that uses a desktop to create and present a virtual world that can interact with the
user. It is mainly used for complex medical and chirurgical procedures, and doesn't have a relevant
application for retailers.
4. Mixed Reality
Mixed reality puts computer-generated inputs together with the user's view of the real world, in
order to create a valuable output. The goal is to add virtual elements creating an improved and
totally different environment. It is a complex technology as it requires the virtual elements to be
realistic enough to fit into the actual world. Like immersive systems, one of the mixed reality's main
use cases is videogames, but it is also used a lot by retailers.
VR & AR immersive system and mixed reality are the most used in retail. Based on these two key
technologies, we mapped three applications:
A. 3D Modeling for product fitting and recommendation
These are software that receive as input photos and/or body measures information and create a VR
avatar that can try clothes. Based on body type and style, customers can receive relevant and
personalized recommendations for products.
This solution is extremely relevant for e-commerce business, allowing them to implement try-
before-you-buy solutions and minimizing the hassle of online shopping. It impacts greatly the
purchase phase of the customer journey (more specifically testing & evaluating), and, depending on
how mature the solution is, it may impact consideration if it is able to provide accurate
recommendations based on body images.
B. Virtual showrooms
These are virtual environments where customers can visualize the products as if in an in-store
experience, usually with the help of HMD or at a desktop. The virtual showroom can be much more
dynamic than a real world one, as retailers can change products faster and even personalize them
depending on the customers' profile and shopping behavior. Also, customers have a lot more
information about products, such as price, reviews, color and personalization options.

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DIAKSE, which was one of the companies we interviewed during our research, is a French startup
providing virtual showrooms for retailers. They believe retailers must create different and
personalized shopping experiences in order to beat competition, and they say their solution has
proven to increase by 4 times the average time spent on the website and to boost conversion rate
by 25%.
Virtual showrooms make product research much easier, as customers don't need to leave their
houses to visit the store/showroom and companies don't need huge physical spaces to storage
products. Therefore, it has a direct impact on consideration and purchase (more specifically on
product search) phases of the customer journey.
C. Virtual fitting rooms and smart mirrors
These solutions work with body scanners and cameras that are able to recognize and represent the
customers' body virtually, allowing them to try on products by displaying an AR image on the fitting
room mirror or in any other screen. It differs from solution A () in the sense that virtual fitting rooms
use dynamic and real time images captured by a camera, so the challenge is making the product fit
perfectly while the customer is moving.
Modiface is a Canadian company that provides this solution for all of the biggest makeup companies.
It has not only solutions for physical stores but also for mobile shopping, allowing customers to
virtually try on makeup by using webcam while using shopping by app or e-commerce.
The following exhibit sums up the three applications powered by VR and AR technology impacting
Exhibit 4: Applications powered by VR & AR impacting customer journey in retail.
Metail, a British company for 3D modeling for product fitting and recommendation; DIAKSE, a french

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company for virtual showrooms; and Modiface, a Canadian company for virtual fitting rooms and
smart mirrors.
Natural Language Processing
Natural Language Processing (NLP) is a branch of AI that helps computers understand, interpret and
manipulate human language. The technology draws from many disciplines, including computer
science and computational linguistics and its major goal is to fill the gap between human
communication and computer understanding.
NPL has two main business applications:
1. Translation
Algorithms that understands the relationship between words and meaning of sentences to translate
text from one language to another. This is mainly used in automated translation softwares and has
no direct application for retail.
2. Natural Language Generation
Algorithm that uses text analytics and speech recognition, which transforms spoken words into
written words, to gather and understand data. It, then, generates language as response (written or
spoken) taking into consideration the context it is included. Such technology has been proving to be
very useful for retailers as it can provide personalized interaction with customers and provide
optimal solutions based on specific needs.
Natural language generation is applied in two different solutions for retail businesses:
A. Smart Personal Assistants
These are usually hardware that use speech recognition and NLP to transform voice into text and
understand customers demand. Depending on the request, the output assistants provide may be
products recommendation and/or purchase through the device. Examples of such devices are
Google Home, Amazon’s Alexa and Apple’s Siri.
The solution acts directly on the consideration phase of the customer journey, since it has the ability
of answering questions like “Where can I find a cocktail dress for my party tonight?”. The purchase
phase is obviously optimized too, since most of the solutions offer the option of voice purchase –
“Alexa, please buy more milk”.
In addition to the big technology firms’ personal assistants, some companies like the American
MindMeld are developing, and providing for retailers, conversational interfaces and chat assistants
that promise to streamline common daily tasks, such as finding stores or product information and
even placing orders at a local restaurant.
B. Chatbots

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Chatbots use NLP to simulate human conversations, which are most of the time written but can also
be spoken in more complex interfaces. They are usually softwares that are integrated into the
companies’ website, call center and/or social media. Chatbots may have many functionalities, such
as: customer support answering to general questions, providing product recommendations based
on specific needs, assisting in-store product search, assisting on the actual purchase process and
engaging customers into post-purchase processes by receiving product reviews and general
feedback.
For this variety of functionalities, chatbots can impact customers on three different phases of the
journey: consideration, purchase and post-purchase.
The following exhibit sums up the two applications powered by NLP technology impacting customer
journey in Retail.
Exhibit 5: Applications powered by NLP impacting customer journey in retail.
MindMeld, an American company for smart personal assistants; and Kik, a Canadian company for
chatbots.
Robotics
Robots are hardware that interact and modify the physical world. Not all robots, however, can be
considered AI. To do so, a robot should have an intelligent software, and therefore have the ability
to learn.
Robotics have two business applications:
1. Perform and optimize activities
These robots (usually powered by smart vision solutions aligned with machine learning techniques)
are designed to optimize processes and to achieve desired results. They are mainly used to improve
operational efficiency in very repetitive tasks.

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2. Interact socially and solve problems
These robots (usually powered by a combination of smart vision, NLP and deep learning techniques)
are able to interact socially, simulating conversations and reasoning upon verbal and non-verbal
communication, in order to provide recommendations and suggestions to specific individual
problems.
Robotics have three applications for retail businesses:
A. Inventory robots
These robots track shelving stock, refill, support inventory forecast and even grab products for
customers. In some cases, it can also analyze buying patterns and can provide data for demand
forecasts. It can be very similar to the in-store shelf auditing solution if the smart vision cameras are
applied into a robot.
Fellow Robots, a Silicon Valley company is building inventory robots that scan the entire store daily
and collect high definition images of products, price and placement. Complementary software can
support retailers in inventory management and to gather insight about in-store behaviors.
This solution, like the in-store shelf auditing, guarantees that consumers will find the desired
products exactly where it should be in the store, affecting the purchase phase of the journey
(specifically product search).
B. Delivery robots
These robots are used for timely and last-minute deliveries. AI algorithms help robots continuously
improve routes in faster and safer ways.
Starship, an American startup, created the famous Domino's Pizza delivery robot, that is able to
make deliveries within two-mile radius and works autonomously. The robots travel mostly on
sidewalks and can go as fast as 10mph.
C. Personal assistant
These robots provide in-store assistance, interacting with clients and giving any type of information
they may need, such as directions, product information, specific recommendations, and even
receiving feedback and reviews. Their machine learning powered algorithm helps them always
improve the quality of their answers. Most complex robots are even trying to anticipate clients
demand.
One of the most famous personal assistant robots is Pepper, created by the French SoftBank
Robotics. Pepper usually is located at the entrance of banks and stores, such as Uniqlo. It provides
in-store services, like greeting customers, introducing products and services, personalized
assistance by linking to customer data from CRM.
Even though nowadays there aren't robots providing solutions to all phases of the customer journey
yet, we believe that personal assistant robots have the potential to tackle the entire journey, from
creating product and brand awareness to customers to dealing with post-purchase processes.

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The following exhibit sums up the three applications powered by robotics technology impacting
Exhibit 6: Applications powered by robotics impacting customer journey in retail.
Fellow Robots, an American company for Inventory robots; Starship, also American for delivery
robots; and SoftBank Robotics, a French company for personal assistants.
Finally, next exhibit 2 summarizes the 5 technologies and 15 applications presented in this chapter,
providing a complete view of how each application affects the customer journey.
Exhibit 7: AI technologies and applications affecting the customer journey in retail

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From this point of view, it is clear to see that consideration and product search are the phases with
biggest number of solutions available in the market.
3. AI applications assessment for customer experience in Retail
After mapping, defining and understanding key AI applications currently available impacting
customer experience in Retail, further questions arouse to identify what drives retailers to opt to
invest in a given application.
First decision-driver identified was applications’ key benefits. We identified this to be a key decision
driver during our interviews with retailers and start-up. When asked what was important in order
to prioritize and decide investments in AI applications, interviewees almost unanimously pointed
out to two factors:
a) The need for the AI team (in Marketing, IT, etc), with budget responsibility, to have a good
understanding of the business strategy and needs;
b) Decide on investing in applications that will support business goals achievement, and this
requires understanding what are the applications' key benefits.
As a result, we analyzed each of the 15 applications to identify what were their key benefits and by
doing that, we narrowed down to four main benefits. They are described as follows:
● Targeting: key benefit for applications aiming to improve customers’ targeting activities for
retailers. Usually they help to identify potential customers and propose/offer accurate
recommendations, personalized for them (product, pricing and advertising personalization);
● Visibility & Availability: key benefit for applications aiming to improve visibility & availability of
products for retailers. Activities can be, for instance, to reinforce advertising and mitigate in-
store out-of-stock;
● Convenience: key benefit for applications aiming to provide more convenience and save time
for customers during their entire customer journey. Usually key activities provide time-saving
solutions for non-value-added activities such as check-out, delivery, etc;
● Operational cost savings: key benefit for applications aiming to reduce in-store operational
costs. Usually they support automatizing non-value-added human activities such as shelf-
fulfillment, in-store inventory replacement, answering FAQ, etc.
However, even applications with same benefits might not have the same impact for retailers
because they can differ in their level or maturity, it means, not all of them can offer the same
personalization result.
Another point that was made clear and voiced throughout our interviews is that experts, users and
developers expect that in the future, the more mature applications will be the ones providing the
utmost personalization for customer experience, culminating in a co-creation solution for them.

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We identified four maturity stages, from the least to the most advanced level. We also observed
that evolution is cumulative, meaning that stage two contains stage one; stage three contains stages
one and two, etc.
Overall, the four key maturity levels are characterized as follows:
1. Optimize: applications automate and optimize activities processing huge amount of data,
leading to descriptive reports; no inferences or extrapolations are made;
2. Predict demand: applications optimize activities and have the ability to learn from all data
processed, predicting demand based on historical data in an individual or category basis; cause-
effect inferences start to be made;
3. Predict needs: applications, additionally to learn from historical data, can cross different data-
sources, making inferences about consumer future needs in more complex contexts in an
individual or category basis;
4. Co-create: applications, additionally to predict needs, can influence retailers’ solution current
portfolio and suggest/create a personalized solution to customers, sized to meet their unique
needs.
These maturity levels will impact applications with different benefits in slightly different ways. The
table below shows what to expect from application with different key benefits in each maturity
level.
Exhibit 8: Applications key benefits and maturity levels
Interviewees believe that currently no application is well established in the last level of maturity.
Our later analysis proved that right.
However, we discovered that, for e-commerce businesses, born in the digital age and leaders in the
adoption and usage of AI technologies, applications are usually at a more mature level. This is why
we further breakdown the applications’ level of maturity assessment for the e-commerce and
physical store scenarios.

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Our analysis can be summarized in the following Exhibits 4 and 5.
Exhibit 9: Applications key benefits and level of maturity in physical stores
Exhibit 10: Applications key benefits and level of maturity in e-commerce
The previous analysis shows that Learning Algorithms and NLP are the more developed
technologies. In terms of benefits, they are mainly supporting retailers in targeting.
Another finding is that not all applications used in physical stores are used in e-commerce and vice-
versa. Main reasons are: (1) e-commerce benefits from easier data gathering and learning
algorithms solutions are more widely used and available; (2) some applications only make sense in
physical store (e.g.: automatized check-outs, smart vision to in-store auditing & management).
The next chapter tests key benefits and level of maturity concepts in three real case examples. This
test aims to evaluate if the developed framework holds true in reality and it if could be further
deployed to any retailer so they can evaluate and access their AI investment portfolio.

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4. Retailers benchmark
As previously described, the goal of our benchmark analysis was to: (1) identify what the retailers’
leaders in AI deployment are currently doing and (2) test if the ideas and framework developed in
the previous chapter apply in reality.
The criteria used to select the benchmarks were (a) the company needs to be recognized by the
media as a reference in AI adoption and (b) we wanted to bring retailers from different sectors to
have a broader view of AI adoption in the industry. As a result, Alibaba, Walmart and Sephora were
selected.
The following pages will analyze each company individually to:
● Map applications directly impacting customer experience;
● Detail each application by:
○ Providing its brief description;
○ Identifying its key benefits;
○ Assessing, qualitatively, its value creation potential for the retailer;
○ Describing its type of development (in-house or outsourced);
○ Providing examples of how it is being used.
● Define applications level of maturity in the retailer
Finally, we close this chapter with some overall findings.
Alibaba
Alibaba was founded in 1999 in China by 18 people, led by Jack Ma (former English teacher). The
company’s founders shared the belief that Internet would be a platform for smaller business to
compete in a global landscape and, as a result, they launched a website to support small Chinese
businesses to sell internationally.
Since then, Alibaba has grown into a global ecommerce leader with almost USD 40bi in revenues
and USD 10.2bi in profits in 2018.
Alibaba has three main websites (Tmall, Taobao and Alibaba.com) serving hundreds of millions of
users in 190 countries, also holding 80% of China’s online shopping market.
In terms of AI adoption, Alibaba is a recognized leader with many initiatives throughout the whole
value chain. We mapped 10 applications direct improving customer experience in Retail.
Bellow we describe each application used by Alibaba.

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Exhibit 11: Alibaba’s applications mapping and analysis

22
Once applications were mapped and described, we identified, for each of them, which stages of the
customer journey they were impacting and at which level of maturity they were positioned. The
figure below shows the result of this analysis.

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Exhibit 12: Alibaba’s application impacting customer journey and level of maturity
We can observe that Alibaba has a diversified portfolio of AI applications, covering all stages of
customer journey. Even though Alibaba has applications also covering all key benefits mapped, we
see a focus on applications that can improve targeting, followed by convenience and operational
savings.
Also, Alibaba has many applications at higher levels of maturity level (three and above) and those
more mature applications are mainly supporting in targeting.
Walmart
The giant American retail corporation operates a chain of hypermarkets, discount department
stores and grocery stores that sums up to more than 11,200 stores in 27 countries. It was founded
in 1962 by Sam Walton and in 2018 was appointed by Forbes to be the world's largest company by
revenue - over USD 500 billion.
Walmart has been investing hard on cutting edge technologies, aiming to transform the retail
experience by using AI, Internet of Things and Big Data. Their biggest goal is to create a seamless
experience between what customers do online and what they do in store. To do so, they have filled
dozens of patents focusing on checkout processes, over 25 related to drones and several others
related to machine learning and predictive algorithms. Walmart also created a tech incubator, called
Store No.8, in the Silicon Valley to incubate, invest and work with other startups to develop its own
proprietary robotics, VR and AR, machine learning and AI technologies.
Walmart has faced tough competition from the digitally born retailers like Amazon, but now is trying
to use the best of the both worlds - leveraging its brick and mortar strength with its online
experience. Below, we describe all AI applications that are being used by Walmart:

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Exhibit 13: Walmart’s applications mapping and analysis

25
Exhibit 14: Walmart’s application impacting customer journey and level of maturity
We can see that, differently from Alibaba, Walmart's solutions are much more focused on the
benefits of convenience and operational cost savings, which makes sense given the low margins of
the sector and the nature of the business ("Everyday low prices"). Also, as long as they are well
advanced in covering many phases along the customer journey with AI applications, we observe that
there are still opportunities for testing and evaluation in purchasing and in the post-purchase.
Despite the big investments in AI, Walmart's applications are still not as mature as Alibaba's and the
most developed ones are at a level two of maturity - predict demand.
Sephora
Founded in 1970 in Paris, Sephora is a multinational chain of personal care and beauty stores
featuring nearly 300 brands along with its own private label. It was acquired by the LVMH
conglomerate in 1997.
Sephora is differentiating from other cosmetic retailers, who usually rely heavily on department
store sales, by using technology to provide a personalized shopping experience. Quoting Mary Beth
Laughton, Sephora's executive VP of omni retail: "We are very focused on our customers, and we
know that their life is increasingly reliant on digital. So, we know that to be successful as a retailer,
we've got to be where our clients are, and give them tools and experiences that meet their needs."
Bellow we describe all AI applications used by Sephora.

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Exhibit 15: Sephora’s applications mapping and analysis

27
Exhibit 16: Sephora’s application impacting customer journey and level of maturity
From this table we can see clearly that Sephora is focusing its AI investments in applications
improving its customer targeting and convenience. This is a different behavior from what we saw in
the other benchmarks, especially Walmart.
Sephora's AI applications are focused mainly on the initial part of customer journey, and still haven't
developed solutions for payment & delivery and post-purchase. Also, like Walmart, Sephora's
solutions are still at a medium-low level of maturity, with opportunities to develop and create even
more personalized experiences for customers.

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Key findings
On the table below, we can see a summary of all three benchmarks’ mapped AI applications, and
how they are impacting the customer journey.
Exhibit 17: benchmarks summary
We observe that Alibaba is the most advanced retailer in the adoption of AI, not only with more
applications, but also more diversified (using all key AI technologies) and more developed ones,
covering the whole customer journey.
The previous table also points out to an opportunity for both Walmart and Sephora to start
deploying AI on the post-purchase stage.
Another key finding is that, from previous analysis, we observed that retailers opted to use solutions
with different key benefits; our hypothesis is that this difference is related to retailers’ focus to
either provide a unique shopping experience or good value-for-money products.

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Exhibit 18: Benchmarks and most used applications summary
This hypothesis was worked and confirmed by our interviewees during our discussion and we
believe this is a powerful insight for retailers. Retailers must first understand their unique
proposition for customers (more towards experience or more towards transaction) and then select
the AI applications with benefits and characteristics that can support them in achieving their
business’ goals.
Conclusions
Our extensive research provided us with powerful insights about the adoption of artificial
intelligence solutions by retailers. First, we can conclude that AI is positively impacting the Retail
industry performance and, therefore, it became a focus area of investment for companies.
According to TechEmergence, Retail is the third heaviest industry in terms of AI investments, only
after Technology & Internet and Telecom, which proves that retailers are aware of the gains AI can
bring to businesses.
The figure below shows three interesting quotes from interviewees during our research that
exemplify the positive impacts AI is bringing to retailers.
Exhibit 19: Quotes from interviewees on the impact of AI for retailers

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Other quantifiable examples of positive impact derived from our desk research and benchmark
analysis:
● McKinsey studies shows AI learning algorithms to personalize advertising and promotions
can bring 1-2% of incremental sales for brick-and-mortar retailers;
● 60% of Alibaba’s sales derive from its learning algorithm for online advertising (Alimama);
● Sephora’s learning algorithm to personalize advertising and product recommendation
resulted in +7% online cart volume and +50% click through rate;
● Learning algorithm to personalize virtual storefront helped improve 20% conversion rate in
Tmall;
● Alibaba’s chatbot (Ali Xiaomi) is capable of handling 95% of customers’ inquiries,
significantly reducing call-center costs (payback in less than 2 years).
Even though retailers are investing heavily on AI and are aware of how relevant the technology is to
their business, there is still a gap between the usage of AI by e-commerce and by physical stores.
We found that e-commerce's AI applications are more advanced than physical stores', and reach
higher levels of maturity close to stage four (co-creation). This happens for two main reasons:
1. Ecommerce are usually digitally mature companies and are more prepared to implement AI
on its operations.
a. They were born in the digital era and therefore it is easier for them to gather data
and to use data in their decision-making process to improve operations;
b. Also, these companies are more likely to have employees with know-how to
develop and implement AI solutions (e.g. data scientists);
c. Finally, AI solutions for e-commerce require less investment in hardware and are
easier to launch and test, given the availability of data.
2. Companies see AI as an opportunity to narrow the gap between the physical store and
online shopping experiences, helping to keep pushing online sales. The main gaps that can
be filled are:
a. Lack of human contact: Chatbots that can interact socially will ultimately co-create
solutions for customers by combining seamless amount of individual’s information;
b. Testing products beforehand: 3D modeling and Virtual Fitting Rooms are being
implemented, so customers can have an experience similar to trying products in-
store;
c. Delivery convenience: last-mile and delivery robots to deliver what the client needs
just-in-time and whenever they are.
Physical stores also have great potential when investing in AI, and despite some common beliefs
they will not disappear with the expansion of e-commerce. We found that AI solutions can be used
as a driver for stores to achieve their goals.
We mapped three main different types of stores (brand sanctuary, specializes stores and sell-sell-
sell), each of them has very different business goals and therefore should focus on specific AI
applications to differentiate and generate value for customers. The figure below, summarizes our
findings.

31
Exhibit 20: Matrix of store type and key benefits brought by AI
Finally, last question to make is: which applications will evolve faster (to reach stage four of
maturity, “co-creation”) and why?
Based on our primary and secondary research, discussions with experts and consultants, we expect
that the applications that will first reach co-creation maturity level are the ones that (a) receive the
most investments and (b) are currently at more evolved stages.
In terms of maturity level, it is easy to follow the rationale that more evolved applications will
probably reach last stage of maturity faster.
However, key driver for applications development is retailers’ investments. During our work, we
observed that investments in AI mainly depend on:
● Value creation potential for customer: or the perceived value of an application for
customers. In theory, the higher the perceived value, the more investments a given
application will receive. However, this rule is not always true because even applications not
perceived by customers can bring big impact for business (e.g.: cost saving and efficiency
gains). As we saw in the previous chapter, retailers’ investments should be primarily driven
by their business strategy;
● Usage breadth: applications that are cross-sectors (e.g.: used in E-commerce and different
types physical store) can expect to receive more investment and evolve faster.
The following exhibit shows our qualitative analysis on these key development drivers and our
expectation for each application to reach “co-creation” stage.

32
Exhibit 21: Applications expected pace of development, based on investments, current maturity
level, value creation potential and usage breadth
As observed, we expect that applications powered by learning algorithms will be the ones to first
reach “co-creation” stage given their broad application, especially in e-commerce (still driving AI
development), current maturity and level of investment.
These applications evolvement will be followed by AI personal assistants (chatbots or robots). They
bring tremendous value for customers. Independently or in collaboration with human assistants,
these solutions will support providing personalized recommendations before or during the
purchasing phase, helping customers to get exactly what they want and need.
In the short-midterm, we expect smart vision solutions to gain track. These applications can support
retailers to better target and recommend products to customers (e.g.: image diagnosis and product

33
recommendation applications) or improve in-store product availability, reducing operational costs
(e.g.: smart vision and robotics for in-store auditing and management).
Other applications will keep evolving, but at a slower pace, being expected to reach full maturity in
the mid and long terms.
Finally, we believe that any investment in AI by retailers should be a strategic decision, grounded by
companies’ beliefs and aligned with their long-term vision, as opposed to just following what
competition is doing.
In light of all the findings shared in this report, we finish our project by providing a step-by-step
approach for successful investments in AI by retailers.
1. Identify retailers’ goals and align implications to AI investment’s decisions. E.g.:
● If goal is to provide utmost personalized experience for customers, AI investments
should maximize perceived value for customers. Applications with targeting and
convenience as a primary benefit are highly recommended;
● If goal is to achieve lean operations, AI investment should maximize retailers’ return
on investment. Applications reducing operational costs and maximizing conversion
rate (e.g.: assortment and product recommendation optimization) should be the
focus.
2. Select applications by:
● Assessing current retailers’ pain points in customer journey;
● Sizing potential value creation for retailers (probably varying with: application level
of maturity, retailers' AI usage and knowledge).
3. Define how to develop and deploy the application:
● In-house
● Partnership/collaboration
● Outsourced
To conclude, this research that helps define and understand AI applications currently impacting
customer experience in Retail.
We deepened our understanding of each application in terms of benefits they can bring to retailers
and customers, usage, maturity level and expected level of investments received from retailers.
From a retailers’ perspective, we tested our findings in some real case examples (Alibaba, Walmart
and Sephora) and further discovered that different applications can serve different retailers’ goals.
Retailers need to have a clear view of their purpose and strategy in order to prioritize AI investments
accordingly.
Our research and interviews also showed that physical stores will not disappear and, as it happens
with AI investments’ prioritization, retailers need to adjust their physical store role to support their
business strategy (e.g.: have store to venerate brands or to optimize transactions).
We finally close our work with an analysis on applications’ expected pace of development and our
3-step approach to help retailers assess AI investments.
As an exploratory research, this report has a limited scope and further analysis is needed in order
to size AI applications’ impact for customers and retailers.
Even though, we believe this research provides a broad and detailed overview of how retailers and
other business should evaluate and assess AI opportunities, deployment purpose, prioritization and
expected impact for their success.

34
Bibliography
Most important articles and reports:
Hoong, Victor and Boermann Morris. Retail 360: Connected Stores. Deloitte Digital, 2018.
Turing, Alan. Computing Machinery and Intelligence. Article from 1950.
Amed, Imran and Berg, Achim. The State of Fashion 2018. BOF & McKinsey, 2018.
The smarter Store. CBInsights, 2017.
The future of AI in Customer Experience. CBInsights, 2017.
Artificial Intelligence in Retail. Reports breakdown in 3 parts. Coresight Reseach, 2018.
Online Retail. Tracxn, 2017.
Global Equity Research. An Investor’s Guide to Artificial Intelligence. J.P.Morgan, 2017.
Artificial Intelligence - Next "bold play". Deloitte, 2017.
McKinsey Global Institute. Artificial Intelligence - the next digital frontier?. McKinsey, 2017.
McKinsey Analytics. The age of analytics: Competing in a data-driven world. McKinsey, 2016.
Competing in the age of AI. Boston Consulting Group, 2017.
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Retail Reimagined. EY, 2016
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defined.html
https://lp.planetretail.net/rs/895-ENN-359/images/SOTF%20Final%2011%2010%202017.pdf
http://www.mytotalretail.com/article/ais-impact-on-the-customer-experience-and-what-it-
means-for-retail/
https://www2.dimensiondata.com/it-trends/customer-experience-2018
http://www.digitalistmag.com/customer-experience/2017/09/19/ai-machine-learning-spell-end-
of-retail-as-we-know-it-05350268
https://coresight.com/research/artificial-intelligence-retail-part-1-applications-across-customer-
facing-functions/
https://fashionista.com/2017/11/fashion-brands-stylists-ai-artificial-intelligence-chatbots
https://www.forbes.com/sites/bernardmarr/2017/08/29/how-walmart-is-using-machine-learning-
ai-iot-and-big-data-to-boost-retail-performance/#39714c996cb1

35
https://www.techrepublic.com/article/how-sephora-is-leveraging-ar-and-ai-to-transform-retail-
and-help-customers-buy-cosmetics/
https://www.forbes.com/sites/veronikasonsev/2018/04/12/how-sephora-makes-beauty-a-two-
way-conversation/#3e32cafa7f51
http://www.cmo.com/features/articles/2018/1/5/5-companies-offering-a-glimpse-into-the-store-
of-the-future.html#gs.1deLRRU
https://www.businessoffashion.com/articles/bof-exclusive/inside-farfetchs-store-of-the-future
http://www.alizila.com/at-alibaba-artificial-intelligence-is-changing-how-people-shop-online/
https://www.psfk.com/2017/11/alibaba-has-introduced-an-ai-fashion-assistant.html
https://www.technologyreview.com/s/603494/10-breakthrough-technologies-2017-paying-with-
your-face/
https://whatsnext.nuance.com/customer-experience/2017-chatbots-customer-service-and-
artificial-intelligence/
https://www.jllrealviews.com/industries/voice-recognition-technology-the-future-of-retail/
https://blog.euromonitor.com/2017/05/shopping-via-voice.html
https://www.crunchbase.com/organization/mindmeld#section-overview
https://www.cmo.com.au/article/628066/8-brands-using-voice-activation-boost-brand-
engagement/
https://www.ibm.com/blogs/watson/2017/10/10-reasons-ai-powered-automated-customer-
service-future/
https://www.topbots.com/project/burberry-facebook-messenger-chatbot-guide/
https://www.luxurysociety.com/en/articles/2017/03/chatbots-5-luxury-brand-examples/
https://newscenter.io/2017/03/race-singularity-five-ai-startups-watch/
http://fortune.com/2017/02/23/artificial-intelligence-companies/
https://www.techemergence.com/robots-in-retail-examples/
http://www.urlab.eu/robot-introducing-spoon-non-humanoid-emotions/
https://roboticsandautomationnews.com/2017/11/09/humanoid-robot-market-to-grow-to-4-
billion-in-five-years/14884/
https://www.recode.net/2017/1/4/14171436/softbank-robot-pepper-sales-brick-and-mortar-
retail-ces
https://blog.robotiq.com/whats-the-difference-between-robotics-and-artificial-intelligence

36
Appendix
Learning Algorithms startups

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Smart Vision startups

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VR & AR startups

40
NLP startups

41
Robotics startups

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How is AI improving customer experience in Retail?

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