See how your Retail organization can apply some of the most cutting-edge techniques in Data Science across the end-to-end customer experience lifecycle.

1. SUFFIYAN SYED
Applying Data Science
Across the Ten Stages
of the Retail Lifecycle

2. As techniques in Machine
Learning begin to prove
themselves, we’re
beginning to see every major
technology player make
key strategic bets in the field
of Artificial Intelligence.
Google is uniquely posi-
tioning itself as an “AI-first
company” – organizing every
product team to embody a
data-centric approach.
Facebook is training every
single developer in the field
of Machine Learning enabling
adoption at scale. Along with
setting a 10-year roadmap
with AI at its core.
Amazon has been rolling out
product after product with
a heavy focus on Artifi-
cial Intelligence, Machine
Learning, Deep Learning,
and Augmented Reality.
Microsoft is focusing on
building its Azure cloud
services, which is central
to leveraging and democra-
tizing Artificial Intelligence
at its core.
Apple is infusing their entire
lineup of products with
Machine Learning – along
with launching a range of
processors that can take
advantage of ML.
The benefits of Machine
Learning—how it
fundamentally works,
what makes it an important
area to invest in, and how it’s
delivered at scale to impact
customer experiences—will
have a profound impact
on business strategy by
becoming a key
differentiator for brands.
Retail Executives are
beginning to take notice,
recognizing that Machine
Learning has passed the
hype curve and is beginning
to become a critical area of
strategic focus.
Here you’ll discover a list
of comprehensive use cases
across the End-to-End
Customer Lifecycle that will
help the retail executive gain
a deeper understanding of
how techniques in Applied-
Machine Learning and Data
Science can help tackle
ongoing business chal-
lenges. There are a plethora
of opportunities across the
customer journey that can
be elevated through the use
of Data Science. Directly
impacting key areas of
business strategy, customer
experience, and business
outcomes.
This list focuses on areas that
show tremendous promise
along with areas that in
general are new ways to
look at existing business
challenges. It is meant to
be used as guardrails to
think about better, more
meaningful outcomes –
instead of being perceived
as a comprehensive list
of opportunity areas
that mandate focus
and investment.
Artificial Intelligence, Machine
Learning, Deep Learning and
Data Science all bring with
them a unique advantage
of building a modern data
centric organization. One that
leverages data at its core to
build meaningful customer
experiences that drive both
business and human value.
02
D ATA S C I E N C E
A N E W F R O N T I E R
F O R R E TA I L I N N O VAT I O N
We’re beginning
to see every
major technology
player make key
strategic
bets in the field
of Artificial
Intelligence.

4. E M E R G I N G T E C H N O L O G Y
P L AT F O R M S
VIRTUA L ASSI STA N TS & C HATBOTS
NATUR A L LA N G UAG E UN DER STAND ING
SPEECH R ECOG N I TI ON
VISUA L R ECOG N I TI ON
Virtual assistants and chatbots are the most commonly understood use cases of AI. We’ve seen them in movies, and
with the introduction of popular platforms like Amazon’s Alexa and Google Home these technologies are becoming
a lot more accessible and familiar to the end user.
The most frictionless way for people to interact with computers is through natural language. This is why Natural
Language Understanding is seen as one of opportunity areas to elevate customer experience. Advances in
algorithms focused on analyzing understanding, and deriving meaning from human language are opening up
new and profound applications for the end consumer
As we begin to get better with Natural Language Understanding, so does our ability to understand Voice. Instead
of forcing your customers to be heads down in a screen and constantly push them to enter their input in the form
of keystrokes, we can use voice and allow users to speak out commands and opinions through voice.
One of the most powerful applications of modern AI is its ability to see – to visualize a user’s environment and
identify the things they interact with. This opens up new avenues of customer interaction by understanding the
things they’re holding, touching, feeling, and using. With visual recognition, the Retail industry is no longer
confined to a physical store or a digital touchpoint – it can truly be anywhere.
04

5. AUGMEN TED R EA LI TY
VIRTUA L R EA LI T Y
EXPER T SYST EM S
ROBOT I C SYSTEM S
With almost every big player heavily invested in AR (Google Tango, Facebook CameraEffects, Apple AR Kit), it is
very well positioned to be the next platform for innovation. Snapchat and Pokemon Go have proven AR can work at
scale and turned into something meaningful. If there has ever been a time to invest in AR it is right now.
Virtual Reality allows you to transport consumers into entirely new worlds that are limitless. The ability to build and
curate environments which you as a brand can dictate is something that can fundamentally change how consumers
think about a brand. While VR is achieving scale, it brings with it great promise and a whole new way to engage
with the consumers.
Expert Systems are automated systems powered by Artificial Intelligence to solve one or more complex problems.
With intelligence at their core, an expert system can leverage the latest advances in Applied Machine Learning
techniques to find solutions to existing problems in whole new ways – through the power of new computing
standards and capabilities.
Advances in Robotic technology powered by AI are finding whole new ways to enter the Retail landscape. We’re
beginning to see new applications ranging from Inventory Management Bots (Amazon), to Retail Service (Lowe’s
Bot), to Delivery Bots (Starship Delivery Bot) enter the Retail environment and truly change how we go
about designing consumer experiences.

6. 06
P R O A C T I V E LY E N G A G E A N D
B U I L D N E W A U D I E N C E S
Look-A-Like Audience Modeling
Develop a model that discovers, engages, and builds
new target segments based on a significant overlap of
characteristics and patterns by cross referencing existing
customers.
A U D I E N C E U N D E R S TA N D I N G
Social Insights And Listening
Manage social interactions around the globe by
automatically analyzing and understanding audience
sentiment, tone, nuances, and cultural attributes to inform
business strategy.
C U R AT E L O V E AT
F I R S T S I G H T
Intelligent Recommendations
Increase average order values by converting more
anonymous visitors by recommending relevant
products; tailor content and offers across channels
based on visitor preferences.
A N A LY Z E R E S P O N S E T O
M A R K E T I N G / C A M PA I G N S
Response Modeling
Maximize overall performance of marketing campaigns
and promotional events by finding a set of promising
segments that are most likely to convert based on
promotional information.
A U D I E N C E I N T E L L I G E N C E
D A S H B O A R D
Visualize Audience Relationships
Build an audience dashboard that allows you to visualize
and monitor customer needs based on trends and behavioral
patterns – driving confidence in decision-making during
execution.
TA I L O R E D A U D I E N C E
M E S S A G I N G
Content Analysis And Management
Optimize content based on formats that resonate the most
with audiences. Personalize messaging by combining content
analysis during campaigns with developed inferences.
S TA G E 1 : AWA R E N E S S & I N S P I R AT I O N

7. P E R S O N A L I Z E D
R E C O M M E N D AT I O N S
Recommendation Systems
Build intelligent models to anticipate, predict, prescribe,
rank, and refine tailored recommendations to users based
on user tastes and preferences.
I D E N T I F Y K E Y C U S T O M E R
S E G M E N T S
Customer Segmentation Modeling
Understand customers at a finer level by modeling key
customer segments based on their digital footprints to
engage with them through more meaningful experiences.
I N F U S E P R O D U C T I N T E L L I G E N C E
Product Attribute Graph
Rank products based on how they perform and resonate
across key customer segments and use that ranking
criteria to showcase the right products to the appropriate
customers.
S T O C K P R O D U C T S T O M A X I M I Z E
R E T U R N S
Category Management
Calculate the appropriate balance of products across
varying physical shelf/window/aisle constraints in a way to
maximize gross margins and substitute the right products.
S E A R C H R A N K I N G
Recommendation Systems
Build models that rank product and multi-user taste
preferences across a range of critical parameters in
order to return accurate results by considering
context and need.
A N T I C I PAT E C U S T O M E R N E E D S
Needs-Based State Machines
Build predictive models that consider the different states a
user may belong to by understanding their needs within each
state and possible triggers that generate new needs.
P R I C I N G S T R AT E G Y I N L I N E
W I T H B U S I N E S S G O A L S
Pricing And Profit Optimization
Model a pricing strategy by keeping in mind overall business
goals and quarterly targets in mind. Assign individual price to
each customer in order to maximize overall revenue.
A N T I C I PAT E F U T U R E D E M A N D
Demand Modeling
Recognize key trends that are both internal (customer/
business related) and external (market/ industry related)
that may influence the increase/ decrease of demand
for key products.
E L E VAT E D S E A R C H E X P E R I E N C E
Predictive Search
Enable data-driven search recommendations that
allow the user to perform rich search queries that
hand hold them to the right products in the most
customized way possible.
A U T O M AT E D C ATA L O G C U R AT I O N
Catalog Ingestion
Automate SKU creation, curation, and management
by recognizing patterns from available
merchandizing data and use conflict resolution as a
means to increase confidence in results.
P O W E R F U L F I LT E R I N G C R I T E R I A
Collaborative Filtering
Allow customers to use meaningful and relevant filtering
criteria to arrive at products they’re looking for – learn from
patterns and behaviors to inform better product placement.
S TA G E 2 : C O N S I D E R AT I O N & E VA L U AT I O N

8. FA C I L I TAT E U N A N T I C I PAT E D
S T R U G G L E
Struggle Detection
Automatically detect struggle with little or no human
intervention by analyzing critical user flows and tasks and
learn through observations to increase user experience
where required.
I M P R O V E D C R O S S - S E L L A N D
U P - S E L L
Merchandizing Analytics
Recognize patterns in customer behavior across
buying patterns to improve product associations and
recommendations in the shopping cart.
M A R K E T B A S K E T
U N D E R S TA N D I N G
Basket Analysis
Analyze how customers make purchases and the behaviors
that inform the products they buy to create relevant
product associations enabling a smarter purchase.
S E C U R E T R A N S A C T I O N
P L AT F O R M S
Fraud Detection
Take advantage of cutting edge security solutions
in fraud detection that recognize behavioral signals
of fraudulent transactions and employ counter
measures to void them.
O P T I M A L P R I C I N G D E C I S I O N S
Dynamic Pricing / Price Discrimination
Make optimal pricing decisions, keeping customer
and business value in mind, by setting optimal prices
to increase revenues and engaging with price-sensitive
customers.
I D E N T I F Y K E Y S A L E S D R I V E R S
Sales Value Decomposition
Identify how much proportion of sales is being driven by
which driver factors that pertain to internal and external
triggers. Use them to test hypothesis and validate through
experiments.
P R E D I C T I N G C U S T O M E R B E H AV I O R
Propensity Modeling
Conduct experiments and build models to predict the key
shopping behaviors that determine likelihood of conversion
versus likelihood of abandonment.
S TA G E 3 : S H O P P I N G C A R T

9. M E A N I N G F U L C U S T O M E R
R E L AT I O N S H I P S
Response Modeling
Understand how customers respond to using your
product by employing a mindset of collecting
feedback and analyzing the responses to inform the
design and experience of products.
R I C H C U S T O M E R P R O F I L E S
Profile Insights
Build rich and informative customer profiles that allow
a deeper understanding of customers, segmentation,
preferences, and needs that inform product strategy
and roadmaps.
L I K E L I H O O D O F N O - R E T U R N
Customer Churn Analysis
Build models to understand why customers might
not return to the brand and fill the gaps across the
user journey in order to better engage with customers.
C R O S S - S E L L & U P - S E L L
A N A LY S I S
Propensity To Category Expansion
Increase likelihood of selling related or complementary
products to customers by making data-driven
recommendations that are highly personalized.
S TA G E 4 : F I R S T P U R C H A S E
S TA G E 5 : N E W C U S T O M E R
S TA G E 6 : S E C O N D P U R C H A S E
I N C E N T I V I Z I N G N E X T P U R C H A S E
Discount Modeling
Build unique models of discounting and incentivizing
customer segments to increase the likelihood of their next
purchase by understanding motivations and triggers.
R E A C H - O U T P R O G R A M S
Call Back Analysis
Determine accurate, contextual, and highly personalized
strategies to conduct reach-out programs with actionable
triggers that can be measured.
M U LT I -TA S T E P R E F E R E N C E
U N D E R S TA N D I N G
Preference Modeling
Model multi-taste customer preferences based on collected
data to understand what customers are looking for in products
and services that can be used to inform strategic thinking.

10. 10
E S T I M AT E C U S T O M E R
L I F E T I M E VA L U E
Customer Lifetime Value Modeling
Combine unique targeted models in varying granularity
across different categories to determine the measurement
in dollars associated with customer to identify their
lifetime value.
R E C O G N I Z E L I F E - C H A N G I N G
E V E N T S
Propensity To Change
Shopping Habits
Identify likelihood of life-changing events (e.g., relocation,
wedding, etc.) triggering new patterns of customer
behavior and how that might open up new categories
to purchase from.
S E R V I N G L OYA L C U S T O M E R S
D I F F E R E N T LY
Uplift Modeling
Understand key patterns of repeat customers and how
their behaviors may be different to regular shoppers in
order to serve them differently, enabling a substantial
sales boost.
I N C R E A S E C U S T O M E R S P E N D
Share Of Wallet Modeling
Increase percentage of customer spending within categories
that resonate with them by understanding the context
in which they engage with the brand and make purchase
decisions.
I D E N T I F Y C U S T O M E R S L I K E LY
T O D I S - E N G A G E
Risk Modeling
Build a process to identify customers early-on in the funnel
who are most likely to dis-engage with the brand and most
likely to not return to make another purchase.
S TA G E 7 : O N E A N D D O N E
S TA G E 8 : R E P E AT C U S T O M E R

11. 11
R E C O G N I Z I N G A N D R E TA I N I N G
L OYA L C U S T O M E R S
Advocate Definition
Building an acute understanding of what it means to
be a loyal customer of the brand and what are the set of
parameters that a customer needs to fulfill in order to
sustain it.
R E T E N T I O N P R O G R A M
Churn Modeling
Quickly identify customers with high propensity to churn
and target them with retention campaigns that may bring
them back through a discount, gift, or other monetary
benefit.
P E R S O N A L I Z E D L OYA LT Y
P R O G R A M
Gift Card/Loyalty Card Analysis
Go from Personas to People with a highly personalized loyalty
program that keeps individual preferences in mind and
engages in a contextualized relationship.
S TA G E 9 : L O YA L C U S T O M E R
S TA G E 1 0 : L O S T C U S T O M E R

12. S U P P LY C H A I N O P T I M I Z AT I O N
Minimize distribution costs, warehouse assignment costs,
route management, scheduling, product grouping, etc. by
understanding the most optimal ways to manage your Supply
Chain by leveraging Applied Machine Learning techniques.
O R D E R M A N A G E M E N T
Automate order management, scheduling, delivery, and pick-up
by understanding the crucial factors and risks involved to the
overall business. Optimize models based on a dynamic data and
cadence of smooth operations.
S A L E S F O R E C A S T I N G
Predict likelihood of sales and conversion across different
properties by evaluating trends and sales pattern data. Analyze
how you can do better and constantly implement new strategies to
fuel new ways of selling.
P R E D I C T I V E I T
Predict abnormal behavior early-on in systems that may
potentially lead to downtimes/crashes. Understand historical
patterns/triggers to actively manage peak system performance –
allowing you to constantly maintain efficient systems.
WA R E H O U S E A S S I G N M E N T
Calculate optimal warehouse assignment routes by understanding
unique parameters that influence the matching of inventory to
warehouses to either store or customer delivery.
P R I C I N G A N D P R O F I T O P T I M I Z AT I O N
Build accurate pricing and profit models based on a range of
parameters and pricing elasticity models. Predict likelihood of
customer spend in line with business goals and profit margins.
I N V E N T O R Y F O R E C A S T I N G
Predict optimal store or fulfillment center inventory based on
historic and likelihood patterns of what customers may and may
not order. Align with sales data to host the right products in the
right geographies and store locations.
I N T E L L I G E N T S E Q U E N C I N G
Detect which product combinations make sense for each
customer segments and see how you can take advantage of
a deeper understanding of these relationship-mapping of
products to consumers.
A N O M A LY D E T E C T I O N
Automate the detection of anomalies within systems for
applications like Fraud Prevention, Fake Reviews, Coupon
Transactions, etc. Build a deeper understanding of anomalies
and employ counter measures to actively tackle them.
D E M A N D P R E D I C T I O N
Build a Demand Prediction model that analyzes product
properties, sales events, shopping trends, and inventory status
to model accurate demand and distribution.
L O G I S T I C S O P T I M I Z AT I O N
Map the shortest, most optimal routes across various handoff
stages for all logistical operations across the end-to-end
customer experience in order to save costs or prioritize
investment in crucial areas that have a direct business impact.
C O G N I T I V E TA G G I N G
Build a cognitive tagging mechanism for individual
products across all consumer segments that defines a
multi-dimensional attribute model to build product
intelligence, which can be leveraged across multiple use
cases and applications.
S A L E S E V E N T P L A N N I N G
Automate sales event planning for flash sales, seasonal
sales, clearance sales, coupons, etc. by optimizing revenue
management systems that predict and model sales events
based on customer data and propensity to buy.
DATA P L AT F O R M
Democratize data across the organization by building a
robust, scalable data platform allowing seamless access for key
departments to leverage data and build a data-centric strategy
and execution plan.
F O U N D AT I O N A L P L AT F O R M E L E M E N T S

13. SapientRazorfish, part of Publicis.Sapient, is a new breed of transformation
partner designed to help companies reimagine their business through radical
customer-centricity. With more than 12,000 people and 70 offices around the
globe, our capabilities span growth and business model strategy, new product
and service innovation, enterprise digital transformation, IT modernization,
omni-channel commerce, customer experience strategy, change management,
digital operations, digital innovation, data strategy and advanced analytics.
Business_Reimagined for a Connected World
About
SapientRazorfish
SUFFIYAN SYED
Applied Machine Learning
Strategy Lead
A B O U T T H E
A U T H O R
GET IN TOUC H
To learn more about how financial
services institutions can benefit
from our unique combination of
industry and innovation expertise,
contact Suffiyan Syed at
suff.suffiyan@sapientrazorfish.com
Suff is the Applied Machine Learning Strategy Lead at
SapientRazorfish, where he’s responsible for the application of Machine
Learning in critical customer and business problems across key clients.
He believes in combining meaningful design and intelligent technology
to enable human value. He’s a self-taught Machine Learning engineer
and a non-conformist.
Prior to SapientRazorfish, Suff has worked at and advised startups
building creative and enterprise solutions. He’s lead Design and Tech
teams on agile projects through Design Thinking and Systems Thinking
approaches; he is passionate about designing products and services
that solve problems, delight people, generate user adoption, and drive
strategic business value.
His in-depth knowledge and practical experience in software design,
from design concept creation to development and release, enable him
to build strong strategic partnerships with Research, Strategy, Design,
Engineering, and Marketing teams to deliver innovative end-to-end
solutions. He maintains a fine balance between leading, managing,
and being hands-on collaborative.

14. SUFFIYAN SYED
Leveraging Data Science
Across The End-to-End
Retail Customer Journey