As professionals working in product development, we've all experienced how the system has evolved. From being driven by creating requirements to now, a more user driven methodology through data and insights. But how can we take this further and humanise the user's journey and step forward into the future of product development? By bringing ML & AI in tandem with your users journeys. In this talk, Mansi will talk about her experience through this evolution and how see the future of products and systems. She will touch on: How she helped evolve product systems throughout her career; What challenges she encountered at each step; and Based on her current project, how can she further push her business to the future by humanising user journey interactions

1. Strictly
Confidential
Mansi Kamdar, Principal Product Manger
Walmart
1
Product Evolution
The journey of humanizing digital experiences
PRODUCT ORG | DIGITAL SERVICES

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Confidential
MANSI KAMDAR
“MISTAKES ARE PAINFUL WHEN THEY HAPPEN, BUT YEARS LATER IT IS WHAT WE CALL EXPERIENCE”
~RANBIR KAPOOR
PRINCIPAL PRODUCT MANAGER
WALMART
“CREATING AND BUILDING PRODUCTS EVERYDAY!”
“BEING FEARLESS AS A WOMAN, MOTHER AND LEADER”
“MUSIC, FITNESS, COOKING”

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Confidential
BUILDING PRODUCTS – PRODUCT DESIGN &
DEVELOPMENT
Product Development analogy, captain of a ship, orchestra
director….
Product building is what we do everyday in our lives
North star, vision, roadmap, stakeholder management,
influence, backlog, execution, project plans, implementation,
deployments, product launches, promotions and marketing,
feedback……

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Confidential
HOW DID WE GET HERE?
REQUIREMENTS
DRIVEN
PRODUCT
CENTRIC
DESIGN
THINKING AND
CUSTOMER
CENTRIC
DATA
DRIVEN

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Confidential
PRODUCT EVOLUTION
TIRE – SWING CARTOON
CUSTOMER
CENTRICITY IN THE
DIGITAL AGE
PRODUCT
CENTRIC
DATA DRIVEN PRODUCT
DEVELOPMENT – HUMANIZING
EXPERIENCES

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Confidential
RETAIL SHOPPING: BUILDING BLOCKS AND
PROGRESS
CAN'T TRUST ONLINE
SAVE MONEY
AND WIDER
ASSORTMENT
SAVE MONEY,
TIME, ENHANCE
EXPERIENCE –
WHERE AND
HOW I WANT IT

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Confidential
PEOPLE, CULTURE, WAYS OF WORKING
• DESIGN
THINKING
• CUSTOMER
JOURNEY AND
FLOWS
• PROTOTYPING
AND EASY OF
USABILITY
• STRONG AND
SCALABLE
ARCHITECTURE
• SOLUTIONS TO THE
PROBLEM STATEMENT
• ENABLERS TO FASTER
TIME TO MARKET
• CUSTOMER
CENTRIC
PRODUCT VISION
AND ROADMAP
• PROBLEM SOLVING
• FEEDBACK AND
ITERATING
• BUSINESS DOMAIN
• NEEDS AND
BUSINESS DRIVERS
• PROBLEM
STATEMENT
BUSINESS PRODUCT
DESIGN
ENGINEERING

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Confidential
PRODUCT TOOLS AND TECHNIQUES
PRODUCT
STRATEGY
PRODUCT
DISCOVERY
CONCEPT
REVIEW
DEVELOPM
ENT
LAUNCH TO
MARKET
ONGOING
OPTIMIZATI
ON AND
LEARNING
CUSTOMER CENTRIC

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Confidential
SHOPPING TODAY
KEYWORD
BASED
SEARCH
DISCOVERY
YOUR
NEEDS
RECOMME
NDATIONS
CUSTOMIZE
AND
PERSONALIZE
INTELLIGENT
PERSONALIZ
ED
RECOMMEND
ATIONS
INTERACTIVE
AND
HUMANIZED
EXPERIENCE
People are the boss

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Confidential
MACHINE LEARNING
Machine Learning: the science (and art) of programming computers so they can learn from
data ~ Aurélien Géron
RULES
• RULE BASED
SYSTEMS
• INPUT
• FIXED SET
OF RULES
• OUTPUT
BIG DATA
• DATA
COLLECTION
AND
AGGREGATION
• RELIANT ON
MANUAL
ANALYSIS
DATA
SCIENCE
• MACHINE
LEARNING
• DATA
ENGINEERING
• TRAINING
• PREDICTION
VIA MODELS

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Confidential
ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE: CREATING INTELLIGENCE FROM THE MACHINE LEARNING; ML IS
A SUBSET OF AI
ARTIFICIAL
INTELLIGENCE
MACHINE
LEARNING
DEEP
LEARNING
Automated Voice Response
Rules Intelligent Assistance Human like bidirectional conversation
MIMIC THE INTELLIGENCE OR BEHAVIOR PATTERN OF
HUMANS
LEARN FROM DATA WITHOUT USING A COMPLEX SET OF
RULES
MACHINE LEARNING INSPIRED BY NETWORK OF
NEURONS

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Confidential
UNIQUENESS OF THE RETAIL WORLD
APPLICATIONS ARE LIMITLESS ML/AI IN RETAIL
SHOPPING
ASSISTANCE
MUSIC
RECOMMEN
DATIONS
FRONT
OFFICE
SUPPORT
PERSONALIZ
ATION
FULFILLMEN
T
AUTOMATIO
N
FRAUD
DETECTION

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Confidential
TRADITIONAL APPROACH
PRODUCT SOLUTION
TECHNOL
OGY
PROBLEM
S
PRODUCT
PROBLEMS
BUSINESS
PROBLEMS
Enablers = ML and AI

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Confidential
MODELS PROVIDING INSIGHTS
CROSS SELLING
OPPORTUNITIES
IMAGE BASED PRODUCT
SEARCH
OPTIMIZED
PRICING
MACHINE LEARNING AND AI INSIGHTS
AND LEARNING

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Confidential
CHALLENGES
▪ AI take over
▪ Human grunt work is being optimized
▪ Time spent on greatest value
▪ Models and their predictions could be biased
▪ Models are not biased “If you have a mind, you have bias”
▪ Mindset and culture to problem solved unbiased
▪ Remove the bias from data – needs human intervention
▪ Model monitoring - concept and data drift
▪ Predictions become less accurate over time as data evolves
▪ Re-enforced learning gather feedback and improve the models
▪ How do we know the models are truly learning – need humans to
evaluated the success of them

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Confidential
REFERENCES
https://oligarchmedia.com/how-to-interpret-the-research-data-in-new-product-development/
https://speakerdeck.com/sambaiga/machine-learning-fundamentals?Slide=4
https://global.hitachi-solutions.com/blog/ai-in-retail
Inspired By The Book – Human + Machine – Paul Daugherty and H. James Wilson

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Confidential
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7
PRODUCT ORG | DIGITAL SERVICES
Thank You