ibm watson for retail 2017
TRANSCRIPT
Keith MercierGlobal Retail LeaderCognitive Business Solutions@keithmercier
Bringing Watson to Life in Retail
2010 2020
Sensors & Devices
Text
Enterprise Data
Images/Multimedia
44 Zettabytes
Gap
Traditional
You are here
2017
of customer data stored by Walmart
every hour.
>2.5PBof mobile traffic by 2019, up from 30 exabytes in 2014
292 exabytes
of data produced by a cancer patient every
day.
1TB
We face an overwhelming amount of data in every industry
Humans excel at:
DILEMMAS
COMPASSION
DREAMING
ABSTRACTION
IMAGINATION
MORALS
GENERALIZATION
Cognitive Systemsexcel at:
COMMON SENSE
NATURAL LANGUAGE
LOCATING KNOWLEDGE
PATTERN IDENTIFICATION
MACHINE LEARNING
ELIMINATE BIAS
ENDLESS CAPACITY
Cognitive systems are creating a new partnership between humans and technology
How do Cognitive systems work?
REASON
They can reason, grasp underlying concepts, form hypotheses, and infer and extract ideas.
UNDERSTAND
Cognitive systems understand imagery, language and other unstructured data like humans do.
LEARN
With each data point, interaction and outcome, they develop and sharpen expertise, so they never stop learning.
INTERACT
With abilities to see, talk and hear, cognitive systems interact with humans in a natural way.
Watson is an evolving set of Cognitive capabilities
Personality Insights
Alchemy Language
Conversation DocumentConversion
Language Translator
Natural Language Classifier
Retrieve & Rank
Tone Analyzer
Language
Speech to Text
Text to Speech
Speech
Visual Recognition
Vision
Tradeoff Analytics
Alchemy Data News
Data Insights
Social Media
Values
Psycholinguistic Analytics
Big5
Attitude EmotionStyle
Needs
Personality Portrait
Personality Insights
Developer Cloud Partner EcosystemSolutions
What is Watson today?
Watson at Work in Retail
Contextual Interactions
Guided shopping
The North Face and Watson guides customers to find the perfect jacket by asking where and when you’re going to use the jacket, and whether you’re looking for men’s or women’s, and what sort of activities you plan to engage in.
In its conversational interface, GWYN, the 1800-Flowerscognitive concierge guides customers to finding the perfect Mother’s Day gift.
Partnering with another luxury retailer, Westfield helped create
the cognitive gifting app that helped shoppers buy gifts based on custom personality profiles.
Individualized experience• Profiles drive the visual offerings on the web
for a personalized experience• Shopping history and customer analytics
drive unique product recommendations• Each click and action informs further
personalization and recommendations• Known and Unknown - Engaging ALL
consumers throughout entire journey
Cognitive gamification
Cognitive merchandise attribution
• Apply Natural Language Processing to description and reviews to derive attributes
• Additive to understanding of existing attributes
• Visual search can also be applied
Interactive digital ads Continuously collect customer and product insights
Associate/Agent Assist
• Understand and speak in natural language• Have a humanlike personality• Understand intent• Include intuitive tooling • Run across multiple messaging platforms• Have domain knowledge• Hook into back-end systems• Constantly learn
We believe that all Bots should…
Store associate bot
Cognitive customer service: Conversational bot
Key Capabilities • Understand and speak in natural language • Have a humanlike personality • Understand intent • Be easy to use and develop • Hook into existing systems
Where can it run?
»Reduce "inbound" call volume»Decrease average handle time»Decrease agent-to-agent transfers»Increase customer satisfaction and NPS
»SMS Text»Mobile app messaging»Web based chat»Facebook Messenger and Slack
»Order Status FAQs»Shipping and Delivery FAQs»Watson can execute simple changes including canceling an order, initiating a return, updating delivery options and editing shipping address.
Why is it so important?
What can it do?
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KoziKaza@ikeausa
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Chat and Visual Recognition
Use CaseFind image and with Watson’s
Vision Bot framework, offer your customers the ability to get
immediate product recommendations in the same
channel.
Direct message
Tweet
@kozikaza
KoziKaza
#KoziKazabot
Cognitive customer service/ call center
• Active listening along side of the agent or associate
• Natural language understanding
• Real time location of supporting documents and data
Data Insights
Augmenting the design process
• Scaled past design images to identify color palatte and link to emotions
• Accelerated fabric research best suited for dress design
• Socially ”aware” dress that changed colors based on live social sentiment
23
Trend identificationDevelop persona related to overall product category
Function First (41,800)Fashion First (2770)Flare Enthusiast (1470)Style Me (3465)Denim Diva (13,150)
Identify trending styles based on social mentions and activity
Identify clusters with positive sentiment towards identified trend.
Audience insights
• Leverage social-sign for permission-based access to social data
• Understand images, text and personality
• Append CRM records with new attribution for better targeting and egagement
Cognitive contact center analytics
• Retroactively evaluates contact center call recordings
• Speech to text API
• NL classifier used to determine the intent
• Sentiment and intent can be used for intelligent routing and prediction of handle times by calls type
Getting Started
Understand
Discover Evaluate
Prototype
• A proven approach with core practices specific to IBM• “Co-creation with customers” and “testing and learning” is at the heart of our principles• This is a human centered framework for moving from design to operations
We propose to use IBM Design Thinking approach to identify the business priorities for initial cognitive use cases
What will you do with Watson?