Reshaping the brand experience through AI, a strategic challenge Chapter produced in collaboration with
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Use Case
8:55 AM - 9:10 AM
Brands' relational promise boosted by Machine Learning FRANÇOIS POITRINE Co-founder Ekimetrics
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#digitbench1 9
SPEAKER’S PRESENTATION LONDON
DUBAI
FRANÇOIS POITRINE
PARIS NEW YORK
Co-founder
HONG KONG
EKIMETRICS
Ecole Polytechnique / HEC-Entrepreneurs 15 years experience in Data Science projects
YEARS & 1000+ PROJECTS IN DATA SCIENCE
Entrepreneur Scientific - Applied Maths Business expert Automotive
-
12
Financiers
+20 0 DATA SCIENTISTS WORLDWIDE
services,
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RESHAPING THE BRANDÂ EXPERIENCE THROUGH AI: A STRATEGIC CHALLENGE ABOVE ALL
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3 TEMPTING OPPORTUNITIES TO TAKE UP THE CUSTOMER EXPERIENCE 5%
Technological Promise
Data Promise
AI Promise
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WHY DO WE WANT TO BELIEVE IN TECH? MVP
Industrialization
PoC
Performanc e achieved
€ Effort made WIFI digitbench19
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WHY DO WE WANT TO BELIEVE VERY HARD IN TECH?
DEEP NEURAL NETS
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BACK TO BASICS
Technological Promise
Data Promise
AI Promise
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BACK TO BASICS: THE RELATIONAL PROMISE
Technological Promise
Data Promise
AI Promise
Relational Promise
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BACK TO FUTURE: THE RELATIONAL PROMISE... INDUSTRIALIZED
Technological Promise
Data Promise
Relational Promise
AI Promise
+ People / Organization / Change WIFI digitbench19
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INSURANCE DATA SCIENCE AT THE SERVICE OF BUSINESS GOALS: EVOLVING THE BRAND PROMISE
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2 BIG TRANSFORMATION CHALLENGES FOR THE INSURANCE INDUSTRY
1
REFOCUS
SIMPLIFY
on customers
the operating model
2
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DECIPHER & DESIGN A MEANINGFUL CUSTOMER JOURNEY, DESPITE COMPLEXITY Initial contact
Meeting
Training
Contact
CRM
Request for information
Deliberatio n
Coverage request
Request approved
Claim Claim
Payout received
Time
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LEVERAGE MACHINE LEARNING TO STICK TO THE REALITY OF CUSTOMERS' BEHAVIOR Static modelling
vs .
Sequential modelling
Customers
Customers
Aggregated events
Events
Time
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PREDICT CLIENT SATISFACTION ADJUST ACTION PLANS, FOR THE RIGHT CLIENT IN THE RIGHT CONTEXT Attrition Score
Weeks
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INDUSTRIALIZATION: BUILDING NEW TECHNOLOGICAL CAPABILITIES People
Organization
Technology
Processes
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COSMETICS DATA SCIENCE AT THE SERVICE OF BUSINESS GOALS: SUPERCHARGING THE CLIENT PROMISE
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THE MODERN RELATIONAL PROMISE Recommendation
Prescription
Predicting the product you may want
Predicting the product you need
Based on customer behaviour and lookalike customers
Based on experts recommendations on a diagnosis
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THE 5 CHALLENGES OF PRESCRIPTION RELEVANCE PERSONNALIZATION
Prescriptions must be cosmetically relevant with the scientific expertise
Prescriptions must be unique and adapted to each customer profile
at the core
SUSTAINABILITY Prescriptions must be stable and coherent despite business priorities, data or experts changes over time
INTERPRETABILITY Customers must know why a specific prescription would fit her needs and/or solve her problems
SCALABILITY Prescription system must be able to adapt to different brands and countries
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DOUBLE USER EXPERIENCE RECONNECTED THROUGH RELEVANCY BRAND’S USER User inputs
Clinical signs
COSMETICS EXPERT User profile
Priorities
Modiface algorithm
Human expertise
Learning to imitate the expert
User tagging Profile corresponding to the Beauty Profile from the Global Data Model
Skin type Age… Diagnosis
AI Prescription system
Recommende d routine in the application
Matching algorithm
Finetuning algorithm
Learning generalized rules from experts rules
Learning the exceptions
Augmentin g the expert
API
Web application Check, input monitor, update and correct the results of the algorithm
… 1
Objectivizing the rules of prescriptions
7
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USER EXPERIENCE POWERED UP BY AN ADVANCED PRESCRIPTION SYSTEM BRAND’S USER User inputs
Clinical signs
COSMETICS EXPERT User profile
Priorities
Modiface algorithm
User tagging Profile corresponding to the Beauty Profile from the Global Data Model
Skin type Age… Diagnosis
Prescription system at the core
Human expertise
Learning to imitate the expert
AI Prescription system
Recommende d routine in the application
Matching algorithm
Finetuning algorithm
Learning generalized rules from experts rules
Learning the exceptions
Augmentin g the expert
API
Web application Check, input monitor, update and correct the results of the algorithm
… 1
Objectivizing the rules of prescriptions
7
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USER EXPERIENCEÂ POWERED UP BY AN ADVANCED PRESCRIPTION SYSTEM COSMETICS EXPERT Human expertise
Learning to imitate the expert
User tagging Objectivizing the rules of prescriptions
Optimize the UX of the cosmetics expert
Matching algorithm
Finetuning algorithm
Learning generalized rules from experts rules
Learning the exceptions
Augmentin g the expert
Web application Check, input monitor, update and correct the results of the algorithm
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USER EXPERIENCEÂ POWERED UP BY AN ADVANCED PRESCRIPTION SYSTEM COSMETICS EXPERT
Algorithm accuracy*
1
Human expertise
Learning to imitate the expert
User tagging To adopt the same framework
User tagging
50%
1 2
Matching algorithm To objectivize the decision process 2
90%
3
Objectivizing the rules of prescriptions
Matching algorithm
Finetuning algorithm
Learning generalized rules from experts rules
Learning the exceptions
Finetuning algorithm To learn the exceptions
99%
* % of prescription an expert would have done
Augmentin g the expert
3
Web application Check, input monitor, update and correct the results of the algorithm
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INDUSTRIALIZATION: REINFORCE MOMENTUM ON EXISTING CAPABILITIES People
Organization
Technology
Processes
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80 / 20? WIFI digitbench19
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AI INDUSTRIALIZATION: MUCH MORE THAN AN ALGORITHM Data Verification Configuration
Data Collection ML Code
Feature Extraction
Machine Resource Management
Monitoring Service Infrastructure
Analysis Tools Process Management Tools
https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
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AI INDUSTRIALIZATION: STRATEGIC OR NOTHING... Data culture Hybrid profiles
Last on the decision chain Flexibility
People
Technology
Organization
Break silos High level of governance
Processes
Reshape working process Evolve business model
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Thank you!
Global consultancy, European leader in Data Science.
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