Although Personalization figures at the top of "to get right" list of strategic objectives, hardly a few get it right. It's much more than a bunch of algorithms, product-market fit, a pretty front end design or even lots of incentives. It's complete re-architecting of the soul of the organization! It's a journey from being in love with the product that was created and finding the customers to solving a "user's problem" sustainably, efficiently and effectively. This talk will be a walk through of the process of creating a vision, the strategic goals and execution of a Personalization program with Data Driven Optimization. It will touch upon the advancement along the Analytics Maturity Curve from the right metric creation for the Strategic Objectives, shoring up the Data Platform, a "Learn-Listen-Test" framework of iterative execution and finally scaling with Machine Learning solutions. The intention is to share the lessons along the journey with the audience and take back insights from their own personal experience of pulling this off. Presented at Predictive Analytics Summit 2019 at San Diego run by Innovation Enterprise.

1. 1
The Personalization Cookbook
Ramkumar Ravichandran – Data science TLM, Google Shopping
Disclaimer: Participation in this summit is purely on personal basis and is not meant to represent Google’s position on this or any other
subject, in any form or matter. The talk is based on learning from work across industries and firms. Care has been taken to ensure no
proprietary or work related information of any firm is used in any material.
http://smartvectorpics.com/free-vector/chef-cooking-in-the-kitchen/

2. 1. Define “Personalization”
1. Define Personalization
Contents
2
2. All it takes to make it happen!
3. Key Takeaways

3. Let us define “Personalization” first...
3
https://imgflip.com/i/2zo720
•not pretty front end
•not giving discounts all the time
•not bunch-a algorithms
•not loyalty programs
•not omni channel campaigns

4. ...it’s all this but much much more
Simon Sinek - Do you love your wife?
4

5. 5
https://imgflip.com/i/2zqo22

6. At the core, it’s all about loving your customers!
6
https://giphy.com/gifs/schittscreek-schitts-creek-l1OBiDMmjQqXPpcSa4/media
“it’s all about knowing the
need of the customer, knowing
where they are in the intent
funnel at a time and doing
whatever you can do to ensure
they come to you when they
have made up their mind”

7. 7
https://www.google.com/url?sa=i&source=images&cd=&ved=2ahUKEwjh4I_Z4vPhAhVNpZ4KHX6RBo4QjRx6BAgBEAU&url=https%3A%2F%2Fmemecrunch.com%2Fmeme%2FI8Z7%2Fst
ate-the-obvious&psig=AOvVaw2axuaLcBcUpbh1peDyY3pJ&ust=1556574643076354

8. •Needs are “complex, complicated and always evolving”
Don’t get me wrong, it’s not easy!
•“Law of Diminishing Returns”
•Difficulty in identifying “intent stages”, “influencing
drivers” even with abundant data
•Technical complexity of Scalable & “agile” architecture
•Choice between “Optimization” vs. “Disruptive
Innovation” and switchover costs 8

9. 9
https://www.cleverism.com/mass-customization-what-why-how/
Mass Customization vs. Mass Market

10. 10
https://pdfs.semanticscholar.org/1cec/2e444714f300ce4a61610394f79eba84a957.pdf
Mass Customization Framework

11. Maturity Levels of Personalization set up
11
1
Autonomous Engines
Responsive learning systems for a set of goals &
constraints
3
System Dynamics
Recommended Actions based on expected value
across multiple BUs and KPIs
5
Segmented Flow (Needs/Customers)
Guided Paths based on segments
2
Serendipity Drivers
Latent Factor/Push
Personalization
4
Predictive Impact Driven
Recommended Actions based on
expected value on a BU KPI
Only with DS, ML & AI critical for agility, scalability, efficiency & RoI is possible. Not only does it help
reduce time to act, but also opens up resources to focus on new ideas & opportunities!

12. 1. Define “Personalization”
Contents
12
3. Key Takeaways
2. All it takes to make it happen!

13. It takes entire organization to make it happen!
13
Marketing & Sales
05
● Key role: “Growth & Partnership” -Customer
& Partner Lifecycle Management
Technology
04
● Key role: “Build, Deliver & Integrate”
-Agile Infra, QA/Release, DevOps/SRE
Data Science, ML &
other AI
03
● Key role: “Learn-Listen-Test Optimization”
-Integrate, Augment, Predict & Prescribe
Product Management
02
● Key role: “Create & Manage” - Design
Thinking, Product-Market Fit Iterations
Strategy, Finance &
Operations
01
● Key role: “Envision, Plan & Commercialize” -
Values, Vision & Success Criteria

14. 14
Double clicking on certain critical focus areas...

15. Values
15
● “Love” your customers and not your products
● “Long” term focus on lifetime value vs. profitability
● “Learn” with humility & growth mindset
● “Lead” with value instead of chasing the success stories
● “Law” abiding, ethical & uncompromising on integrity

16. Design Thinking = “Customer-Needs-Context”
16
!!!Design Thinking has to be the driving force of all things Personalization!!!
● “USP” identify the core need & problem being solved differently
● “Target” use cases, features and benefit (Marketing Message)
● “User Flow” embedded in mental model & expectations (Design)
● “Understand” accepted tradeoffs (Technical Development)
● “Allay” any apprehensions (Privacy, Legal & Machine Learning)

17. Extend
Manage
Build
Prototype
Design
Plan
Product Development Lifecycle
• Critical Review of Existing solutions
(optimization or differentiator)
• Use Cases (Opp, Impact, RoI)
• Goals, Success/Stop Criteria
• Readiness (Customer, Provider,
Regulator, Creator)
• Design iteration decisions (by user, by
needs, by context)
• Tactical: Platform, Program, Process
• Use Case Scoring & Prioritization
• POC- Success/Lessons, RoI
• Optimization/Customization
• Review, Stress Test, UAT
• Plan & Timelines - Milestones
• Evangelize & Engage
• Data Driven Optimization
• Support, Operations & Distribution
• Refine, Revamp or Retire?
• ”Learn-Listen-Test” launch
• Deploy, Monitor & Iterate
• Usage Protocols : Guide & Comply
• Innovation & Upgrade
17

18. Optimization across Intent Journey Funnels
18
Intent Journey (from low to high)
Behavior: Browsing,
Searching & Clicking
Focus on: Making it
Immersive & memorable,
Offer up Choices, Keep
them hooked
Inspiration & Discovery
Behavior: Bookmark/
Favorite/Save/ATC
Focus on: Nudge drivers
(Mails, Urgency Messages,
Popularity Tags, Others
viewing tags), Funding
Options
Consideration
Behavior: Checking similar
items, Branded Searches,
Reviews/Ratings, Return
Policies, Bookmarks
Focus on: Comparison tools,
Price Drops, “Other
products”, Editorial Pages
Research
Behavior: Checkout Funnel
Focus on: Simplifying
Checkout Funnel,
Frictionless Payments,
Funding Options
Purchase
Behavior: Repeat
Visits/Orders, Funnel
Initiation
Focus on: Loyalty
Messaging, Exclusive Offers,
Custom
UX/Messaging/Campaigns
Engagement
Behavior: Social
Endorsements (Likes), NPS,
Growth, LTV, CSAT
Focus on: Influencers,
Collaborative Product
Developments, Word of
Mouth Campaigns, Ads
Loyalty

19. Critical components to deliver Personalization
at Scale, Speed & Efficiency
19
Data Store
●Consolidated Data
Layer stitching
together data sources,
levels, granularity,
refresh frequencies. It
will also host tables for
monitoring
performance,
implement policy and
troubleshoot.
●Data Models: RDBMS,
NoSQL, Graph as
required
●Use Cases:
Accounting, Reporting,
Analyses, Modeling
Feature Store
●ML Features: Feature
Engineering, Reduction
& Monitoring
●“On the fly” Feature
Engg & Treatment:
Field, granularity of
aggregation, type of
aggregation,
math/logic supported,
real time frequency
●Logging of feature
trials & results
●Feature pipeline
grouping & reuse
●Model Governance
implications
Learning
Platform
●Varied types of
models: Learning &
Decision Models
●Choice to develop
manually for baseline
setting or
Autonomous Machine
Learning (AML) for
impartial dev and
scalability/efficiency
●Model Validation/
QA/Integration/Unit
Testing, DevOps
●Documentation &
Governance
●Expert Feedback
Learn-Listen-
Test Infra
●Model Performing
Monitoring:
Champion-Challengers,
Integration with User
Feedback & Expert
Feedback Loop
●Alerts and Drift
Monitoring
●Rollback/Scale Up
protocols and set up
Serving Layer
●APIs & Microservices
hosting Model Feed, QA &
Incident Management
(Flagging & Customer
Complaints)
●Policy Implementation
●CS/RM Troubleshooting &
Referencing Layer

20. Data Governance critical requirement of success!
Critical Requirements
● Data Quality in Acceptance Criteria
● Data QA part of Release Certification
● Data Pipeline Monitoring & Alerts set up
● Bug triages, escalation paths & resolution SLAs
● Data Loss protocols: Business Continuity,
Disaster Recovery & Redundancies
● Privacy Design & Access Restrictions
● Serving Layer Integration with Analyses Layer
20
Data
Governance
Data
Ingestion
Data
Transformation
Data Lineage &
Documentation
Data Blending
& Integration

21. Application Layer
CXM Logs
Data Store for Efficiency, Scalability, Lineage &
Reliability
21
Machine Learning
Feature Store
ETL Tables
Orders Inventory
3rd Party
(Bureau,
Industry,
DMP)
Others
(Tests,
Labeling,
Expert)
RDBMS
Aggs
Data Products,
Custom Tables
● Data Governance, Lineage &
Integration
● Data Quality Alerts
Enriched
Aggs
● Feature & Model Governance
Raw Feed
Data Lake
Specialized
needs: NoSQL,
Graph, Columnar
Streaming
● Policy Implementations
Raw
Format
Finance/Compliance,
Reporting, Analytics,
Testing, User Research
Model
Dev
Performance Monitoring with,
1. Expert Feedback
2. Learn-Listen-Test Framework
3. Drift Monitoring

22. Personalization AI can’t supersede Analytics Maturity
https://www.intel.com/content/dam/www/public/us/en/documents/guides/analytics-planning-guide.pdf
22

23. 4. Lifetime Value
Net Profit over lifetime
3. Personas
Combination of Demographics,
needs, attitudes & motivations
2. Activity Buckets
RFM Buckets, Purchase/Visit Profile,
Shopping Use Cases for Targeting
Customer Hyper Segmentation Schema (illustrative)
23
1. Engagement Segments
Portfolio Monitoring, Strategic
Decisions
http://chittagongit.com/icon/icon-customer-6.html

24. Sensitivity & Preferences
(Price, Delivery, Returns)
Expected Conversion
An illustrative (non exhaustive) Personalization Model
Tree...
24
https://www.flaticon.com/icon-packs/contact
Visitor Segmentation
Predicted LTV
Predicted Fraud/Abuse
Next Best Product
Recommended Incentive
Customer & Need
Segments
CX Issue Severity Buckets
Predicted NPS
Predicted Retention
Expected Amount of Spend
Sentiment Buckets
Intent Funnel Stage
Identification (Need/CTA)
Time to Next Purchase
Recommended Channel
Recommended Marketing
Mix & Campaign
Lookalike Segments
Online-Offline Deployment Mix
...other key AI products are also critical (and leveraged appropriately) to deliver personalization are Chatbots, Anomaly Detection
Systems, Social/Brand Issue Tagging/Monitoring & Impact Sizing, Serendipity Engines (Data Products).

25. Learning Platform Framework
Data & Analytics
(Customer, UX, Mktg,
Sales, Finance)
OPTIMIZATION
Platform Monitoring
(Logs, Anomaly, Data
Quality)
VoC & CX Management
Experimentation Layer
25
Feedback

26. Learning Platform Tools Landscape
26

27. Strategy
Data
Tagging
Data
Platform
Reporting
Analytics
Research
Scale
Iterative
Loop
Optimization
“Learn-Listen-Test” Optimization Framework
✓ Reporting for “monitoring” KPIs
✓ Analytics provides insights into “user
behavior”
✓ Research context on “motivations”
✓ Testing helps verify the “tactics”
27

28. 1. Define “Personalization”
Contents
28
2. All it takes to make it happen!
3. Key Takeaways

29. Key takeaways
• Personalization is incredibly complicated & complex but imperative for
survival. Requires commitment, persistence & passion to care & serve!
• Pillars of a successful personalization: Design Thinking + Data Science/ML &
AI + Technical Maturity + Marketing of “USP” to “needs” segment +Policy
• Success hinges on sound “Data Governance”, seamless “Data Store”, “Feature
Store”, end to end “ML Platform”, “DevOps” & “Optimization” loop
• Augmentation opportunities through Partnerships & Network effects, keeping
in mind Compliance & policy considerations.
• Personalization has to be figured out through “Learn-Listen-Test”
Framework.
29

30. Thank you! We would love to hear from you...
https://twitter.com/decisions_2_0
http://www.slideshare.net/RamkumarRavichandran
https://www.youtube.com/channel/UCODSVC0WQws607clv0k8mQA/videos
https://www.linkedin.com/pub/ramkumar-ravichandran/10/545/67a
RAMKUMAR RAVICHANDRAN
30

31. DATA SCIENCE LIFECYCLE & PLATFORM
Intended for Knowledge Sharing only https://www.dominodatalab.com/resources/managing-data-science/

32. • Full end-to-end platform: Must be noted that our need is more than just AML, starts from problem conceptualization through the
documentation stages. Eventual set up may be Data Ingestion & Processing layer, AML and Programming layer, Rally for Project Management,
Sharepoint and Native Site for Communication
• AML specific:
▪ Customizability and interpretability of models (We can’t work with Blackbox solutions)
▪ Breadth of algorithms and use cases supported. Although Classification & Regression account for a sizeable proportion, we also need
support for Survival, Panel Data, Forecasting, Text Handling/NLU, DN/RNN/CNN, etc.
▪ Support for Prescriptive Analytics
• Platform specific: Ease of integration with existing set up and potential AML/packages. Coverage of the entire data lifecycle (Support Admin,
Testing, Monitoring, Alerting).
• Input data types supported and level of pre-data operations required.
• Learning curve and level of support for training team and stakeholders.
• Costs: Fixed, Operational, Integration, Training and Migration cost. Net RoI positive.
• Documentation: Model Governance, Lineage, Integration, support documentation customization & analyzable.
• Deployment ready: API, POJO, FTP dumps (APIs can be used to connect with Testing/Research/Analytics tools).
DATA SCIENCE PLATFORM: BUILD VS BUY DECISIONS
Intended for Knowledge Sharing only
The maturity of the analytics set up and specific organizational nuances go into the build vs. buy decision…

33. DATA SCIENCE PLATFORM NEEDS
Intended for Knowledge Sharing only
Specific needs at each step of the Data Science Lifecycle

34. DATA SCIENCE PLATFORM: TOOLS LANDSCAPE
Intended for Knowledge Sharing only
…the effort will be split in standing up the end-to-end platform (H20 Python, Datatron, GitHub, Sharepoint, Rally) and
H20 Driverless in parallel

35. An illustrative set of personalization use cases
Optimize Product Lifecycle
• Strategy
• Experience
• Development
• Management
Metrics: Click Through Rate, Conversion,
%Happy Path, Speed, Distribution
Minimize Risk
• Decrease in Standard Risk
• Successful Prevention Rate
• New Risk Detection Efficiency
• Rule efficiency: FPR/FNR, Agent Reviews,
Reported
• Implementation cost: CXM, CSS
Metrics: Bad Rate Changes, %bad prevented,
%leak through, business KPI impact
Optimize User Journey
• Campaign Strategy
• Performance Attribution
• Funnel Management (Omni)
• Cost Optimization
• Brand Management
Metrics: Awareness, Sentiment, Adoption,
CPE/CPM/CPC, Engagement, NPS, LTV
Optimize Marketing and/or Sales Process
• Goal Setting, Monitoring & Tweaking
• Prospect Scoring & Prioritization
• Lead Funnel Management: Rate, Speed, Cost
• Retention & Growth
• Turnover
Metrics: Topline, Time to Live, Cost of
Acquisition & Retention, Account growth/ NPS
Optimize Strategy & Operations
• Strategy Development & Execution
• Innovation Delivery
• Performance Tracking & Intervention
• Business Operations
• Resource Investment Decisions (Finance)
• Strategic Research: Competitive Monitoring,
Regulatory, Policies, Legal
Metrics: Earnings Growth, Guidance delivery,
Investor Confidence
Optimizely Technology Delivery Cycle
• Development Prioritization
• Delivery Quality & Monitoring
• Cost of Development
• Platform Management
• Scalability: Compatibility, Detection,
Pre-emption & Prevention
Metrics: Uptime, Performance, #Story points to
Develop/Scale/Iterate, #Bugs/Bug Rate
35

36. Illustrative Data Science
Cognitive
Prescriptive
Predictive
Diagnostic
Descriptive
o Dashboards by Functions (e.g., #Checkins, Time Spent, Emotions)
o Real Time Monitoring & Alerts (Flagged phrases, Escalations)
o Visualizations & Storyboarding
o Drilldowns/Segment Insights (Analytics+Research+Testing)
o Investigations, Deviations, Sizing & Opportunity Assessment
o UX Personalization: Individuals & Professional use cases
o Tracking: Sentiment, Themes, Entity, Clustering
o Scoring & Actions: Conversations, Individuals, Seasons, Events
o Preventive Escalations & Interventions: Causations
o Medical Studies, Professional reviews, Intervention Effectiveness
o Grid Strategy for the business & domain
o Core Algorithm augmentation, expansion or retirement
o Scalability: Geo, User Segments, Needs, Domain
36

37. Key Takeaways
•“Design” thinking- anchored to “User-Needs-Context”
•“Engagement” focus right from start
•“Learn-Listen-Test” launch & ramp
•“Partner” ecosystem & support
•“Loyalty” pricing
37