This document discusses using artificial intelligence to increase revenue on ecommerce websites. It outlines the CRISP-DM process for data mining, including defining the business problem, identifying and cleaning data sources, building and testing a model, and deploying and maintaining the model. The document also describes how AI can be used to recommend products to customers, predict customer behavior, and analyze similar customers to dictate trends.

1. Product for Artificial Intelligence

2. 1
q Product Managers driving AI initiatives
q Product Managers who want to drive AI initiatives
q Just curious because AI is a “buzz word”
Who do we have in the audience?

3. 2
INCREASE REVENUE ON AN ECOMMERCE WEBSITE
q Recommend the right products to the right customers at
the right time
q Target Customers with products having an increased
propensity to buy
q Dictate Trends
q Predict Customer’s behavior
q Analyze look a-likes

4. CRISP-DM (CROSS INDUSTRY PROCESS FOR DATA MINING)
Define Business
Problem
Identify Sources of
data and lineage
Extract and Cleanse
Data
Define and Build Model
Test ModelDeploy ModelMaintain Model
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5. 4
q Why
q What
q Metrics
q Budget
q Resources
q Timeline
Define Business Problem and Metrics

6. 5
q Available & Complete Data
q Privacy
q On Premise or Cloud
q Batch or Streaming
q Internal or External
q Lineage
Identify Data Sources and Lineage

7. 6
q Data Acquisition
q Extract Transform Load
q Store in Repository
q Structured vs Unstructured
q Validate Data
Extract and Cleanse Data

8. 7
q Select Model
q Build
q Feature Engineering
q Select Relevant Attributes
q Identify Patterns
q Discover Insights
Define and Build Model

9. 8
q Train the model
q Fit the model
q Evaluate for Bias
q Report
q Tune the model
q Retrain
Test Model Iteratively

10. 9
q Stabilize
q Scale for deployment
q Publish as a web service
q Store in repository
q Online vs Offline
q Document
Deploy and Operationalize at Scale

11. 10
q Changes in data
q Test Periodically
q Update model
q Store
q Document
q Track usage
Maintain Model Relevance

12. 11
PULLING IT ALL TOGETHER
Increase Sales on
eCommerce site
Behavioral,
demographic,
click through
ETL, Quality
Look a likes,
Recommendations
Test, Train,
Monitor
Operationalize
@Scale
Maintain,
Document, Test
Data
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13. 12
Team Structure with Roles and Responsibilities
Product
Manager
Data Engineer
Data Scientist
UX Designer
Program
Manager
Build Program
Plan
Build POC with
UX
Collect data,
Build scrapers Integrate APIs
Build Model Deploy Model Maintain Model
Manage Program
Plan
Report Results
Manage Model
Library
Define Business
Problem
Business
Problems Library
Data Cleanup at
scale
Pull internal &
external data
Data Collection
at scale
Update UX
Design
Test Model
Re-define
Problem
Measure Business
KPIs
Data Preparation

14. Five Phases in the Maturity of Analytics
From Reactive (“Look Back”) to Proactive (“Looking Ahead”) to Cognitive (“Mimic Human Behavior”) …
Prescriptive
Analytics
(What actions
are needed?)
Predictive
Analytics
(What will
happen/why
will it
happen?)
Diagnostic
Analytics
(Why did it
happen?)
Descriptive
Analytics
(What
Happened/is
happening?)
Cognitive
Analytics
(What
decisions
would a human
make?)
Analyze intentions/
sentiment/influenc
e to predict future
behavior
Analyze patterns
of buying behavior
and target
offering to the
group using
optimization/
simulation and
multi-criteria
decision modeling
• Ingest vast volumes
of data to detect
patterns and
interpret/predict
human behavior
• Boost
personalization of
experiences
• Co-relate
sentiments to
corresponding
business events
• Cause and
effect analysis
using inferential
statistics,
behavioral
analytics
Identify emerging
trends by
clustering themes
and topics
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15. Technologies supporting the phases in Maturity
• Business
Reporting
• Scorecards
• BI
• Predictive
Modeling and
Statistical
Analytics
• Regression
Analysis
• Forecast
Modeling
• Agile
Dashboards
• Cause and
Effect
• Correlations
• Behavioral
Analytics
• Data and
Text Mining
• Optimizations
• Artificial
Intelligence
• Machine
Learning
• Simulations
From Reactive (“Look Back”) to Proactive (“Looking Ahead”)…
Prescriptive
Analytics
(What actions
are needed?)
Predictive
Analytics
(What will
happen/why
will it
happen?)
Diagnostic
Analytics
(Why did it
happen?)
Descriptive
Analytics
(What
Happened/is
happening?)
Cognitive
Analytics
(What
decisions
would a human
make?)
• Behavioral
Journey
Science
• Real time
personalization
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16. 15
Thoughts and Reactions?

17. Thank you