Regardless of the size and nature of businesses, rapid adoption of Machine Learning and Artificial Intelligence is a must-win battle for enterprises. Non-adoption of those can put them in disadvantageous trajectories. Enterprises fail to capture the true potential of ML and AI because of:  Suboptimal approaches due to lack of contextualization of ML and AI to their businesses.  Slow adoption due to the complexities involved in connecting to different data, tools, technologies, techniques, and, nuances of deployment at scale.  Lack of disciplined approaches, operating in silos, and limited collaboration amongst functions and teams.  Ineffective traditional methods to institutionalize the experiences and learnings as well as significant dependencies on human talent. An analytics eco-system that is self-sufficient within an organization and enables all the stake- holders to strive towards a common goal is a solution. In this talk Satyamoy will dwell on the different components of an analytics ecosystem and how Analyttica is operating at the confluence of “Analytics experience”, “Data science expertise”, and “Technology excellence” to create significant value for enterprises in their ML & AI endeavors by via an adaptive analytics ecosystem.

1. An Analytics Ecosystem to Accelerate
Enterprises’ Analytics and AI Journey

2. Value Ecosystem Innovation

3. 85%
of the technology enabled companies
today have a data strategy
77%
of them have implemented some form
of AI technology
31% of them have realized RoI

4. Why
Only
31% ?

5. 01 02
Data
Situation?
03 04
Problem
Definition?
Why
Only
31% ?
Talent
Situation?
Tools &
Technologies?

6. 05
OR
Something Else ?
01 02
Data
Situation?
03 04
Problem
Definition?
Why
Only
31% ?
Talent
Situation?
Tools &
Technologies?

7. How do we measure the return on Analytics & AI Investment?
Acquire
EngageRetain
Example: Large Complex Bank

8. How do we measure the return on Analytics & AI Investment?
Acquire
EngageRetain
Example: Large Complex Bank
Revenue Customer Satisfaction
Cost Risk

9. Descriptive
Analytics
Prescriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics

10. Acquire
New to Bank Existing Customers InactiveProspects
-Acquisition Programs (Pre-
approved, Invitation to
apply)
-Point of sale prospecting
-Acquisition Balance
consolidation
-On-boarding & fulfillment
-Sales Activation programs
-Early MOB management
and retention
- Upgrade potential
-One and done analytics -Incremental sales programs
-Balance growth initiatives
-Cross sell offers
-Credit Line Management
-Customer service & loyalty
programs
-Conversion(Upgrade /
Downgrade) / Migration
-Reward programs
-Sales growth usage offers
-Retention & Win-back
programs/offers
-Inactive reactivation
programs/offers
-Proactive Sales Management
Analytics for Acquisition Analytics for Customer Management Analytics for Retention
DescribePredictOptimize
Prospect Economics
Prospect Segmentation
Prospect Campaign Effectiveness
Prospect Potential
Prospect Fraud Likelihood
Prospect Price Sensitivity
Prospect Offer Response Estimation
Prospect Offer Palette Management
Prospect Offer Optimization
Customer Profitability
Customer Segmentation
Product Usage
Default Likelihood & Severity
Transaction Fraud Likelihood
Product Usage Opportunities
Product Innovation
Service Channel Optimization
Default Management Optimization
Estimate Wallet Share
Customer Campaign Effectiveness
Cross-Sell Win/Loss Analytics
Customer Lifetime Value
New Account Fraud Likelihood
Customer Price Sensitivity
Customer Offer Response Estimation
Event-Driven Offer Optimization
Customer Relationship Optimization
Voice of Customer Analysis
Historical Attrition Analysis
Retention Campaign Effectiveness
Customer Attrition Likelihood
Customer Value-at-Risk
Loss Mitigation Effectiveness
Sentiment-Based Offer Optimization
Macro Economic Data Driven Offer Optimization
Retention Tactic Optimization
Example Use Cases Across the Customer Lifecycle – Credit Card

11. Case
Data
Tool
Talent CostReturn
Revenue
W/O Analytics & AI
Return on Investment

12. Case
Data
Tool
Talent
Revenue
CostReturn
Revenue
Cost
Analytics
Cost
W/O Analytics & AI W/ Analytics & AI
Return on Investment

13. So…, In order to create impact
We need to increase the marginal return on analytics investment
Unanswered business questions
Took longer time than expected
Stakeholders agreed to disagree
Could not deploy at scale
Re-invented the wheel next time

14. Can the answer be a cohesive localized
analytics and AI Ecosystem?
The Data Guy The Analytics TranslatorThe Business Guy
The Math GuyThe Deployment GuyThe Governance Guy

15. The Localized Analytics & AI Ecosystem
The Business Guy The Data Guy The Analytics
Translator
The Governance Guy The Deployment Guy The Math Guy
Contribute
to
Consume
from
Contribute
to
Consume
from Contribute
to
Consume
from
Contribute
to
Consume
from
Contribute
to
Consume
from
Contribute
to
Consume
from

16. The Localized Analytics & AI Ecosystem
The Business Guy The Data Guy The Analytics
Translator
The Governance Guy The Deployment Guy The Math Guy
Background,
Objective,
Purpose
Attain the
business
metrics Volume,
Velocity,
Variety,
Veracity
Pattern to
optimize
data strategy Business
problems to
analytics
objectives
Optimize the
impact
formulation
Pattern for
balanced
approach
Ensure
adherence to
policies
Recommend
ations for
timely
actions
Implement
solutions at
scale
Data and
feedback
loop
Apply
algorithms

17. A Balanced Localized Analytics
& AI Ecosystem

18. ATH Precision presents an intelligent man-machine
ecosystem for building and productionalising data
analytics and machine learning solutions via a tool
and system agnostic framework; which enables
agility, scalability, leverage, and governance

19. ATH Precision presents an intelligent man-machine
ecosystem for building and productionalising data
analytics and machine learning solutions via a tool and
system agnostic framework; which enables agility,
scalability, leverage, and governance

20. satyamoy.chatterjee@analyttica.com
https://www.analyttica.com/precision
Thank You

21. APPENDIX

22. www.beres.com
ML/AI Solution Development and Operationalization Process
Deployed solution is triggered each time an input is sent
through API and returns output
End
Consumer
Client
Application
Automated data queries to fetch the data for
model development and retraining
Database SQL Services
Assess the need to develop a model, contextualize the in-built
ML algorithms and learnings from the past projects
Machine Learning Institutionalize
A
T
H
Model results are continuously monitored for accuracy & re-trained at regular frequency.
Model Training, selection, solution creation & deployment is done through ATH.
Data
Processing
Model
Development
Solution
Extraction
Solution
Deployment
A
T
H

23. www.beres.com
ML/AI Solution Adaptive Machine Learning Process
Model Training – First Time Model Implementation – Continuous Model Retraining – Continuous
Historical Data
Pre-Processing
Data Treatment
Feature
Selection
Model Iterations
and Final Model
Selection
New Data
Pre-Processing
Data Treatment
Feature
Selection
Model
Retraining
Historical Data
Feedback
Consolidation
Feedback Loop
Correct Incorrect
Prediction/ Prescription
New Data
Pre-Processing
Data Treatment
Feature
Selection
Leverage
Trained Model
Adaptive
Machine
Learnin
g