Roadmap for Systems Analytics - product & business solutions; theory & algorithms.

1. SYSTEMS Analytics – present & future
PG Madhavan, Ph.D.
Chief Algorist
Syzen Analytics, Inc.
Seattle, WA, USA

2. Practical Framework for Prescriptive Analytics
2
 Prescriptive Analytics is NOT “one and done”
 Need “closed-loop” solution.
 TRACKING solution much like “flu shots”
 Treat, monitor, update & treat again over time.
 “SYSTEMS Theory” is the natural approach.
Systems Analytics Roadmap!
Models
ad hoc
State-space Models
Network Models
Spatio-temporal Models
2015
2016
2017
2018
GROUP
Recommendation Engine
Closed-loop &
Real-time Social Network
Integration
“Global Effects”
1. Retail Chains: In USA & India with many 100 stores
2. Consumer Product Goods firms: Majors in USA
3. Retail technology OEMs: Global leaders

3. 44

4. 5
Models
ad hoc
State-space Models
Network Models
Spatio-temporal Models
2015
2016
2017
2018
GROUP
Recommendation Engine
Closed-loop &
Real-time Social Network
Integration
“Global Effects”
Systems Analytics Roadmap

5. 6
Products & Business Solutions Roadmap
GROUP Recommendation
Engine
1. Retailer & CPG
2. Product Assortment .
3. E-commerce warehouse.
4. Ad buy mix.
5. . . .
Models
ad hoc
State-space Models
Network Models
Spatio-temporal Models
2015
2016
2017
2018
Closed-loop & Real-time
1. IoT
2. Retail business
solutions.
3. Mobile management.
4. Machine monitoring.
5. Fraud detection.
6. Payment monitoring.
7. CRM.
8. Gaming.
9. JIT marketing.
10. . . .
Network flow
1. Viral marketing
2. Infection spread
monitoring.
3. Health monitoring.
4. “Process control”.
5. . . .
Global effects
1. Large scale effects.
2. Dynamic flow control.
3. . . .

6. Theory & Algorithm Roadmap
Model-based Clustering
1. Projometry™.
2. Eigen-decomposition.
3. Kernel PCA.
4. . . .
Models
ad hoc
State-space Models
Network Models
Spatio-temporal Models
2015
2016
2017
2018
Closed-loop & Real-time
1. Kalman filer.
2. System identification.
3. ML unification.
4. . . .
Network Graph
1. Social network effects.
2. Time-delay models.
3. Infection models.
4. . . .
Global effects
1. Random Field theory.
2. New theories.
3. . . .
Already Developed
1. Projometry™ algorithm
2. ML Unification
3. Network effect – basic theory & spatial algorithm
4. Random Field theory – basic theory & Scale of Fluctuation algorithm
6

7. Milestone Details

8. Recommendation Engine – for Groups
10
Behavioral Segmentation Projometry™
Local Store Shopper Sub-population in DSGs
Local Store PROPORTIONAL
Assortment Shares
Systems Analytics
Algorithms
(non-standard)T-log
Segregated
DOMINANT Shopper Groups (DSGs)
Based on
Purchase DATA

9. Systems Analytics – Retail Commerce
11
Figure 3: Canonical model of Retail commerce DEMAND chain.

10. Systems Analytics - Introduction
12
Model: Structure Learning Algorithm Objective
Perceptron Perceptron nodes Perceptron Algorithm
Find g  f
Logistic Regression Multiple Regression Gradient Descent
Neural Network Multiplayer Perceptron Back Propagation
SYSTEMS State-space Kalman Filter
Table 1: Systems Analytics and how it fits within ML taxonomy.

11. Systems Analytics for Retail Merchandising
13
LIF-ML Framework
1. We have a framework for a
closed-loop Analytics solution.
2. We have a framework for real-
time Analytics.
• Goal-seeking solution that
improves and delivers results
over time.
• Time period may be days or
milliseconds depending on
the application.

12. Spatio-temporal Network Dynamics
14

13. Network Dynamics – behind the scenes
15

14. Random Field Theory
16
Scale of Fluctuation: “θ”
• Spatio-temporal &
other multi-
dimensional data.
• Coupled systems.
“Network Dynamics & Coupling:
Shannon’s Reverie Reprised”
“Random Fields: Analysis & Synthesis”
- Erik Vanmarcke, MIT Press, 1988

15. Machine Learning Unification
17
• Recommendation Engine
• Language Translation
• Fraud Detection
• and many others . . .

16. 1818
Article link & Contact Information
“Future of Analytics – a definitive Roadmap”
http://www.jininnovation.com/FutureofAnalytics_Reformat.pdf