Applications with predictive analytics are able to deliver massive value to end users. But what steps should product managers take to add predictive analytics to their applications? In this webinar, we’ll walk through an end-to-end lifecycle of embedding predictive analytics inside an application. Find out how a real-world application decided what predictive questions to ask, sourced the right data, organized resources, built models, deployed predictive analytics in production, and monitored model performance over time.

1. Logi Analytics Confidential & Proprietary
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The Complete Predictive Analytics
Lifecycle for Application Teams
Sriram Parthasarathy Hannah Flynn
With: Moderated by:
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2. Logi Analytics Confidential & Proprietary
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Logi Analytics helps application teams create, deploy, and constantly
improve analytic applications that engage users and drive revenue. In 2000,
Arman Eshraghi founded Logi Analytics, formerly LogiXML, to help web
developers easily embed compelling data visualizations inside websites.
This core technology evolved into the Logi platform, providing an
extraordinarily fast and easy way to embed analytics into any application.
Today, over 1,800 application teams use Logi to create more valuable
applications, engage users, and differentiate their software products.

3. Logi Analytics Confidential & Proprietary
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3
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interact with the presenters
https://www.productmanagementtoday.com/webinar-series/dashboards-that-set-your-app-apart/

4. Logi Analytics Confidential & Proprietary
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About Sriram Parthasarathy
Sriram is Chief Product Owner of the Predictive Analytics platform at Logi Analytics. He works with customers to
embed Predictive Insights directly in to the applications business users use on a daily basis. Sriram has over 20 years
of experience in designing enterprise and OEM Analytical products. Prior to Logi Analytics, Sriram was with
MicroStrategy for 15 years, where, as an early employee, he was integral to building & launching several product /
modules. As a practicing Data Scientist, Sriram is passionate about making it easy for business users to predict what
is going to happen and take preventive actions. In his free time, Sriram coaches kids for competitive Math and Science
competitions.
About Hannah Flynn
Hannah went to The University of Chicago, where she majored in Environmental Studies with a concentration in
Economics and Policy. She now works with Aggregage on social media strategy and webinar production on sites such
as Product Management Today, B2B Marketing Zone, and Supply Chain Brief.

5. Logi Analytics Confidential & Proprietary
Click to edit Master title styleAgenda
 Overview
 Part 1: Predictive Life Cycle
 Identify the Right Question
 Assemble the Data
 Training / Prediction Strategies
 Distribute & Act
 Model Monitoring and Retraining
 Part 2: Live Demo
 Summary
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6. Logi Analytics Confidential & Proprietary
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Predictive Analytics Overview

7. Logi Analytics Confidential & Proprietary
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What
happened?
What will
happen
Take
preventive
action
Newapplications
Oldapplications
Application Strategic Blueprint
Embedded machine-learning based analytics gives a strategic
advantage to your application
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8. Logi Analytics Confidential & Proprietary
Click to edit Master title styleWhat is Machine Learning
Traditional Programming
Data
Rules
Answers
Machine Learning
Data
Answers
Rules
Field of study that gives computers the ability to learn
without being explicitly programmed.
- Arthur Samuel
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9. Logi Analytics Confidential & Proprietary
Click to edit Master title styleSimple Machine Learning Examples
Patient Information
Readmitted or
Not
Readmitted Model
Demographics +
Invoices
Paid late or
Not
Late Payment Model
Data Answers Rules
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10. Logi Analytics Confidential & Proprietary
Click to edit Master title styleIdentify the Data to Answer your Predictive Question
Prep
Source Historical Data
Predictors Objective
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11. Logi Analytics Confidential & Proprietary
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Create Predictive Models Based on Existing Outcomes
from your Historical Data
Prep
Source ModelHistorical Data
Predictors Objective
1. Train using Historical dataset
Algorithms
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12. Logi Analytics Confidential & Proprietary
Click to edit Master title stylePredict Future Outcomes with New Data, On-demand
and in Real-Time
Prep
Source ModelHistorical Data
Predictors Objective
Predicted
Results
New Data
2. Predict on New dataset
Algorithms
1. Train using Historical dataset
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13. Logi Analytics Confidential & Proprietary
Click to edit Master title styleEmbedding Predictive Insights in your Application Gives your
Business a Strategic Advantage
Prep
Source ModelHistorical Data
Predictors Objective
New Data
2. Predict on New dataset
Algorithms
1. Train using Historical dataset
3. Embed & Act on Predicted insight
Predicted
Results
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14. Logi Analytics Confidential & Proprietary
Click to edit Master title styleContinuously Retrain and Improve the Model
Prep
ModelHistorical Data
Predictors Objective
Predicted
Results
New Data
2. Predict on New dataset
3. Embed & Act on Predicted insight
Algorithms
1. Train using Historical dataset
Source
4. Analyze & Retrain with actual results
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15. Logi Analytics Confidential & Proprietary
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15
Predictive Life Cycle Overview

16. Logi Analytics Confidential & Proprietary
Click to edit Master title styleAgenda
Overview
Part 1: Predictive Life Cycle
Identify the Right Question
Assemble the Data
Training / Prediction Strategies
Distribute & Act
Model Monitoring and Retraining
Part 2: Live Demo
Summary
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17. Logi Analytics Confidential & Proprietary
Click to edit Master title style#1. Identify the real problem
1. A problem with significant pain
2. Everyone in the company
understands
3. Has a clear way to measure ROI
in a given timeframe
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18. Logi Analytics Confidential & Proprietary
Click to edit Master title styleExample ROI: Customer Churn
 Retail Customer with 50 million ARR
@ 6% churn
 Annual loss = $3 million
 Goal = 1% savings = $500k per yr
 Question: Identify customers who are
highly likely to churn
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19. Logi Analytics Confidential & Proprietary
Click to edit Master title styleExample: Real-World Predictive Applications
 Will this customer churn?
 Will this customer pay late?
 How many sales orders?
 How many will call my call center?
 Fraudulent transaction / claim
 Incorrect invoice
 High / Low risk cases
 High / Low profitable customers
#1 Predict Future Outcome #2 Predict Future Metric
#3 Identify Anomalies in Real Time #4 Group / Segment Customers
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20. Logi Analytics Confidential & Proprietary
Click to edit Master title styleAgenda
Overview
Part 1: Predictive Life Cycle
Identify the Right Question
Assemble the Data
Training / Prediction Strategies
Distribute & Act
Model Monitoring and Retraining
Part 2: Live Demo
Summary
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21. Logi Analytics Confidential & Proprietary
Click to edit Master title styleData from Multiple Systems Need to be Assembled to
Answer a Predictive Question
Timestamp
Weather
Location
Transactions
Distance Billing
Products Census
 Customer Demographics
 Product Demographics
 Transactions
 Known outcome
Data could be present in different
systems
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22. Logi Analytics Confidential & Proprietary
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Customer
Product /
Service
Transactions /
Interactions
Outcome
Assembling your Data for Solving a Predictive Problem
Example: Customer churn
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23. Logi Analytics Confidential & Proprietary
Click to edit Master title styleAgenda
Overview
Part 1: Predictive Life Cycle
 Identify the Right Question
 Assemble the Data
 Training / Prediction Strategies
 Distribute & Act
 Model Monitoring and Retraining
Part 2:
 Live Demo
Summary
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24. Logi Analytics Confidential & Proprietary
Click to edit Master title styleFive Common Questions from Customers for Model Training
 How to give more importance
to the latest data?
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 How to formulate the right
question?
 What Algorithm to use?
 How much Data is needed?
 How to deal with different
regions / products?
Question depends on what will you
do with the answer?
Train with few and pick the one
with the best accuracy
Statistical data samples
Combined or individual model
Example: Q1 , Q2 & Q3 model and
do a weighted average

25. Logi Analytics Confidential & Proprietary
Click to edit Master title stylePrediction Strategy
Real time prediction
Customer calls the support trigger real time prediction
On demand
New data comes in @ 7 PM. Trigger prediction @ 9 PM
and write the predictions to a database
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26. Logi Analytics Confidential & Proprietary
Click to edit Master title styleAgenda
Overview
Part 1: Predictive Life Cycle
 Identify the Right Question
 Assemble the Data
 Training / Prediction Strategies
 Distribute & Act
 Model Monitoring and Retraining
Part 2:
 Live Demo
Summary
26

27. Logi Analytics Confidential & Proprietary
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Distribute Predictive Insights to End Users, other
Applications and Processes
Predictive
Insights
Embed predictive insights
in to your application
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28. Logi Analytics Confidential & Proprietary
Click to edit Master title styleEnable in Application Workflows to Recommend Actions
Users Can Take
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29. Logi Analytics Confidential & Proprietary
Click to edit Master title styleMultiple Models Intelligently Running your Business
Engage
Model Predict best campaign to engage
2
Late Payment
Model
Predict who will pay late1
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30. Logi Analytics Confidential & Proprietary
Click to edit Master title styleAgenda
Overview
Part 1: Predictive Life Cycle
 Identify the Right Question
 Assemble the Data
 Training / Prediction Strategies
 Distribute & Act
 Model Monitoring and Retraining
Part 2:
 Live Demo
Summary
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31. Logi Analytics Confidential & Proprietary
Click to edit Master title styleContinuous Improvement / Deployment
Semi-Automate the Process to
retrain and redeploy
Model Accuracy
Predicted Volume
Accuracy from predictions
80%
300
72%
Behaviors may have changed
Retrain recommended
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32. Logi Analytics Confidential & Proprietary
Click to edit Master title stylePoll: Have you Considered Predictive Features for your
Product?
1. Its in my roadmap for the next 12 months
2. My customers have asked for it
3. My competitor already has it
4. My management is not sold on it
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33. Logi Analytics Confidential & Proprietary
Click to edit Master title styleAgenda
Overview
Part 1: Predictive Life Cycle Life Cycle
 Identify the right question
 Assemble the data
 Training / Prediction Strategies
 Distribute & Act
 Model monitoring and Retraining
Part 2:
 Live Demo
Summary
33

34. Logi Analytics Confidential & Proprietary
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34
Live Demo

35. Logi Analytics Confidential & Proprietary
Click to edit Master title styleTelecom Customer Churn
Question: Will this customer churn?
Input Data: Historical Churn data
New Data: Predict on new customers
Action: Engage with customers who will churn or
do cross sell / up sell with customers who
will not churn

36. Logi Analytics Confidential & Proprietary
Click to edit Master title styleAgenda
Overview
Part 1: Predictive Life Cycle
 Identify the right question
 Assemble the data
 Training / Prediction Strategies
 Distribute & Act
 Model monitoring and Retraining
Part 2:
 Live Demo
Summary
36

37. Click to edit Master title style
37 | LOGI ANALYTICS
Summary: Roadmap for Next 12 months
1.Pick a well-defined predictive problem with good ROI
2.Leverage your in-house application team
3.Predict, Embed, and Act using the insight
4.Time to market is key. Get started and iterate with
customer feedback
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38. Logi Analytics Confidential & Proprietary
Click to edit Master title stylePoll: Would you like to see a custom Predict demo using
your data?
1. Yes
2. Not at this time
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39. Click to edit Master title style
39 | LOGI ANALYTICS
Take Logi Predict for a Spin Using your Data
LogiAnalytics.com/Predict

40. Click to edit Master title style
40 | LOGI ANALYTICS
40
Q&A
Hannah Flynn
With: Moderated by:
Senior Director of Predictive Analytics, Logi Analytics
Linkedin page: /in/srparthasarathy/
Twitter ID: @logianalytics
Website: logianalytics.com
Sriram Parthasarathy
Site Editor, Product Management Today
Linkedinpage:/in/hannahmichaelflynn
TwitterID:@prodmgmttoday
Website:productmanagementtoday.com
https://www.productmanagementtoday.com/webinar-series/dashboards-that-set-your-app-apart/