Post on 06-Dec-2014
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Predictive Analytics: Executive Primer
Ryan WithopLead Predictive Analytics & Product StrategyYouSendIt.com
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YouSendIt
Online, mobile, desktop solutions for sharing files that are too big for email
Basic level service – free for majority of usersPremium service – expanded file size / storage capability for monthly or annual recurring costEnterprise service – Multi-seat, Direct Sales
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Ryan Withop
Lead Predictive Analytics at YouSendIt
Who are our best customers? What actions lead to paid subscriptions?
Previously founded Dash Media, song recommendation engine for digital radio
What song is the best match for you right now?
@RyanWithop
(The opinions expressed here are my own, and do not necessarily reflect the opinions of my employers.)
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What is Reporting and What is Predictive Analytics?Reporting:
What happened?
Predictive Analytics: Which users might react to a given input?
Ok to raise price
Likely to cancel in 30 days – win back?
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Examples of Reporting vs Predictive Analytics
Reporting: Is that peak in registrations due to my external ad campaign last week?
How much revenue did we see this quarter?
Predictive Analytics: Who is likely to register in response to my ad campaign?
How much revenue could I expect if I target a specific group?
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What can you do with Data Mining? (Predictions)1. Decision Trees -> Business Rules, Scoring
ex. If you save 4 movies and came from this channel, you will convert at X%.
2. Clusteringex. What people/companies acting like this company we closed?
3. Time Series and Sequence Analysisex. User went to this page then this page, so serve them an ad for this.
4. Anomaly Detectionex. Who are the early adopters, heavy users, subscribers, spammers?
5. Market Basket – Association Analysisex. People who buy these products (or anything together) also buy these
6. Social Network Analysisex. Which groups are collaborative? Which are fraudulent?
Text Mining – Identifying brand sentiment, topics, clustering usersAbc
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How has Predictive Analytics been used at YouSendIt?Users from SEM entry points, in specific industry, who send a
professional file on Day 0 had 10x likelihood of paying
People who use “save” feature only once act like other non-payers
Moving users from web to apps -> 20% increase in lifetime value
Spammers / Scammers high usage indicate fraudulent payments
People who expired their files manually, also used Password Protection features
These users are at center of network - keep 10 users subscribing
Sales comments with “individual account” mentioned have higher likelihood of upsell/consolidation of seatsAbc
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VisionYou have a right to know which user has the highest likelihood of responding to what message, and what time
Predictive Analytics can automate that selection
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Technologies & Methods for Further Research
IBM SPSS Modeler – predictive algorithms with solid user interface and text ontologies
R – free, open source, command line predictive algorithms
NodeXL – Social Network graphing
ManyEyes – word visualizations
Python – open source programming language, good data libraries – easiest to write with
Neural Networks – algorithms similar to networks in brain
Ensemble models – combines the best of each algorithm into a super algorithm
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More Resources for Starting in Predictive AnalyticsCoursera
“Machine Learning”, Andrew Ng, Stanford Professor“Intro to Data Science”, Bill Howe, University of WA
Practical Text Mining by Andrew Fast, PhD
www.statsoft.com/Textbook/Data-Mining-Techniques
Job Titles: Data Scientist, Data Modeler, Data Mining, Predictive Analytics
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Questions?
@RyanWithop