Predictive Analytics as a Product

Intended for Knowledge Sharing only

Predictive Analytics as a ProductFeb 2017


Disclaimer: Participation in this summit is purely on personal basis and is not meant to represent VISA’s position on this or any other subject and in any form or matter. The talk is based on learning from work across industries and firms. Care has been taken to ensure no proprietary or work related information of any firm is used in any material.


Quick recap of what it is


Data Scientist, eh…

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FEELS LIKE A ROCKSTAR, DOESN’T IT?

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http://modernservantleader.com/servant-leadership/narcissism-kills-morale/

http://modernservantleader.com/servant-leadership/narcissism-kills-morale/




..BUT A KANYE & NOT COLDPLAY

5https://imgflip.com/memegenerator/7064654/Kanye-West

https://imgflip.com/memegenerator/7064654/Kanye-West




So what happened?

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Intended for Knowledge Sharing onlyIntended for Knowledge Sharing only

SOME CHALLENGES

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Unrealistic expectations on RoI.

Operates in siloes, not complemented by user research/other internal or external data/experimentation results.

Field testing & iterative development still predominantly offline.

Deployment, Post Deployment management & monitoring expensive. Not easy to turn on/off, tweak, flip, scale.

Predictions driven significantly by historical trends and relationships. Expectations modeled as simulations.




Explain it a bit more...

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COMPLICATION 1: PREDICTIVE ANALYTICS IS INTRICATE & COMPLEX

Intended for Knowledge Sharing only 9

Objective

Translation to Analytical Framework

Data Collection and Preparation

Analysis, Validation & Verification

Actionable insights and impact sizing

A/B Testing

Rollouts

• Understand need, fit with Strategic needs, actionability, stakeholders buy-in, engineering RoI, project management

• Decide on the Analytical methodology based on nature of the problem, dependent variable, frequency, sample, time, required precision, actionability

• Hypothesized driver list

• Data Collection: Internal & external sourcing• Data Preparation: Blending, aggregations• Data Transformations: Outlier, Missing, math transformation, interactions, redundancy

treatments, variable selections• Sampling methodology & split

• Model development and validation: In-time, Out-of-time • Stand alone, ensemble• Performance diagnostics & cross check with other sources

• Recommendations, impact sizing, cross leverage scores

• Field Testing (Champion vs. Challenger)• Iteration plan based on user feedback (VOC), performance

• Model deployment, post deployment monitoring & management• Integration with Product Line– New product,

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Description

COMPLICATION 2: MULTIPLE AUDIENCE, PRIORITIES, DEPENDENCIES


Objective

Translation to Analytical Framework

Data Collection and Preparation

Analysis, Validation & Verification

Actionable insights and impact sizing

A/B Testing

Rollouts

• Analyst & Stakeholder

• Analyst, Data Instrumentation, Data Manager, Stakeholder

• Analyst, Data Instrumentation, Data Manager

• Analyst

• Analyst, Stakeholder, Cross Functional team, Leadership

• Analyst, Experimentation Team, User Researcher, Developer, Stakeholder

• Analyst, Developer, Stakeholder, Leadership

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Who does it?

• Agile and may undergo iteration

• Changes in Strategic goals, newer initiatives, releases, discoveries, reorgs

• Sourcing/Blending challenges: Data handovers between systems, blending challenges

• Scalability/automation• Data movements/latencies/

teams/approvals

• Evolution of hypotheses, data changes/errors, success criteria

• Competing priorities, data movements, Scenario Simulations

• Success criteria, integration with research/testing tools, iterations

• Integration with host systems, engineering investment, model tweaking, monitoring, customization

Key Challenges

COMPLICATION 3: OUTPUT OF ONE CAN BE INPUT/ADDITION TO ANOTHER


Behavioral

Merchant Performance

Clickstream/ Ops

Campaign Performance

VOC/Social/ CRM

• Probability of Engagement/LTV Growth/Churn/Loyalty

• Life event changes• Product/Price Migrations

• Probability of Growth/Churn• Next Best Product/Offer• Network partners

• Conversion Rate Optimization

• Server Response Times• Time to Purchase

• Campaign Responses• Next Best Product/Offer• Cross Channel target

• Promoter/Detractor & drivers• Brand Appeal• Theme/entity of

engagement

Data Lake: Enriched

with predictions

e.g., Uber’s cross sell platform,

Google Calendar, VDP

COMPLICATION 4: REAL DECISION MAKING NEEDS ADDITIONAL REASONING BEYOND ANALYTICS


Analytics provides insights into “actions”, Research context on “motivations” & Testing helps verify the “tactics” in the field and everything has to be productized…

Strategy

Data Tagging

Data Platfor

m

Reporting

Analytics

Research

Data Product

s

IterativeLoop Why such

complexity?

Focus on Big WinsReduced WastageQuick FixesAdaptabilityAssured executionLearning for future initiatives

Optimization


COMPLICATION 5: DEMANDS ON PREDICTIVE ANALYTICS HAVE INCREASED

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Predictive Analytics

Behavioral Analytics

What are the customers

doing?

Voice of Customer

What are the customers telling you?

Platform PerformanceHow are you delivering? Competitive

Are the customers

buying elsewhere?

Social ListeningHow are

customers discussing

you?

…aaanddd Better, Faster, Cheaper, Monetizable




So, what do we need then?

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• Extensible• Scalable• Flexible• Easy to integrate

with other techniques


HIGH LEVEL SUMMARY OF NEEDS: MODULAR, SHAREABLE & MONETIZABLE

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keywordsuggest.org Iconfinder WebPT

• Documentation• Governance• Integration with

project management tools (collaboration)

• Security/Privacy Management

• Value Abstraction• API-able

Modular Shareable Monetizable

http://keywordsuggest.org/gallery/339077.html

https://www.iconfinder.com/icons/257513/analytics_dollar_earning_income_monetization_profit_sales_icon

https://www.webpt.com/products/emr




Potential Solutions

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TWO DEPLOYMENT SOLUTIONS- PMML & PFA


Data Mining Group an independent Vendor Led Consortium that develops Data Mining Standards has come up with PMML (Predictive Model Mark Up Language) and PFA (Portable Format for Analytics)

http://www.kdnuggets.com/2016/01/portable-format-analytics-models-production.htmlhttp://dmg.org/https://www.ibm.com/developerworks/library/ba-predictive-analytics4/ba-predictive-analytics4-pdf.pdfhttps://www.ibm.com/developerworks/library/ba-ind-PMML1/http://www.kdnuggets.com/faq/pmml.htmlhttps://journal.r-project.org/archive/2009-1/RJournal_2009-1_Guazzelli+et+al.pdf

PMML PFA

File XML JSON & YAML

Maturity Mature but expanding Evolving

Nesting/Customization Model Parameters

Control Structures (Type System of Model

Parameters & data - Callback function allowed)

FlexibilityStandard across

most scoring engines (better

than custom code)

More flexible than PMML but safer than Custom

Code

ScopeData prep,

Modeling, Scoring, Sharing

+Pre/Post processing, enforced memory model

http://www.kdnuggets.com/2016/01/portable-format-analytics-models-production.html

http://dmg.org/

https://www.ibm.com/developerworks/library/ba-predictive-analytics4/ba-predictive-analytics4-pdf.pdf

https://www.ibm.com/developerworks/library/ba-ind-PMML1/

http://www.kdnuggets.com/faq/pmml.html

https://journal.r-project.org/archive/2009-1/RJournal_2009-1_Guazzelli+et+al.pdf

PMML PROJECTS

Intended for Knowledge Sharing only 18http://data-informed.com/pmml-puts-big-data-to-work/

http://data-informed.com/pmml-puts-big-data-to-work/

POSITIONING OF PFA

Intended for Knowledge Sharing only 19http://data-informed.com/pmml-puts-big-data-to-work/

http://data-informed.com/pmml-puts-big-data-to-work/




Why this, Why now, why here?

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BIGGER TRENDS THAT ARE SHAKING UP THE ANALYTICS WORLD FROM INSIDE OUT…

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Demand Pressures: Complexity and nature of problems and their solutions, type of audience & consumption framework evolving

Monetization opportunities- Direct, Indirect, Recurring

Artificial Intelligence, IoE and “Smart”ening of devices/systems faster than expected.

Evolution of input data sources and integration of multiple insights sources into decision making (A/B Testing, Research, Predictions/Scores from other models)

Evolution from Service to Product to Platform (Build Once, Use Everywhere)

…APIs are eating up our world




The parting words…

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SUMMARY


Predictive Analytics has stopped being “one-off competitive edge project exercise” – it’s a necessary survival initiative for organizationsScale, complexity, breadth of needs (including Monetization) demand Platform approach.

“Build Once, Use Everywhere” -consumption of predictive analytics outputs need to be easy to use, integrate, re-use/collaborate across multiple initiatives

As everything becomes Productized via APIs, together they can become a business problem solving ANI

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Streaming Analytics is quickly evolving into Streaming Predictive Analytics




Appendix

THANK YOU!


Would love to hear from you on any of the following forums…

https://twitter.com/decisions_2_0

http://www.slideshare.net/RamkumarRavichandran

https://www.youtube.com/channel/UCODSVC0WQws607clv0k8mQA/videos

http://www.odbms.org/2015/01/ramkumar-ravichandran-visa/

https://www.linkedin.com/pub/ramkumar-ravichandran/10/545/67a

RAMKUMAR RAVICHANDRAN

https://twitter.com/decisions_2_0











Disclaimer: Participation is purely on a personal basis and does not represent VISA,Inc. in any form or matter. The talk is based on learning from work across industries and firms. Care has been taken to ensure no proprietary or work related info of any firm is used in any material.

Director, Insights at Visa, Inc. Enable Decision Making at the Executives/ Product/Marketing level via actionable insights derived from Data.

RAMKUMAR RAVICHANDRAN

Data & Analytics

Predictive Analytics as a Product