LYTIQ GmbH Vorhersagen besser treffen mit Predictive...

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LYTIQ GmbH

Vorhersagen besser treffen – mit Predictive AnalyticsKöln, April 2015

Jun.-Prof. Dr. Artus Krohn-Grimberghe

Chief Data Scientist

April 2015, LYTIQ GmbH. Jun.-Prof. Dr. Artus Krohn-Grimberghe

Agenda

Introduction

Predictive Analytics Use Cases

An Azure Architecture for

Predictive Analytics

Azure Machine Learning Demo


IntroductionLeveraging Predictive Analytics and Machine Learning

- From structured and unstructured data

- For Big Data and small data scenarios


LYTIQ employees have worked for the following customers


What is Machine Learning / Predictive AnalyticsFind out how Machine Learning & Predictive Analytics works

to better understand where and how to apply it.


What is Machine Learning (ML)

Making

Predictions

X

Input Data

Customer

attributes & past

transactions

y

Purchase

Indicator

Target Data


What is Machine Learning (ML)

Making

Predictions

X 𝑦

Input Data

h( )So we try some

functions h that

we do know and

see which does

best

h(X) generates

predictions 𝑦.

Should be close to

original 𝑦.

Target Data

Customer

attributes & past

transactions Purchase

Predictions


What is Predictive Analytics (PA)

It is an oversimplification…

… and as with all marketing terms there is lots of dispute…

But to me:

Predictive Analytics is applying ML to business problems.

I.e.:

Customer churn should be predicted.

Historic data is available.

Available data actually explains that what should be predicted.

Data is shaped to differentiate between customers in the various lifecyclestates.

Now ML models are built to score the customer‘s propensity to churn giving hisbehaviour.


Machine Learning / Predictive Analytics Use Cases Find out how Machine Learning & Predictive Analytics can help you

make complex decisions better and faster.


Predictive Analytics Now Span Several

Industries

Web app

optimization

Smart meter

monitoring

Targeted

Advertising

Life sciences

research

Fraud

detection

Healthcare

outcomes

Weather

forecasting

Natural resource

exploration

Social network

analysis

Telemetry Data

Analysis

Buyer Propensity

Models

Predictive

Maintenance

Churn Analysis

Network Intrusion

Detection


Predicting Part Maintenance

No sensor for measuring the thickness

of a brake pad or wear of a tire• Need to predict when a failure will occur.

Inform the driver• Change of parts might be necessary in a few

weeks

Order parts and prepare an individual

maintenance package before the driver

comes to the garage

Analytical Task: • Predict the possibility of next break pad failure using data collected from sensors and telemetry

1

3

2

Assigndrivers into categories • Sports driver, Grandma / Grandpa driver

Provide offerings based on

driver type• Sports package for the sports driver

Make a special offer for the

next car• Sporty look for the sports driver

Analytical Task: • Cluster the relevant diagnostics data to find groups of drivers with common driving habits

1

3

2

Use Case: Customer Segmentation


Use Case: Improving Vehicle Recalls

OnBoard

Storage

Aggregated

Data

Archive

Additional Data Collection for selected vehicles

Scored Vehicles

On service

contact


Use Case: ATM Failure Prediction


Other IoT Use Cases

Improved Production Monitoring

- Trace errors in production to machine components and process steps

- Dynamic production tolerances

Product Lifecycle Management

- Find true requirements for new components

- Improve Weibull Bayes models


Use-Case: Targeted Mailing Optimization

DWH

CMS

Campaign-

Management


Other Predictive Analytics Use Cases

Customer Analytics

- Unified Customer View

- Customer Segmentation

- Market Segments

- Customer Clusters

- Full individual-level personalization

- Customer Retention / Churn Detection

- Prospective Client Analysis

- Dynamic Personalization / Recommendations

- Cross-Selling, Up-Selling

- During car configuration

- For direct sales contact


How about Elevators & Escalators?

Data from *lots* of machines having

dozens of sensors

Data generated per minute (practically

up to seconds)

Event Hubs

Complex Event Processing


Predicting & Optimizing Traffic

Full routes known

Predicting ETA / trip time

Improving ETA predictions

Predicting load on route segments

Detect traffic jams and

consequences

Central traffic planning?

No, social gaming

Load based traffic pricing and taxing?

“Improved London Congestion

Charge”


Improve Logistics to Reduce Traffic

Project proposed (BMBF)

Central logistics planning DB & service

Increase transparency

Reduce trafficLogistics

Marketplace


Ground Image Data

Commercial satellite imagery available

on at least daily basis

planet.com, 2x per day

spire.com, 24x per day

5m or better GSD

Improve traffic models!

Detect roads, (illegal) construction,

land use,…

Can tell different kinds of trees or

crops apart


Other Topics

Joint intelligent transportation systems

Trains, long distance & local commute

Rental cars

Cabs

Bicycles

Electricity

Water

Waste

Pollution

Emergency prediction & management

Other public services


SQL Server

enables data

mining of

databases

Computers

work on users

behalf, filtering

junk email

Microsoft

Kinect can

watch users

gestures

Microsoft

launches

Azure Machine

Learning,

making years

of innovation

available

Microsoft

search engine

built with

machine

learning

Bing Maps

ships with ML

traffic-

prediction

service

Successful,

real-time,

speech-to-

speech

translation

Microsoft & Machine Learning20 years of realizing innovation

John Platt, Distinguished scientist at

Microsoft Research

1999 201220082004 201420102005

Machine learning is pervasive throughout

Microsoft products.“”

Microsoft & Machine Learning


What is Azure ML


Advanced analytics architecture

• HDFS• RDBMS• NoSQL stores• Blobs and tables

ML

Studio

API

Marketplace


Revolution Analytics R

Acquisition still pending close. Treat them as a partner

until then.

http://blog.revolutionanalytics.com/2015/01/revolution-acquired.html

http://blogs.microsoft.com/blog/2015/01/23/microsoft-acquire-revolution-

analytics-help-customers-find-big-data-value-advanced-statistical-analysis

http://blogs.microsoft.com/blog/2015/01/23/microsoft-acquire-revolution-analytics-help-customers-find-big-data-value-advanced-statistical-analysis

http://blogs.microsoft.com/blog/2015/01/23/microsoft-acquire-revolution-analytics-help-customers-find-big-data-value-advanced-statistical-analysis

Microsoft and Revolution Analytics

Strategic Rationale

28

Revolution R

Open

Revolution R

Enterprise

R Language Engine with multi-core processing Included Supported

R Reproducibility Toolkit & MRAN Included Supported

ParallelR: Parallel Programming Toolkit Supported

RHadoop: R interface to Hadoop MapReduce Supported

DeployR Open: Web Services API Supported

RRE DeployR – Multi-server, enterprise authentication Licensed & Supported

RRE ScaleR – Big Data toolkit and PEMAs for R Licensed & Supported

RRE DistributedR – EDW, Grids, Hadoop Licensed & Supported

AdviseR Technical Support Included

Open Source Assurance Included

Revolution Analytics Services (Consulting / Training) Available Available

Search for a model

All Industries

Banking

Education

Government Customer Churn Customer Conversion Product Recommendation House Pricing

Churn Prediction // CH_M5

“We reduced our customer turnover by 25% using

this model, it identified customers most at risk and

enabled us to improve our relationship overall with

our customers.”

Alice Wing, Customer Relations Director, Contoso Corp


LYTIQ GmbH is a Solution Provider for Predictive Analytics.

Contact person:

Jun.-Prof. Dr. Krohn-Grimberghe, Artus

LYTIQ GmbH

Technologiepark 19

33100 Paderborn

+49 5251 682207-0

[email protected]

www.lytiq.de

Whether “Big Data” or “Small Data”-

analytics – we are your reliable partner.


LYTIQ GmbH Vorhersagen besser treffen mit Predictive...

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