Deriving Value From Big Data Visual, Predictive, GeoLocation and Event Analytics

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Deriving Value From Big Data

Visual, Predictive, GeoLocation and Event Analytics

Nick Young

Solutions Consultant - APJ

[email protected]

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• Analytics

• Insight to Action

• Analytics Journey

• Visual Analytics and Dashboards

• Predictive & Prescriptive Analytics

• GeoSpatial Analytics

• Event Analytics

• Wrap-Up / Questions

• Putting it all together with some examples

Increase

Productivity

Grow

Revenue

Value

Reduce

Risk

ROI

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Execution

Operations

Tactical

Strategic

Time Increment

Seconds/

Minutes

Process Zone

Batch Analysis

Hours / Days

/ Weeks

Months /

Quarters

Quarters /

Years

Real-Time Analysis

Importance of Alerts

Not Important

Important

Necessary

Insight to Action

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Immediate Long-Term Competitive Advantage

Value to the Organization

Get business value across this Analytics Spectrum

Self-service

Dashboards Event Processing

Predictive and Prescriptive Analytics

Measure Diagnose Predict Optimize Operationalize Automate

Analytics Maturity

Insight to Action

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Value to the Organization

Get business value across this Analytics Spectrum

Self-service

Dashboards

Measure Diagnose Predict Optimize Operationalize Automate

Analytics Maturity

Predictive and

Prescriptive Analytics Event Processing

Insight to Action

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Value to the Organization

Get business value across this Analytics Spectrum

Self-service

Dashboards

Measure Diagnose Predict Optimize Operationalize Automate

Analytics Maturity Event Processing Predictive and Prescriptive Analytics

Insight to Action

Analytics

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Value to the Organization

Get business value across this Analytics Spectrum

Self-service

Dashboards Event Processing

Measure Diagnose Predict Optimize Operationalize Automate

Analytics Maturity

Insight to Action

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Bar chart

Line chart

Pie chart

Scatter Plot

Bar chart

Combo chart

Parallel Coord Plot

3d Scatter Plot

Dashboards - Some Basic Charts and Plots

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Graphical Table

Box Plot

Table

Heat Map

Tree Map

Cross Table

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Visual Analytics - Interactive Brush-Linked

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Visual Analytics - Interactive Brush-Linked

3D rotate

Surface

Polar

Contour

Network

Funnel

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Extending the Palette – JS, D3

Sankey

Venn

Chord

Donut

Dials

Gantt

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Map Layer

Marker Layer

Feature Layer

Image Layer

WMS Layer

Map Layers

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• Color

• Shape

• Size

• Relative amounts

• Size

Marker Layer

Feature Layer

• Color

Marker or Feature Layer

• Tooltip

• Labels

Map Elements

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Non Geographic Data – Interactive Charts

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Non Geographic Data – Interactive Charts

Airplane Seating Chart

Football Field Seating Chart

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Value to the Organization

Get business value across this Analytics Spectrum

Self-service

Dashboards

Measure Diagnose Predict Optimize Operationalize Automate

Analytics Maturity

Predictive and

Prescriptive Analytics Event Processing

From Dashboards to Value

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Big Data Analytics

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Data

a

Insight

a

Action

Data Access

& Prep

Exploratory

Data Analysis

Features

Visual

Dashboard

Model &

Predict

Deploy

Model

Test &

Learn

Channel

Churn

Loyalty

Campaign

Filter

Map

Merge

Shape

Propensity

Affinity

Improve

Guided

---

Deploy

---

In-Line

Explore Data

Aggregate

Prepare Data

Business Case

Increase

Productivity

Grow

Revenue

Ensemble Forest Regression Additive Models

Segment

Visualize

Pricing

Promotion

Challenger

Models

At Rest

In Motion

Value

Theses

Reduce

Risk

ROI

Purchase

Dashboard

Updates

Fast Data

Big Data

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April – 21 Customers

Exploratory Data Analysis (EDA) & Features

Features: Telco Churn

• Business Problem: who is likely to leave?

• Insight: people who spend time talking to

people who have already left the plan –

are more likely to leave!

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May – 2 Deactivations

Exploratory Data Analysis (EDA) & Features

Features: Telco Churn

• Business Problem: who is likely to leave?

• Insight: people who spend time talking to

people who have already left the plan –

are more likely to leave!

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June – 4 Deactivations

Features: Telco Churn

• Business Problem: who is likely to leave?

• Insight: people who spend time talking to

people who have already left the plan –

are more likely to leave!

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July – 7 Deactivations

Features: Telco Churn

• Business Problem: who is likely to leave?

• Insight: people who spend time talking to

people who have already left the plan –

are more likely to leave!

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Key Feature

Features: Telco Churn

• Feature: % incoming calls in last <90>

days from customers who cancelled in last

<30> days

• Those with >10% = “churn chatters”

• Feature not in any database !

• Voice a little better than text

• Call count a little better than call length

• Inbound closer than outbound

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Tenure (days)

Calls to Call Center

Time Left in Plan (days)

Time Since Last Call (days)

Features – Telco Churn

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All EM/EM+ Subs Top 5% Per Base Model Top Churn Chatters Feb

Mar Apr May

Comparing Churn Rates Over Time

No Social Networking in the Base Model

Pct

C

hu

rn

in

g

in

Ea

ch

3 0-d

ay

pe

rio

d

0.

00

0.

05

0.

10

0.

15 Social Amplification of Features / Not Long to Act!

Time Left in Plan (days)

Red

= churn chatters

Churn chatters

gone in 90 days

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Contextual Analytics

- Right click

- Forecasting

Contextual Analytics

- Menu

- Machine Learning

Contextual + Simple Predictive Analytics

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Extensible Predictive Analytics – Expressions

Lines by TERR( contourLines(x,y) )

Color by TERR( kmeans(x1,x2) )

Interactive Advanced Analytics

- Expression – on the fly R code

- Points: kmeans(), …

- Lines: contourLines(), spline(), …

- Expression Functions – sharing

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Customers within drive/walk from location (store)

• Store open/close effects

• Competitor response

• Expected sales*products from demographics

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Analysis Workflows – Data Functions

Interactive Analytics with R

- Data Function

- Robust Cluster Analysis

- Any Analysis in R / CRAN

Variables driving segments

- Random Forest

Revenue by product

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Customer

Acquisition

Customer

Retention

Relationship

Growth

Customer

Lifecycle

Customer & Marketing Analytics

Segmentation

Propensity

Affinity

Cross-Sell, Up-Sell

Churn

Test & Learn

Customer Attributes

Purchase History

Social, sentiment

Location

Satisfaction (NPS)

Analytics

Data

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Segmentation: @CIBC

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• Response Data

• Acceptance of credit card offer

• Feature Variables

• Bank transactions: ATM, checking;

deposit, withdrawal, balance, …

• Demographics: sex, age,

profession, nationality

• Customers Identified

Likely to respond to credit card offer

Propensity: Respond to Credit Card Offer

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Citibank saw time-to-market for

new product offerings cut by 80%

(from 8 weeks to 8 days) after

implementing cross-sell programs.

Cross-Sell: @Citibank

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Value to the Organization

Get business value across this Analytics Spectrum

Self-service

Dashboards

Measure Diagnose Predict Optimize Operationalize Automate

Analytics Maturity

Event Processing

From Dashboards to Value

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Campaign

Effectiveness

Offer Analysis

Customer

Analytics

Customer

Experience

Real Time - Data Customer Analytics

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All Login events

tracked and

monitored.

Login Event Capture

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Search events

evaluated

against

Analytics Rules

and Models.

Intent to Cancel – Intervention

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Offer to Customer trying to Cancel (Sarah)

Customer is

given an offer

based on their

profile and

actions.

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Notify Call Center and Sales Team

Web site data

sent to Call

Center and

Sales Team.

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Campaign

Effectiveness

Offer Analysis

Customer

Analytics

Customer

Experience

Real Time - Data Customer Analytics

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Optimize

pricing

Check for

fraud

Make offer

to customer

Restock

inventory

transport

Reroute

Give customer

service

Proactively

maintain machines

Real Time Data Analytics – Interventions

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