turning discovery analytics into operational action · pdf filemarketingmarketing executives...

December 9, 2013

Turning Discovery Analytics into Operational Action

2 Teradata Confidential Teradata Confidential 12/13/2013

New business insights from all kinds of data with all types of analytics for all types of enterprise users with rapid exploration

Data

Large Volumes

Interaction Data

Structured

Multi-structured

1

Analytics

Relational/SQL

MapReduce

Graph

Statistics, R

Time Series

2

Users

Business Users

Analysts

Data Scientists

3

Discovery/Action

Fast

Iterative

Investigative

Action

4

Big Data Discovery Analytics Alignment Big Data Discovery is about a process to provide:

UNIFIED DATA ARCHITECTURE

ACCESS MOVE MANAGE Marketing

Executives

Operational

Systems

Frontline

Workers

Customers

Partners

Engineers

Data

Scientists

Business

Analysts'

Math

and Stats

Data

Mining

Business

Intelligence

Applications

Languages

Marketing

APPLICATIONS

USERS

DISCOVERY ANALYTICS

INTEGRATED DATA WAREHOUSE

ERP

SCM

CRM

Images

Audio

and Video

Machine

Logs

Text

Web and

Social

SOURCES

DATA MANAGEMENT

USUALLY HYBRID: • ENTERPRISE ETL • CUSTOM ETL • ENTERPRISE BAR • HADOOP/ELT

TERADATA ASTER


Iterative Analytical

Ideas

Visualize & Evaluate Results SQL and non-SQL

Analysis

Operational DB or EDW

Operationalize or Move On

Faster Discovery

Discovery Analytics Iterative Discovery Approach Accelerate and Operationalize New & Supplemental Insights

New Data Integration


Aster Discovery Analytics Visualization Options

• A visualization tool built upon Aster’s SQL-

MapReduce framework.

• Browser-based visualizations are produced

using result sets from popular Aster

operators such as nPath and cFilter.

• GraphGen is perfect for visualizing path &

pattern and graph for iterative analysis.

• Tableau and MicroStrategy connect to

the Aster platform using ODBC.

• Aster’s relational output is easily fed

into and visualized by these partner

tools (and other standard BI Tools).

• Great for visualizing query results and

for interactive reporting.

Visual SQL-MapReduce® Functions

http://en.wikipedia.org/wiki/File:QlikTech_20xx_logo.svg


Discovery Analytics Approach Example: Multi-Channel Attrition & Probability

Acquire and Prepare

Rapidly Iterate Analysis

Combine Analytical Techniques

Operationalize Insights

• Create 360° view of customer based on interaction data across channels • Acquisition: Acquire data from data mgmt. tier, direct sources and relational systems • Preparation: XML and Apache Log parser; Sessionization

• Rapidly iterative analysis in secs and mins on all data • Analyze interaction paths leading to account closure • Functions: nPath (cross channel time series pattern analysis)

• Discover: new at risk churners and new behaviors • Multi-Genre Analytics : Text Analysis + Path Analysis

+ SQL + Statistical Predictive Modeling • Functions: nPath; Text Categorization;

Attribution; Naïve Bayes

• Retain at risk customers through proactive timely and relevant action

• Integrate insights with Integrated Data Warehouse, Real Time and Campaign Mgmt. and Call Center applications


• Acquire and prepare all types of interaction data from all channels and transform using MapReduce capabilities into common format (row/column)

• Start with 2-3 channels to validate business use case and grow over time

Step 1: Acquire Multi-Channel View of Customer

Text Category (Intent)

Page Type, Action

Branch ID, Mgr ID, Event

Event ATM ID, Event, $$

Teller ID, Question ID, Response ID

Online Branch ATM Call center Email Store Survey

Teller ID, Event

Discovery Platform


• Identification – Identify the customer or groups of customers in the data

• Sojournize – Stitch together sessions to recreate cross-channel journey

07:05:32 09:20:23 09:25:32 11:05:48 1:05:06 1:35:12 1:42:58 1:45:14 3:05:58 4:15:22

Recreating the Customer Journey Through all Interactions

Step 2: Prepare the Data for Iterative Analysis


Step 3: Perform Iterative Discovery Analysis See patterns and paths of events across time leading to “events”

Event Path to “Event of Interest”

Network

Care

Billing

Offers

Online Survey

Web Self-Service

Collections

Store

IVR

Identification Window

(Proactive)

Correction Window

(Reactive)

Reactive Window

(Too late)

Event of Interest - Purchase - Churn - Fraud - Other events

Business Question(s):

• Are there any identifiable patterns of behavior across time and all customer interaction channels prior to a specified “Event of Interest”?

• If so, what does this pattern look like?


Integrated with CRM, IVR, Web, BI, & Enterprise Apps

Customer Browses

Web Logs Captured

CAPTURE | STORE | REFINE

INTEGRATED

DATA WAREHOUSE

DISCOVERY

PLATFORM

Analytic Discovery Operational Deployment

This is the critical step to drive value!

Step 4: Take Actions on New Insights High-Level Concept Flow of making it operational


Cable: Gateway Product & Services Affinity

Cable Services

Voice Services

Broadband Internet

Interactive affinity graph analysis

It can show product and service

recommendations and relationships across all offerings, specifics categories or even for a

single customer.

It is easy to generate by running an out-of-box SQL-MR function

and visualization operator.

One could even generate a personalized, high resolution recommendation (PDF file)

for every customer!

Identify “gateway” links and connections into other products/services, categories and sub-relationships indicated by different colored

nodes. Node color is automatically calculated based on recommendation clusters.


Manuf: Warranty Part Failure Affinity Analysis Interactive Sigma Graph


Retail: Time Based Affinity & Recommendations Time Ordered Collaborative Filtering Cuff Links are usually “first in basket”.

Time based collaborative filtering + instant

visualization clearly shows many arrows

pointing away from the cuff links with a

high rate (> 30%) of successive

purchases.

In contrast, the cuff links are less

frequently placed in the basket after other

items (< 5%).


Healthcare: Medicare Complaint Paths

• Reduce the noise to find the signal in

the data. Iterative analysis enables the

analyst to discover interesting patterns

and trends.

• Use Standard SQL and BI Tools in

order to refine result sets and focus

only on specific paths and patterns in

the data.


Telco: Post Activation Behavioral Path Analysis (predict customer service post activation, new bundling opps,...)


Use Case Business Qualification Process

1. What is the potential business use case focus?

2. What is the current analytical process being used to address this use case?

3. What data sources are needed and available to address this use case?

4. Are there dedicated data/business resources assigned to this use case?

5. What is the estimated business value of addressing this business use case?

6. What is the success criteria and how will it be measured and validated?

7. Who is the business sponsor for this use case project?


Key Takeaways

1. Prioritize Use Cases Based on Qualified Business Value

2. Identify Existing Resources and Hire to Fill Dedicated Gaps

3. Make sure it is Actionable & Measurable (Avoid Science Projects)

4. Iterate (Repeat the Use Case Qualification Process)


Thank You!

Questions

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