Quality Progress - September 2017 - 37

TA B L E   1

Social media analytics products
Company

Tool

Description

IBM

SPSS
modeler-
Premium

Ability to process structured, unstructured and semi-structured
data to discover relationships between concepts and sentiments,
and create the most accurate models possible. This includes data
mining, text mining and web mining.

SAS

SAS
analytics

Provides an integrated environment for predictive and
descriptive modeling, data mining, text analytics, forecasting,
optimization, simulation and experimental design.

Oracle

Oracle
advanced
analytics

Provides comprehensive platform for real-time analytics
that delivers insight into key business subjects, such as churn
prediction, product recommendations and fraud alerting.

Microsoft

XLMiner

Provides a comprehensive data mining add-in for Excel.*

*Source: Galit Shmueli, Nitin R. Patel and Peter C. Bruce, Data Mining for Business Intelligence:
Concepts, Techniques and Applications in Microsoft Office Excel With XLMiner, Wiley, 2011.

leads to fact-based decision making and complex problem solving at strategic, tactical and operational levels.
Organizations can benefit by supplementing traditional statistical data analysis tools with advanced
analytics tools. Benefits of doing this include:8-11
+ Gaining new insights.
+ Identifying interesting and meaningful new hidden
patterns and unknown associations.
+ Identifying changes, anomalies and trends.
+ Identifying risks and appropriate mitigation plans.
+ Saving time, money, resources, and optimizing business processes and performance.
+ Understanding, predicting and anticipating consumers' attitudes, behaviors, priorities and preferences to
deliver innovative products and services.
+ Making smart, accurate, timely and effective decisions
at operational, tactical and strategic levels.
+ Breaking silos and applying systems thinking to solve
problems, answer questions, engage customers and
improve stakeholder satisfaction.
+ Gaining a 360-degree view of operations and
customers.

Advanced analytics software

Leading IT companies such as IBM, SAS, Microsoft and
Oracle released social media analytics products that can
be integrated with existing data analysis tools. These are
summarized in Table 1.
According to Paško Konjevoda and Nikola Štambuk,
open-source advanced analytics software such as R
and Weka is completely comparable to commercial
software in terms of functionality and reliability.12 Other

examples of open-source advanced analytics software
are Tanagra, Orange and Past.
Some scenarios in which organizations can leverage
advanced analytics include:
+ Formulating or adjusting strategies by evaluating
strengths, weaknesses, opportunities and threats.
For example, a health insurance organization might
use information gathered through advanced analytics
to work with healthcare providers to devise a new
healthcare strategy, such as offering remote healthcare for certain segments of members.
+ Identifying an untapped customer segment that
could lead to a new product or pricing strategy. A
health insurance organization using information from
advanced analytics may decide to offer nontraditional
health plans-such as consumer-driven health plans-
to certain segments of members.
+ Redesigning or changing existing processes or
removing bottlenecks in a current process. A health
insurance organization call center manager may use
advanced analytics for the call center staffing process
or the call center maintenance process. Changes to the
call center staffing process may include adjusting call
center staffing rates based on an analysis of current
and projected call volume gathered through advanced
analytics. Changes to the call center maintenance process may include adjusting website maintenance hours
and call center hours to meet customer needs based
on a real-time review of web use logs, social media
information and call logs.
+ Identifying potential customers. A health insurance
organization can do a targeted marketing campaign to

qualityprogress.com ❘ September 2017

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Table of Contents for the Digital Edition of Quality Progress - September 2017

Seen and Heard
Progress Report
Mr. Pareto Head
Career Coach
Expert Answers
Field Notes
Data Disruption
The Deal With Big Data
Better Intelligence
A Study in Measurement
Innovation Imperative
Statistics Spotlight
Standard Issues
ASQ's 2017 Quality Resource Guide
Marketplace
Footnotes
Back to Basics
Quality Progress - September 2017 - Intro
Quality Progress - September 2017 - cover1
Quality Progress - September 2017 - cover2
Quality Progress - September 2017 - 1
Quality Progress - September 2017 - 2
Quality Progress - September 2017 - 3
Quality Progress - September 2017 - 4
Quality Progress - September 2017 - 5
Quality Progress - September 2017 - Seen and Heard
Quality Progress - September 2017 - 7
Quality Progress - September 2017 - Progress Report
Quality Progress - September 2017 - 9
Quality Progress - September 2017 - Mr. Pareto Head
Quality Progress - September 2017 - 11
Quality Progress - September 2017 - Career Coach
Quality Progress - September 2017 - 13
Quality Progress - September 2017 - 14
Quality Progress - September 2017 - Expert Answers
Quality Progress - September 2017 - Field Notes
Quality Progress - September 2017 - 17
Quality Progress - September 2017 - 18
Quality Progress - September 2017 - 19
Quality Progress - September 2017 - Data Disruption
Quality Progress - September 2017 - 21
Quality Progress - September 2017 - 22
Quality Progress - September 2017 - 23
Quality Progress - September 2017 - 24
Quality Progress - September 2017 - 25
Quality Progress - September 2017 - The Deal With Big Data
Quality Progress - September 2017 - 27
Quality Progress - September 2017 - 28
Quality Progress - September 2017 - 29
Quality Progress - September 2017 - 30
Quality Progress - September 2017 - 31
Quality Progress - September 2017 - 32
Quality Progress - September 2017 - 33
Quality Progress - September 2017 - Better Intelligence
Quality Progress - September 2017 - 35
Quality Progress - September 2017 - 36
Quality Progress - September 2017 - 37
Quality Progress - September 2017 - 38
Quality Progress - September 2017 - 39
Quality Progress - September 2017 - 40
Quality Progress - September 2017 - 41
Quality Progress - September 2017 - A Study in Measurement
Quality Progress - September 2017 - 43
Quality Progress - September 2017 - 44
Quality Progress - September 2017 - 45
Quality Progress - September 2017 - 46
Quality Progress - September 2017 - 47
Quality Progress - September 2017 - Innovation Imperative
Quality Progress - September 2017 - 49
Quality Progress - September 2017 - 50
Quality Progress - September 2017 - Statistics Spotlight
Quality Progress - September 2017 - 52
Quality Progress - September 2017 - 53
Quality Progress - September 2017 - Standard Issues
Quality Progress - September 2017 - 55
Quality Progress - September 2017 - 56
Quality Progress - September 2017 - 57
Quality Progress - September 2017 - ASQ's 2017 Quality Resource Guide
Quality Progress - September 2017 - 59
Quality Progress - September 2017 - 60
Quality Progress - September 2017 - 61
Quality Progress - September 2017 - 62
Quality Progress - September 2017 - 63
Quality Progress - September 2017 - 64
Quality Progress - September 2017 - 65
Quality Progress - September 2017 - 66
Quality Progress - September 2017 - 67
Quality Progress - September 2017 - Marketplace
Quality Progress - September 2017 - 69
Quality Progress - September 2017 - Footnotes
Quality Progress - September 2017 - 71
Quality Progress - September 2017 - Back to Basics
Quality Progress - September 2017 - cover3
Quality Progress - September 2017 - cover4
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