Predictive Business Analytics: The Way Ahead

Abstract

This paper delves the various aspect and application of predictive analytical tools because it provides greater transparency, and can find and analyze past and present trends, as well as the hidden nature of data. However, past and present insight and trend information are not enough to be competitive in business. Business organizations need to know more about the future, and in particular, about future trends, patterns, and customer behavior in order to understand the market better so at this juncture understanding the predictive business analytics is the need of the hour to predict the future outcome. Traditional analytics help gain insight for what was right and what went wrong in decision-making. Today's tools merely provide rear view analysis. However, one cannot change the past, but one can prepare better for the future and decision makers want to see the predictable future, control it, and take actions today to attain tomorrow's goals.

Full text

Predictive Business Analytics:
The Way Ahead
Dr Naresh Sharma, Manish Dadhich
Abstract
This paper delves the various aspect and application of predictive analytical
tools because it provides greater transparency, and can find and analyze past
and present trends, as well as the hidden nature of data. However, past and
present insight and trend information are not enough to be competitive in
business. Business organizations need to know more about the future, and in
particular, about future trends, patterns, and customer behavior in order to
understand the market better so at this juncture understanding the predictive
business analytics is the need of the hour to predict the future outcome.
Traditional analytics help gain insight for what was right and what went
wrong in decision-making. Today’s tools merely provide rear view analysis.
However, one cannot change the past, but one can prepare better for the
future and decision makers want to see the predictable future, control it, and
take actions today to attain tomorrow’s goals.
Key Words: Clinical analytics, Predictability, Descriptive modeling and
Inferential modeling
Journal of Commerce & Management Thought
Vol. 5-4, 2014, pp 652-658
DOI : 10.5958/0976-478X.2014.00012.3
652

Introduction
Predictive analytics are used to determine the probable future outcome of
an event or the likelihood of a situation occurring. It is the branch of data
mining concerned with the prediction of future probabilities and trends.
Predictive analytics is used to automatically analyze large amounts of data
with different variables; it includes clustering, decision trees, market basket
analysis, regression modeling, neural nets, genetic algorithms, text mining,
hypothesis testing, decision analytics, and more. The core element of
predictive analytics is the predictor, a variable that can be measured for an
individual or entity to predict future behavior. For example, a credit card
company could consider age, income, credit history, and other demographics
as predictors when issuing a credit card to determine an applicant’s risk factor.
Multiple related predictive models can produce good insights to make strategic
company decisions, like where to explore new markets, acquisitions, and
retentions; find up-selling and cross-selling opportunities; and discovering
areas that can improve security and fraud detection. Predictive analytics
indicates not only what to do, but also how and when to do it, and to explain
what-if scenarios.
Predictive analytics employs both a microscopic and telescopic view of
data allowing organizations to see and analyze the minute details of a business,
and to peer into the future. Traditional BI tools cannot accomplish this
functionality. Traditional BI tools work with the assumptions one creates, and
then will find if the statistical patterns match those assumptions. Predictive
analytics go beyond those assumptions to discover previously unknown data;
it then looks for patterns and associations anywhere and everywhere between
seemingly disparate information.
Types of Predictive Analytics
Generally, the term predictive analytics is used to mean , “scoring” data
with predictive models, and . However, people are increasingly using the term
to refer to related analytical disciplines, such as descriptive modeling and
Predictive Business Analytics... 653

decision modeling or optimization. These disciplines also involve rigorous
data analysis, and are widely used in business for segmentation and decision
making but have different purposes and the statistical techniques underlying
them vary.
•Predictive Model
Predictive modeling identifies and mathematically represents underlying
relationships in historical data in order to explain the data and make
predictions, forecasts or classifications about future events. Predictive
models typically analyze current and historical data on individuals to
produce easily understood metrics such as scores. Predictive models can
also detect the likelihood of a transaction being fraudulent (Risk
Detection). Predictive models are frequently used in mission-critical
transactional systems and drive decisions and actions in near real time.
•Descriptive model
Descriptive models quantify relationships in data in a way that is often
used to classify customers or prospects into groups. Unlike predictive
models that focus on predicting a single customer behavior (such as credit
risk), descriptive models identify many different relationships between
customers or products. Descriptive models do not rank-order customers
by their likelihood of taking a particular action the way predictive models
do. Instead, descriptive models can be used, for example, to categorize
customers by their product preferences and life stage. Descriptive
modeling tools can be utilized to develop further models that can simulate
large number of individualized agents and make predictions.
•Inferential model
Inferential model is concerned with the various test of significance in
order to determine with what validity data can be said to indicate some
conclusions. It is also used to analyze past performance to assess how
likely a customer is to exhibit a specific behavior in order to improve .
This category also encompasses models that seek out subtle data patterns
Journal of Commerce and Management Thought 5 - 4654

to answer questions about customer performance, such as fraud detection
models. Predictive models often perform calculations during live
transactions, for example, to evaluate the risk or opportunity of a given
customer or transaction, in order to guide a decision. These models can
be used in optimization, maximizing certain outcomes while minimizing
others. Decision models are generally used to develop decision logic or
a set of business rules that will produce the desired action for every
customer or circumstance.
General Applications of Predictive Analytics
The following are some examples of how Predictive Analytics can be
applied in various sectors in order to get the maximum utilization of
available resources.
•Analytical financial service
The cost of making the right decision yields marginal benefits whereas
wrong decision looses both time and revenue. Often the focus of analysis
is the financial product, portfolio, firm, industry or even the economy.
For example, credit card companies are able to determine who is most
likely to default on their credit cards in the next six months by applying
predictive analytics to customer’s and demographics
•Analytical retailing and customer relationship management
In retail, understanding customer is essence of success. Retailers have
provided customized shopping experiences by using predictive analytics
to understand the drivers of profitability, loyalty and other activity for
each customer panel and frame specific strategy for each segment. This
technique has helped both offline and online retailers determine which
products to carry, optimize marketing plan, and develop promotional and
loyalty programs. Another example is knowing what a customer is most
likely to purchase next, so that your staff or website can make informed
recommendations. Analytical uses predictive analysis in applications for
marketing campaigns, sales, and customer services to name a few. These
tools are required in order for a company to posture and focus their efforts
effectively across the breadth of their customer base.
Predictive Business Analytics... 655

•Analytical clinical decision support systems
Experts use predictive analysis in health care industries specially to
determine which patients are at risk of developing certain disease like
diabetes, asthma, heart disease, and other lifetime illnesses. Moreover,
advanced incorporate predictive analytics to support medical decision
making at the point of every care aspect.
•Analytical manufacturing support systems
Manufacturing is nothing but knowing about what, where and how much
to produce in order to yield maximum benefits with consumption of
minimum resources. Predictive analytics have helped manufacturer to
manage their supply chain, inventory and production schedules by proper
forecasting demand and have helped to produce final products in such a
way to cater the maximum demand. For example, PBA has helped
manufacturers with tens of thousands of units as sales each month to
forecast sales within 10 to 15 units, so that they can optimize production
schedules and supply chain. Moreover It also gives us an idea why
different managers have different level of output, employee turnover,
injuries and system failure.
•Analytical study of collection
Every portfolio has a set of delinquent customers who do not make their
payments on time. The financial institution has to undertake collection
activities on these customers to recover the amounts due. A lot of
collection resources are wasted on customers who are difficult or
impossible to recover. Predictive analytics can help optimize the
allocation of collection resources by identifying the most effective
collection agencies, contact strategies, legal actions and other strategies
to each customer, thus significantly increasing recovery at the same time
reducing collection costs.
•Analytical customer retention
With the number of competing services available, businesses need to
focus efforts on maintaining continuous , rewarding and minimizing .
Businesses tend to respond to customer attrition on a reactive basis, acting
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only after the customer has initiated the process to terminate service. At
this stage, the chance of changing the customer’s decision is almost
impossible. Proper application of predictive analytics can lead to a more
proactive retention strategy. By a frequent examination of a customer’s
past service usage, service performance, spending and other behavior
patterns, predictive models can determine the likelihood of a customer
terminating service sometime in the near future.
•Analytical marketing
When consumer products and services, there is the challenge of keeping
up with competing products and consumer behavior. Apart from
identifying prospects, predictive analytics can also help to identify the
most effective combination of product versions, marketing material,
communication channels and timing that should be used to target a given
consumer. The goal of predictive analytics is typically to lower the or .
Conclusion
In this research paper we discussed the role of predictive business
analytics with various dimension and how to assess and use the predictive
power of empirical model. Predictive model can lead to the discovery of new
constructs, new relationships and interpret the most appropriate outcome of
future. Predictive models which are kind of data mining that support the
extraction of information from large datasets and form a variety of data
structure. Predictive assessment provides a straightforward way to assess the
practical relevance of theories, to compare competing theories, to compare
different construct operation and to assess the predictability of measurable
phenomena that is why we can say that the predictive analytics has proved its
significance at various walks of life. However modern predictive analytics
tools are no longer restricted to IT specialists. As more organization adopt
predictive analytics into decision-making process and integrate it into their
operation so as to improve core competence of an organization. Predictive
analytics tools have become sophisticated enough to adequately present and
dissect data problems, so that any data-savvy information worker can utilize
them to analyze data and retrieve meaningful, useful results.
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The Authors
Dr Naresh Sharma is an Associate Professor in University of Rajasthan, Jaipur.
Manish Dadhich is a Research Scholar in University of Rajasthan, Jaipur.
Email : manishclef6@gmail.com • Received on: 12, June.2014
Journal of Commerce and Management Thought 5 - 4658