Page 84 - ICDEBI2018

International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456Trend in Scientific Research and Development (IJTSRD) ISSN: 2456Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 | IF: 4.101

implementing Business Intelligence into decisionmplementing Business Intelligence into decision-
i
making processes [14]

3. Survival time Analysis
T
This technique shows how loyal the customer is to his technique shows how loyal the customer is to
the brand and what is the probability of it that he
w
will switch to another brand.ill switch to another brand. The organization
receives this be havioral information to prolong a havioral information to prolong a
customer’s survival time.

4. Forecast the development of strategic business orecast the development of strategic business
process
Applications of BA in marketing T
The use of historical, present and anticipated data he use of historical, present and anticipated data
Marketing department of an organization has the organization has the c
can predict the future of the company.an predict the future of the company. The
responsibility of identifying, satisfying and retaining responsibility of identifying, satisfying and retaining potential behavior of the customer can be otential behavior of the customer can be
p
the customers using their product or services.the customers using their product or services. The analyzed which predict future sales,nalyzed which predict future sales, profit and
a
data driven digital marketing belongs to the emerging data driven digital marketing belongs to the emerging overall strategies of the business.strategies of the business.
trends in marketing along with cross channel and with cross channel and
content marketing.BA proves to be very effective in es to be very effective in
these marketing activities.BA can be used effectively these marketing activities.BA can be used effectively
in below area of marketing.
1. Customer Segmentation and ProfilingCustomer Segmentation and Profiling
The marketing decisions are depend upon the depend upon the
results derived from the application of customer results derived from the application of customer
segmentation and profiling techniques.profiling techniques. The model
used here is RFM model.(figure). This model used here is RFM model.(figure). This model
divides the customers into groups according to the divides the customers into groups according to the
following three metrics values: recency meaning recency meaning
how recently the customer made a purchase; how recently the customer made a purchase; Figure 2.RFM model (Source: Hsu (2012))RFM model (Source: Hsu (2012))
frequency, standing for how often theyfrequency, standing for how often they purchase;

and monetary value, or how much they spend.value, or how much they spend. The
A
Application of BA in social mediapplication of BA in social media
other segmental information like demographical other segmental information like demographical
M
Many authors believe that social media analytics any authors believe that social media analytics
segmentation (Age, sex, marital status,status, education) presents a unique opportunity for businesses to treat ty for businesses to treat
and behavioral segmentation (How(How often they t the market as a dialog between businesses and he market as a dialog between businesses and
purchase a product) can be also determined by purchase a product) can be also determined by
customers; instead of the traditional businessustomers; instead of the traditional business-to-
c
BA. It also studies the migration of customers so studies the migration of customers
c
customer marketing approaches [15]ustomer marketing approaches [15]
from one segment to the other and can be used for from one segment to the other and can be used for

effective decision making regarding a product.effective decision making regarding a product. Different analytics techniques are used in social ifferent analytics techniques are used in social
D
media. These are
2. Supportive analysis for cross selling & up Supportive analysis for cross selling & up 1. Natural language programming (NLP) Natural language programming (NLP)
selling
I It is the most common technique and may not be t is the most common technique and may not be
Here the previous purchases of specific customer Here the previous purchases of specific customer
used for processing of real time data.of real time data. [16]
are taken into consideration while selling the sideration while selling the
products. The market basket analysis identifies products. The market basket analysis identifies 2. Opinion Mining
interdependencies between the products and interdependencies between the products and
The Opinion Mining Technique is defined as the he Opinion Mining Technique is defined as the
T
clustering them as a model can be used in BA. clustering them as a model can be used in BA.
effort of finding valuable information contained in ffort of finding valuable information contained in
e
The affinity grouping model identifies which The affinity grouping model identifies which
user-generated data [17]
product attract the sale of other products. Tproduct attract the sale of other products. These
factors increase the sale of the product factors increase the sale of the product 3. Sentiment Analysis
remarkably. Cross selling and up selling are remarkably. Cross selling and up selling are
Sentiment analysis software discovers the analysis software discovers the
considered to be the most attractive marketing considered to be the most attractive marketing
business value in opinions and attitudes expressed s and attitudes expressed
objectives organizations hope to be achieve when nizations hope to be achieve when
on social media, the news, and in enterprise ews, and in enterprise
@ IJTSRD | Available Online @ www.ijtsrd.comwww.ijtsrd.com | Conference Issue: ICDEBI-2018 | | Oct 2018 Page: 76