1. to get a full 360-degree view of the customer
(Bipin)

2. Why Data Analysis?
 Collecting and analysing data on the customers allows
companies to augment their services by examining
customer sentiment.
 Data can provide businesses with metrics on sales,
marketing and other areas to gauge performance and
quality.
 It can also help make better forecasting decisions by
allowing for real-time decision-making as well as
giving information on product inventories, customer
segmentation and assist in the development of
products and services.

3. Importance of Customer Data
 When referring to customer data, big data refers to large
amounts of either transactional data or analytical data.
 The data is at the heart of customer analytics and is
divided in to segments , each segments is intuen split into
descriptive data (such as name, address and other
attributes), behavioural data (transactions such as orders
and payments), interaction data (including email
messages, chat scripts, Web streams and CRM notes) and
attitudinal data (such as customer feedback, market
research and social media comments).

4. Continued..
 Eventually companies need to bring together,
rationalize and analyze all customer data, from any
source, type (structured, unstructured and event-
based) or time frame; the more they include, the fuller
their customer view will be.

5. How to do Customer Analytics ?
 Customer analytics has tools then extract this data
from multiple data sources and information
management platform brings the data, predominantly
structured data, text and social media, together, and
makes it available for the analytics platform.
 These requirements and steps for customer analytics
requires integrating all of this information requires big
data technology and further integrated into its existing
customer focused efforts and further embrace its
current big data analytic.

6. Data Visualization
 This includes tools that support data and text mining,
business rules management, entity analytics,
sentiment analysis and business intelligence; together
they help companies carry out predictive modeling,
sentiment analysis, forecasting and simulation, social
analytics, and customer feedback analysis.
 The results are shown in scorecards, dashboards and
reports that support the latest visualization
techniques, and which support real-time decision-
making.

7. Conclusion
 There is no denying that customers have changed their
purchasing and communication habits. To keep up,
companies need the fullest customer view they can
obtain. Smart customer analytics goes a long way
toward meeting these needs.