Transcript Business Intelligence

Metodi Quantitativi per Economia,
Finanza e Management
Lezione n°2
Management & Quantitative Methods
Management & Quantitative Methods
112 Keywords
57 Data Analysis & Quantitative Methods
Management & Quantitative Methods
Gartner's(*) Top Three Predictions for 2009-2010
•
IT Cut Costs. Inside of traditional Information Technology we're
going to find a lot of new ways to quickly cut costs. ….
•
High-Scale BI. Business Intelligence (BI) will require a move up
scale to larger sets of data, larger sets of content, and more
mingling or joining of disparate types of data and content in order to
draw inferences about what the customers are willing to do and pay
across both B2B and B2C activities.
•
Social Data-CRM Mash-ups. The role of social media and networks
will continue to grow and be impactful for enterprises, as marketers
and sales-people begin to look to these organizations from the
metadata and inference about what customers are willing to buy,
particularly under tight economic conditions. There's going to be a
need to tie traditional Customer Relationship Management (CRM)
and sales applications with some sort of a process overlay into the
metadata that's available from these Web-based cloud
environments, where users have shared so much inference and
data about themselves. I look for some mash-ups between social
data and the sales and business development applications and
data. (**)
(*) Gartner, Inc. (NYSE: IT) is the world’s leading information technology research and advisory company.
Management & Quantitative Methods
Metodi Quantitativi per Economia,
Finanza e Management
Agenda:
• Business Intelligence & Data Sources
• Internal Data - External Data
• Le ricerche di mercato
• Il Campionamento
Business Intelligence
Business intelligence(*) (BI) refers to skills, knowledge, technologies,
applications, quality, risks, security issues and practices used to
help a business to acquire a better understanding of market
behavior and commercial context. For this purpose it undertakes
the collection, integration, analysis, interpretation and
presentation of business information.
BI applications provide historical, current, and predictive views of
business operations, most often using data already gathered into
a Data Warehouse or a Data Mart.
BI applications tackle sales, production, financial, and many other
sources of business data to support better business decisionmaking. Thus one can also characterize a BI system as a
Decision Support System (DSS).
(*) http://en.wikipedia.org/wiki/Business_Intelligence
Business Intelligence & Data Sources
Business Intelligence systems are data-driven DSS.
Internal Data
• Operational digital transaction
• CRM digital transaction
External Data
• Public Data Base (Bureau of Census, Central Bank,..)
• Private Data Base (Consodata, D&B,..)
• Market Research
Business Intelligence & Internal Data
Operational & Strategic Marketing Hints
Business
Intelligence
DW
agents
call center Management
systems
portals
operations
data collection
data modelling
& processing
data analysis
Business Intelligence & Internal Data
Data Warehouse
Multi
Level
Summary
OLAP
DMA
Analisi
Statistica
Business Intelligence & Internal Data
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•
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Interaction between Customers & Company
Digital transactions
Billions of data
Data Warehousing
– Marketing Data Mart - Customer DataBase
• Data Mining(*)
• Customer Profiling
(*) Data Mining is the process of extracting hidden patterns from data. As more data
are gathered, data mining is becoming an increasingly important tool to transform
this data into information. It is commonly used in a wide range of profiling
practices, such as marketing, fraud detection and scientific discovery.
http://en.wikipedia.org/wiki/Business_Intelligence
Customer Profiling & Data Mining
Segmentation
How to select target
marketing segments?
Evaluation
of results
Identify business
area
Marketing
Datamart Make behavioural
Marketing plan
data available
implementation
Analysis and
classification
Strategic decisions
Marketing
Datamart
Propensity Models
Who are the best prospect
to target for the campaign?
Identification of prior
cross-selling segment
Evaluation
of results
Campaign
implementation
Marketing
Datamart
Extract
sample data
Scoring model
building
Tactical actions
Customer Profiling & Data Mining
Scoring Model
Behavioural Segmentation
Credit Scoring
Basel II
Credit Scoring
Acceptance Score Card
Needs Based
Segmentation
2000
1990
Mail Order
Teleco
New Media
Finance
Publishing
Social
Network Analysis