This document discusses the growth of data and analytics in business. It notes that the amount of data generated each day will grow exponentially from 2.5 exabytes currently to 40 zetabytes by 2020. It also outlines some of the key processes involved in business intelligence like extract, transform, load (ETL) and data management and analytics. Finally, it provides some examples of metrics used in analytics like financial, visibility, user-centric and content-centric metrics.

1. Business Intelligence and
Big Data
Ana Brambilla, September/2018
anabrambilla@gmail.com

2. 2020:
40 Zetabytes of
Data
TODAY:
2.5
Exabytes
per DAY
1 EXA = 1 million of Terabytes
1 ZETA = 1 billion of Terabytes

3. Source: The State of Business Intelligence
https://www.forbes.com/sites/louiscolumbus/2018/06/08/the-state-of-business-intelligence-2018/#61561da77828

5. TRANSFORMEXTRACT LOAD
ETL process
The challenge is transform the subjective in to objective data (thoughts, behavior, emotions etc).
NOISE: bots, fakes, unrelated content, bad configuration, technical problems etc.

6. DATA MANAGEMENT:
processes and
supporting technologies
designed to acquire
and store data.
DATA ANALYTICS:
set of techniques used
to analyze, visualize
and produce
intelligence from big
data.

7. Financial metrics:
- CTR
- conversion (sales)
- CPC
Visibility metrics:
- prints (ad display)
- CPM
- pageviews
- views
User-centric metrics:
- users
- session
- conversion (login, sign up)
Content-centric metrics:
- page/session (browsing
depth)
- time spent
- bounce rate
WEBSITEMETRICS

8. Bounce Rate & Exit Rate
Monday: Page B Page A Page C EXIT
Tuesday: Page B EXIT
Wednesday: Page A Page C Page B EXIT
Thursday: Page C EXIT
Friday: Page B Page C Page A EXIT
Bounce Rate is how many sessions started by an X page, generating just 1 pageview.
Exit Rate is how many times an X page was the last one visited in each session. %

9. Bounce Rate & Exit Rate
Monday: Page B Page A Page C EXIT
Tuesday: Page B EXIT
Wednesday: Page A Page C Page B EXIT
Thursday: Page C EXIT
Friday: Page B Page C Page A EXIT
Bounce Rate is related to how many sessions started by an X page, generating just 1
pageview.
Exit Rate is related to how many times an X page was the last one visited in each
session.
%
How much is the Page A Exit Rate this
week?

10. Bounce Rate & Exit Rate
Monday: Page B Page A Page C EXIT
Tuesday: Page B EXIT
Wednesday: Page A Page C Page B EXIT
Thursday: Page C EXIT
Friday: Page B Page C Page A EXIT
Bounce Rate is related to how many sessions started by an X page, generating just 1
pageview.
Exit Rate is related to how many times an X page was the last one visited in each
session.
%
How much is the Page C Bounce Rate
this week?

11. KnowledgeData Information
Insights
MAKE DECISIONS
● Descriptive
● Diagnostic
● Predictive

12. DESCRIPTIVE
1. Is there some behavior pattern in the graphic below?
2. How can we interpret these curves?
Dark blue: users
Light blue: bounce rate

13. DIAGNOSTIC
1. Is there some behavior pattern in the graphic below?
2. What kind of insight can we extract from this report?
Dark blue: users
Light blue: page/session (browsing depth)

14. PREDICTIVE
1. Which user has more probability to
cancel the services?
2. Which user has more probability to
cancel the services?
Time Spent / Month

17. Why to use Big Data?
“... a strategy that creates value by some form of
company-customer interaction at the fabrication and
assembly stage of the operations level to create
customized products with production cost and monetary
price similar to those of mass produced products”
(Kaplan & Haenlein, 2006)

19. Neuroscience applied to Consumer Behavior
ATTENTION
EMOTION
MEMORYEEG
Eye-
tracking
Biometrics
Facial
coding

23. Neuroscience applied to Consumer Behavior
ATTENTION
EMOTION
MEMORY

24. Webpage Analytics

32. USER = volume
BROWSING DEPTH
= color
TREEMAPS (Acquisition > All traffic > Treemaps)
USER FLOW (Acquisition > Social >
User Flow)

33. USER FLOW (Acquisition > Social > User Flow)

34. 1. How many users (%) made some purchase,
over the total of August audience?
2. What was the home’s bounce rate in the last 2
months?
3. What was the traffic source that generated the
deepest browsing in 1Q/2018 ?
4. What are the market interests of users who
made a purchase at Google Merchandising
Store in 2Q/2018?
Challenges:
Audience > Overview
Acquisition > All Traffic >
Source
Acquisition > Interests >
Overview
Behaviour > Site Content >
All Pages

35. Extra
References in the Blackboard

36. Big Data applied to Marketing Research

37. MKT Perspectives on Big Data

38. Semantic & Punctuation...
Killer sentenced to die for secondtime in 10 years
I saw the man with the binoculars
… a fragile point!