65
Information Technology Program Aalto University, 2015 Dr. Joni Salminen [email protected], tel. +358 44 06 36 468 DIGITAL ANALYTICS 1

Digital analytics: Visualization (Lecture 5)

Embed Size (px)

Citation preview

Information Technology Program

Aalto University, 2015

Dr. Joni Salminen

[email protected], tel. +358 44 06 36 468

DIGITAL ANALYTICS

1

DIGITAL…….ANALYTICS Dr. Joni Salminen

Aalto University

ITP 2015

1

Welcome back!

Here’s what we have left:

1. Data visualization (hip!)

2. Analytics problems (hip!)

3. Optimization (hooray!)

4. A word about Big Data (wow)

5. Building and managing an analytics team (’nuff)

6. Future of analytics (dude…)

7. Wrap-up (& goodbye!)

2

CAN’T WAIT,

CAN’T WAIT!!!1

VISUALIZATION

3

Contents (today & tomorrow)

a. principles of visualization

b. chart types & how to choose them

c. tools: GA, Tableau (fun fun)

d. lying with data (ooohhh)

4

Exercises in Tableau

Why Tableau?

Tableau is one of the most used business tools for

analyzing and visualizing ”big data”. The learning curve

to get started is low, yet the software is super powerful

(almost as powerful as R :)

5

Exercises in Tableau

What to do first:

1. Go to www.tableau.com/tft/activation

2. Download and install Tableau Desktop software

3. Go to Basecamp: download license key (text file)

and exercise files (three documents)

6

Exercises in Tableau

• We will go through the use of Tableau in the class

with the help of example exercises

• You can use it for making analyses and visualisations

for your GA audit report (not mandatory, but gives

extra point).

7

Instructions: Click on the link and select Get

Started. On the form, enter your university

email address for “Business email”; and

under "Organization", please input the name

of your school.

8

What is this?

9

What is this? (It’s a representation of a pipe)

10

In a same way, data (or its visualization) is

not reality – it’s a representation of reality.

And representations have all kinds of

sketchy features, as we’re about to see…

11

”No chart is reality” –Jones, 2013

What is visualization?

Visualization is a form of data presentation. The

visualizer holds power of inclusion and exclusion of

relevant data, as well as portraying various visual

cues, such as size (relative and absolute), coloring,

positions, and so on.

The viewers of visualization are subject to cognitive

limitations, and will draw conclusions based on what

they are shown.

12

(Famous) examples

13

March on Moscow (Minard, 1869)

14

London cholera cases (John Snow, 1854)

15

Most common

birth dates

(Stiles, 2012)

16

The point of visualization is to simplify data,

i.e. make something complex more easily

understandable (cf. dashboards).

17

From this:

18

To this:

19

Why visualize? (Anscombe, 1973)

20

Visualization shows relationships

(Anscombe, 1973)

21

Which is more impressive?

Version A: ”Population grew exponentially throughout

the 20th century.”

22

Version B:

23

Two purposes of visualization…

(Underwood, 2013)

1. Explore → discover what the data is telling (gain a

descriptive understanding of the phenomenon)

2. Explain → tell that to others (highlight certain

aspects)

24

The workflow of statistical analysis

First visualize, then create hypotheses.

a. visualization = exploration

b. hypotheses = testing

25

Why visualize? Data brings credibility

26

Using visualizations in marketing: infographs

27

Data can be visualized in many ways. Let’s

start from charts which are a form of

graphs. We’ll look at some charts and

graphs, and discuss their properties as we

go along.

28

Types of charts (…and when to use them)

a. bar chart

b. histogram

c. line chart

d. pie chart

e. bubble chart

f. sparkline

g. treemap

h. heatmap

i. network analysis

j. geospatial radius

29

Bar chart: a common way to visualize

30

Bar chart: two variables

31

Histogram shows distributions

32

Bar charts vs. histograms

• bar chart = categorical data

• histogram = continuous data

33

Line chart shows trends

34

Pie charts (Whitelaw-Jones, 2013)

35

Visualization geeks hate pie charts!

36

I DISLIKE PIE.

“Pie charts are terrible at showing trends in

absolute numbers.” (Holllins, 2013)

37 (Hollins, 2013)

Versus stacked bar chart (with trend lines)

(Holllins, 2013)

38

From this:

39

(Hollins, 2013)

To this:

40

(Hollins, 2013)

Pie charts are bad (?)

““Pie charts (or any kin thereof) = bad” was the

message. I don’t really want to fight about whether they

are good, nor bad—the reality is probably in between.

(Tufte, the most cited source to the ‘pie charts are bad’

rhetoric, never really said pie charts were bad, only that

given the space they took up they were, perhaps

less informative than other graphical choices.) Do

people have trouble reading radians? Sure. Is the

message in the data obscured because of this? Most of

the time, no.” (Whitelaw-Jones, 2013)

41

Bubble chart (Microsoft, 2015)

42

x y

z

Sparklines (Sparkline.com, 2013)

43

Effective

in using

space.

Treemaps (GA)

44

When to use tree maps?

45

”Use Treemaps to display large numbers of values that

exceed the number that can effectively be shown in a

bar graph.” (Underwood, 2013)

Treemaps & principles of visualization

(Kaushik, 2014)

46

Treemaps & principles of visualization

(Kaushik, 2014)

47

Treemap: example in digital marketing

”When we are doing a keyword analysis for an

SEO/SEM client, we present it in a TREEMAP. When

you are working with a Fortune 100 client that has

thousands of potential target keywords, the best way to

present that data is in the form.” (Stewart, 2014)

48

Heatmaps: banner blindness

“Heatmaps from eyetracking studies: The areas where users looked the most

are colored red; the yellow areas indicate fewer views, followed by the least-

viewed blue areas. Gray areas didn't attract any fixations. Green boxes were

drawn on top of the images after the study to highlight the advertisements.”

(Nielsen, 2006) 49

Heatmap: Google Golden Triangle

50

Heatmaps tell

us where the

user is

focusing her

attention on

Heatmaps: landing page optimization

51

Knowing the psychology behind

perception, attention can be

guided towards desired ends.

Alternative ways to accomplish heatmaps

a. Click maps (e.g., Google Analytics)

b. Eye-tracking (costly but more data)

52

Heatmaps: in-page analysis in GA

• [JONI SHOWS]

53

Network analysis (Richardson & Jackson,

2010)

54

Geospatial radius (Mathieux, 2014)

55

Geospatial radius (Mathieux, 2014)

56

Geospatial radius (Mathieux, 2014)

57

Geospatial radius (Mathieux, 2014)

58

Choosing the right chart type

59

Tools: they are not the most important thing

(the most important thing is knowing how to

use them ;)

60

Visualization options in Google Analytics

• [JONI SHOWS]

• pie chart, bar, pivot, motion graph

• ”plot rows” option

61

Behavior flow (GA)

62

Goal flow (Google, 2015)

63

What’s the difference between goal flow

and conversion path?

Conversion path (SearchEngineLand.com, 2013)

64

(there can be many sources for one

conversion; together they form a path,

and these paths can be examined in

conversion path report.)