Brand Management Advertising and Brand Attitudes P R O F. M A X J O O Objectives Does advertising influence consumers’ attitudes toward a brand? ◦ Perceived quality ◦ Perceived value ◦ Recent satisfaction How do we estimate the effects? 2 Brand attitude Consumers’ positive/negative association with a brand ◦ Is the brand associated with “good quality”? ◦ Is the brand associated with “good value”? ◦ Is the brand associated with “satisfaction”? 3 Brand attitude Consumers’ positive/negative association with a brand ◦ Is the brand associated with “good quality”? ◦ Is the brand associated with “good value”? ◦ Is the brand associated with “satisfaction”? 4 Brand attitude Consumers’ positive/negative association with a brand ◦ Is the brand associated with “good quality”? ◦ Is the brand associated with “good value”? ◦ Is the brand associated with “satisfaction”? Uniform quality, credibility and experience beyond a single product ◦ Positive attitudes offer competitive advantages ◦ Readily available from GfK, Millward Brown, TNS and YouGov ◦ Managers track a brand’s health over time using brand attitude surveys 5 Why do we care? Brand attitudes ◦ Predict measurable lower-funnel metrics like sales and online searches ◦ Create differentiation and reduce pricing pressure ◦ Are inherently valuable, as reflected in valuations ◦ Might be a useful proxy to set ad budgets, especially for advertisers who can’t estimate direct effects of ads on sales 6 Why do we care? Brand attitudes ◦ Predict measurable lower-funnel metrics like sales and online searches ◦ Create differentiation and reduce pricing pressure ◦ Are inherently valuable, as reflected in valuations ◦ Might be a useful proxy to set ad budgets, especially for advertisers who can’t estimate direct effects of ads on sales Advertising potentially influences brand attitudes, the brand attitudes may influence choice ◦ It offers an intermediate or surrogate measure of marketing effectiveness ◦ Facebook’s brand lift ◦ Scientific research has been skeptical 7 Du, Joo, Wilbur (2019) How do brand attitudes change with own and competitor ads? How do these relationships vary across attitudes and ad media? Can brand attitudes be attributed to ads? 8 Data 575 established brands/ 37 industries/ 252 weeks/ $264B ad spend ◦ Meta-analytic scope without publication bias ◦ 37% of national ad spend, over 10 million surveys YouGov BrandIndex ◦ A panel of more than 1.5 million US consumers ◦ Each panelist completes up to one survey each month ◦ We focus on the following three questions: ◦ “Which of the brands do you associate with good quality?” ◦ “Which of the brands do you associate with good value-for-money?” ◦ “Would you identify yourself as a recent satisfied customer of any of these brands?” 9 Data 575 established brands/ 37 industries/ 252 weeks/ $264B ad spend ◦ Meta-analytic scope without publication bias ◦ 37% of national ad spend, over 10 million survey ...

1. Brand Management
Advertising and Brand Attitudes
P R O F. M A X J O O
Objectives
Does advertising influence consumers’ attitudes toward a
brand?
◦ Perceived quality
◦ Perceived value
◦ Recent satisfaction
How do we estimate the effects?
2
Brand attitude
Consumers’ positive/negative association with a brand
◦ Is the brand associated with “good quality”?
◦ Is the brand associated with “good value”?
◦ Is the brand associated with “satisfaction”?
3

2. Brand attitude
Consumers’ positive/negative association with a brand
◦ Is the brand associated with “good quality”?
◦ Is the brand associated with “good value”?
◦ Is the brand associated with “satisfaction”?
4
Brand attitude
Consumers’ positive/negative association with a brand
◦ Is the brand associated with “good quality”?
◦ Is the brand associated with “good value”?
◦ Is the brand associated with “satisfaction”?
Uniform quality, credibility and experience beyond a single
product
◦ Positive attitudes offer competitive advantages
◦ Readily available from GfK, Millward Brown, TNS and
YouGov
◦ Managers track a brand’s health over time using brand attitude
surveys
5
Why do we care?
Brand attitudes
◦ Predict measurable lower-funnel metrics like sales and online
searches

3. ◦ Create differentiation and reduce pricing pressure
◦ Are inherently valuable, as reflected in valuations
◦ Might be a useful proxy to set ad budgets, especially for
advertisers who
can’t estimate direct effects of ads on sales
6
Why do we care?
Brand attitudes
◦ Predict measurable lower-funnel metrics like sales and online
searches
◦ Create differentiation and reduce pricing pressure
◦ Are inherently valuable, as reflected in valuations
◦ Might be a useful proxy to set ad budgets, especially for
advertisers who
can’t estimate direct effects of ads on sales
Advertising potentially influences brand attitudes, the brand
attitudes
may influence choice
◦ It offers an intermediate or surrogate measure of marketing
effectiveness
◦ Facebook’s brand lift
◦ Scientific research has been skeptical
7

4. Du, Joo, Wilbur (2019)
How do brand attitudes change with own and competitor ads?
How do these relationships vary across attitudes and ad media?
Can brand attitudes be attributed to ads?
8
Data
575 established brands/ 37 industries/ 252 weeks/ $264B ad
spend
◦ Meta-analytic scope without publication bias
◦ 37% of national ad spend, over 10 million surveys
YouGov BrandIndex
◦ A panel of more than 1.5 million US consumers
◦ Each panelist completes up to one survey each month
◦ We focus on the following three questions:
◦ “Which of the brands do you associate with good quality?”
◦ “Which of the brands do you associate with good value-for-
money?”
◦ “Would you identify yourself as a recent satisfied customer of
any of these brands?”
9
Data
575 established brands/ 37 industries/ 252 weeks/ $264B ad
spend

5. ◦ Meta-analytic scope without publication bias
◦ 37% of national ad spend, over 10 million surveys
YouGov BrandIndex
10
Data
575 established brands/ 37 industries/ 252 weeks/ $264B ad
spend
◦ Meta-analytic scope without publication bias
◦ 37% of national ad spend, over 10 million surveys
Kantar Media Stradegy
◦ Comprehensive ad placement and expenditure data
◦ #1 in competitive advertising intelligence
11
Data
575 established brands/ 37 industries/ 252 weeks/ $264B ad
spend
◦ Meta-analytic scope without publication bias
◦ 37% of national ad spend, over 10 million surveys
Ford
12

6. 0
10
20
30
40
50
60
Po
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e
B
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A
tti
tu
de
(%
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8. 9
16
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21
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ep
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ov
−0

9. 9
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10. 6−
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13. ●
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15. ●
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16. ●
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23. ●
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24. ●
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25. ●
●
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0
10
20
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A
dv
er
tis
in
g

26. E
xp
en
di
tu
re
($
M
ill
io
n)
● Quality Value Satisfaction
Data
575 established brands/ 37 industries/ 252 weeks/ $264B ad
spend
◦ Meta-analytic scope without publication bias
◦ 37% of national ad spend, over 10 million surveys
Toyota
13
0
20

27. 40
60
Po
si
tiv
e
B
ra
nd
A
tti
tu
de
(%
)
3−
M
ar
−0
8
5−
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ay

28. −0
8
7−
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l−
08
8−
Se
p−
08
10
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ov
−0
8
12
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an
−0
9
16
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ar
−0
9

29. 18
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ay
−0
9
20
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ul
−0
9
21
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ep
−0
9
23
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ov
−0
9
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an
−1
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29

30. −M
ar
−1
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31
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ay
−1
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−1
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ct
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0
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ec
−1
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31. Fe
b−
11
11
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pr
−1
1
13
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un
−1
1
15
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32. −1
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pr
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ct
−1

33. 2
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34. ●
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42. ●
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46. ●
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●
0
10
20
30
40
50
A
dv
er
tis
in
g
E
xp
en
di

47. tu
re
($
M
ill
io
n)
● Quality Value Satisfaction
Data
575 established brands/ 37 industries/ 252 weeks/ $264B ad
spend
◦ Meta-analytic scope without publication bias
◦ 37% of national ad spend, over 10 million surveys
Coke
14
0
20
40
60
Po
si

48. tiv
e
B
ra
nd
A
tti
tu
de
(%
)
3−
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l−

49. 08
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10
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ov
−0
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12
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an
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ar
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9
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ay
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9

50. 20
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9
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51. −M
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53. −1
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54. ●
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55. ●
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56. ●
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57. ●
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58. ●
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59. ●
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60. ●
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61. ●
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62. ●
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63. ●
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64. ●
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65. ●
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66. ●
● ●
●
●
●
●
●
●
●
●
●
●
0
10
20
30
A
dv

67. er
tis
in
g
E
xp
en
di
tu
re
($
M
ill
io
n)
● Quality Value Satisfaction
Data
575 established brands/ 37 industries/ 252 weeks/ $264B ad
spend
◦ Meta-analytic scope without publication bias
◦ 37% of national ad spend, over 10 million surveys
Apple

68. 15
0
20
40
60
Po
si
tiv
e
B
ra
nd
A
tti
tu
de
(%
)
3−
M
ar
−0

69. 8
5−
M
ay
−0
8
7−
Ju
l−
08
8−
Se
p−
08
10
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ov
−0
8
12
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an
−0
9
16
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70. ar
−0
9
18
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ay
−0
9
20
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ul
−0
9
21
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ep
−0
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23
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ov
−0
9
25
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71. an
−1
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29
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ar
−1
0
31
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ay
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2−
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−1
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4−
O
ct
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ec

72. −1
0
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b−
11
11
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pr
−1
1
13
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un
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ug
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ct
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73. 1
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1
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ug
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74. 29
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ct
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75. ●
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76. ●
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77. ●
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78. ●
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79. ●
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80. ●
●
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81. ●
●
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82. ●
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●

83. ●
●
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84. ●
●
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●

85. ●
●
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●
●

86. ●
●
●
●
●
●
●
●
●
●
●
●
●
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●
●
●
●

87. ●
●
●
●
●
●
●
●
●
●
0
10
20
30
A
dv
er
tis
in

88. g
E
xp
en
di
tu
re
($
M
ill
io
n)
● Quality Value Satisfaction
Regression finds
Own national traditional ad spend increases all three brand
attitude
metrics
Own local traditional ad spend tends to improve perceived
quality and
perceived value, but not recent satisfaction
Own digital ad spend increases perceived value, but not other
two
Your ad spend influences consumers’ attitudes toward your

89. brand
◦ This updates the existing knowledge that ads cannot
16
Regression finds
Competitors’ national traditional ad spend negatively impacts
perceived
quality, but not perceived value or recent satisfaction
Competitors’ local traditional ad spend tends to reduce all three
brand
attitude metrics
Competitors’ digital ads tend to reduce perceived quality and
recent
satisfaction
If your competitors spend on ads but you do not, consumers’
attitudes
toward your brand are negatively influenced
17
Takeaways
Advertising helps to formulate positive attitudes toward a brand
beyond
awareness
Competitive advertising may hurt
These effects are observed in various types of media

90. 18
Now let’s do it
Make sure to be ready to use Radiant using one of the three
options:
1. Install on your computer: https://radiant-
rstats.github.io/docs/install.html
2. Log on to the virtual lab at http://ucr.apporto.com/ using your
UCR Net ID
and remotely work on Radiant from the virtual lab
◦ Instructions for the virtual lab is here:
https://ucrsupport.service-
now.com/ucr_portal/?id=kb_article&sys_id=8b5964291b84d490
26bd635bbc4bcbd7
◦ In case that you have trouble working on the virtual lab, you
will need to directly contact the IT
office
3. (Emergency protocol, but not recommended) Use online
version of Radiant
at https://vnijs.shinyapps.io/radiant/
◦ Functionality is limited, and security is a concern
Download your ad-brand dataset from eLearn, Course Materials
19
https://radiant-rstats.github.io/docs/install.html

91. http://ucr.apporto.com/
https://ucrsupport.service-
now.com/ucr_portal/?id=kb_article&sys_id=8b5964291b84d490
26bd635bbc4bcbd7
https://vnijs.shinyapps.io/radiant/
Radiant
First run “Rstudio”
20
Radiant
Click “Addins”
21
Radiant
Click “Start radiant (browser)”, then Radiant will show up on
your web
browser
22
Radiant
On the left panel, click the drop down menu under “Load data
of type”,
then choose “csv”
23

92. Radiant
Click “Load”, then select the folder you stored the data file.
Select the file (ad_brand_data_reg.csv) then click “Select”
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Your data
Ad_brand_data_reg.csv
◦ Brand: brand identifier (anonymized)
◦ Industry: industry that a brand belongs to (anonymized)
◦ time_period: exact time that the data point was measured
◦ Yrqtr: Year and quarter of “time_period”
◦ qua: % of respondents who associated the brand with “good
quality”
◦ val: % of respondents who associated the brand with “good
value”
◦ sat: % of respondents who associated the brand with
“satisfaction”
◦ nat_1m: national media advertising in million dollars
◦ loc_1m: local media advertising in million dollars
◦ dig_1m: online advertising in million dollars
◦ comp_...: competitor advertising in the same industry
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Regression analysis
Click “Model” on top menu, and select “Linear regression”

93. 26
Regression analysis: Step 1
Select “qua” as “Response variable”
Select “nat_1m”, “loc_1m”, and “dig_1m” as “Explanatory
variables”
Click “Estimate model”
Coefficient for “nat_1m” is 0.004, with “***”
◦ What does it mean?
◦ Unit increase in national ads ($million) is associated with
0.004 unit increase
in % of people who perceive the focal brand as good quality
◦ “***” on the right hand side indicates that
◦ Coefficient is statistically distinguishable from zero
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Regression analysis: Step 1
Select “qua” as “Response variable”
Select “nat_1m”, “loc_1m”, and “dig_1m” as “Explanatory
variables”
Click “Estimate model”
Coefficient for “loc_1m” is -0.000, without “*”

94. ◦ What does it mean?
◦ Unit increase in local ads ($million) does not exhibit a
relationship with % of
people who perceive the focal brand as good quality
◦ No “*” on the right hand side indicates that
◦ Coefficient is NOT statistically distinguishable from zero
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Regression analysis: Step 1
Select “qua” as “Response variable”
Select “nat_1m”, “loc_1m”, and “dig_1m” as “Explanatory
variables”
Click “Estimate model”
Coefficient for “dig_1m” is 0.011, with “*”
◦ What does it mean?
◦ Unit increase in online ads ($million) is associated with 0.011
unit increase in
% of people who perceive the focal brand as good quality
◦ “*” on the right hand side indicates that
◦ Coefficient is statistically distinguishable from zero
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Regression analysis: Step 2

95. Select “qua” as “Response variable”
Select “nat_1m”, “loc_1m”, and “dig_1m” as “Explanatory
variables”
Add “Industry” and “yrqtr” to the “Explanatory variables”
◦ Ctrl + Click multiple variables
Click “Re-estimate model”
How did coefficients change?
◦ All three coefficients are statistically distinguishable from
zero with control
variables!
◦ Now how can we interpret the coefficients?
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Interpretation, on average
$1m increase in national ads is associated with 0.3% (=0.003)
increase
in the % of people who perceive the focal brand as good quality
$1m increase in local ads is associated with 0.2% (=0.002)
increase in
the % of people who perceive the focal brand as good quality
$1m increase in digital ads is associated with 1.1% (=0.011)
increase in
the % of people who perceive the focal brand as good quality
Control variables help researchers obtain more accurate
coefficients

96. 31
Assignment 8: Due May 23rd
Carefully review the slides in pages 18 ~ 30, and run the
following regression analyses
1. Perceived quality
◦ Regress qua on nat_1m, loc_1m, and dig_1m, and interpret
coefficients
◦ Regress qua on nat_1m, loc_1m, dig_1m, industry, and yrqtr,
and interpret coefficients
◦ Discuss how control variables (industry and yrqtr) influenced
advertising effectiveness coefficients
2. Perceived value
◦ Regress val on nat_1m, loc_1m, and dig_1m, and interpret
coefficients
◦ Regress val on nat_1m, loc_1m, dig_1m, industry, and yrqtr,
and interpret coefficients
◦ Discuss how control variables (industry and yrqtr) influenced
advertising effectiveness coefficients
3. Satisfaction
◦ Regress sat on nat_1m, loc_1m, and dig_1m, and interpret
coefficients
◦ Regress sat on nat_1m, loc_1m, dig_1m, industry, and yrqtr,
and interpret coefficients
◦ Discuss how control variables (industry and yrqtr) influenced
advertising effectiveness coefficients
4. Repeat regressions in 1~3 with competitive advertising
(compnat_1m, comploc_1m,
and compdig_1m) and discuss how control variables help

97. Note: Please attach screenshot of each work
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