Thesis: Digital Media Advertising Attribution In Multi-Channel And Multi-Media Contexts Thesis Defense: Pass, March-15-2019

1. DIGITAL MEDIAADVERTISING
ATTRIBUTION IN MULTI-CHANNEL
AND MULTI-MEDIA CONTEXTS
PhD Candidate: Zhao Yunkun
Advisor: Dr.Goh KhimYong
Date: 15 MAR 2019
1
THESIS DEFENSE

2. General Introduction
Marketing Attribution
• Individual customer will attribute observed purchase outcomes to
the underlying causes, such as internal stimuli and external stimuli
(e.g., brand/product characteristics and advertising messages)
2
It was challenging to
attribute conversion
behavior to the underlying
causes very accurately, in
the multi-channel multi-
media contexts

3. General Introduction
3

4. Introduction: Purchase Journey
4
Product
Line
Strategy
Online
Offline
Organic
Search
Study I
Study II

5. Online Advertising, Salesforce and
Product Line Strategies in Online-to-
Offline Environments
5
STUDY II

6. Introduction: Purchase Journey
6
Product
Line
Strategy
Online
Offline
Organic
Search
Ads
Strategies
Brand-oriented
Product-oriented

7. Motivation I
• Companies devoted enormous resources ($77 billion) into online
advertising (eMarketer 2016).
• Companies shrunk their salesforce by selectively keeping skilled salesmen
(SCMP 2017; TheAtlantic 2013).
• Most customers led by online ads into offline channels may have already
done extensive online search to refine their needs, and thus casting doubts
on the role of salesforce in offline stores (Mckinsey 2009).
Whether the impacts of online advertising
can be strengthened by salesman skills ?
7

8. Motivation II-Multi-brand Multi-Product
Company Brand 1
Brand 2
Brand N
Line 1
Product 1
Product 2
……
Product M
Product 1
Product 2
……
Product N
Product 1
Product 2
……
Product P
Line 2
Product 1
Product 2
……
Product Q
Product 1
Product 2
……
Product R
Product 1
Product 2
……
Product S
Suppose you own multiple products (of the same/different lines),
should you choose to advertise the brand as a whole, or the
specific products?
8
……
……

9. Motivation II
Many multi-brand multi-product companies:
• Indiscriminately promoting multiple brands/products
• Without appropriate leverage or integration of advertising strategies with
their product line strategies
• Waste advertising expenditure (Azcentral 2012; Marketing91 2017).
9
How to match
online
advertising
strategies with
product line
factors ?
“Ads-Cake” Sharing

10. Research Questions
• RQ1: Interactions between online advertising and offline
salesforce skills attributes
• Whether and how are the impacts of online advertising influenced
by salesman skills (e.g., brand tenure and sales training) ?
• RQ2: Interactions between online advertising and product line
length
• Whether and how are the impacts of online advertising strategies
influenced by product line management strategies (e.g., product
line length across different brands and within the same brand) ?
10

11. Literature and Research Gaps
• Online advertising strategies
• The impact on customers’ brand knowledge and emotional attitude
(Biehal and Sheinin 2007; Kolsarici and Vakratsas 2010), not actual
purchase/conversion behavior
• Salesman efforts
• Examined whether customers’ being served by salesman moderates
advertising effectiveness in a non-granular way (Gatignon and
Hanssens 1987; Gopalakrishna and Chatterjee 1992; Eizenberg, A. 2016)
• No study shows which exact salesman attributes moderate
advertising effectiveness
• Product line management (Bayus and Putsis 1999; Cottrell and Nault
2004; Borle et al. 2005; Moreno and Terwiesch 2016)
• No study examines the interactions of advertising strategies with
product line management strategies.
11

12. Research Framework
12
Brand-Oriented
Advertising
Online Ads Strategies
Product-Oriented
Advertising
Dependent Variables
Demand
Salesman Skills
Brand Sales Tenure
Completing Sales
Training
Product Line Factors
Product Line Length
Across Different
Brands
Product Line Length
Within The Same
Brand
H1A&H1B: +
H2A: -
H2B: -
H2C: +
H2D: -

13. Research Hypotheses
- Online Ads and Salesman Skills Attributes
• (1) Online advertising activities and offline salesforces may play
a complementary role
• (2) Customer-salesperson interactions may create added values to
online advertising activities
13
• H1A: The effect of online advertising exposure on a
customer’s demand for the target product will be stronger
when the salesperson has higher brand sales tenure.
• H1B: The effect of online advertising exposure on a
customer’s demand for the target product will be stronger
when the salesperson has completed sales job training.

14. Research Hypotheses
- Ads Strategy and Product Line Length Management
• Product line length across different brands
• The number of same-line items across different brands
•  brand competition intensifies  distracting brand
interests/attention  lower responsiveness or reactions to brand-
/product-related ads activities
14
• H2A: The effect of the brand-oriented advertising exposure of the
target product on a customer’s demand for the target product will be
weaker when the target product’s product line length across different
brands is longer.
• H2B: The effect of the product-oriented advertising exposure of the
target product on a customer’s demand for the target product will be
weaker when the target product’s product line length across different
brands is longer.

15. Research Hypotheses
- Ads Strategy and Product Line Management
• Product line length within the same brand
• The number of same-line items of the same brand
•  higher brand/line power  more varieties of this brand to satisfy
heterogeneous tastes  more favorable responsiveness or reactions
to brand-related ads activities
15
• H2C: The effect of the brand-oriented advertising exposure of the
target product on a customer’s demand for the target product will be
stronger when the target product’s product line length of the same
brand is longer.

16. Research Hypotheses
- Ad Strategy and Product Line Management
• Product line length within the same brand
• The number of same-line items of the same brand
• Accessibility-diagnosticity framework for product judgment (Biehal and
Sheinin 2007; Lynch et al. 1988)
•  unfamiliar product attributes within this brand  more needs for
external product info  product interests/attention  lower
responsiveness or reactions to the target product’s ads activities
16
• H2D: The effect of the product-oriented advertising exposure of the
target product on a customer’s demand for the target product will be
weaker when the target product’s product line length of the same
brand is longer.

17. Research Framework
17
Brand-Oriented
Advertising
Online Ads Strategies
Product-Oriented
Advertising
Dependent Variables
Demand
Salesman Skills
Brand Sales Tenure
Completing Sales
Training
Product Line Factors
Product Line Length
Across Different
Brands
Product Line Length
Within The Same
Brand
H1A&H1B: +
H2A: -
H2B: -
H2C: +
H2D: -

18. Research Context
• Provider
• A multi-brand multi-product automobile manufacturer in
China
• Raw Data
• Collect (1) advertising campaign details, (2) customer offline
store visit records, (3) customer transactions/decisions for each
visit, (4) dealership salesperson attributes data
• Web-Crawl automobile product characteristics
• Time Window
• From January 2014 to June 2016
18

19. Research Framework
19
Brand-Oriented
Advertising
Online Ads Strategies
Product-Oriented
Advertising
Dependent Variables
Whether to Buy
Salesman Skills
Brand Sales Tenure
Completing Sales
Training
Product Line Factors
Product Line Length
Across Different
Brands
Product Line Length
Within The Same
Brand
H1A&H1B: +
H2A: -
H2B: -
H2C: +
H2D: -
Store-Visits &
Transactions
Individual-
Level Analysis
Product-Level
Analysis

20. Results and Findings
- Online Ads and Salesman Skills Attributes
• Data Cleaning
• 551,056 observations; 524,991 customers
• customer-product-day
• Information: customer advertising exposure, offline store-visit,
purchase decisions
• Time Window
• From Jan/2014 to Jun/2016
20

21. 21
Table 3-1. Summary of Variables
Variable Definition and Operationalization Mean S.D. Min Max
Choiceijt Whether customer i chooses to buy
product j on day t (=1 buy, =0
otherwise)
0.004 0.065 0 1
BrandAdijt Whether customer i is led by brand-
oriented advertising for product j
into offline official store to visit on
day t
0.141 0.348 0 1
ProductAdijt Whether customer i is led by
product-oriented advertising for
product j into offline official store to
visit on day t
0.411 0.492 0 1
SpBrandTenureit The number of months the
salesperson (who served customer i
on day t) has been responsible for
the car product j
26.182 22.173 0 279
SpPassTrainit Whether the salesperson (who
served customer i on day t) has
passed a sales training program
0.388 0.487 0 1
Ads Strategies
Salesman Skills

22. Model Specification
22
( 1) ( )ijt ijtPr Choice X   
0 1 2 3 4
5 6
7 8
=
* *
* *
ijt ijt ijt it it
ijt it ijt it
ijt it ijt
X BrandAd ProductAd SpBrandTenure SpPassTrain
BrandAd SpBrandTenure BrandAd SpPassTrain
ProductAd SpBrandTenure ProductAd SpPassTrai
     
 
 
    
 
 
9 10
it
jt jt
i j t
n
ProLineDiffBrand ProLineSameBrand ControlVariables 
  
  
  
• Estimation with Logit and Probit model
• i: Customer
• j: Product
• t: Day
Ads Strategies Salesman Skills
Inter-
actions

23. 23
H1B: +

24. Results and Findings
- Ads Strategy and Product Line Length Management
• Data Cleaning
• 2,493 observations; 35 products
• product-week level
• Time Window
• From January 2014 to June 2016
24

25. 25
Table 3-4. Summary of Variables
Variable Definition and
Operationalization
Mean S.D. Min Max
Transactionjt The total number of
transactions of product j on
week t
6.828 15.563 0 186
Visitjt The total number of offline
store visits of product j on
week t
1125.156 2281.252 1 35276
TotalBrandAdjt The total number of brand-
oriented advertising
exposures of product j on
week t
119.823 487.765 0 12179
TotalProductAdjt The total number of product-
oriented advertising
exposures of product j on
week t
416.703 1164.487 0 33634
ProLineDiffBrandjt The number of same-line
different-brand products
specific to product j on week
t
2.558 1.931 0 6
ProLineSameBrandjt The number of same-line
same-brand car models
specific to car j on week t
2.301 0.803 0 4
Ads Strategies
Product Line
Factors

26. Model Specification
26
ln(1 )jt jt jtTransaction X    
ln( )jt jt jtVisit X   
0 1 2
3 4
5
6
7
=
*
*
jt jt jt
jt jt
jt jt
jt jt
X TotalBrandAd TotalProductAd
ProLineDiffBrand ProLineSameBrand
TotalBrandAd ProLineDiffBrand
TotalProductAd ProLineDiffBrand
TotalBran
   
 



  
 



8
*
*
jt jt
jt jt
t
dAd ProLineSameBrand
TotalProductAd ProLineSameBrand
ControlVariables



 
• Estimate with Panel FE and RE
• j: Product
• t: Week
Ads Strategies
Product Line Factors
Interactions

27. 27
-23.7%
+13.2%
-18.5%
H2C: +
H2D: +
H2A: -

28. 28
Identification Strategies
Endogeneity IssuesApproach
1. Ads strategies
exposures are
endogenous
• Instrumental variable estimation
2. Salesman may not
be randomly chosen
• Unobserved customer valuation
• Add brand/product/line dummies
• Unobserved customer interest
• Add DeciLevel
• How strong is the customer i’s intention to purchase
product j when visiting the offline store at day t
• Falsification test
• Randomly shuffling assignments of salesmen to
customers
3. Simultaneity
between Ads and
Sales
• Three-Stage Least Squares (3SLS)

29. Robustness Checks
• Customer market(province)-level and individual-level
heterogeneity on sensitivities of focal variables
• Multi-level linear model
• Hierarchical bayesian model
29

30. Practical Implications
• Help brick-and-mortar companies predominantly
conducting online advertising to understand the
complementarity between online advertising and
salesforce skill attributes
• Help multi-brand multi-product companies to better
make marketing plans, by strategically matching
marketing plans with product line strategies
• Main ads effect (independent marketing plan):
• Actual ads effect (coordination of marketing plan with product
line factors):
30
Ads
ProductLineFactorsAds  

31. • Scenario 1:
Product line
length within the
same brand
(ProLineSameBrand)
• Suppose
interaction
coefficient
λBrandAd = +1 &
λProductAd= -1
0
1
2
3
4
5
6
Product A:
ProLineSameBrand=1
Product B:
ProLineSameBrand=2
Main
Actual=Main+Int
eraction
Figure 1A: Marginal
Benefit of Brand-
Oriented Ads
3 3
4
5
0
0.5
1
1.5
2
2.5
3
3.5
Product A:
ProLineSameBrand=1
Product B:
ProLineSameBrand=2
Main
Actual=Main+Inter
action
Figure 1B: Marginal
Benefit of Product-
Oriented Ads
3 3
2
1
ProductLineFactorsAds  

32. 2.7
2.75
2.8
2.85
2.9
2.95
3
3.05
Product A:
ProLineDiffBrand=1
Product B:
ProLineDiffBrand=2
Main
Actual=Main+Intera
ction
0
0.5
1
1.5
2
2.5
3
3.5
Product A:
ProLineDiffBrand=1
Product B:
ProLineDiffBrand=2
Main
Actual=Main+Inter
action
Figure 2A: Marginal
Benefit of Brand-
Oriented Ads
3 3
2
1
Figure 2B: Marginal
Benefit of Product-
Oriented Ads
3 3
2.9
2.8
• Scenario 2:
Product line
length across
different brands
(ProLineDiffBrand)
• Suppose
interaction
coefficient
λBrandAd = -1 &
λProductAd= -0.1
ProductLineFactorsAds  

33. Theoretical Contributions
• Fills the gap in prior literature on advertising strategies
by investigating their impacts on actual conversion
behavior.
• Fills a gap in salesforce literature by examining the
interdependency of online advertising strategies and
offline salesman skills
• Unravels the interdependency between online
advertising strategies and product line management
strategies
33

34. Limitations
• This study is limited by:
• What information or content in the advertising copies
shape the effectiveness of brand- and product-
oriented advertising, so this study is unable to
examine the underlying mechanism
• Focusing on purchase stage, not pre-purchase
information collection stage
• Future research can also focus on customers’ show-
rooming behavior
34

35. Evaluating the Effects of Online Customer
Touchpoints in Omni-Channel Marketing
Environments on Purchase Behaviors
35
STUDY I

36. Introduction: Purchase Journey
36
Product
Line
Strategy
Online
Organic
Search
Study I

37. Introduction
37
Customer
Touchpoints come
into play

38. Touchpoint Definition Example
Owned media Media that are owned by
companies
 Retailer-owned
pages/accounts in
online social
networks (e.g.,
Twitter/Weibo)
 Mobile apps
Paid media Media that are paid by companies
to boost their campaigns
 Search ads (e.g., on
Baidu.com)
 Online
display/banner ads
(e.g., on wsj.com)
Earned media Media that are not controlled by
companies and activities are
generated mainly by customers or
fans
 User referrals in
review sites (e.g., on
Yelp.com)
 Consumer reviews on
micro-blogs (e.g., on
Twitter.com)
38
Online Customer Touchpoint
(Smartinsights, 2012; Stephen, A. T., and Galak, J., 2012)

39. Research Questions
• RQ1: Relative effectiveness of touchpoints
• What is the relative effectiveness of different customer
touchpoints (i.e., owned media, paid media and earned media)
in affecting customers’ demand for the focal products?
• RQ2: Rival brands’ touchpoint exposure spillovers
• Whether and how do touchpoint exposures from rival brands
influence customers’ demand for the focal products?
• RQ3: Interdependencies among touchpoints
• Whether and how does exposure to one customer touchpoint
influence the effectiveness of another touchpoint in affecting
the demand for the focal products?

40. 40
Data Background
Provider
• A globally branded omni-channel retailer selling cosmetic
goods and fashion goods of different brands in China
Data
• Collect (1) customer purchase history, (2) customer
clickstream records, (3) customer demographics
• Web-Crawl product characteristics
• 4,608 customers; 103,361 observations; 14,917 transactions
• 4,079 products; 5 categories; 113 subcategories; 110 brands
• Time window
• January 2014 - August 2014
WebParsing

41. Variable Definition and Operationalization Mean S.D. Min Max
Expendijt
Purchase expenditure of customer i for
product j at day t
51.185 170.49 0 5000
Qtyijt
Purchase quantity of customer i for
product j at day t
0.163 0.465 0 20
Choiceijt
Whether customer i chooses to buy
product j at day t (=1 buy, =0 otherwise)
0.144 0.351 0 1
Ownedijt
Number of owned media exposures for
product j received by customer i up to day
t
0.148 0.487 0 13
Paidijt
Number of paid media exposures for
product j received by customer i up to day
t
0.076 0.397 0 15
Earnedijt
Number of earned media exposures for
product j received by customer i up to day
t
0.004 0.067 0 4
OwnedRivalijt
Number of owned media exposures for
product j’s rival brands received by
customer i up to day t
0.231 0.902 0 17
PaidRivalijt
Number of paid media exposures for
product j’s rival brands received by
customer i up to day t
0.154 1.011 0 25
EarnedRivalijt
Number of earned media exposures for
product j’s rival brands received by
customer i up to day t
0.008 0.107 0 5
Measure
Rivals

42. Purchase expenditure Purchase quantity
Purchase choice
Specification
42
( 1) ( )ijt ijtPr Choice X   
ln( 1)ijt ijt ijtExpend X     ln( 1)ijt ijt ijtQty X    
0 1 2 3
4 5 6
7 8 9
10 11 12
13
+
* * *
ln( )
ijt ijt ijt ijt
ijt ijt ijt
ijt ijt ijt ijt ijt ijt
jt jt j
i
X Owned Paid Earned
OwnedRival PaidRival EarnedRival
Owned Earned Paid Earned Owned Paid
Price New StoreBrand
Gender
    
  
  
  

    
 
  
  
 14 15 16 17 18 19
20
30 3140 4150 51i i i i it it
j i t
Age Age Age Age Tenure GoldCard
CategoryDummy
     
  
     
  
Model Specifications and Results
Main Impacts
Spillovers
Interdependencies

43. (1) ln(Expend+1) (2) ln(Qty+1) (3) Choice
VARIABLES FE-Linear FE-Linear CRE-Probit
Owned 0.529*** 0.073*** 0.403***
(0.013) (0.002) (0.011)
Paid 0.364*** 0.049*** 0.328***
(0.017) (0.002) (0.015)
Earned 0.186* 0.024* 0.239**
(0.098) (0.013) (0.095)
OwnedRival -0.025*** -0.002** -0.042***
(0.008) (0.001) (0.011)
PaidRival -0.008 -7.37e-04 -0.030**
(0.007) (9.39e-04) (0.013)
EarnedRival 0.022 0.006 -0.021
(0.056) (0.007) (0.081)
Owned*Earned 0.132 -0.004 0.091
(0.175) (0.024) (0.148)
Paid*Earned 0.383** 0.083*** 0.348**
(0.174) (0.023) (0.168)
Owned*Paid -0.178*** -0.024*** -0.148***
(0.012) (0.002) (0.012)
Controls √ √ √
MeanTimeVary - - √
Note: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
Relative Effectiveness
(1) Purchase choice:
Logarithmic ratio of
standardized coefficients
(2) Purchase expenditure:
Dominance analysis
(3) Purchase quantity:
Dominance analysis
Owned vs.
Paid
Paid vs.
Earned
0.579*** 2.902**
(0.053) (1.100)
Owned Paid Earned
0.016 0.002 1.01e-05
Owned Paid Earned
0.016 0.002 1.59e-05

44. 44
Identification Strategies
Endogeneity Issues Approach
1. Self-select into touchpoint
media exposures
• Propensity score matching
• Coarsened exact matching
2. Unobserved time-variant
factors
• Instrumental variable
estimation
• Control function approach
3. Incidental data truncation
of dependent variables at the
transaction level
• Heckman two-step estimation
4. Simultaneity between
customer demand and
touchpoint media exposures
• Three-Stage Least Squares
(3SLS)

45. Robustness Checks
Granger causality test
Simultaneous equations estimation
• Unobserved factors driving customers’ purchase choices may
also be correlated with their purchase expenditure/quantity
• Need to account for cross-model correlations in the error
structures
Sensitivity test of different attribution methods
• Attribution method 1: prior 90 days
• Attribution method 2: prior 30 days
• Attribution method 3: account for memory decay by time
45

46. 46
Robustness Checks
Mechanism:
Negative rival spillovers on purchase outcomes
(Hoban and Bucklin 2015; Joo et al. 2014; Lewis and Reiley 2014;
Lopez et al. 2015)
Procedure
• Step-1: Rival brands’ owned media exposures 
Customers’ interests in rival brand products 
Search/browse rival brands
• Step-2: Search/browse rival brands  Less responsive to
the focal products’ touchpoint media exposures  Purchase
outcomes

47. Contributions
Theoretical contributions
• Literature on information channel integration and
information channel interdependencies in omni-channel
contexts
• Literature on information channel spillovers in omni-channel
contexts
Managerial implications
• Help omni-channel companies to understand the differences
in touchpoint effectiveness and interdependencies
• Improve firm’s advertising and CRM resources allocation in
omni-channel marketing practices
47

48. 48

49. Question & Answer
49/24

50. Appendix:
Study I
50/24

53. Data: Identifying Touchpoints
Suppose a customer sees promotion
content on retailer-owned weibo official
page and clicks the link into the store,
then store server will also receive a
query package with different keys.
For example, the key “utm_source”
contains which social media platform
customers are from (i.e., weibo), and
key “utm_medium” indicates whether
this exposure is from retailer-owned
page or not.

54. Data: Identifying Touchpoints
If a customer sees promotion content
generated by customers on weibo
platform, then this is a typical query
package that store server will receive:
“utm_source” in this example contains
the weibo user ID who generates this
promotion content, and “utm_medium”
indicates whether this exposure is
referral content or not (i.e., content).

55. Data: Measurement
Procedure (e.g., touchpoint exposure)
Mathematically, we operationalized the focal variables Owned, Paid
and Earned as this cumulative term (Ansari et al, 2018):
• nijr,Owned (nijr,Paid or nijr,Earned) indicates the number of owned
(paid or earned) touchpoint exposures regarding product j
that customer i receives at day r (Our data is left-censored,
so r=0 means the first day of our observation period)
,0
,0
,0
t
ijt ijr Ownedr
t
ijt ijr Paidr
t
ijt ijr Earnedr
Owned n
Paid n
Earned n










56. Data: Rival Brands
• In our study, across the 5 broad product categories in our dataset (e.g.,
haircare, makeup, skincare, perfume and tool), there are a total of 112
sub-categories (e.g., cleansing milk, lip balm, sunshine cream, etc.) that
can clearly distinguish between each product’s attributes. We assumed
that if two products belong to the same sub-category, their attributes are
similar. Thus for example, if focal product A and product B belong to the
same sub-category (e.g., cleansing milk), but are of different brand
names, then we consider product B as a rival brand to focal product A.
• Based on t-tests, product attributes are insignificantly different between
focal and rival brands.
• In terms of rival brand pairs, we have 105 unique brands and 2,399
unique products in our data. There are 209,268 focal-rival product pairs
in our data. On average, each focal product has around 87 rival brand
products. It appears that brands or manufacturers are competing in a
nearly perfect competition or monopolistic competition market.

57. Identification: Matching
Procedure (e.g.: owned media touchpoint exposure)
• (1) Latent utility of a customer i’s choice of exposure to product
j’s owned media
• (2) Obtain propensity scores and match each treated individual
to one (or more) non-treated individual on propensity scores
• (3) Obtain ATT by comparing purchase outcomes (quantity and
expenditure) differences between the matched pairs in treated
and non-treated groups
*
50 1 2 3 4
76 8 8
+ 30 3140
4150 51 j j j
ij ij ij i i i
i i ijStoreBrand AvgPrice CategoryDummies
TreatOwned UrlClick VisitSession Gender Age Age
Age Age w
     
      
    
 
*
1 if 0, and 0 otherwiseij ij ijTreatOwned TreatOwned TreatOwned  
( 1| ) ( ),
( 0| ) 1 ( )
ij
ij
Prob TreatOwned W W
Prob TreatOwned W W


  
  

58. Identification: Matching
(1) Owned Media (2) Paid Media (3) Earned Media
Treatment Treatment Treatment
MATCHING METHODS ATT ATT ATT
Dependent Variable: Expend
Propensity Score Matching 61.938*** 41.784*** 3.577
- 3NN (2.592) (3.515) (11.747)
Propensity Score Matching 63.564*** 39.846*** 11.317
- Radius (caliper = 0.1) (2.405) (3.230) (9.181)
Mahalanobis Metric 55.519*** 40.114*** 5.332
(3.021) (4.072) (14.124)
Coarsened Exact Matching 62.678*** 34.823*** 22.721*
(2.903) (4.299) (12.211)
Dependent Variable: Qty
Propensity Score Matching 0.181*** 0.126*** 0.006
- 3NN (0.007) (0.009) (0.026)
Propensity Score Matching 0.185*** 0.118*** 0.004
- Radius (caliper = 0.1) (0.006) (0.009) (0.019)
Mahalanobis Metric 0.153*** 0.115*** -0.022
(0.008) (0.011) (0.038)
Coarsened Exact Matching 0.167*** 0.107*** 0.041
(0.008) (0.011) (0.029)

59. Identification: IV & CF
Instrument variables
• Number of touchpoint exposures from same-brand but
different-category products via Owned, Paid and Earned
media
Reasons
• A brand coordinates and conducts marketing campaigns for
its products holistically (i.e., “inclusive restriction”)
• Advertising efforts of the same-brand different-category
products do not influence the focal product’s demand
directly (i.e., “exclusion restriction”)

60. (1) ln(Expend+1) (2) ln(Qty+1) (3) Choice
VARIABLES IV-FE-Linear IV-FE-Linear IV-FE-Linear
Owned 0.492*** 0.067*** 0.086***
(0.027) (0.004) (0.005)
Paid 0.252*** 0.034*** 0.044***
(0.040) (0.005) (0.007)
Earned 0.153 0.027 0.032
(0.199) (0.027) (0.035)
OwnedRival -0.025*** -0.002** -0.004***
(0.008) (0.001) (0.001)
PaidRival -0.007 -6.05e-04 -0.001
(0.007) (9.40e-04) (0.001)
EarnedRival 0.015 0.004 0.005
(0.056) (0.007) (0.010)
Owned*Earned 0.172 -0.002 0.023
(0.196) (0.026) (0.035)
Paid*Earned 0.441** 0.086*** 0.059
(0.215) (0.029) (0.038)
Owned*Paid -0.135*** -0.019*** -0.024***
(0.018) (0.002) (0.003)
Controls √ √ √
R2/Log-likelihd 0.015 0.014 0.013
Note: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
Consistent
with main
results

61. (1) ln(Expend+1) (2) ln(Qty+1) (3) Choice
VARIABLES CF-FE-Linear CF-FE-Linear CF-FE-Linear
Owned 0.497*** 0.068*** 0.087***
(0.025) (0.003) (0.004)
Paid 0.270*** 0.036*** 0.047***
(0.032) (0.005) (0.006)
Earned 0.096 0.018 0.020
(0.145) (0.017) (0.025)
OwnedRival -0.028*** -0.002*** -0.004***
(0.005) (5.75e-04) (8.26e-04)
PaidRival -0.005 -2.92e-04 -5.93e-04
(0.004) (5.98e-04) (7.61e-04)
EarnedRival 0.026 0.006 0.007
(0.038) (0.004) (0.006)
Owned*Earned 0.112 -0.006 0.014
(0.183) (0.023) (0.032)
Paid*Earned 0.437** 0.090** 0.061**
(0.171) (0.041) (0.030)
Owned*Paid -0.185*** -0.025*** -0.033***
(0.012) (0.001) (0.002)
Controls √ √ √
R2/Log-likelihd 0.046 0.060 0.055
Note: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
Consistent
with main
results

62. Identification: Heckman Model
Reason
• Customers’ purchase quantity and expenditure can only be
observed if they have purchase transactions at this retailer
(i.e., incidental truncation in dependent variables) (Konuş et
al. 2014; Xie and Lee 2015)
Procedure
• (1) Latent utility that a customer i chooses to purchase the
product j at time t at this omni-channel retailer:
• (2) Compute Inverse Mills Ratio
• (3) Include Mijt as a regressor in the purchase expenditure
and quantity model during their estimations
*
*
1 if 0 and 0 otherwise
ijt ijt ijt
ijt ijt
Choose Z u
Choose Choose
 
 
ˆ ˆ( ) / ( )ijt ijt ijtM Z Z    

63. (1) Choice (1) ln(Expend+1) (2) ln(Qty+1)
VARIABLES Heck-Step-1 Heck-Step-2 Heck-Step-2
Owned 0.380*** 0.216*** 0.143***
(0.009) (0.071) (0.047)
Paid 0.260*** 0.150*** 0.100***
(0.012) (0.051) (0.034)
Earned 0.099 0.101 0.070
(0.087) (0.075) (0.050)
OwnedRival -0.154*** -0.078** -0.052**
(0.009) (0.032) (0.021)
PaidRival -0.120*** -0.057** -0.037**
(0.012) (0.027) (0.018)
EarnedRival -0.292*** -0.181** -0.119**
(0.074) (0.087) (0.057)
Owned*Earned -0.064 -0.088 -0.062
(0.131) (0.101) (0.067)
Paid*Earned 0.639*** 0.651*** 0.439***
(0.186) (0.191) (0.126)
Owned*Paid -0.156*** -0.089*** -0.059***
(0.011) (0.031) (0.020)
InverseMillsRatio 0.675*** 0.446***
(0.249) (0.165)
Customer dummy - √ √
Product attributes - √ √
Demographics √ √ √
Note: Standard errors in
parentheses:
*** p<0.01, ** p<0.05,
* p<0.1
Consistent
with main
results

64. Identification: 3-Stage Least Squares
Reason
• Positive feedback loop between customer demand and
touchpoint media exposures
• Approach: 3SLS (Duan et al. 2008; Lu 2013)
Procedure
• Models for purchase expenditure and purchase quantity
0 1 2 3
4 5 6
7 8 9
10 11 12
13
+
* * *
ln( )
ijt ijt ijt ijt
ijt ijt ijt
ijt ijt ijt ijt ijt ijt
jt jt j
i
X Owned Paid Earned
OwnedRival PaidRival EarnedRival
Owned Earned Paid Earned Owned Paid
Price New StoreBrand
Gender
    
  
  
  

    
 
  
  
 14 15 16 17 18 19
20 1
30 3140 4150 51i i i i it it
j i t ijt
Age Age Age Age Tenure GoldCard
CategoryDummy
     
   
     
   
ln( 1)ijt ijtY X  

65. Identification: 3-Stage Least Squares
0 1 2 , 1 3 , 1 4 5 6
7 8 9 10 11 12
2
ln( 1) ln( 1) 30 3140
4150 51
ijt ijt ij t ij t i i i
i i it it ijt ijt
t ijt
Owned Y Y Owned Gender Age Age
Age Age Tenure GoldCard UrlClick VisitSession
      
     
 
         
     
 
0 1 2 , 1 3 , 1 4 5 6
7 8 9 10 11 12
3
ln( 1) ln( 1) 30 3140
4150 51
ijt ijt ij t ij t i i i
i i it it ijt ijt
t ijt
Paid Y Y Paid Gender Age Age
Age Age Tenure GoldCard UrlClick VisitSession
      
     
 
         
     
 
0 1 2 , 1 3 , 1 4 5 6
7 8 9 10 11 12
4
ln( 1) ln( 1) 30 3140
4150 51
ijt ijt ij t ij t i i i
i i it it ijt ijt
t ijt
Earned Y Y Earned Gender Age Age
Age Age Tenure GoldCard UrlClick VisitSession
      
     
 
         
     
 
• Models for focal touchpoint exposures

66. Identification: 3-Stage Least Squares
0 1 2 , 1 3 , 1 4 5
6 7 8 9 10
11 12 7
ln( 1) ln( 1) 30
3140 4150 51
ijt ijt ij t ij t i i
i i i it it
ijt ijt t ijt
EarnedRival Y Y EarnedRival Gender Age
Age Age Age Tenure GoldCard
UrlClick VisitSession
     
    
   
        
    
   
0 1 2 , 1 3 , 1 4 5 6
7 8 9 10 11 12
6
ln( 1) ln( 1) 30 3140
4150 51
ijt ijt ij t ij t i i i
i i it it ijt ijt
t ijt
PaidRival Y Y PaidRival Gender Age Age
Age Age Tenure GoldCard UrlClick VisitSession
      
     
 
         
     
 
0 1 2 , 1 3 , 1 4 5 6
7 8 9 10 11 12
5
ln( 1) ln( 1) 30 3140
4150 51
ijt ijt ij t ij t i i i
i i it it ijt ijt
t ijt
OwnedRival Y Y OwnedRival Gender Age Age
Age Age Tenure GoldCard UrlClick VisitSession
      
     
 
         
     
 
• Models for rival brands’ touchpoint exposures

67. (1) ln(Expendt+1) (2) ln(Qtyt+1)
VARIABLES 3SLS 3SLS
Ownedt 0.313*** 0.047***
(0.019) (0.003)
Paidt 0.146*** 0.021***
(0.023) (0.003)
Earnedt 0.218 0.033
(0.188) (0.025)
OwnedRivalt -0.002 7.74e-04
(0.008) (0.001)
PaidRivalt 0.003 6.71e-04
(0.007) (9.57e-04)
EarnedRivalt 0.027 0.006
(0.061) (0.008)
Ownedt*Earnedt 0.427** 0.031
(0.188) (0.025)
Paidt*Earnedt 0.439** 0.086***
(0.206) (0.028)
Ownedt*Paidt -0.042*** -0.007***
(0.013) (0.002)
Controls √ √
R2 0.227 0.240
Note: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
We only show the main model results here.
Mostly
consistent
with main
results

68. (1) PageRival (2) ln(Expend+1) (3) ln(Qty+1) (4) Choice
VARIABLES Step-1 Step-2 Step-2 Step-2
Owned 0.539*** 0.074*** 0.096***
(0.014) (0.002) (0.002)
Paid 0.435*** 0.059*** 0.078***
(0.019) (0.003) (0.003)
Earned 0.223** 0.028** 0.040**
(0.103) (0.014) (0.018)
OwnedRival 10.640***
(0.051)
PaidRival 9.317***
(0.047)
EarnedRival 18.430***
(0.374)
PageRival -0.001*** -6.53e-05 -1.73e-04**
(4.78e-04) (6.40e-05) (8.49e-05)
PageRival * Owned -0.002*** -2.61e-04*** -3.31e-04***
(5.27e-04) (7.06e-05) (9.37e-05)
PageRival * Paid -0.002*** -2.81e-04*** -3.75e-04***
(2.86e-04) (3.83e-05) (5.08e-05)
PageRival * Earned -0.003 -2.54e-04 -5.23e-04
(0.002) (2.51e-04) (3.33e-04)
Other Controls √ √ √ √
R2 0.094 0.016 0.015 0.014

69. (1) TimeRival (2) ln(Expend+1) (3) ln(Qty+1) (4) Choice
VARIABLES Step-1 Step-2 Step-2 Step-2
Owned 0.542*** 0.075*** 0.096***
(0.014) (0.002) (0.002)
Paid 0.432*** 0.059*** 0.077***
(0.019) (0.003) (0.003)
Earned 0.221** 0.027* 0.040**
(0.103) (0.014) (0.018)
OwnedRival 12.860***
(0.065)
PaidRival 9.000***
(0.061)
EarnedRival 21.510***
(0.480)
TimeRival -0.001*** -7.44e-05 -1.49e-04**
(3.75e-04) (5.03e-05) (6.67e-05)
TimeRival * Owned -0.001*** -2.33e-04*** -2.90e-04***
(4.22e-04) (5.66e-05) (7.51e-05)
TimeRival * Paid -0.002*** -2.82e-04*** -3.78e-04***
(2.90e-04) (3.88e-05) (5.15e-05)
TimeRival * Earned -0.003 -2.60e-04 -5.62e-04
(0.002) (2.93e-04) (3.89e-04)
Other Controls √ √ √ √
R2 0.065 0.016 0.015 0.014

70. 70
Mechanism Checks
Negative rival spillovers on purchase outcomes
(Hoban and Bucklin 2015; Joo et al. 2014; Lewis and Reiley 2014;
Lopez et al. 2015)
Procedure
• Step-1: Rival brands’ owned media exposures 
Customers’ interests in rival brand products 
Search/browse rival brands
• Step-2: Search/browse rival brands  Less responsive to
the focal products’ touchpoint media exposures  Purchase
outcomes

71. Robustness: Granger Causality Test
Purpose
• Whether the time series of touchpoint exposures are useful
in predicting the time series of purchase outcomes
Procedure
• Compute the daily sum of Owned, Paid, Earned, Expend
and Qty to form the time series data
• Estimate autoregressive models for Expend and Qty with
touchpoint media exposures
• Run Wald tests to examine whether touchpoint exposures
Granger-cause purchase outcomes
Results
• Owned and Paid media Granger-cause Expend and Qty
• Earned media does not Granger-cause Expend and Qty

72. Robustness: Simultaneous Estimation
Reason
• Cross-model correlations across the residual error terms
might lead to inconsistent estimators
Procedure
• Assume multivariate normal distribution structures
• The residual errors for purchase expenditure , purchase quantity
, and purchase choice are assumed to satisfy the following
multivariate normal distribution structures:
• Maximum likelihood estimations
,ijt E
,ijt Q ,ijt C
, , , ,[ , ] ( , ) and [ , ] ( , )ijt E ijt C EC EC ijt Q ijt C QC QCMVN MVN      
22
2 2
and
Q QCE EC
EC QC
EC C QC C
  
   
  
      
      

73. (1) (2)
ln(Expend+1) Choice ln(Qty+1) Choice
VARIABLES Linear Linear Linear Linear
Owned 0.529*** 0.094*** 0.073*** 0.094***
(0.028) (0.005) (0.004) (0.005)
Paid 0.364*** 0.065*** 0.049*** 0.065***
(0.068) (0.012) (0.009) (0.012)
Earned 0.186** 0.032** 0.024** 0.032**
(0.083) (0.016) (0.011) (0.016)
OwnedRival -0.025*** -0.004** -0.002* -0.004**
(0.009) (0.002) (0.001) (0.002)
PaidRival -0.008 -0.001 -7.37e-04 -0.001
(0.007) (0.001) (7.81e-04) (0.001)
EarnedRival 0.022 0.006 0.006 0.006
(0.045) (0.007) (0.005) (0.007)
Owned * Earned 0.132 0.018 -0.004 0.018
(0.276) (0.046) (0.033) (0.046)
Paid * Earned 0.383** 0.052* 0.083*** 0.052*
(0.180) (0.032) (0.019) (0.032)
Owned * Paid -0.178*** -0.032*** -0.024*** -0.032***
(0.019) (0.004) (0.003) (0.004)
Controls √ √ √ √
Cross-model
Correlations
0.984*** 0.968***
(0.000) (0.002)
Log-likelihood -48139.965 -122216.450
Note: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
Consistent
with main
results

74. Robustness: Alternative Attributions
Time/Memory decay attribution method
• Considering memory decay parameters to account for chorological
orders of the exposures. This is an exponential process nonlinear
attribution method.
• Mathematically, we operationalized the focal variables Owned, Paid
and Earned as:
nijr,Owned (nijr,Paid or nijr,Earned) indicates the number of owned (paid or
earned) touchpoint exposures regarding product j that customer i
receives at day r
r=0 means the first day of our observation period), while t-r measures
the elapsed days since day r until day t (i.e., current day).
,0
,0
,0
*
*
*
t t r
ijt ijr Owned Ownedr
t t r
ijt ijr Paid Paidr
t t r
ijt ijr Earned Earnedr
Owned n
Paid n
Earned n
















75. Robustness: Alternative Attributions
(1) 90-days (2) 90-days (3) 90-days (4) 30-days (5) 30-days (6) 30-days
ln(Expend+1) ln(Qty+1) Choice ln(Expend+1) ln(Qty+1) Choice
VARIABLES FE-Linear FE-Linear CRE-Probit FE-Linear FE-Linear CRE-Probit
Owned 0.534*** 0.073*** 0.407*** 0.573*** 0.078*** 0.428***
(0.013) (0.002) (0.011) (0.014) (0.002) (0.012)
Paid 0.362*** 0.049*** 0.330*** 0.411*** 0.055*** 0.360***
(0.017) (0.002) (0.015) (0.018) (0.002) (0.016)
Earned 0.168* 0.021 0.209** 0.106 0.014 0.144
(0.099) (0.013) (0.097) (0.103) (0.014) (0.102)
OwnedRival -0.035*** -0.003*** -0.061*** -0.047*** -0.005*** -0.075***
(0.008) (0.001) (0.012) (0.011) (0.001) (0.015)
PaidRival -0.016* -0.002 -0.058*** -0.039*** -0.005*** -0.108***
(0.009) (0.001) (0.016) (0.012) (0.002) (0.021)
EarnedRival -0.005 0.003 -0.039 0.011 0.004 0.016
(0.058) (0.008) (0.085) (0.072) (0.010) (0.093)
Owned * Earned 0.190 0.010 0.133 0.241 0.015 0.171
(0.196) (0.026) (0.166) (0.219) (0.029) (0.183)
Paid * Earned 0.355** 0.083*** 0.281* 0.446** 0.099*** 0.279*
(0.178) (0.024) (0.159) (0.186) (0.025) (0.161)
Owned * Paid -0.177*** -0.025*** -0.151*** -0.189*** -0.027*** -0.159***
(0.013) (0.002) (0.013) (0.017) (0.002) (0.015)
Controls √ √ √ √ √ √
R2/Log-likelihd 0.015 0.014 -36283.345 0.016 0.015 -36275.503
Note: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
Consistent
with main
results

76. Appendix:
Study II

77. Brand-/Product-Oriented Advertising
Brand-Oriented
Advertising
Product-Oriented
Advertising

78. Exploratory Data Analysis
(a) Offline Store Visits and Brand Advertising
78
(b) Transactions and Brand Advertising
• The Relationships between Advertising Strategies and Offline Visits or
Transactions

79. Exploratory Data Analysis
(c) Offline Store Visits and Product Advertising
79
(d) Transactions and Product Advertising
• The Relationships between Advertising Strategies and Offline Visits or
Transactions

80. Exploratory Data Analysis
(a) The Interaction between Brand Advertising
and Brand Sales Tenure
80
(b) The Interaction between Product
Advertising and Brand Sales Tenure
• The Interactions between Advertising Strategies and Salesperson Brand
Sales Tenure

81. Exploratory Data Analysis
(c) The Interaction between Brand Advertising
and Proportion of Completing Sales Training
81
(d) The Interaction between Product Advertising
and Proportion of Completing Sales Training
• The Interactions between Advertising Strategies and Sales Training
Completion

82. Exploratory Data Analysis
(a) Aggregate Offline Store Visits
82
(b) Aggregate Transactions
• The Interactions between Brand-oriented Advertising and Product Line
Length across Different Brands

83. Exploratory Data Analysis
(a) Aggregate Offline Store Visits
83
(b) Aggregate Transactions
• The Interactions between Product-oriented Advertising and Product Line
Length across Different Brands

84. Exploratory Data Analysis
(c) Aggregate Offline Store Visits
84
(d) Aggregate Transactions
• The Interactions between Brand-oriented Advertising and Product Line
Length of the Same Brand

85. Exploratory Data Analysis
(c) Aggregate Offline Store Visits
85
(d) Aggregate Transactions
• The Interactions between Product-oriented Advertising and Product Line
Length of the Same Brand

86. Identifications I-
Ads exposures are endogenous
• Instrument Variable
• BrandAdDLineSBrand and ProductAdDLineSBrand
• Brand-/product-oriented advertising exposures from different-line
same-brand products
• Inclusion restriction: Same-brand
• Exclusion restriction: Different-line
86

87. Identifications I-
Advertising exposures are endogenous

88. Identifications I-
Advertising exposures are endogenous

89. Identifications II-
Salesman may not be randomly chosen
Company may endogenously initiate the interaction with
potential customers, so such unobserved non-random assignment
of salespersons may lead to omitted variable bias (Eizenberg 2016).
• (1) Unobserved customer valuation
• Add brand/product/line dummies
• (2) Unobserved customer interest
• Add DeciLevel
• How strong is the customer i’s intention to purchase product j when visiting
the offline store at day t
• (3) Falsification test
• Randomly shuffling assignments of salesmen to customers
89

90. Identifications II-
Salesman may not be randomly chosen

91. Identifications III-
Simultaneity between Ads and Sales
91
ln( )ijt ijtY X 
0 1 2 3
4 5
6
7
=
*
*
*
jt jt jt jt
jt jt jt
jt jt
jt
X TotalBrandAd TotalProductAd ProLineDiffBrand
ProLineSameBrand TotalBrandAd ProLineDiffBrand
TotalProductAd ProLineDiffBrand
TotalBrandAd P
    
 


   
 


8 *
jt
jt jt
t jt
roLineSameBrand
TotalProductAd ProLineSameBrand
ControlVariables

 

  
0 1 2 , 1 3 , 1 4
5 6 2
ln( ) ln( )jt jt j t j t jt
j j ijt
TotalBrandAd Y Y TotalBrandAd BaiduSearchIndex
BrandDummies TypeDummies
    
  
     
  
0 1 2 , 1 3 , 1 4
5 6 3
ln( ) ln( )jt jt j t j t jt
j j ijt
TotalProductAd Y Y TotalProductAd BaiduSearchIndex
BrandDummies TypeDummies
    
  
     
  

92. Identifications III-Simultaneity between Ads and Sales

93. Identifications III-Simultaneity between Ads and Sales

94. Robustness - Multi-Level Analysis

95. Robustness-Hierarchical Bayesian
Estimation
• Data likelihood function:
• Where N is the number of observations or sample size and m is province
(m=1,…,34);
0
0 0
1
0
1
1
( | , , ) ( ) ( )
1 1
ijmt
ijmt ijmt
ijmt ijmt
XN
Choice Choice
ijmt ijmt X Xn
e
p Choice X
e e
 
   
 


  
 
 
0 1 2 3 4
5 6
7 8
=
* *
* *
ijmt m m ijmt m ijmt m imt m imt
ijmt imt ijmt imt
ijmt imt ijmt
X BrandAd ProductAd SpBrandTenure SpPassTrain
BrandAd SpBrandTenure BrandAd SpPassTrain
ProductAd SpBrandTenure ProductAd SpPassTra
     
 
 
    
 
 
9 10 11
12 13 14 15
16 17 18ln( ) ln( ) ln(
imt
m jmt m jmt ijt
it it it it
m jmt j
in
ProLineDiffBrand ProLineSameBrand DeciLevel
SpMidSchool SpCollege SpGraduate SpAge
Price Displacement FuelCon
  
   
  
  
   
   19)j j
i j t
sumption Seats
  

  

96. Robustness-Hierarchical Bayesian
Estimation
Suppose we have k+1 coefficients including intercept term. We have
hierarchical province and customer-level random coefficients (e.g., βh,
including Intercept, BrandAd, ProductAd, SpBrandTenure, SpPassTrain,
ProLineDiffBrand, ProLineSameBrand and Price) and non-hierarchical
customer level random coefficients (e.g., β-h, the rest of coefficients):
Each non-hierarchical customer level random coefficient βc
-h (c=1,…,k-7):
0 0( , ) ( , , )
where hierarchical coefficients:
( , , , , , , )
h h h
h
BrandAd ProductAd SpBrandTenure SpPassTrain ProLineDiffBrand ProLineSameBrand Price
    




( 7) 1 ( 7) 1 ( 7) ( 7)( , )h h h
k k k kN   
      

97. Robustness-Hierarchical Bayesian
Estimation
Hierarchical province and customer level coefficients β0 and βd
h
(d=1,…,7):
Hyperpriors of non-hierarchical coefficients:
These customer level random coefficients represent customer-level
differentiated individual sensitivities;
Hyperpriors of hierarchical coefficients:
0 8 1 8 8( , ) ( , )h h h
MVN    
2
( 7) 1 ( 7) 1 ( 7) ( 7) ( 7) ( 7)0 , 10h h
k k k k k k  
            
2
8 1 8 1 8 8 8 1 8 1 8 8 8 8
2
8 8 8 8 8 8 8 8
( , ) where 0 , 10
( , ) where 10 , 10
h h h h h
h h h
N
InverseWishart S df S df
        
   
    
   

98. Robustness-Hierarchical Bayesian
Estimation
Then the posterior distribution is:
• Use MCMC to draw the parameter values from the posterior
distribution
• Select 20,000 as the number of iterations and select the first 15,000
iterations as “burn-in” samples.
• Coefficients are generally converging to their posterior means after
10,000 iterations, so the rest of 5,000 iterations are selected to calculate
the posterior means and posterior standard errors of parameters.
• MCMC acceptance rate is over 25%, which is in an appropriate level.
0 0 0( , , | , ) ( | , , , ) ( | , ) ( , | , )
( | , ) ( | , )
h h h h h h h h h h
ijmt ijmt ijmt ijmt
h h h h h
p X Choice p Choice X p p
p p S df
          
 
    
  
 

99. Robustness-Hierarchical Bayesian
Estimation