Summary articles Digital Marketing Intelligence
Week 1 Chapman, C., Feit, E.M. (2019). Association Rules for Market Basket
Digital Analytics Analysis. In: R For Marketing Research and Analytics. Use R!. Springer,
And Cross Media Cham. [only pages 341-343]
Campaigns Kannan, P. K. and Hongshuang Li (2017). Digital marketing: A
framework, review and research agenda. International Journal of
Research in Marketing, 34(1), 22-45.
Kukar-Kinney, M., Scheinbaum, A. C., Orimoloye, L. O., Carlson, J. R., &
He, H. (2022). A model of online shopping cart abandonment:
evidence from e-tail clickstream data. Journal of the Academy of
Marketing Science, 1-20.
Li, J., Abbasi, A., Cheema, A., & Abraham, L. B. (2020). Path to
Purpose? How Online Customer Journeys Differ for Hedonic Versus
Utilitarian Purchases. Journal of Marketing.
Van Ewijk, B. J., Stubbe, A., Gijsbrechts, E., & Dekimpe, M. G. (2021).
Online display advertising for CPG brands:(When) does it work?.
International Journal of Research in Marketing, 38(2), 271-289.
Week 2 -
Wehkamp –
Digital Analytics
and
Experimentation
Week 3 Bollinger, B., Gillingham, K., Kirkpatrick, A. J., & Sexton, S. (2022).
Social Networks Visibility and peer influence in durable good adoption. Marketing
& Social Influence Science.
Hinz, Oliver, Bernd Skiera, Christian Barrot, and Jan U. Becker (2011),
"Seeding Strategies for Viral Marketing: An Empirical Comparison,"
Journal of Marketing, 75 (6), 55-71.
Risselada, H and J. Van den Ochtend (2021). Social Network Analysis.
In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of
Market Research: Springer.
Week 4 Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., &
Text Mining Schweidel, D. A. (2020). Uniting the Tribes: Using Text for Marketing
Insight. Journal of Marketing, 84(1), 1–25.
Berger, J., & Milkman, K. L. (2012). What makes online content viral?.
Journal of Marketing Research, 49(2), 192-205.
Hirschberg, J., & Manning, C. D. (2016). Advances in natural language
processing. Science, 349(6245), 261-266.
Hartmann, J., Heitmann, M., Siebert, C. and Schamp, C. (2022). More
than a Feeling: Accuracy and Application of Sentiment Analysis,
International Journal of Research in Marketing
Rutz, O. J., Sonnier, G. P., & Trusov, M. (2017). A New Method to Aid
Copy Testing of Paid Search Text Advertisements. Journal of
Marketing Research, 54(6), 885–900.
,Week 5 McDonnell Feit, E. & Berman, R. (2019). Test & Roll: Profit-
Digital Maximizing A/B Tests. Marketing Science 38(6), 1038-1058.
Experiments Goldfarb, A., Tucker, C., & Wang, Y. (2022). Conducting Research in
Marketing with Quasi-Experiments. Journal of Marketing, 86(3), 1–
20.
Brett R. G., Zettelmeyer, F., Bhargava, N., Chapsky, D. (2019) A
Comparison of Approaches to Advertising Measurement: Evidence
from Big Field Experiments at Facebook. Marketing Science,
38(2):193-225.
Shehu, E., Abou Nabout, N. & Clement. M. (2021). The Risk of
Programmatic Advertising: Effects of Website Quality on Advertising
Effectiveness. International Journal of Research in Marketing, 38(3),
663 – 667.
Week 6 Eggers, Felix, Sattler, Henrik, Teichert, Thorsten, Völckner, Franziska
Choice based (2018). Choice-Based Conjoint Analysis. Handbook of Market
conjoint Research, Springer.
experiments
Week 7 Goić, M., Jerath, K., & Kalyanam, K. (2022). The roles of multiple
Attribution channels in predicting website visits and purchases: Engagers versus
Models closers. International Journal of Research in Marketing, 39(3), 656-
677.
Kannan, P.K., and Hongshuang (Alice) Li (2021), “Multitouch
Attribution in the Customer Purchase Journey,” Impact at JMR,
(June),
Li, H., & Kannan, P. K. (2014). Attributing conversions in a
multichannel online marketing environment: An empirical model
and a field experiment. Journal of marketing research, 51(1), 40-56.
, WEEK 1 – Digital Analytics and Cross Media Campaigns
1. Chapman, C., Feit, E.M. (2019). Association Rules for Market Basket Analysis. In: R For
Marketing Research and Analytics. Use R!. Springer, Cham. [only pages 341-343]
In this chapter we examine a strategy to extract insight from transactions and cooccurrence data:
association rule mining. Association rule analysis attempts to find sets of informative patterns from
large, sparse data sets. We demonstrate association rules using a real data set of more than 80,000
market basket transactions with 16,000 unique items [23]. We then examine how rule mining is
potentially useful with nontransactional data and we use association rules to explore patterns in the
subscription data from Chap. 5.
An association is simply the co-occurrence of two or more things. A transaction is a set of items that
co-occur in an observation. The market basket is the set of things that are purchased or considered
for purchase at one time.
Association rules are used to identify the relationships between different products that are
frequently purchased together by customers. For example, a retailer may observe that customers
who purchase bread are also likely to purchase milk. This information can be used by the retailer to
place these products closer together in the store, to increase the likelihood of customers buying both
products.
The authors introduce the concept of support, confidence, and lift, which are used to measure the
strength of association rules. Support (joint probability of finding pairs AB across all baskets) is
defined as the proportion of transactions that contain both items in the rule. If {hot dogs, soda}
appears in 10 out of 200 transactions, then support({hotdogs,soda}) = 0.05. Confidence (probability
of purchase B given a purchase of A) is the proportion of transactions that contain the antecedent (or
left-hand side) of the rule that also contain the consequent (or right-hand side) of the rule. Lift is a
measure of the strength of the association rule and is calculated by dividing the confidence of the
rule by the probability of the consequent occurring on its own.
To interpret the output of a Market Basket Analysis in R, you need to look at the association rules
generated by the "arules" package. The output will typically include information on the support,
confidence, and lift of each rule.
• Support: This measure indicates the proportion of transactions that contain both items in the
rule. For example, if the support of a rule is 0.05, it means that the items in the rule were
purchased together in 5% of all transactions.
• Confidence: This measure indicates the proportion of transactions that contain the
antecedent (or left-hand side) of the rule that also contain the consequent (or right-hand
side) of the rule. For example, if the confidence of a rule is 0.8, it means that 80% of the
transactions that contain the antecedent item also contain the consequent item.
• Lift: This measure indicates the strength of the association between the antecedent and
consequent items, relative to what would be expected if they were independent. A lift value
greater than 1 indicates a positive association, while a value less than 1 indicates a negative
association. A value of 1 indicates that there is no association. For example, if the lift of a rule
is 1.5, it means that the items in the rule are 1.5 times more likely to be purchased together
than if they were purchased independently.
In interpreting the output of the analysis, you should focus on high support, high confidence, and
high lift rules. These rules indicate strong associations between items and can be used by retailers to
make data-driven decisions, such as product placement, cross-selling, and targeted promotions.
However, it's important to note that the output of Market Basket Analysis should be interpreted in
the context of the specific business problem being addressed. It's also important to consider other
, factors, such as seasonality, promotions, and customer demographics, that may influence buying
behavior.
Finally, the authors discuss some of the limitations of Market Basket Analysis, including the fact that
it only captures relationships between products that are purchased together and does not account
for other factors that may influence buying behavior, such as price or promotions. Nevertheless, the
authors argue that Market Basket Analysis is still a valuable tool for retailers to gain insights into
customer behavior and make data-driven decisions.
2. Kannan, P. K. and Hongshuang Li (2017). Digital marketing: A framework, review and
research agenda. International Journal of Research in Marketing, 34(1), 22-45.
Abstract
We develop and describe a framework for research in digital marketing that highlights the
touchpoints in the marketing process as well as in the marketing strategy process where digital
technologies are having and will have a significant impact. Using the framework we organize the
developments and extant research around the elements and touchpoints comprising the framework
and review the research literature in the broadly defined digital marketing space. We outline the
evolving issues in and around the touchpoints and associated questions for future research. Finally,
we integrate these identified questions and set a research agenda for future research in digital
marketing to examine the issues from the perspective of the firm.
Digital marketing is defined as an adaptive, technology enabled process by which firms collaborate
with customers and partners to jointly create, communicate, deliver, and sustain value for all
stakeholders. The adaptive process enabled by the digital technologies creates value in new ways in
new digital environments. Processes enabled by digital technologies create value through new
customer experiences and through interactions among customers. Digital marketing itself is enabled
by a series of adaptive digital touchpoints encompassing the marketing activity, institutions,
processes and customers.
Environment 5 C’s – Customers, collaborators, competitors, context and company
Key concepts and elements of the framework are:
• Digital technologies are rapidly changing the environment within which firms operate,
specifically they are reducing information asymmetries between customers and sellers.
o Consumer behavior is changing as a result of access to a variety of technologies and
devices both in the online and mobile contexts. Customers can influence other
Week 1 Chapman, C., Feit, E.M. (2019). Association Rules for Market Basket
Digital Analytics Analysis. In: R For Marketing Research and Analytics. Use R!. Springer,
And Cross Media Cham. [only pages 341-343]
Campaigns Kannan, P. K. and Hongshuang Li (2017). Digital marketing: A
framework, review and research agenda. International Journal of
Research in Marketing, 34(1), 22-45.
Kukar-Kinney, M., Scheinbaum, A. C., Orimoloye, L. O., Carlson, J. R., &
He, H. (2022). A model of online shopping cart abandonment:
evidence from e-tail clickstream data. Journal of the Academy of
Marketing Science, 1-20.
Li, J., Abbasi, A., Cheema, A., & Abraham, L. B. (2020). Path to
Purpose? How Online Customer Journeys Differ for Hedonic Versus
Utilitarian Purchases. Journal of Marketing.
Van Ewijk, B. J., Stubbe, A., Gijsbrechts, E., & Dekimpe, M. G. (2021).
Online display advertising for CPG brands:(When) does it work?.
International Journal of Research in Marketing, 38(2), 271-289.
Week 2 -
Wehkamp –
Digital Analytics
and
Experimentation
Week 3 Bollinger, B., Gillingham, K., Kirkpatrick, A. J., & Sexton, S. (2022).
Social Networks Visibility and peer influence in durable good adoption. Marketing
& Social Influence Science.
Hinz, Oliver, Bernd Skiera, Christian Barrot, and Jan U. Becker (2011),
"Seeding Strategies for Viral Marketing: An Empirical Comparison,"
Journal of Marketing, 75 (6), 55-71.
Risselada, H and J. Van den Ochtend (2021). Social Network Analysis.
In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of
Market Research: Springer.
Week 4 Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O., &
Text Mining Schweidel, D. A. (2020). Uniting the Tribes: Using Text for Marketing
Insight. Journal of Marketing, 84(1), 1–25.
Berger, J., & Milkman, K. L. (2012). What makes online content viral?.
Journal of Marketing Research, 49(2), 192-205.
Hirschberg, J., & Manning, C. D. (2016). Advances in natural language
processing. Science, 349(6245), 261-266.
Hartmann, J., Heitmann, M., Siebert, C. and Schamp, C. (2022). More
than a Feeling: Accuracy and Application of Sentiment Analysis,
International Journal of Research in Marketing
Rutz, O. J., Sonnier, G. P., & Trusov, M. (2017). A New Method to Aid
Copy Testing of Paid Search Text Advertisements. Journal of
Marketing Research, 54(6), 885–900.
,Week 5 McDonnell Feit, E. & Berman, R. (2019). Test & Roll: Profit-
Digital Maximizing A/B Tests. Marketing Science 38(6), 1038-1058.
Experiments Goldfarb, A., Tucker, C., & Wang, Y. (2022). Conducting Research in
Marketing with Quasi-Experiments. Journal of Marketing, 86(3), 1–
20.
Brett R. G., Zettelmeyer, F., Bhargava, N., Chapsky, D. (2019) A
Comparison of Approaches to Advertising Measurement: Evidence
from Big Field Experiments at Facebook. Marketing Science,
38(2):193-225.
Shehu, E., Abou Nabout, N. & Clement. M. (2021). The Risk of
Programmatic Advertising: Effects of Website Quality on Advertising
Effectiveness. International Journal of Research in Marketing, 38(3),
663 – 667.
Week 6 Eggers, Felix, Sattler, Henrik, Teichert, Thorsten, Völckner, Franziska
Choice based (2018). Choice-Based Conjoint Analysis. Handbook of Market
conjoint Research, Springer.
experiments
Week 7 Goić, M., Jerath, K., & Kalyanam, K. (2022). The roles of multiple
Attribution channels in predicting website visits and purchases: Engagers versus
Models closers. International Journal of Research in Marketing, 39(3), 656-
677.
Kannan, P.K., and Hongshuang (Alice) Li (2021), “Multitouch
Attribution in the Customer Purchase Journey,” Impact at JMR,
(June),
Li, H., & Kannan, P. K. (2014). Attributing conversions in a
multichannel online marketing environment: An empirical model
and a field experiment. Journal of marketing research, 51(1), 40-56.
, WEEK 1 – Digital Analytics and Cross Media Campaigns
1. Chapman, C., Feit, E.M. (2019). Association Rules for Market Basket Analysis. In: R For
Marketing Research and Analytics. Use R!. Springer, Cham. [only pages 341-343]
In this chapter we examine a strategy to extract insight from transactions and cooccurrence data:
association rule mining. Association rule analysis attempts to find sets of informative patterns from
large, sparse data sets. We demonstrate association rules using a real data set of more than 80,000
market basket transactions with 16,000 unique items [23]. We then examine how rule mining is
potentially useful with nontransactional data and we use association rules to explore patterns in the
subscription data from Chap. 5.
An association is simply the co-occurrence of two or more things. A transaction is a set of items that
co-occur in an observation. The market basket is the set of things that are purchased or considered
for purchase at one time.
Association rules are used to identify the relationships between different products that are
frequently purchased together by customers. For example, a retailer may observe that customers
who purchase bread are also likely to purchase milk. This information can be used by the retailer to
place these products closer together in the store, to increase the likelihood of customers buying both
products.
The authors introduce the concept of support, confidence, and lift, which are used to measure the
strength of association rules. Support (joint probability of finding pairs AB across all baskets) is
defined as the proportion of transactions that contain both items in the rule. If {hot dogs, soda}
appears in 10 out of 200 transactions, then support({hotdogs,soda}) = 0.05. Confidence (probability
of purchase B given a purchase of A) is the proportion of transactions that contain the antecedent (or
left-hand side) of the rule that also contain the consequent (or right-hand side) of the rule. Lift is a
measure of the strength of the association rule and is calculated by dividing the confidence of the
rule by the probability of the consequent occurring on its own.
To interpret the output of a Market Basket Analysis in R, you need to look at the association rules
generated by the "arules" package. The output will typically include information on the support,
confidence, and lift of each rule.
• Support: This measure indicates the proportion of transactions that contain both items in the
rule. For example, if the support of a rule is 0.05, it means that the items in the rule were
purchased together in 5% of all transactions.
• Confidence: This measure indicates the proportion of transactions that contain the
antecedent (or left-hand side) of the rule that also contain the consequent (or right-hand
side) of the rule. For example, if the confidence of a rule is 0.8, it means that 80% of the
transactions that contain the antecedent item also contain the consequent item.
• Lift: This measure indicates the strength of the association between the antecedent and
consequent items, relative to what would be expected if they were independent. A lift value
greater than 1 indicates a positive association, while a value less than 1 indicates a negative
association. A value of 1 indicates that there is no association. For example, if the lift of a rule
is 1.5, it means that the items in the rule are 1.5 times more likely to be purchased together
than if they were purchased independently.
In interpreting the output of the analysis, you should focus on high support, high confidence, and
high lift rules. These rules indicate strong associations between items and can be used by retailers to
make data-driven decisions, such as product placement, cross-selling, and targeted promotions.
However, it's important to note that the output of Market Basket Analysis should be interpreted in
the context of the specific business problem being addressed. It's also important to consider other
, factors, such as seasonality, promotions, and customer demographics, that may influence buying
behavior.
Finally, the authors discuss some of the limitations of Market Basket Analysis, including the fact that
it only captures relationships between products that are purchased together and does not account
for other factors that may influence buying behavior, such as price or promotions. Nevertheless, the
authors argue that Market Basket Analysis is still a valuable tool for retailers to gain insights into
customer behavior and make data-driven decisions.
2. Kannan, P. K. and Hongshuang Li (2017). Digital marketing: A framework, review and
research agenda. International Journal of Research in Marketing, 34(1), 22-45.
Abstract
We develop and describe a framework for research in digital marketing that highlights the
touchpoints in the marketing process as well as in the marketing strategy process where digital
technologies are having and will have a significant impact. Using the framework we organize the
developments and extant research around the elements and touchpoints comprising the framework
and review the research literature in the broadly defined digital marketing space. We outline the
evolving issues in and around the touchpoints and associated questions for future research. Finally,
we integrate these identified questions and set a research agenda for future research in digital
marketing to examine the issues from the perspective of the firm.
Digital marketing is defined as an adaptive, technology enabled process by which firms collaborate
with customers and partners to jointly create, communicate, deliver, and sustain value for all
stakeholders. The adaptive process enabled by the digital technologies creates value in new ways in
new digital environments. Processes enabled by digital technologies create value through new
customer experiences and through interactions among customers. Digital marketing itself is enabled
by a series of adaptive digital touchpoints encompassing the marketing activity, institutions,
processes and customers.
Environment 5 C’s – Customers, collaborators, competitors, context and company
Key concepts and elements of the framework are:
• Digital technologies are rapidly changing the environment within which firms operate,
specifically they are reducing information asymmetries between customers and sellers.
o Consumer behavior is changing as a result of access to a variety of technologies and
devices both in the online and mobile contexts. Customers can influence other
