Summary articles Digital Marketing & Analytics
Table of Contents
Week 1 .............................................................................................................................. 2
In the digital world, not everything that can be measured matters. How to distinguish
“valuable” from “nice to know” among measures of consumer engagement – Fulgoni ........... 2
Attribution modeling in digital advertising – Nisar & Yeung ................................................... 3
Beyond paid media: marketing’s new vocabulary – Edelman & Salsberg ............................... 5
Week 2 .............................................................................................................................. 7
Helping Firms Reduce Complexity in Multichannel Online Data:A New Taxonomy-Based
Approach for Customer Journeys – Anderl, Schumann & Kunz ............................................. 7
Attributing conversions in a multichannel online marketing environment: an empirical model
and a field experiment – Li & Kannan.................................................................................... 10
Customer journey mapping is at the heart of digital transformation ..................................... 13
Competing on customer journeys – Edelman & Singer .......................................................... 14
Week 3 ............................................................................................................................ 16
An integrated marketing communication perspective on social media metrics – Barger &
Labrecque ............................................................................................................................... 16
Can you measure the ROI of your social media marketing? – Hoffman & Fodor ................. 20
Factors influencing popularity of branded content in Facebook fan pages – Sabate, Berbegal-
Mirabent, Canabate & Lebherz .............................................................................................. 22
Week 4 ............................................................................................................................ 25
Search engine advertisements: the impact of advertising statements on click-through and
conversion rates – Haans, Raassens & van Hout .................................................................... 25
Search engine advertising effectiveness in a multimedia campaign - Zenetti, Bijmolt, Leeflang
& Klapper ............................................................................................................................... 26
Online display advertising: targeting and obtrusiveness – Goldfarb & Tucker ..................... 28
Week 5 ............................................................................................................................ 29
Consumer heterogeneity and paid search effectiveness: a large-scale field experiment – Black,
Nosko & Tadelis ...................................................................................................................... 29
Personalization in email marketing: the role of noninformative advertising content – Sahni,
Wheeler & Chintagunta .......................................................................................................... 29
Week 6 ............................................................................................................................ 30
On the go: how mobile shopping affects customer purchase behavior – Wang, Malthouse &
Krishnamurthi ........................................................................................................................ 30
Geo-conquesting: competitive locational targeting of mobile promotions – Fong, Fang & Luo
................................................................................................................................................. 32
Hour-by-Hour sales impact of mobile advertising - Baker, Fang & Luo ................................ 32
,Week 1
In the digital world, not everything that can be measured matters. How to distinguish
“valuable” from “nice to know” among measures of consumer engagement – Fulgoni
The definition of engagement used to be: turning a prospect to a brand idea enhanced by the
surrounding context.
Engagement no longer can be considered to be simply a one-way communication from a
brand to the consumer. It now needs to incorporate consumers’ ability to easily provide
digital feedback of their own – at scale and with the communication being either positive or
negative.
Traditional “softer” measures of advertising engagement (= attitudinal shifts in brand recall,
likability and purchase intent) along with harder measures of sales lift, are used to measure
consumers’ engagement with digital advertisements. The computerized nature of digital also
means that many other engagement metrics (like shares, likes, views) are available today.
The challenge for marketers is to identify the metrics that matter to their return on advertising
investment versus those that either are nice to know or downright misleading.
One of the first measures of online engagements was the “click”. Research has demonstrated
the absence of any relationship between clicks and effectiveness. But click-through rates are
still used as measures of engagement and effectiveness. Simplicity, low cost and speed are
the drivers of continued use of clicks. Other engagement metrics (website reach, number of
page views, time spent & demographics) are relevant to the size and quality of the audience
reached, they relationship of these measures to advertising engagement and the effectives of
brand advertising carried on the side is vague.
Most relevant metrics that need to be taken into account in gauging engagement, are attitude
towards a website, motivations for using it, and overall opinion of the site. Trust in the side
appears to be an extremely important metric for driving consumer response to advertising.
How viewable is your ad?
Digital in-view rates (= percentage of advertising impressions in a campaign that are in-view
to the consumer) often are found to be lower that 50%. An advertisement is classified as
“being in-view” when 50% of a display advertisement is in-view for at least one second. A
study showed that the impact of digital advertisements climbs sharply after the advertisement
has been in-view for some time, suggesting that a longer in-view definition might be more
appropriate.
The battle against ad blocking
Advertisers seek more effective ways to engage with consumers and measure that
engagement, their efforts are hampered by ad-blocking software. Ad-blockers is especially
popular on mobile devices, where slow load times are particularly annoying to users.
Ad-blockers are a problem across any digital platform, including the viewing of video
content.
Social media metrics
Measurement of the number of postings and whether they are positive or negative, how many
times shared and how many users reached, have become important metrics of social
engagement with brands that are initiated by consumers. Social networks are inundated with
negative comments regards brands. The challenge for any marketer is to respond to negative
engagement in a timely manner. 70% of companies fail to do just that, thereby risking
damaging consequences. Monitoring and responding in real time to inbound comments
,expressed on social media should be as important as monitoring the impact of a marketer’s
outbound communications.
Positive consumer engagement on social media can yield impressive results. Brands can use
paid or organic communications to reach their target segment and then benefit from any
engagement (e.g. sharing) that subsequently occurs and which extends persuasive reach.
Measures of actual sharing of content have been shown to deliver measurable marketing ROI.
Mobile-engagement metrics
The most significant change in consumer engagement has been the result of the use of mobile
devices. It is vital for marketers to ensure they capture their “fare share” of consumers’
mobile engagement.
Mobile devices initiate engagement with branded content whenever and wherever they
choose à also called micro-moments. Google has found that consumers often are more
attentive to their in-the-moment needs than they are loyal to a particular brand or product.
They’re attracted to those brands that best address those in-the-moment needs. The winners
will be brands that are able to derive methods to measure and understand consumer demands
for mobile engagement and then deliver against those needs.
Conclusion
Marketers have only scratched the surface of measuring new levels of consumer engagement,
given a digital marketplace that remains in flux.
Attribution modeling in digital advertising – Nisar & Yeung
The click-through rates are declining fast. Researchers discovered that only a small
proportion of visits translated into final purchase. Researchers also found that banner
advertising was not an effective online media strategy. Banner advertisement can play a
significant role in consumer retention.
The last-click method of attribution is flawed, because it fails to take account of the influence
of all tough points except the last one. Companies adopting the last-click model forfeit the
chance to better optimize their advertising spend.
The last-click model ascribes 100% of the credit to the last advertisement the user clicked on
before a purchase conversion.
The advertising industry proposed the alternative concept of multichannel attribution. This
framework assumes that more than one channel impression or touch point can have a fraction
of the credit for a sale, on the basis of the true influence each impression has on the
conversion. Individual advertising channels should not be evaluated in isolation, and credit
must be assigned equitably with respect to the campaign goals on these channels.
Within this article the authors examined the nature and scope of the rule-based attribution
models in measuring the performance of online channels in consumer journeys. The study
investigated:
- whether multichannel attribution models give different channel valuations than last-
click models
- whether these channel valuations vary significantly among the various multichannel
models
- whether statistical multi-attribute models have predictive validity
, Attribution in digital advertising
Multiple vendors, publishers or search engines serve advertisements so there can be a number
of touchpoints or channel impressions with digital advertising in a purchase funnel.
Attribution modeling becomes interesting only when one considers the impact of several
channel impressions together. The process of attribution can be understood as the assignment
of conversion credit when multiple advertising channels reach a given online user.
A consumer can take different choice actions at different stages of a purchase funnel. There
can be three roles in a customer journey:
- introduction à plays the initial role in starting the process, often populated by search
- assist à any contributing website that enables a transaction after the introduction.
Consumer behavior turns to a goal-directed search.
- Conversion à final step before the purchase is made.
It is difficult to observe cross-channel effect in an interactive situation. Paid search and
organic search may complement each other in the purchase funnel.
Four rule-based models that can be used for measuring the performance of an advertising
campaign:
1. Time-decay model = adjusts credit so that the closer and impression is to a
conversion, the more credit it receives. The rewards can be apportioned so that the
last contributor achieves maximum credit. Best for short-lived deals or promotional
offers.
2. Uniformly distributed attribution model = value of each conversion is distributed
uniformly to all impressions. Because the shares of each conversion are divided
equally among all channels, the model does not consider where the touch points occur
3. Position-based model = Pareto distribution model à places more importance on the
first and last touch points than on all the others in between. The model assumes that
the first impression is important because it attracts the user’s attention, and the last
one is important because of the role it plays in concluding the transaction.
4. Statistics-based attribution model = based on empirical observations rather than
theoretical assumptions. In light of the increasing complexity of digital advertising,
researchers have endeavored to develop a true data-driven methodology to account for
the influence of each interaction on the final user decision.
Multi-channel attribution models
Attribution model Modeling approach Advantages disadvantages
Last-click model Helps to determine which Does not take in account
channels best lead users to a many vital interactions and
buying decision or final steps in a customer’s
conversion journey to transaction
Time-decay model Rule-based modeling Captures an important -
assumptions aspect of the consumers’
online behavior:
conversions are associated
with a short attention span.
