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Please read the case study Global Attribution as well as chapter 3 (both files on the attachment). Then, answer the following questions:1. Which of the ten media channels do you think was least reliably measured? Do you think the measurement of ad exposure in this channel was biased (systematically higher or lower than the real number) or just highly variant (sometime higher and sometimes lower than the real number)?2. Think up another situation in which you would expect to find endogeneity.3. In general, do you think online media is better for inducing purchase incidence or purchase amount?4. Could the methodology described in the case be applied to determine advertising effectiveness of a consumer packaged good like Tide? Why/why not?Please note that since this case is based on Chapter 3 Web Analytics, you should do some basic calculations and data analysis using data provided in the case to support your answers. By the way, please don’t turn in simple question and answer format. Case analysis should be thoughtful, well organized and support your arguments/answers with data.This work must be done in less than 48 hours.


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Global Attribution
CMO: How is our ad budget allocation changing this year compared to last?
VP of Advertising: We’re doubling our allotment to digital channels like social media ads
and online search ads and paring back our spending on traditional channels like television
and magazine ads. Overall in the advertising industry, traditional ad channels are declining
and digital channels are growing, and we’re leading the way.
CMO: Great! So are we seeing better results now?
VP of Advertising: We are now showing advertisements in over ten different channels, so
consumers are being exposed to our advertisements on more media than ever.
CMO: Okay, good, but are we getting more bang for the buck?
VP of Advertising: We feel that being exposed to more ads in more locations can only help
sell customers on our brand.
CMO: That’s probably true, but is there any evidence that moving budget away from
television to digital channels is bringing in more sales?
VP of Advertising: It’s impossible to know for sure, but we think keeping ahead of recent
trends is a good idea.
In the internet era, many customer actions can be measured. As a result, advertisers are under
increasing pressure to use this customer data to show that their ads are increasing sales. But
even with careful tracking of all possible customer data, problems with attribution can cause
faulty conclusions about the effectiveness of various online ads. For example, last-click
attribution typically exaggerates the effect of search marketing efforts, and first-click
attribution can give highly errant results with small changes in an arbitrary time window
assumption. No ready solution to the attribution problem has yet been developed, so
marketing analysts must simply keep in mind that their data is not 100% reliable.
Even more difficult than the attribution problem within digital marketing is the attribution
problem across an entire advertising budget, including both online and offline ad spending.
Even if a marketing analyst could be confident in attributing sales to marketing efforts in
email, online search, social media, and display advertising, how could she determine the
relative effectiveness of marketing efforts in television, billboard, magazine, and catalogs?
An analysis that would accurately determine the relative effectiveness of the myriad
advertising channels would be extremely valuable to any business, but an analysis of this
kind is extremely difficult. The largest marketing research company in the world, The
Nielsen Company, along with another marketing research heavyweight, Arbitron, undertook
a joint project in 2005 to make such an analysis possible. The project was terminated three
years later and deemed an expensive failure
In 2013, Peter Danaher and Tracey Dagger, marketing scholars from Monash University, in
Melbourne, Australia, published the results of a research project for a large Australian retailer
in which they were able to measure the relative effectiveness of advertising expenditures
across ten different advertising channels spanning both online and offline advertising
activities. In other words, Danaher and Dagger were able to solve the attribution problem, not
just for the digital marketing, but for all marketing channels. This case describes the methods
they used to collect the data and run this analysis.
Collecting Data
When a marketing analyst is trying to determine the effect of advertising expenditures on
sales, what she is trying to determine is whether seeing an advertisement caused an individual
(or several individuals) to make a purchase. Advertising is only effective if it changes
individuals’ behavior. As a result, the only way to reliably determine the effectiveness of
advertising is to measure both advertising exposure and purchasing at the individual level.
That is, a company would need a list of its customers along with data on their purchasing and
amount of exposure to all forms of advertising done by the company. The company could
then analyze this data and determine whether customers who saw more television ads
subsequently spent more than customers who saw fewer television ads for the company.
Collecting such data is challenging. Many marketing research companies collect portions of
this data, but none of them collect all of this data at the individual level. To collect this data,
Danaher and Dagger used the loyalty program members of the Australian retailer. (The
retailer wishes to remain anonymous, but it is an upscale department store analogous to
Macy’s in the United States.) Specifically, they sent an invitation to an online survey to
20,000 randomly- selected members of the loyalty program (hereafter LP) who fit the target
market (women between the ages of 25 and 54) on the day after the conclusion of a major
four-week-long sale and accompanying advertising campaign. The survey measured LP
members’ exposure to the retailer’s ads across all 10 advertising channels used by the retailer
during the ad campaign for the sale. The LP program maintained a database of each
member’s purchase history, so sales of each LP member could be retrieved from this database
and matched to the data on her advertising exposure.
The sale began on Wednesday, September 22, 2010 and concluded on Sunday, October 17,
2010. This sale was accompanied by a four-week-long advertising blitz across ten advertising
channels, including mass media channels (television, newspapers, radio, and magazines),
electronic media outlets (online display ads, Google search ads, and social media ads), and
direct media (catalogs, postal mail, and e-mail). Across all media, ads were consistent in their
appearance and messaging, announcing “massive discounts” on a wide range of products or
on specific featured items. Table 1 shows the relative spending on these ten advertising
channels and various measures of the resulting reach.
Table 1
1: GRP stands for gross ratings points, which is a standard way to measure advertising
exposures. GRP is calculated as Reach (%) × Average frequency (#). A GRP of 100 indicates
enough ad exposures to cover the entire population, though this score could come from a
reach of 50% and average frequency of 2 or a reach of 100% and frequency of 1.
Television’s GRP of 1,048 indicates that people on average saw the advertisement over 10
Measuring an individual’s exposure to multiple advertising channels is a difficult task.
Market research companies have developed sophisticated measurement techniques for
measuring exposure to a single medium, such as Nielsen’s People Meter panel for television
and Arbitron’s panel for radio. These companies typically require participants to keep a diary
of every exposure to the medium in question. For example, participants in Arbitron’s radio
panel will record every instance of radio listening for a week, including the radio station
listened to and the length of time spent listening. Keeping such diaries is labor-intensive for
one medium and thus would be impossible for ten media.
As a result, media exposure was measured through the survey sent after the sale and ad
campaign concluded. Because the retailer had a known media plan, the survey could be
limited to asking about the media on which the retailer had advertised. For example, instead
of asking an LP member for every instance of TV viewing during the four-week advertising
campaign, the survey asked, “In the past four weeks, how many episodes of Desperate
Housewives have you seen?” For newspapers, LP members were asked, “On which days did
you read or look into these newspapers in a typical week?” To measure exposure to online
display ads and social media ads, participants were asked their frequency of visiting the sites
on which the retailer had placed banner ads. To measure exposure to Google search ads, the
survey asked, “About how many times did you do a Google search for [retailer] in the past 4
weeks?” To measure exposure to radio ads, the survey asked respondents about their typical
weekly radio-listening habits.
Because the purpose of the study was to determine how ad exposure influences purchasing,
measurements of media exposure must be converted to measurements of ad exposure. Ad
exposure was measured using the traditional GRP, with a major difference being that GRP in
this case indicates an individual’s exposure to ads in that channel rather than the populationlevel exposure. Individual-level GRP was calculated from the individual’s exposure to the
medium in question combined with the number of times an ad was shown on that medium.
For example, if an individual watched 3 of 4 episodes of Desperate Housewives and the
retailer advertised on this show twice, the individual’s GRP would be 150 for this show (100
× 3⁄4 × 2). The same calculation would be carried out for all television shows on which the
retailer advertised, and the individual’s television GRP would be a summation of the GRP
numbers for all television shows on which the retailer advertised.
Fitting the Model
This case is not meant to provide an in-depth study on statistical modeling, so it will skirt
many of the details of the model, but some of the basic aspects of the model must be
discussed if the reader is to develop an understanding of this research project and have any
hope of replicating it. Table 2 shows a small portion of the data as they were formatted to
enable fitting of the statistical model.
Table 2
The desired end result of the statistical model is measurement of the effectiveness of each
advertising channel. That is, we wish to know whether and by how much advertising
expenditures in a given channel increased sales. In order for advertising to influence sales, it
has to influence an individual to either (1) make a purchase when she otherwise would not
have purchased or (2) spend more money than she otherwise would have. To determine
whether advertising influenced the first behavior, or purchase incidence, we could run a
logistic regression or probit regression model with the Purchase variable as the dependent
variable and the GRP data as independent variables. This model would indicate which
advertisement channels influenced LP members to shop when they otherwise might not have
shopped. But we would not be able to determine whether advertising influenced the amount
of money they spent. To determine whether advertising influenced the second behavior, or
purchase amount, we could fit a linear regression model with the Spending variable as the
dependent variable and the same GRP data as independent variables. But the Spending
variable has several 0s in it. Roughly 45% of LP members in the sample made no purchases
at all during the sale period. Running a regression on data with these 0s violates the
assumptions of linear regression, so our results would be biased.
The model used by Danaher and Dagger is called a Type II Tobit model. The model first fits
a probit model to the Purchase variable to determine whether advertising influenced purchase
incidence. It then fits a linear regression model to the Spending variable but ignores the 0
data to determine whether advertising influenced purchase amount2.
A number of important statistical issues arise in the fitting of this model. This case will
briefly discuss three of these issues. They are:
1. customer heterogeneity
2. purchase/viewing bias
3. endogeneity
Other issues besides these three arise, but we select these three issues for discussion because
they illustrate important points about analyzing market data that every marketer should
Customer heterogeneity refers to the fact that customers differ from one another. One
important way in which customers differ from one another for our model is a difference in
underlying purchase propensity. If one customer spends $500 and another spends $100
during the sale period, the model will attribute the first customer’s higher spending to the
media outlets to which this customer received more exposure. But it could be that this $500
expenditure was a drop from her usual $1000-per-month spending at this store while the $100
expenditure made by the second customer was an increase from a typical expenditure of $0.
The model will be biased if it does not correct for the customers’ baseline level of spending.
To correct for this difference in baseline spending, the model also included a variable
expressing the amount spent by each customer in the nine-month period before the start of the
The purchase-viewing bias refers to the fact that someone who is a frequent shopper at the
retailer might also be a heavy media viewer. If so, the model would incorrectly infer that it
was the exposure to the many ads that led to her large purchase level. But this correlation
could be spurious. To correct for this, the model included a variable measuring each LP
member’s general level of media consumption.
Endogeneity, the third issue, is a very technical problem that arises frequently in marketing
data. Though it is a problem for statistical models, it is often a sign of good strategic
marketing decisions. In the case of the current data, endogeneity problems arise because the
marketing managers for this retailer were strategic in directing their advertising to the
customers who were most influenced by the advertising. The retailer obviously wants to
encourage this kind of optimal advertising allocations, but it makes modeling more difficult
because it biases the model results. The example data shown in Table 3 illustrates why.
Consider a very simple movie store that sells DVDs. Every week, the store advertises the
latest new DVD for sale. Table 3 shows the advertising and sales of DVDs in three
successive weeks. In week 1, a small independent film is the only new title. Knowing the
movie to be of limited appeal, the store invests only $100 in advertising the new film and is
able to sell $1000 worth of DVDs. The next week, an action movie comes out. Because this
movie has a larger market, the store invests $500 into advertising and achieves $10,000 in
sales. Finally, in week 3, a major blockbuster movie with huge market appeal is released. The
store puts $2,000 into advertising this movie and achieves $25,000 in sales.
2: This description is not 100% accurate, but an accurate description would require more
technical detail than this case is intended to give. The gist of this description is accurate even
if a few technical details are omitted.
Table 3
What if we were to analyze the effect of advertising on sales? From this table, it appears that
advertising has a dramatic influence on sales. When advertising increased, sales also
increased. But the market share data reveals that the effect of advertising was not so dramatic.
If anything, the advertising merely preserved the store’s share of the market. What explains
this strong relationship between advertising and sales if advertising is not causing larger
sales? A third variable, audience size, is influencing both advertising and sales. When the
audience size is high, the store advertises more and sales are higher. A portion of the higher
sales level is due to the higher advertising, but that portion is small. If the store had spent
$2,000 to advertise the independent movie from week 1, we would not have observed sales
anywhere near $25,000.
The term endogeneity refers to the fact that advertising levels were not set randomly but were
set strategically to maximize sales, the dependent variable. A third variable, audience size, is
causing an exaggerated relationship between advertising and sales. If we were to run a
regression model predicting sales from advertising, the regression would tell us that
advertising had a much larger effect on sales than it was having in reality. This is a major
concern for the analysis done by Danaher and Dagger. If the retailer was at all strategic in
setting advertising levels, the analysis would be misleading, and since most competent
marketers are strategic in their decisions, the analysis here would be biased if the endogeneity
issue were not addressed. As a result, Danaher and Dagger had to use a statistical technique
known as instrumental variables to account for the fact that advertising levels are
Recall that the Type II Tobit model used to analyze the relationship between advertising and
sales is really two different models—a model of purchase incidence and a model of purchase
amount. Table 4 shows the coefficients of both parts of the model depicting the effect of
advertising exposure on sales. The stars next to the coefficients report whether those
coefficients are statistically significant. The results indicate that exposure television ads,
radio ads, Google search ads, the sale catalog and postal mail ads significantly increased
purchase incidence. Being exposed to those ads significantly increased the likelihood that a
shopper would visit the store and make a purchase. The analysis indicates that newspaper
ads, magazine ads, online display ads, social media ads, and email blasts had no significant
effect on the likelihood of LP members’ making a purchase during the sale. The model of
purchase amount shows similar results, though with some slight differences. The model
indicates that exposure to advertising on television, newspaper, and radio influenced the
amount customers spent, as did exposure to the sale catalog, but exposure to advertising on
other channels had no reliable effect.
Table 4
Surprisingly, the results of this analysis indicate that advertising in the more traditional media
outlets of television, newspaper, and radio are effective, as are catalog distribution and postal
mail ads. On the other hand, of the newer digital media outlets, only search ads were found to
have a reliable effect on sales. Online display ads, emails, and social media ads were all
found to have no reliable effect on either purchase incidence or purchase amount. Given the
large growth in digital advertising, it is disappointing that the data from this study indicate
that most digital advertising techniques provide no significant increase in sales. What
accounts for this surprising result?
The most likely explanation for the ineffectiveness of most of the digital advertising is the
nature of this retailer’s website. This retailer’s website is almost purely informational. Very
few products are available for purchase on the retailer’s website, and none of the items being
discounted during the sale and accompanying ad blitz were available on the website.
Countless other investigations have found that digital advertising positively affects online
sales, so the inability of customers to immediately purchase the advertised item on the
website was likely a huge missed opportunity for additional sales. Indeed, follow-up analyses
show that all forms of digital advertising had a positive effect on visits to the retailer’s
website. If the website had made sale products available, digital advertising would likely
have had a significant effect on purchase incidence and purchase amounts.
An additional possible reason for the ineffectiveness of online display ads was a poor choice
of websites used to show these ads. Most of the online display ads were shown on the web
version of the same newspapers selected to show print ads. The online display ads may have
been more effective had they been shown on different websites and used ad copy that was
more suited to online display rather than the same ads being shown in the physical
Replicating the Research
Other companies should be able to apply the methods described in this case to determine the
relative effectiveness of the various available advertising channels for their company, but
t …
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