1. Abstract— Given the persistent poor, uncertain economic
performance in the print media industry, newspaper
distributors are challenged to visualize and leverage data
from their various distribution routes and business store
points to identify the routes and distribution points along
those routes that hold the potential for profitability. This
paper analyzes the geographic overview and corresponding
data from the prime distribution areas of a newspaper
distribution company operating in the major urban corridors
of southwestern Pennsylvania and north-central New Jersey
regions. Trend line forecasts are then generated to predict
sales performances in each area for specific newspaper
products. A waterfall model further pinpoints the type of
newspaper and product and the best distribution points in the
company’s areas of responsibility.
Index Terms—data visualization, newspaper distribution,
sales trend line forecasting, seasonality
I.INTRODUCTION
Liberty News Distributors, Inc., founded in 2006,
encompasses more than 5,000 national accounts and
distributes more than 1,500 titles including domestic
newspapers, periodicals and international magazines.
Major distribution points include convenience stores,
shopping plazas and airports along with more than 300
select independent and chain stores accounts via the FedEx
Corp.
Recognizing the need to identify sales opportunities and
ongoing positive communication with retailers, distributors
and publishers, the company tasked a research team of
university students, led by a faculty member, to use data
visualization tools (e.g., Tableau and Microsoft Excel) to
finetune daily distribution operations that reflect consumer
buying behavior of newspaper products and the distribution
points at stores they patronize.
This study focuses on distribution of four newspapers in
the Pennsylvania-New Jersey market covered by the
company: Delco Times, The New York Times, New York
Daily Post and New York Daily News.
II.LITERATURE REVIEW
A. Distribution and Seasonality
Newspapers have a short time-sensitive life. For national
dailies, such as The New York Times and The Daily News,
the respective value of each copy is zero the day following
its publication. The lifecycle is rapid, as most readers prefer
to receive the news before 9 a.m. or whatever time their

Manuscript received January 10, 2017. This work was part of a business
administration course for Saint Peter’s University, led by Joseph Gilkey,
Ph.D., and in collaboration with Liberty News Distribution, Inc. A
Ana Maria Garcia, Guen Pak, Francis Oduro, Deondre Thompson and
Karla Erazo, authors, are students at Saint Peter’s University. All queries
should be directed to gpak86@gmail.com or jgilkey@saintpeters.edu.
workday begins, unlike with longer-form media products
(e.g., novels, hardbacks, magazines, or other periodicals).
These problems have been compounded by large-scale
changes in commuting habits of consumers who must
contend with heavy traffic volume, especially during the
morning hours in major urban areas.
The challenge existed long before technological
advancements in information dissemination and
communication began to affect print media’s profitability,
as Fowler’s model of comparative readability of newspapers
and novels has demonstrated (1904, 1933 and 1965). The
newspaper distribution problem also has been compounded
by the geography of distribution and allocation points,
along with fleet routing problems. In this case, Liberty
News Distribution has experienced routing problems, given
the geographical distance of some locations that confound
the economies of scale in distribution. In a 1996 study of a
major U.S. metropolitan newspaper, cost savings were
realized by reducing the number of distribution centers
along with a corresponding decrease in truck fleets and
drivers required to serve the distribution centers from the
newspaper production facilities. Resolving the distribution
component problem requires coordinating and overcoming
the problems of uncertain demands to identify potentially
profitable drop-off points for newspaper products in
targeted areas that can be delivered within the shortest
amount of time possible. The objective is to reduce undue
costs and mitigate risks of sale losses that are aggravated by
high levels of return and stocking costs and transportation
expenses, especially on the least profitable segments of
routes in the company’s areas of responsibility for
distribution.
B. Readership
Newspaper circulation and readership decline continues
a long-term trend. In 2015, the average weekday circulation
fell seven percent, despite annual increase of 2 percent in
digital subscriptions to newspapers. Sunday circulation
during the same year declined by 4 percent, again despite a
4 percent increase in digital Sunday newspaper
subscriptions (Barthel, 2016). The most recent declines
occurred after a brief rebound in print subscriptions in 2013
Despite the declines, print circulation still accounts for
the largest share of readership (78 percent, weekdays; 86
percent, Sunday) and one survey indicates that 59 percent
of consumers who read newspapers still do so in print-only
formats (Barthel, 2016).
III. DATA
The company provided 12 months of aggregate data for
the 2015-16 period so that the research team could prepare
visualizations for analysis. The data include distribution
locations for each route in all three areas designated for
analysis, including street addresses. Sales data for each day
of the week and the sales of individual newspaper editions
are indicated for each location, along with prices, revenues
and gross profit margins. The team accounted for aspects
that might have hindered effective visualizations. One
Seasonality and The Newspaper Distribution Problem:
Using Data Visualization to Improve Trend Line Forecasts
Ana Maria Garcia, Guen Pak, Francis Oduro, Deondre Thompson, Karla Erazo, Joseph Gilkey Jr.

2. instance involved negative numbers in the data signifying a
potential revenue loss due to not properly accounting for
“Draws” from “Returns” to equaling “Sales.” Problems
were resolved by segmenting data (i.e., querying identical
records of data) into measurable pieces by establishing a
coordinated hierarchy covering the four geographical areas
of operations. For example, if a Philadelphia store sells
papers every day of the week, but circulation data separate
weekdays, Saturdays and Sundays (Fig. 1). To make the
analysis more efficient and comprehensible, data records
were formatted to chart weekly sales, as opposed to day-to-
day sales (Fig. 2). A script was created to automate the
process for the entire raw data set
(https://github.com/gpak/SchoolprojectOR/blob/master/Co
mbining%20Cells). This procedure facilitated ease and
efficiency in data visualization and storage.
Fig. 1. The raw data as presented by the company.
Fig. 2. The condensed data after file manipulation.
The research team identified four areas to further
organize the data set: Area 1 (greater Philadelphia and
Delaware County, Pennsylvania), Area 2 (north and central
New Jersey), Area 3 (central and coastal New Jersey) and
Area 4 (newly acquired distribution outlets lacking in
sufficient data timespan set aside temporarily to be
considered in follow-up research).
IV. RESULTS
One example of a linear trend model (0.0878696*Week
of W/E+4379.1) was computed for The New York Times in
Area 1, with Measure Values given W/E Week
(R2
=0.818676; F14, 406 = 140.622; p<0.001).
In the data visualization tool, the user has the option to
choose from any day of the week to highlight data (e.g.,
compare each Sunday to entire data set to capture trends
and seasonality. Data for example, in the Sunday measures
indicated solid sales in Area 1 (t=15.3098, p<0.001).
Sunday sales in the company’s distribution points for Area
1 had increased 55 percent from October 2015 to October
2016, primarily because of a corresponding increase in the
number of distribution points the company was serving in
the area.
Model forecasting relied on the three parameters of
exponential smoothing (levels, trends, and seasonality) to
identify best-case scenarios (Table 1). Seasonality emerged
as a strong predictor.
TABLE 1. MODEL FORECASTING WITH EXPONENTIAL SMOOTHING.
Sum of Mon-Sale
Quality Metrics Smoothing Coefficients
RMSE1
MAE2
MASE3
MAPE4
AIC5
Alpha Beta Gamma
1
Root-Mean-Square-Error (RMSE): is used to calculate the amount of
error there is between the predicted and observed values
2
Mean-Absolute-Error (MAE): is the measure of how close the forecast or
predictions are to the actual outcomes
3
Mean-Absolute-Scale Error (MASE): the measure of the accuracy of
forecasts
4
Mean- Absolute-Percentage-Error (MAPE) is the measure of prediction
accuracy of a forecasting method
5
Akaike Information Criterion (AIC): is the measure of the relative quality
of a statistical model
30 23 0.66 3.6% 436 0.291 0.000 0.089
Sum of Tue-Sale
Quality Metrics Smoothing Coefficients
RMSE MAE MASE MAPE AIC Alpha Beta Gamma
34 25 0.80 4,1% 452 0.193 0.000 0.063
Sum of Wed-Sale
Quality Metrics Smoothing Coefficients
RMSE MAE MASE MAPE AIC Alpha Beta Gamma
30 24 0.76 3.8% 438 0.005 0.052 0.003
Sum of Thu-Sale
Quality Metrics Smoothing Coefficients
RMSE MAE MASE MAPE AIC Alpha Beta Gamma
33 27 0.79 4.2% 449 0.000 0.000 0.029
Sum of Fri-Sale
Quality Metrics Smoothing Coefficients
RMSE MAE MASE MAPE AIC Alpha Beta Gamma
59 44 0.81 6.2% 517 0.040 0.469 0.000
Sum of Sat-Sale
Quality Metrics Smoothing Coefficients
RMSE MAE MASE MAPE AIC Alpha Beta Gamma
39 27 1.31 4.5% 467 0.361 0.000 0.069
Sum of Sun-Sale
Quality Metrics Smoothing Coefficients
RMSE MAE MASE MAPE AIC Alpha Beta Gamma
116 82 1.27 22.5% 597 0.234 0.000 0.159
An additional element of data visualization geographic
overviews of each of the three areas covered in newspaper
distribution and the types of media product that would be
most conducive to profitable sales in the respective areas.
For instance, Area 1 signaled sales support for newspapers
focusing on news about Delaware County, Pennsylvania
and the greater Philadelphia area, while sales of The New
York Times editions were most evident in Area 2, and less
so in Area 3.
Fig. 3. Area 1: geographic overview.

3. Fig. 4. Area 2: geographic overview.
Fig. 5. Area 3: geographic overview.
To handle negative numbers in the original data set, the
researchers segregated the data into a separate set in order
to highlight poor performing distribution locations that
might need further attention. The analysis generated a
waterfall model to track losses over time. For example, one
convenience store location received 10 copies of a particular
newspaper daily. The store, on average, sold six copies per
day and returned the remainder to the company for a refund
to be disbursed at the end of each week, which would offset
revenues over time. The model highlighted this location as
a candidate for revising distribution. In the model, Areas 1
and 3 appeared to have the highest number of locations that
would have a persistently negative impact on revenue,
whether because of poor sales or by client business owners
who might try to exploit the refund transactions. As
observed in the following graph figures (Fig. 5, Fig. 6 and
Fig. 7), the problem of loss-prone distribution locations is
not as prominent in Area 1 as it is in Area 3. The waterfall
model output for Area 1 indicates a slight decline and
stagnating position but in Area 3, there is an across-the-
board decline across the region’s zip codes. This sheds light
on underlying significant revenue losses that likely had not
been tracked as closely as they should have been. The
model output for Area 2 suggests the area is a solid revenue
performer and the results suggest that the company should
address its most serious distribution problems first in Area
3.
Fig. 6. Area 1: waterfall model.
Fig. 7. Area 2: waterfall model.
Fig. 8. Area 3: waterfall model.
Researchers also generated histograms for each area and
for each newspaper publication to highlight specific
distribution locations (such as stores for two popular
convenience chains that are open 24/7) where the potential
for selling more copies of specific newspapers would be the
most promising (e.g., those that consistently showed no
copies of papers being returned for refunds). For example,
in an Area 1 convenience store that is always open, the
model predicted the location could sell more copies than
what already is being sold. The data also alert the
company’s distribution management to learn more about
the factors behind specific locations that tend to perform
well consistently.

4. Figure 9. Day-specific analysis of one newspaper’s sales for
chains of convenience stores.
For example, many of the best-performing locations in
Area 1 are based in neighborhoods that share demographics
with segments who have been identified as loyal print
media consumers (e.g., age groups that spent their
formative years with media before the age of Internet and
digital media). In addition, other locations near
concentrations of schools and that are in lower- and
middle-income areas also are ideal candidates for improved
sales. Media access via traditional formats still matters
especially in lower-income families where children might
need to rely on media for school assignments and might not
always have the opportunity to visit a local library branch.
In addition, data visualization can pinpoint dates when
sales increased significantly, as consumers searched for
information about major breaking news and events. While
no one can anticipate when a major story might break, data
visualization tools can augment a distributor’s capacity to
respond quickly to sudden surges in demands for newspaper
editions that focus on such breaking news. Consumers
might patronize locations in such instances, knowing that a
[articular convenience store, for example, will have
sufficient numbers of newspaper copies for purchase.
Researchers also found that numerous locations only
returned one copy of a particular newspaper during the
week on numerous occasions. Some anecdotal evidence
indicated that the sole remaining copy was held back as a
courtesy for customers who at least had the opportunity to
scan the newspaper if there were no remaining copies
available for sales. That location also might be an ideal
candidate for distributing additional copies for sale.
V. CONCLUSION
The challenges for a company involved in distributing
print media products are immense, as the newspaper
industry continues to consolidate and shrink. However,
survey data indicate legacy print media such as newspapers
remains an important consumer product. National
readership data culled from Nielsen Scarborough’s 2015
Newspaper Penetration Report show that 51 percent of
those who consume a newspaper read it exclusively in print,
even as it is down from 62 percent print-only readership in
2011 and 59 percent in 2012. Print still is a predominant
choice for many consumers but the inevitable decline will
continue. As for newspaper distribution companies, such as
the one profiled in this study, the question turns to
identifying opportunities to leverage the optimal
performance of specific distribution points on various route
points to create a longer period of profitability, as
consolidation continues. The opportunity to extend a
positive trend line forecast even for the short term could
provide the business some critical flexibility as it considers
how its operations will continue to be affected by the
longer-term shrinking of the newspaper industry.
Undoubtedly, while the forecast points to difficult times
ahead, a business such as a newspaper distribution outlet
can remain agile and even profitable by becoming creative
in its response to trends and to target those areas where
both positive and negative challenges present themselves.
Even as some enterprising newspapers look to digital
media products including video and podcasting and niche
websites as options for financial viability, others are
creating new print products including print magazines that
focus on categories such as food and drink and outdoor
recreation as well as high-quality lifestyle publications
featuring long-form journalism. Others are joining
competitors to launch expanded Sunday editions, while
some newspapers have launched specific neighborhood
editions tailored to individual communities. Yet others are
launching premium print editions that incorporate content
that typically was available exclusively on digital and web
platforms.
The quality of relationships with individual distribution
locations will become more critical, as client stores consider
the potential of carrying alternative print media products
launched by newspaper publishers. In the interim,
companies can enjoy some breathing space by finetuning
their distribution models per these types of data
visualizations. This amplifies the purpose of remaining
financially stable so that these new opportunities are not
lost, as they become more frequent in a rapidly changing
newspaper industry.
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