Graphical Display of Data
Slide 1
The goal of this PowerPoint presentation on graphical representation of data is to remind you of
the various ways in which marketing data can be depicted in a graphical form.
Slide 2
As this Duffy cartoon illustrates, graphs can be very powerful devices in communicating the
results of data analyses in marketing. In the case of this cartoon, perhaps Duffy and his
assistant would prefer the analysis not be nearly as powerfully portrayed.
Slide 3
One reason that graphs are so powerful as summaries of marketing data is that one graph can
depict much data. Here’s an excellent example of a single graph that summarizes New York
City’s weather for 1996. You can see we’ve got: temperatures, ranges, precipitation, the norms
for the period. There’s a slew of information summarized by this one simple graph.
Slide 4
I’ll quickly overview the following types of charts and graphs: pie charts, bar charts (vertical and
horizontal), line graphs, area graphs, box and whisker plots, and quadrant analysis.
Slide 5 (No Audio)
Slide 6
Pie charts, like the ones shown here, are useful for depicting what percent of all responses is
comprised by each response category. For example, when asked if the products that they
presently use are tested on animals, 48% of respondents didn’t know, 14% of respondents said
yes, and 38% of respondents said no.
Slide 7
This slide suggests that pie chart information can be presented in a colorful fashion. Here the
sun used as a pie chart and the theme of the slide is ‘how much time off people take’. The mode
or most frequent response is two weeks, but 18% of respondents take off four weeks and 4%
take off as much as six weeks per year.
Slide 8 (No Audio)
Slide 9
The next two slides show stacked bar charts. In this case, we’re looking at thousands of
schools, both public and private, that existed in each of these 10-year periods. As this slide
suggests, the number of schools has declined over time, which implies the number of students
has increased rather than decreased. School populations have grown over time as the oneroom school house has been replaced by the multi-thousand student, multi-session school.
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Slide 10
These stacked bar charts are especially informative. The stack on the left shows the 1999
average retail price, which is comprised of distribution and marketing costs and profits, refining
costs and profits, federal and state taxes, and crude oil. The stack bar on the right indicates
those same percentages for the 2000 average retail price, which rose by $0.26. A good
component of the increase in the price from 1999-2000 is the raw material: the actual crude oil.
The other percentages of the total were relatively stable. This dual graph clearly shows that the
increase in retail price for gasoline was due predominantly to the increase in crude oil prices
from 1999-2000.
Slide 11 (No Audio)
Slide 12
If you have so many data points that a series of bar charts might be a bit on the ugly side, then
you can use a scatter diagram. The top scatter diagram shows market coverage versus size of
sales force. The scatter reveals a somewhat straight line and positive relationship, with the
larger the sales force the higher the market coverage. The second graph shows a negative
relationship between out of stock items versus size of the sales force. The larger the sales force
the fewer the number of out of stock items. In the final graph, average sales per salesperson is
related to sales force size. Seemingly, the two variables aren’t related meaningfully; regardless
of sales force size, the typical salesperson sells roughly the same amount.
Slide 13 to Slide 14 (No Audio)
Slide 15
As the heading indicates, line graphs are useful for highlighting comparisons over time. In this
case, hours per week of work and leisure from 1973 to 2000 are displayed. By 1980, the hours
of work per week increased to roughly 50 and stayed there through 2000. At the same time, the
hours of leisure per week dropped to roughly 20 and stayed stable throughout.
Slide 16
This graph illustrates the hazards of carelessly constructed multi-line graphs. The graph shows
percent changes in medical care expenses, fuel oil expenses, and expenses on all other items
from 1980 to 1999. The graph seems to suggest that the change in the consumer price index is
less stable relative to fuel oil prices but more stable relative to medical and other expenses. As
a result, fuel oil prices are often excluded from the consumer price index. Nonetheless, these
lines are roughly the same color and it’s a difficult story to immediately discern from the graph.
Although multi-line graphs provide a powerful way to present data, you should ensure that they
are easy to read.
Slide 17 (No Audio)
Slide 18
You can think of area graphs as a continuous version of a bar chart. If data aren’t discrete—so
measures are continuous—an area graph like this one is useful. There are rules of thumb
regarding which items are stacked first versus last or bottom versus top in such graphs. To
minimize confusion, you should place the lowest variability items at the bottom and the highest
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variability items at the top. Otherwise, you create the illusion that all items above the high
variability one also vary highly, when in fact they’re relatively stable.
Slide 19 (No Audio)
Slide 20
Box and whisker plots are an effective way to depict data generated by descriptive statistical
analysis. This box and whisker plot indicates the mean, median, inter-quartile ranges, and
standard deviations for this particular variable.
Slide 21 to Slide 22 (No Audio)
Slide 23
Here’s an example of a quadrant analysis for a microwave meal. In this case, the X-axis
captures beliefs about product performance on each attribute and the Y-axis captures
importance ratings. The four quadrants are (1) high importance/low rating, (2) high importance/
high rating, (3) low importance/low rating, and (4) low importance/high rating. The cells of most
interest are high importance/high rating and high importance/low rating. Attributes that fall into
the high importance/high rating quadrant represent important attributes on which the product
scores well. Attributes that fall into the high importance/low rating quadrant represent important
attributes on which the product scores poorly. Attributes in the low importance/low rating
quadrant aren’t as important because those attributes are less important to consumers.
However, attributes in the low importance/high rating quadrant represent product overkill, which
suggests an opportunity to value engineer the product; that is, to reduce production costs by
reducing quality on attributes of little importance to consumers.
Slide 24
As the title indicates, my remaining example shows there are several ways to depict the same
data. You should try to identify the most effective depiction given what you’re trying to
communicate and the audience to which you are trying to communicate.
Slide 25
The data in question, summarized by this table, relate to mean ratings on an evaluative
dimension for three suntan lotions named Fun in the Sun, Serious Tanning, and At the Lake.
Respondents rated these three lotions are on six dimensions: high versus low quality,
informative versus uninformative, good versus bad, persuasive versus non-persuasive, artful
versus artless, and refined versus vulgar.
Slide 26
These scores could be combined and then compared to a central tendency or mean score. In
this case, favorable means ‘above the mean’, neutral means ‘roughly the mean’, and
unfavorable means ‘below the mean’. Such an analysis could produce these piece charts.
Slide 27
As an alternative to the pie chart summary, you could provide summary and intermediate level
data by using a stacked bar chart. If you’re looking at quality, informativeness, good versus bad,
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persuasiveness, artfulness, and refineness for these three suntan lotions, then you can use the
stacked bar chart to indicate the totals and determine what fraction of the totals belong to each
lotion. For example, the quality dimension shows Fun in the Sun is more highly rated than the
other two lotions because it covers the largest portion of that bar. Similarly, At the Lake is rated
lower than the other two lotions on informativeness, as it covers the smallest portion of that bar.
Slide 28
Alternatively, you might use a clustered bar chart, which depicts every number in the original
table as an individual bar. In the original table, there were six scores across three brands; this
clustered bar chart includes 18 bars. Because this chart depicts all 18 numbers in the table, no
information is lost about the original scores.
Slide 29
Finally, you could use a profile analysis to compare the three brands. As I mentioned in an
earlier lecture, I’m not enamored with profile analyses, so I discourage you from using them.
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