| Course | BU 443 Marketing Analytics |
|---|---|
| Unit | Unit 7 |
| Paper type | Dashboard or visualization |
| Length | About 830 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | BS in Marketing |
| Updated | September 2026 |
Free sample paper for BU 443 Unit 7
Five Views for a Monday Meeting, One Question Each: A Dashboard Design for a Composite Online Houseplant Retailer, With a Panel That Says Whether the Data Can Be Trusted
Student Name
Business, Herzing University
BU 443: Marketing Analytics
Instructor Name
July 22, 2026
Five Views for a Monday Meeting, One Question Each: A Dashboard Design for a Composite Online Houseplant Retailer, With a Panel That Says Whether the Data Can Be Trusted
The Decision the Display Supports
Greenline Plant Co., an invented plant seller that ships from Oregon, holds a 30-minute marketing meeting every Monday. The owners, the marketing assistant and the shipping lead make two recurring decisions there: whether to move paid advertising money between channels for the coming week, and whether to hold or change shipments to regions facing heat or cold. Until now, the meeting has opened with four separate reports, each from a different system with its own definitions. This dashboard replaces them with one display built around those two decisions, plus the context the owners need to judge them.
The design starts from the decision rather than from the data available. Few (2006) insists that a dashboard fit on one screen and be readable at a glance, and he traces most failed dashboards to designers who displayed whatever the systems made easy instead of what the viewer had to decide. The first question asked in the design was therefore what the Monday meeting decides; every view that does not help answer that question was left off.
Five Views, One Question Each
Each view answers one question and uses a form chosen for the comparison involved.
| View | Question it answers | Form | Why this form |
|---|---|---|---|
| 1. Weekly net sales | Are we ahead of or behind last year this month? | Line chart, this year and last year, 13 weeks | Trends over time read best as lines on a shared scale |
| 2. Paid channel cost per purchase | Which paid channels are earning their cost? | Paired bars per channel, last-click and first-click side by side | Comparing lengths on a common baseline is read accurately; the pair shows the attribution range |
| 3. Cohort repeat rates | Are new customers coming back? | Table with shaded cells; open windows left blank and labeled | Exact values with counts matter here; shading shows the pattern |
| 4. Replacement credits by region | Is shipping damaging plants? | Small multiples: one line per region with a band for extreme temperature days | Each region on the same scale makes differences visible |
| 5. Data health | Can this week's figures be trusted? | Four numbers with a normal range: consent rate, duplicate purchases, untagged sessions, event failures | Plain numbers with limits are quicker to read than charts for checks |
Why These Forms and Not Others
The forms follow evidence about how accurately people read graphs. Cleveland and McGill (1984) found that viewers judge position along a common scale most accurately, and length close behind, while angles, areas and color intensity are judged far less accurately. That is why the dashboard uses lines and bars for comparisons and has no pie charts, gauges or three-dimensional effects. Shading is used only in the cohort table, where exact numbers are printed in every cell and the shading merely helps the eye find high and low values.
The paired bars in view 2 carry a lesson from the channel analysis. Showing only one attribution model would invite the owners to act on a conclusion that reverses under the other. Showing both side by side makes the uncertainty visible: where the bars agree, the owners can act; where they diverge, as they do for paid social, the view itself says that the question cannot be settled from this data. Tufte (2001) argues that good graphics show the data and avoid distorting what the data say, and hiding the attribution range would distort it.
Definitions Within Reach
Every figure on the dashboard links to its definition. Hovering over any number shows a one-line definition and the system it comes from, taken from the metrics definition sheet written at the start of the course, and a footer on each view names the period and the counting rule. The conversion rate shown is the store's, not the analytics tool's, and says so. Open rate does not appear anywhere, for the reasons set out in the metrics sheet. Figures affected by a known data fault, such as a week before the duplicate-purchase fix, carry a marker linking to the fix date.
What Was Left Off
Several figures the owners were used to seeing were left off on purpose. Sessions and users do not appear on their own, since neither informs either decision and both change with consent rates. Follower counts and email opens are absent for the reasons given in the metrics definitions. The long table of every product's weekly sales, which the old report opened with, is gone; stock decisions happen in a different meeting with the greenhouse manager. Each removal was discussed with the owners before the design was fixed, and each was accepted on one test: if the number disappeared, would any Monday decision be made differently? For every item on this list the answer was no. The test also works in reverse, and it added the data health panel, which no previous report had, because several past decisions had been made on figures later found to be broken.
Layout and Reading Order
The screen is arranged in the order the meeting uses it. The data health panel sits in the top left, because if it shows a problem, for example the consent rate dropping sharply after a banner change, the rest of the dashboard must be read with that in mind. Weekly sales sits beside it as the context for everything else. The two decision views, paid channels and shipping damage, take the center of the screen. The cohort table sits at the bottom, since it changes slowly and is discussed monthly rather than weekly.
The dashboard refreshes at 6:00 a.m. every Monday, after the weekend's orders are complete, and the marketing assistant owns its accuracy. Views that are not used in the meeting for eight consecutive weeks will be removed, so that the display stays focused on decisions rather than growing into a report. Greenline and every figure shown are composites for this course.
References
Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531-554. https://doi.org/10.1080/01621459.1984.10478080
Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media.
Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Graphics Press.
BU 443 Unit 7 instructions, in plain terms
BU 443 Unit 7 asks students to design a dashboard or visualization for a specific audience. Typical requirements include identifying the decisions the display supports, choosing a limited set of views each with a clear question, selecting chart forms suited to each comparison, keeping definitions accessible and explaining the layout. Many instructors ask whether a particular tool is required; most accept a mock-up or a described design, provided the forms and layout are clear enough to evaluate. The write-up should justify each view rather than describe each chart. Research on visual perception or dashboard design strengthens the justification. A view table and explanation in APA 7 are common, and strong submissions explain what was left off.
How the BU 443 Unit 7 example is put together
The design begins with the meeting, its two recurring decisions and the problem of four conflicting reports. A table sets out five views with the question each answers, its form and the reason for that form. A section explains the choice of forms using research on graphical perception and shows how paired bars expose attribution uncertainty. A definitions section describes hover text, footers and fault markers. A section lists figures left off and the test applied to each, including how the test added a data health panel. The final section explains the reading order of the screen, the refresh time, the owner and a rule for removing unused views.
Where the points sit in the BU 443 Unit 7 rubric
Dashboard rubrics at Herzing usually reward alignment with decisions, focused views, appropriate chart forms, accessible definitions and thoughtful layout, together with the quality of the writing. Alignment earns full credit when the design starts from what the audience decides. Focus scores highest when each view answers one question. Chart forms are judged on fit with the comparison being made, supported by evidence about perception. Definitions reward making the counting rule reachable from the figure. Layout earns points for a reading order that matches use. Instructors also credit designs that show uncertainty honestly, such as displaying two attribution results, and that explain omissions as deliberately as inclusions.
Common BU 443 Unit 7 mistakes, and how to avoid them
Dashboards often lose points by showing everything the data systems offer, crowding the screen with views nobody uses. Another common problem is using chart types that are hard to read accurately, such as pie charts for many categories or gauges for single numbers. Students frequently omit definitions, so viewers misread figures computed differently from what they assume. Some hide uncertainty by showing a single attribution result. Others describe each chart in detail without saying what decision it supports. Many forget data quality, so a broken week's figures are read as real changes. Finally, a dashboard with no owner or refresh schedule tends to become outdated quickly. Showing a rough sketch of the layout, even as a simple grid, also helps the instructor see how the views relate on one screen.
Get a BU 443 Unit 7 example written to your instructions
Send the Unit 7 instructions and the BU 443 rubric your classroom posts, plus the dataset and the audience the assignment names. We write a custom example that starts from the decision, gives each view one question, matches every form to its comparison and lists what was deliberately excluded. First custom sample free, back in 24 to 48 hours.
More BU 443 papers and marketing samples
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- BU 443 Unit 4: Customer and cohort analysis
- BU 443 Unit 5: Campaign evaluation
- BU 443 Unit 6: Test design and reading
- BU 443 Unit 8: Analytics recommendation
- BU 302 Unit 7: Organizational culture read
- BU 321 Unit 7: Regression output interpretation
- BU 380 Unit 7: Governance and obligations memo
- BU 421 Unit 7: Quality control chart record
BU 443 Unit 7 questions, answered
Does the dashboard have to be built in a particular tool?
Check your instructions, since some sections name one while others accept a mock built in a spreadsheet or a drawing program. What gets graded is the reasoning behind the layout rather than the software, so a carefully argued mock with real definitions behind it can score above a live build assembled from default panels nobody actually chose.
How many views should the display carry?
Few enough to fit one screen without scrolling, which is usually a handful. Every additional panel costs attention from the ones already there, and a reader forced to hunt has stopped using the display as a display. Where your instructions require more, group them and say which view answers the question a decision maker asks first.
Should the write-up explain each chart?
It should explain each choice, which is a different thing. Say what question the view answers, why that form suits the comparison and what the axis decisions were, rather than narrating what the picture shows. A display needing a paragraph before it can be understood has failed at its job, and saying so about your own work earns more than describing it.
Where can I find a free BU 443 Unit 7 sample paper?
The whole BU 443 Unit 7 dashboard design is free above, with the five-view table, the form reasoning and margin notes. If your audience or decisions differ, tell us and we will sketch a custom design free, typically inside two days.
How many views should a BU 443 Unit 7 dashboard have?
As few as the decisions require. The sample uses five, each answering one question for a Monday meeting, and removes any view that goes unused for eight weeks.