DMA 7040 Module 3 Dashboard Assignment: A Channel Performance Dashboard Example

One Page, Six Channels: A Quarterly Marketing Dashboard for Riverbend Stitch Works

Student Name

MBA in Marketing, Thomas Edison State University

DMA-7040: Digital Marketing Analytics

Mentor Name

Month Day, Year

One Page, Six Channels: A Quarterly Marketing Dashboard for Riverbend Stitch Works

Purpose and Audience

The dashboard is for Riverbend's owner and its part-time marketing coordinator, who meet monthly to decide where the next quarter's $16,500 advertising budget goes. Kaushik (2009) warns that dashboards fail when they report data without context, so every number below is shown with the comparison that makes it meaningful: the previous quarter, a target or a break-even line. The indicators were limited to those that answer the meeting's two questions, which channels earn their cost and where conversion is leaking.

The Dashboard

Orders are credited to the last channel before purchase, the default in Riverbend's analytics setup. Phone orders that followed a website visit are included where the caller used the website's dedicated number.

Reading the Indicators

The break-even line comes from margin. Refurbished machines and parts carry an average gross margin of about 45 percent after the cost of the machine, parts and refurbishing labor, so each advertising dollar must bring back about $2.22 in revenue just to cover itself. Paid search, at 4.6, clears that line comfortably. Paid social, at 1.9, falls below it: on the dashboard's numbers, each $100 spent on social returns $190 of revenue and about $86 of gross profit, a loss of $14.

That conclusion is less certain than it looks. Last-click crediting gives all the value to the final step, and social ads often introduce buyers who later return through a search for "Riverbend sewing" or by typing the address. Li and Kannan (2014) found that crediting conversions for the carryover and spillover between touchpoints gives channels very different values from the metrics firms commonly use, while Blake et al. (2015) found the opposite problem in paid search, where ads on a firm's own name captured buyers who would have arrived anyway. Riverbend's paid search total includes brand-name keywords, so its 4.6 may be partly borrowed from organic search and direct visits.

Email converts best, at 3 percent, because it reaches past customers and restock subscribers who already trust the firm. Its average order value is the lowest, $210, since repeat customers mostly buy parts, feet and bobbins rather than machines. Referral traffic from sewing forums and quilting blogs is small but brings the highest order value, $300, which suggests enthusiasts buying better machines. Overall conversion of 1.59 percent sits near the previous quarter's 1.52 percent.

Recommendations

1. Test paid social before cutting it. For six weeks, switch paid social off in a randomly chosen half of the firm's target states and keep it on in the other half, then compare total orders across all channels between the two groups. If orders in the "off" states fall by fewer than about 32 a month, the channel is not paying for itself and its budget should move to search; if they fall by more, the dashboard's last-click figure has been understating it. Gordon et al. (2019) found that experiments of this kind often give different answers from attribution, which is why the test should come before the decision.

2. Split brand and non-brand paid search. Report the two separately on the dashboard. Pause brand keywords in one month and watch whether organic and direct orders rise to replace them, which would show how much of the 4.6 return is real.

3. Grow email. At a 3 percent conversion rate and no media cost, email is the most efficient channel. Adding a sign-up offer on manual downloads, one of the micro conversions defined in Module 1, could grow the list without new spending.

Dashboard Design Notes

The dashboard fits on one page and uses the same order of channels every month so changes are easy to spot. Paid channels appear with their break-even line drawn in, and any figure that moved more than 15 percent from the previous quarter will be highlighted. Three numbers are deliberately left out, average session duration, bounce rate and social followers, because none of them changes a budget decision.

References

Blake, T., Nosko, C., & Tadelis, S. (2015). Consumer heterogeneity and paid search effectiveness: A large-scale field experiment. Econometrica, 83(1), 155-174. https://doi.org/10.3982/ECTA12423

Gordon, B. R., Zettelmeyer, F., Bhargava, N., & Chapsky, D. (2019). A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Science, 38(2), 193-225. https://doi.org/10.1287/mksc.2018.1135

Kaushik, A. (2009). Web analytics 2.0: The art of online accountability and science of customer centricity. Wiley.

Li, H., & Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research, 51(1), 40-56. https://doi.org/10.1509/jmr.13.0050