MT357 Unit 9 Digital Measurement Framework Example

Metrics That Move With Trade-Ins Outrank Metrics That Look Busy: An MT357 Unit 9 Measurement Framework

[Student Name]

Purdue University Global

MT357: Digital Marketing Platforms and Strategy

Unit 9 Measurement Framework

[Instructor Name]

[Date]

The shop, metrics and test results are composite; the methods and research cited are real.

The Hierarchy

Metrics earn their place by how closely they move with completed trade-ins, the objective set in Unit 2. Four tiers result.

Outcomes sit at the top. They are completed trade-ins per month, drawn from the quote tool's records of quotes that reached payment; cost per incremental trade-in by paid line, drawn from spending and test results; and margin per trade-in, drawn from the resale ledger. These three decide whether the plan is working.

Leading measures come next. They are quote starts per month and the completion rate at each of the quote tool's nine steps. They move before outcomes do and show where sellers drop out.

Diagnostics come third: click-through rate, cost per click, email open rate and video watch time. They help find problems, such as an ad that nobody clicks, but they never judge a channel. A line with a poor click-through rate that produces cheap trade-ins is a good line.

Vanity measures sit in an appendix: impressions, followers, likes and views. They are reported because owners ask about them, but no decision rests on them, and they are never shown in the same chart as outcomes.

Why the Hierarchy Matters

Chaffey and Ellis-Chadwick (2022) warn that digital teams often optimize what is easiest to measure rather than what matters. The Unit 6 reflection showed that happening at this shop: a campaign judged by 41,000 visits when only 23 tracked trade-ins resulted. The hierarchy prevents that by ranking metrics before anyone looks at the numbers.

The Brand Search Test

The ad platform credits brand search with about 108 trade-ins a quarter, roughly 33 every four weeks, at a cost of about $14 each. Blake et al. (2015), running a controlled test for a well-known online marketplace, found that ads on its own name added very little, since searchers found the regular listing on their own. To test whether the same was true here, the shop paused brand search for four weeks in October and compared the result with the previous four weeks.

With ads running, 48 completed trade-ins began with a search for the shop's name, whether the seller clicked an ad or a regular result. With ads paused, 42 did. The ads therefore added about 6 trade-ins in four weeks, roughly 18 percent of the 33 the platform had credited. At about $462 of spending per four weeks, each incremental trade-in cost about $77, not $14.

Brand search was not worthless, but it was more than five times as expensive per real trade-in as the platform reported.

Limits of the Test

Four weeks is short, and October may differ from September. Other things changed in the period, such as new grading videos. A geographic holdout or a longer alternating test would give a better estimate. Lewis and Rao (2015) showed that measuring advertising effects precisely often requires very large samples, so this test should be read as directional.

Decision Rules

For each paid line, the framework sets three rules based on cost per incremental trade-in against the $118 margin. If a line costs less than $90 per incremental trade-in for two consecutive months, its budget is raised by up to 20 percent. If it costs between $90 and $118, it is held. If it costs more than $118 for two months, it is cut or ended.

Brand search, at about $77 per incremental trade-in, is held at its current budget. Nonbrand search and retargeting will be judged once their own holdout tests run in the next quarter.

What the Framework Replaces

Before this framework, the shop's monthly report opened with website visits, followers and email open rates, and completed trade-ins appeared on page three. The new report reverses that order. The change is small but deliberate: the first number the owner sees each month is the one the objective is measured by.

Data Sources

Completed trade-ins and quote steps come from the quote tool's database. Spending comes from the ad platforms. Margin comes from the resale ledger. Phone calls about trade-ins, the blind spot found in Unit 8, will be logged in a simple form from next month.

Why Not Trust the Platforms

Ad platforms report results using their own tracking, and their incentive is to show that ads work. That does not make their figures false, but it means they should be checked. The brand search test showed the platform's figure for brand search overstating its effect more than fivefold. Lewis and Rao (2015) explain why precise measurement is expensive, and the framework accepts rough tests that are honest over precise-looking reports that are not.

Reporting

A one-page monthly report shows the outcomes at the top, leading measures below and diagnostics only where a problem needs explaining. Vanity measures appear only in the appendix.

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

Chaffey, D., & Ellis-Chadwick, F. (2022). Digital marketing: Strategy, implementation and practice (8th ed.). Pearson.

Lewis, R. A., & Rao, J. M. (2015). The unfavorable economics of measuring the returns to advertising. The Quarterly Journal of Economics, 130(4), 1941-1973. https://doi.org/10.1093/qje/qjv023