D378 is MKTG 6010 Digital Marketing Science, and it is worth 4 competency units rather than the 3 that most courses in this sequence carry. The catalog describes it as the four competency unit digital marketing foundation for the Marketing Analytics specialization. That extra unit is the signal to read: this is the analytics track's entry course, and the word science in the title points at the difference. Claims here are expected to be testable.
What the fourth competency unit is telling you
Competency units at WGU are a rough expression of workload, so a four unit course sits alongside three unit courses on your plan while asking for meaningfully more. Two consequences follow, and both are practical.
The first is planning. If you have been pacing three unit courses at a certain rate, this one will take longer, and the students who fall behind in an analytics specialization usually do so here rather than later. Budget for it deliberately rather than discovering it.
The second is the standard of argument. Where the Digital Marketing specialization's foundation course asks you to plan channels, an analytics track foundation asks you to plan channels in a way that can be evaluated afterwards. That means designing for measurement before the campaign exists: knowing what will be tracked, what a result would have to look like to count as success, and what alternative explanation you would need to rule out.
The habit that carries the whole course is turning intentions into testable statements. Increase engagement is not testable. If we shift twenty percent of paid budget from broad targeting to retargeting for one month, cost per acquisition will fall by at least fifteen percent, is testable, and it forces you to specify a baseline, a timeframe and a threshold. Aspects in this course are frequently written around exactly that specification.
WGU records the result as Competent or Not Competent, with no letter grades and no ordinary grade point average, and performance assessment work can be revised and resubmitted with no penalty attached to the result. State the hypothesis, define the measurement, and submit.
Turning scored aspects into a measurable plan
If your course is assessed by a performance assessment, the aspects your evaluator scores are the outline, and each needs a score of 2 on its own.
Here is the arithmetic at four unit scale. Suppose your rubric shows fourteen scored aspects and the deliverable runs to roughly 3,400 words with two tables. Give the tables 300 words of interpretation between them, reserve 250 for the business situation and 150 for a close, leaving 2,700 across fourteen aspects, or roughly 193 words each.
One hundred and ninety three words is a tight unit, and tight is correct here. Analytics writing rewards precision over expansiveness: state the claim, state the measure, state the threshold, state the caveat. Where an aspect asks for a metric definition, define it in one sentence with its numerator and denominator rather than in a paragraph. Where an aspect asks for a justification, spend the words there instead.
A useful pass before submitting: find every number in your document and check that it has a source or a stated assumption, a unit, and a time period. Numbers without those three are the most common cause of returns in analytics flavoured courses, and the check takes ten minutes.
A structure for a measurement led digital plan
The genre here is usually a digital plan with a measurement framework attached, or a measurement framework in its own right. This layout puts the measurement design where it belongs, which is before the activity rather than after it.
| Section | What it specifies | Precision required | Where drafts lose the aspect |
|---|---|---|---|
| Business question | The decision the measurement will inform | One sentence naming the choice | A goal instead of a decision |
| Hypotheses | What you expect to happen and by how much | Direction, size and timeframe | Predictions with no threshold |
| Metric definitions | Exactly how each measure is calculated | Numerator, denominator, source system | Metric named but never defined |
| Data sources | Where each number comes from | System, collection method, known gaps | Assuming data exists that nobody collects |
| Campaign or activity design | What is actually done | Channel, audience, creative, duration | An activity that cannot be isolated for measurement |
| Attribution approach | How credit is assigned across touchpoints | Model named with its limitations | Last click used silently as if it were neutral |
| Analysis plan | What comparison shows whether it worked | Baseline, comparison group or period | Before and after with no control for seasonality |
| Decision rules | What each result would cause you to do | If this, then that, written in advance | Results interpreted after the fact to fit |
Evidence craft and measurement integrity
An analytics course judges your handling of numbers as closely as your handling of sources, so build both habits together.
On sources, platform documentation is primary for how a metric is calculated inside a system, which matters because two platforms reporting a conversion may count different things. Industry benchmarks come from interested parties and belong in the text with a publisher and a date. Peer reviewed marketing and information systems research supplies evidence about effects and about the limits of measurement methods, and it is the correct citation whenever you claim a method is reliable.
On integrity, three practices separate strong work. Distinguish correlation from cause explicitly, and where your design cannot establish cause, say so rather than implying it. Name the confounds that would need controlling, seasonality, concurrent campaigns, price changes, and say how the design handles each. And state the limitations of your attribution model in the same paragraph that introduces it, since every model makes a choice about credit that some other model would make differently.
Doing these things does not weaken a submission. In a course with science in its title, saying what your evidence cannot support is part of what is being assessed.
What earns Competent, and what comes back
Passing work is specified in advance. Hypotheses have numbers. Metrics have definitions. The analysis plan says what comparison will be made and what would count as a real effect. Decision rules exist before results do.
Returns cluster in four places. Metrics named but never defined, so nobody could reproduce them. A measurement plan added after the activity was designed, leaving no way to isolate the effect. Causal language attached to correlational evidence. And an attribution choice made silently, which an evaluator in an analytics specialization will always notice.
Decision rules are the most distinctive requirement in this course and the one most often treated as optional. Writing down in advance what you will do at each possible outcome is what separates measurement from reporting. If cost per acquisition falls below the stated threshold, the budget shift becomes permanent and extends to the second market. If it rises, the test stops at the end of week two and the original allocation is restored. If it moves less than the threshold in either direction, the result is treated as inconclusive and the test is rerun at greater scale. Three sentences, written before any data exists, and they remove the possibility of reading a result to suit a preference afterwards.
The other habit worth building here is stating what the data cannot see. Every digital measurement system has blind spots: conversions that happen offline, people who use several devices, customers who block tracking entirely, and long consideration periods that outrun the attribution window. A short paragraph naming which of these apply to your scenario and roughly how much of the picture they obscure is not an admission of weakness. In a course with science in its title it is a demonstration of the exact judgement being assessed, and it is frequently attached to an aspect of its own.
Six mistakes that cost time in D378
Pacing it like a three unit course. The fourth unit is real workload. Put it on the calendar.
Goals instead of hypotheses. A hypothesis has a direction, a size and a deadline attached.
Undefined metrics. Write the numerator and the denominator. Conversion rate means several different things.
Designing measurement last. If the activity cannot be isolated, no analysis will rescue it afterwards.
Ignoring confounds. Naming seasonality and concurrent activity, and saying how you handle them, is often its own aspect.
Silent attribution. Every model assigns credit differently. Name yours and state what it under counts.
What we do on this course, and what we will not do
We work on the written deliverable: mapping the rubric, budgeting aspects at four unit scale, drafting a model plan and measurement framework you can study and rewrite in your own voice, checking that metric definitions and analysis plans are internally consistent, and reading a returned evaluation to name the edits that will clear it. We do not access analytics or advertising accounts and we do not run activity on your behalf. If D378 carries an objective assessment on your plan, that exam is proctored and our role is preparation only. We are never present during an assessment, never take one for a student, and never ask for or handle WGU portal credentials.
Designing the measurement for D378?
Send the scenario, the task instructions and the rubric. We come back with the hypothesis set, the metric definitions and a straight read on whether your design could actually detect the effect.
Three questions students ask about D378
Why is D378 four competency units when similar courses are three?
How much statistics do I need?
What if the scenario gives me no data at all?
Where D378 sits in WGU's programs
The July 2026 catalog places this code in 1 current WGU program. Open a program page for the complete standard path and term positions. The live Degree Plan remains authoritative after transfer credit, substitutions, and mentor planning.
The assessments, one by one
The public catalog does not publish this course's PA/OA identity or task count. WGU Tutors publishes at most one PA manual per course and only from a WGU-controlled public rubric. Until that source exists, PA help begins from the student's real Course of Study and OA support remains preparation only.