Google Digital Marketing & E-commerce common mistakes
Google Digital Marketing & E-commerce · Understand the traps and how to avoid them.
Treating organic and paid channels as interchangeable when asked which tactic best meets a stated goal, so answers swap SEO for paid search or earned media for owned media without checking cost, speed, or control tradeoffs.
Candidates memorise channel definitions in isolation and never practise matching a channel to a business objective, budget, or timeline. The exam regularly frames a scenario where the constraint (limited budget, need for immediate visibility, long-term authority) determines the correct channel.
What to do instead
Build a comparison grid for owned, earned, and paid media with columns for cost, speed to results, control, and durability. For every practice scenario, write the binding constraint first, then eliminate channels that violate it before choosing.
Confusing the marketing funnel stages with the customer journey stages and mislabelling a tactic as awareness when the scenario describes retention or advocacy.
Both models use similar vocabulary (awareness, consideration, conversion, loyalty) but the exam tests whether you can place a described activity at the correct stage. Candidates answer from the tactic's name rather than from the customer's position in the journey.
What to do instead
Ask 'where is the customer relative to purchase?' before reading the answer options. Practise sorting 20 activities (loyalty program, retargeting ad, review request, blog post) into funnel and journey stages and note where the two models diverge.
Selecting a keyword strategy based only on search volume while ignoring intent and competition, so a high-volume informational term is chosen for a page designed to convert.
Volume is the most visible metric in keyword tools, so it anchors attention. Candidates underweight intent classification and difficulty, which are the actual decision criteria in scenario questions.
What to do instead
For each keyword, record intent (informational, navigational, commercial, transactional) and difficulty alongside volume. Reject any keyword whose intent does not match the page's purpose, regardless of volume, and state that reasoning explicitly in practice answers.
Treating social platforms as a single channel and recommending the same content format everywhere instead of matching format and platform to the stated audience and goal.
Candidates generalise from personal platform use and do not study how audience demographics, content lifespan, and native formats differ across platforms. Scenario questions hinge on that fit.
What to do instead
Make a platform profile table: primary audience, dominant format, typical content lifespan, and best-fit objective. When a question names an audience or goal, select the platform whose profile matches and justify with two profile attributes.
Confusing engagement metrics with conversion metrics, reporting likes and shares as evidence that a lead-generation campaign succeeded.
Engagement numbers are prominent in platform dashboards and feel like success. Candidates do not distinguish upper-funnel interaction from measurable lead outcomes, which the exam tests directly.
What to do instead
Label every metric as engagement, lead, or revenue before evaluating a scenario. If the goal is lead generation, only lead and revenue metrics can validate success; engagement metrics can only support the diagnosis.
Writing calls to action and landing page recommendations that are vague ('learn more', 'click here') when the scenario requires a specific, action-oriented CTA aligned to the offer.
Candidates focus on channel choice and budget and treat copy as an afterthought, missing that CTA specificity is a graded conversion lever.
What to do instead
Practise rewriting ten generic CTAs into offer-specific ones that name the value and the next step. In scenario questions, prefer the option whose CTA states what the user gets and what happens next.
Treating email consent as a formality and recommending purchased lists or pre-checked opt-in boxes to grow a list quickly.
Candidates optimise for list size and speed, underestimating deliverability, legal, and engagement damage. The exam tests consent and list quality explicitly.
What to do instead
Adopt the rule that every list-growth recommendation must be permission-based and double opt-in where appropriate. Reject any answer relying on purchased lists or implied consent, and be able to explain the deliverability consequence.
Judging email campaign performance on open rate alone and ignoring deliverability, click-through, and conversion, or misreading A/B test results without checking sample size and significance.
Open rate is the headline metric in most email tools, and candidates are unfamiliar with how test validity constrains conclusions.
What to do instead
Evaluate email results in sequence: deliverability, then open, then click, then conversion. For any A/B scenario, state the sample size and whether the difference is significant before recommending a winner; refuse to declare a winner from a small or confounded test.
Confusing attribution models and reporting credit to the wrong touchpoint, or mixing metrics of different types (rate, count, currency) in one comparison.
Attribution terminology overlaps and candidates do not practise tracing a conversion path through first-touch, last-touch, and linear models. Unit confusion arises from reading dashboards without checking definitions.
What to do instead
Work through a five-touch conversion path under first-touch, last-touch, and linear attribution and record how credit shifts. Before comparing any two metrics, confirm both share the same unit and denominator.
Ignoring the distinction between platform-reported and analytics-reported data, or treating correlation in a campaign report as proof that the campaign caused the lift.
Platform dashboards overcount and candidates rarely study why numbers disagree across tools, so they accept a single source uncritically.
What to do instead
For any measurement scenario, name the data source and its known bias, then look for a control or holdout before claiming causation. Prefer answers that reconcile sources or acknowledge discrepancy over answers that assert a single number as truth.
Setting up an e-commerce store recommendation that skips checkout friction, payment options, or mobile optimisation, focusing only on product page design.
Candidates concentrate on the visible storefront and underestimate cart abandonment drivers, which the exam tests through abandonment scenarios.
What to do instead
Audit any store scenario against a checkout checklist: guest checkout, payment methods, form fields, shipping cost transparency, mobile layout. Choose the option that removes the largest identified friction rather than the most visually appealing one.
Treating product listing optimisation as keyword stuffing in titles and descriptions rather than matching structured attributes and search intent for shopping feeds.
Candidates transfer SEO habits directly to product feeds without accounting for attribute completeness and feed quality signals.
What to do instead
For product listing questions, prioritise complete, accurate attributes (size, colour, material, identifiers) and clear titles before keyword additions. Reject options that sacrifice accuracy or attribute completeness for keyword density.
Confusing customer loyalty tactics with acquisition tactics, or recommending a discount as the default loyalty mechanism when the scenario calls for retention and lifetime value growth.
Discounts are the most familiar tactic, so candidates reach for them without considering margin erosion and the difference between transactional and emotional loyalty.
What to do instead
Classify each tactic as acquisition, retention, or advocacy before choosing. For retention scenarios, prefer tactics that build relationship or habit (rewards, personalisation, service) and reserve discounting for clearly stated price-sensitivity cases.
Measuring loyalty program success by enrolment count rather than by repeat purchase rate, retention, or customer lifetime value.
Enrolment is easy to report and looks impressive, while retention metrics require cohort tracking that candidates rarely practise.
What to do instead
For loyalty scenarios, name the outcome metric (repeat purchase rate, churn, lifetime value) and the comparison baseline. Treat enrolment as an input metric that cannot by itself demonstrate program success.