Sample
McKinseyE-Commerce Margin Erosion
Intermediate
A business case that unfolds in stages — structure the problem, work through the data, and commit to a recommendation you can defend.
What to do
1
Think through the business problem as it unfolds
The case progresses in stages; the AI interviewer shares data exhibits as the analysis develops.
2
The AI will probe your structure, reasoning, and recommendation
Expect follow-up questions throughout the case.
3
The AI closes the case and provides a debrief
Your Interviewer
AI Generated
McKinsey case interviews test your structured problem-solving through hypothesis-driven analysis. Expect exhibits, quantitative questions, and synthesis.
Interview
My hypothesis is that the home goods expansion is the primary driver - impacting both gross margin and fulfillment costs.
Contribution Margin Bridge: 32% to 24%
The waterfall confirms my hypothesis. Product mix and fulfillment are the two biggest drivers of margin decline - both point to home goods as the culprit.
I'd like to see the gross margin and fulfillment costs broken down by category to quantify the impact.
The diagnosis is that home goods is hurting margin through a three-part mechanism: structurally lower gross margin, structurally higher shipping cost, and a much worse return profile. This is not just one bad quarter.
Sizing the gap: home goods is 25% of $2B, so roughly **$500M** of revenue. A **23-point gross margin gap** versus apparel implies about **$115M** of gross profit dilution before we even account for the heavier shipping and return burden. So the category is large enough to matter, and the economics are directionally wrong across the full value chain.
I'd recommend a staged decision, not an immediate exit.
**First 90 days:** raise free-shipping thresholds for bulky home-goods SKUs, cut the worst-returning tail of the assortment, and renegotiate packaging or carrier rules on oversized shipments. In parallel, track contribution margin after fulfillment and returns, not just gross margin.
**Decision test:** if the bottom-quartile SKUs are removed and the category still cannot get to an acceptable post-fulfillment contribution margin, then it likely does not belong in the portfolio. If economics improve materially after pruning, we keep a narrower home-goods offer rather than treating the whole category as a write-off.
I'd run a cohort analysis separating basket size from retention. The question is not whether home-goods buyers spend more once; it's whether they stay longer, buy apparel more often, or become cheaper to reacquire.
So I'd compare apparel-only cohorts versus apparel-plus-home-goods cohorts on 12-month retention, repeat purchase frequency, and blended contribution margin after returns. If home-goods buyers have bigger baskets but no retention lift, the CEO's LTV argument is overstated and we should shrink or exit. If retention and cross-category frequency are materially higher, we optimize the category instead of exiting it.
Debrief
Debrief
E-Commerce Margin Erosion
1
Structure
Distinctive
Distinctive
2
Analytical
Strong
Strong
3
Synthesis
Strong
Strong
4
Communication
Good
Good
You communicated the framework clearly from the start, so each branch of the analysis felt connected to the margin question.
Your signposting was effective, especially when moving between diagnosis and recommendation, which kept the discussion easy to track.
The final synthesis needed a crisper top-line message and fewer words so an executive could grasp the recommendation in one pass.
Original
"My recommendation: 1. Short-term: Raise free shipping thresholds for home goods to offset costs 2. Medium-term: Renegotiate supplier contracts or exit lowest-margin home goods SKUs 3. Strategic: Evaluate whether home goods fits the brand - it may be diluting focus"
Revised
"My recommendation is to fix home goods profitability in the short-term, then decide if it's strategic. First, raise free shipping thresholds for home goods immediately - this is a quick win. Second, exit the bottom 20% of SKUs by margin within 6 months. Third, run the cohort analysis we discussed to test the LTV hypothesis before making a keep-or-kill decision."
Improvements
Structure
Lead with the core message before listing actions
Specificity
Add concrete targets (bottom 20% SKUs, 6 months)
Impact
Connect to earlier analysis (cohort analysis, LTV hypothesis)
Skill Progress
Quantitative Reasoning
ExpertDemonstrated this skill in the session.
Next Focus
Keep practicing to strengthen this skill.