Question 1
Difficulty: medium
How do you use sales, margin, and inventory data to decide whether a product assortment is working?
Sample answer
I start by looking at the full picture, not just sales volume. A strong assortment should move product efficiently, protect margin, and support the customer need for that category. I typically review sell-through, weeks of supply, gross margin return on investment, markdown rate, and stock-out frequency together. If sales are strong but margin is weak, I look at discounting, product mix, and whether lower-margin items are crowding out better performers. If inventory is high but sell-through is slow, I segment by style, size, color, or region to see whether the issue is demand or allocation. I also compare performance against plan and against similar periods to spot trend changes early. From there, I make recommendations such as reducing depth on weak items, rebalancing inventory, or expanding winners. The key is to translate the data into an assortment decision that improves both profit and customer availability.
Question 2
Difficulty: medium
Tell me about a time you found a merchandising issue in the data and had to explain it to a non-technical team.
Sample answer
In a previous role, I noticed that one category looked healthy at the top-line level, but the details told a different story. Overall sales were up, yet several key SKUs were underperforming and one size curve was creating excess inventory in a few locations. I pulled the data into a simple summary showing sales, margin, stock levels, and store-by-store trends. Instead of focusing on formulas, I framed it around business impact: we were tying up cash in slow-moving inventory while losing sales on the best sellers. I met with the merchandising and store teams and walked them through the pattern using a few clear charts. That helped everyone see the issue quickly. We adjusted replenishment, shifted inventory to higher-demand stores, and trimmed future buys on the weaker SKUs. The experience reinforced that good analysis only matters if people can understand it and act on it.
Question 3
Difficulty: easy
What metrics do you consider most important when evaluating product performance in merchandising?
Sample answer
The most important metrics depend on the category, but I usually focus on a core set first. Sales tell me what is moving, margin tells me whether it is profitable, and sell-through shows how quickly inventory is turning. I also watch weeks of supply, inventory turnover, markdown percentage, and out-of-stock rate because those help explain whether we are overbought, underbought, or simply misallocated. For launch items, I pay close attention to early velocity and conversion since those are better indicators than long-term trends. I also like to look at performance by segment, such as store cluster, channel, or customer profile, because a product may be successful in one area and weak in another. The most useful metric is often the one that answers the current business question. If the issue is inventory health, I go deeper into turns and aging. If the issue is assortment quality, I focus more on productivity and markdown risk.
Question 4
Difficulty: medium
Describe a time when you had to make a recommendation with incomplete data.
Sample answer
I once had to support an assortment decision before all the latest sales data was fully available because the team needed a recommendation for a buying meeting. Rather than waiting, I used the best information we had and was transparent about the gaps. I reviewed trend data from the prior weeks, compared similar products, and looked at inventory position, store feedback, and promotional history. I also checked whether the missing data would materially change the recommendation or just shift the level of confidence. In that case, the trend was clear enough to make a directional call. I recommended holding back on one item group and reallocating spend to a stronger subcategory. I made sure to label the decision as provisional and suggested a follow-up review once the complete data came in. That approach helped the team move forward without pretending we knew more than we did. I think good analysts balance speed, judgment, and honesty about uncertainty.
Question 5
Difficulty: hard
How would you identify underperforming SKUs in a large product assortment?
Sample answer
I would approach it in layers so I do not miss the context. First, I would set a benchmark for what good performance looks like by category, season, and channel. Then I would rank SKUs by sales, margin, sell-through, and weeks of supply to identify the obvious laggards. After that, I would separate true underperformance from items that are newly launched, heavily allocated to the wrong stores, or affected by recent promotions. I also like to look at velocity by week to see whether an item is declining steadily or just had a temporary dip. If possible, I would segment by size, color, or location to find whether the issue is the product itself or the execution around it. Once I identify the underperformers, I would categorize them into action buckets: reduce depth, markdown, transfer inventory, or exit the item. The goal is not just to find weak SKUs, but to know what to do about them quickly.
Question 6
Difficulty: medium
How do you handle a situation where the merchandising team wants to keep a product that the data says is underperforming?
Sample answer
I try to treat that as a business discussion, not a disagreement. Merchandising teams often have product knowledge that pure data does not capture, so I start by asking what they are seeing in the market, with vendors, or with customer feedback. Then I bring the data in a way that connects to the decision at hand. For example, I would show whether the item is underperforming across most stores or only in certain clusters, whether the margin is strong enough to justify the slower turn, and how much inventory risk we are carrying. If there is a strong strategic reason to keep the product, I would support a limited test or a smaller buy rather than a full-scale commitment. That way, we respect the merchant’s perspective while still managing risk. I find the best outcomes happen when analysis and commercial judgment work together instead of competing with each other.
Question 7
Difficulty: hard
Walk me through how you would forecast demand for a new product launch.
Sample answer
For a new launch, I would combine historical analogs with current business context. I would first look for similar products by category, price point, season, and customer segment to create a baseline forecast. Then I would adjust for differences such as brand strength, placement, marketing support, and distribution breadth. I would also review any early indicators like pre-orders, web traffic, or store feedback if available. Since launches are uncertain, I would build a forecast range rather than a single number and identify the assumptions behind it. I like to separate the first few weeks from the longer-term run rate because launch behavior often spikes early and then settles. I would also set up a review cadence so we can compare actuals against forecast quickly and adjust replenishment or future receipts if needed. My goal is to make the forecast useful, not just accurate on paper, so the buying and inventory teams can act on it in time.
Question 8
Difficulty: medium
Tell me about a time you improved a reporting process or analysis workflow.
Sample answer
In one role, the weekly merchandising report was taking a lot of manual effort because data had to be pulled from multiple sources and cleaned before anyone could use it. I looked for the most repetitive steps and found that the same calculations were being rebuilt every week. I created a standardized template with consistent metric definitions and automated part of the data refresh so the team could spend more time analyzing and less time formatting. I also added a short summary section at the top that highlighted key changes, exceptions, and recommended actions. That made the report much easier for stakeholders to digest. The result was not only faster turnaround, but better decisions because the team had more time to discuss what the numbers meant. What I learned from that project is that process improvements do not have to be flashy to be valuable. Even small changes can create a big gain in efficiency and clarity.
Question 9
Difficulty: easy
How do you prioritize your work when multiple categories or stakeholders need analysis at the same time?
Sample answer
I prioritize based on business impact, deadlines, and decision urgency. If a request is tied to a buying meeting, a promotion launch, or an inventory issue that could affect sales immediately, it moves to the top. I also look at whether the analysis will change a decision or just provide background information. That helps me avoid spending too much time on work that is interesting but not actionable. I like to clarify the exact question early so I can scope the request properly and prevent rework. If several teams need support, I communicate early about timing and what I can deliver first. Sometimes I will give a quick directional answer and then follow up with a deeper dive. I have found that stakeholders value transparency more than unrealistic promises. Staying organized and keeping people updated usually prevents small delays from becoming bigger problems. It also helps me stay calm when the workload spikes, which is pretty common in merchandising.
Question 10
Difficulty: hard
How would you use merchandising data to support a markdown or clearance strategy?
Sample answer
I would use data to identify which products need price action and which ones still have enough demand to hold price. I would start with aging inventory, sell-through trends, current margin, and stock levels by SKU or variant. If an item is slow-moving and building weeks of supply, I would look at whether it is already in a natural decline or whether it just needs better exposure or reallocation. I would also check whether similar products are still selling at full price, because that helps protect margin where possible. For markdown planning, I would segment inventory into groups like high risk, moderate risk, and healthy, then recommend different action levels instead of applying a blanket discount. I would also monitor the impact after the markdown starts so we can adjust quickly if needed. The best clearance strategy balances inventory reduction with margin protection, and the data should guide both sides of that tradeoff.
