Merchandiser Interview Questions

I start with the data from the previous equivalent season, looking at sell-through by category, by price point, and by option count. I want to understand where we over-ranged and held excess stock into markdown, and where we under-ranged and left potential sales on the table. I then overlay the buying team's direction and any trend data to identify where we are growing the range and where we are editing it. I build the OTB plan from the sales forecast, working back to a buy value that delivers the target margin at the planned full-price sell-through rate. I also check depth versus width at option level: it is usually better to have stronger depth on fewer options than shallow depth across many, because out-of-stocks on a best seller are more damaging than a smaller range.

As soon as a line is tracking below plan by more than 10% in the first three weeks of trading, I treat it as an action item rather than a monitoring item. First, I check whether the issue is ranging, allocation, or presentation: is the stock in the right stores? Is it presented well? Is there a pricing or promotional issue that might be suppressing demand? If the product is genuinely not converting, I look at markdown timing: taking an early mark on a slow-seller recovers more cash and space than waiting. I model the margin impact of two or three markdown scenarios and recommend the one that recovers the most value over the trading period. I also feed the learnings back into the next range plan: a line that misses plan is information about what the customer does not want, not just a problem to solve.

My relationship with buying is most productive when I get involved early, before the range is committed, rather than after the buy is done and I am just allocating what arrived. I contribute the data perspective: last season's performance by attribute, size curve analysis, depth-versus-width trade-offs, and OTB constraints. The buyer brings supplier relationships, trend direction, and product knowledge. The best ranges come from genuine dialogue between those two inputs. I make a point of preparing clear data summaries before range reviews, so the conversation is grounded in evidence rather than opinion. When we disagree, I present my view with the data behind it and then defer to the buyer's judgment on the product side: they have market visibility I do not. The goal is a range we both stand behind.

Allocation is where you either do the range justice or undo it. I start by tiering stores based on their trading profile for the relevant category: volume, space, and historical sell-through index. High-tier stores get deeper stock on core options and first allocation on new lines. Lower-tier stores get edited selections that match their customer profile. I build the initial allocation to cover the first three to four weeks of trading at the planned sales rate, then I set a review trigger: if any store drops below a defined days-of-cover threshold, I top up from central reserve or replenish from a slower-moving store. I track sell-through by store weekly and redistribute proactively rather than waiting for stores to report gaps. Late reallocation of slow stock into faster-selling stores can recover a meaningful amount of markdown.

Three weeks into a new season, I noticed that one of our hero product categories was tracking at 65% of planned sell-through in our top ten stores. Most of my team assumed it was early-season softness. I dug into the data and found that the issue was concentrated in one size: a particular run had been under-allocated because the size curve we used was based on the prior year's data, and the buying team had shifted the range toward a slightly different fit that was attracting a different customer profile. I flagged it to buying immediately, arranged a reallocation of stock from stores where the size was sitting, and worked with the allocation team to adjust the replenishment model for future drops. We recovered most of the planned sell-through by week eight. The data had been sitting in the daily report for three weeks: the issue was being looked at but not properly interrogated.

A buying director wanted to extend the life of a slow-selling line by running a promotional event rather than taking a markdown. My analysis showed that the promotion would move volume but at a gross margin that was lower than a straight early markdown, and the promotional space costs would eat into most of the remaining margin. I presented a side-by-side comparison of three scenarios: hold and promote, early markdown at 25%, and early markdown at 33%. The data showed that the 25% markdown recovered more total cash and released the space four weeks earlier for the new season stock. The director pushed back twice, citing supplier relationship considerations. I held the position because the numbers were clear, but I offered to loop in the supplier account manager to manage the communication. The 25% markdown was approved, and sell-through cleared within three weeks as modelled.

In my second year as a merchandiser I convinced the buying team to go deeper than planned on a trend-led category based on strong early sell-through in a small number of stores. The full estate rollout did not replicate those results: the category had strong appeal in high-footfall urban stores but was misaligned with the customer profile in most of our suburban and out-of-town locations. We ended up with a markdown position that was about 15% worse than the seasonal target. I learned two things: a fast start in a subset of stores is not always a reliable signal for a broad range extension, and I had not done enough demographic profiling by store cluster before recommending the deeper buy. I introduced a store-cluster analysis step into the range plan review process the following season, and we have not repeated the same pattern of over-investment in trend-led categories.

My forecasting process starts with the prior year's actual sales for the equivalent period, adjusted for any known changes in the trading environment: new store openings, closures, format changes, or macro trends. I then apply a like-for-like growth rate based on the business plan and adjust at category level for any changes in the range or pricing strategy. I build the OTB from the top-down sales plan, working back to a buy value that delivers the plan at the target full-price sell-through percentage and the planned achieved margin. I also build a scenario model with a minus-10% and plus-10% sales case, so I can see the markdown risk and the stockout risk under different trading conditions. I review the forecast weekly in-season against actuals and reforecast if the trend changes by more than 5% in either direction.

I track sell-through at three levels: total category, option, and store. At category level, I compare weekly sell-through against the seasonal plan and against the prior year to understand whether the trend is ahead, in line, or behind. At option level, I rank lines by sell-through rate weekly and flag anything below 70% of the category average in the first four weeks: that is my early-warning list. At store level, I look at sell-through index by cluster to understand whether a performance issue is widespread or concentrated. The most actionable data is usually at the intersection of option and store cluster: a line that is performing well in one cluster but badly in another tells me something specific about ranging or presentation that I can act on. I also track rate of sale versus stock on hand to forecast when options will reach their reorder threshold.

I start the clearance plan six to eight weeks before the end of the season, not at the end of it. By that point I have a clear picture of which lines will not clear at full price, and I can model the markdown depth and timing needed to clear them within the season rather than carrying them into the next. I prioritise clearance of lines with high stock volume and low sell-through, because those create the most margin drag. I also look at where the stock is sitting: concentrating it in fewer stores often lifts the sell-through rate by improving product visibility. I set a weekly clearance target by line and track against it, adjusting markdown depth if a line is not clearing at the rate modelled. My goal is always to enter the new season with no carryover stock above the agreed level, because clearance stock cannibalises space and attention from new season product.