A category analyst is the person who can tell you why the whole salty snacks aisle is soft, not just one brand in it. You live in scan data, shelf plans and promo calendars, and you turn them into a story a buyer or a sales team will act on. The day below follows an analyst at a snack manufacturer who works on one big grocery account.
The weekly refresh from Circana or NielsenIQ is in. You pull the category view for your retailer, compare it to the rest of the market, and check which segments moved. Kettle chips are up, tortilla chips are flat, and the private label bag is taking share at the low end. You write three bullet points before anyone asks.
Last month's two-for-one deal on the family size bag looked great in units. Your job is to say whether it grew the category or just pulled forward sales and stole from the smaller bag. You bring a lift chart and a cannibalization read, and the sales manager doesn't love the answer.
The retailer's buyer emails the account team: they want to cut a slow item and ask which one you'd drop. The plan for the day is gone. You rank every item in the set by sales per point of distribution, flag which ones bring in shoppers nobody else reaches, and check whether the incremental numbers from the household panel back you up.
You open the shelf plan in a space planning tool like JDA Space Planning or Blue Yonder and test what happens if the slow item comes out and a better seller gets two more facings. Days of supply matter here. A top seller with too little shelf runs out by Thursday.
The quarterly category review with the retailer is coming. You work on the deck in PowerPoint: the category story on one page, the shopper insight on the next, then the recommendation. The rule you follow is simple. Lead with what's good for the whole aisle, not just your brand, or the buyer stops listening.
The title means two slightly different jobs. At a brand or manufacturer, you're often the category captain's analyst: you build the case for the whole aisle and hope the retailer trusts it, even though your company pays your salary. At a retailer, you work beside the buyer or category manager and look at every brand at once, including the store's own label, with a closer eye on margin, vendor funding and inventory. Retailer roles tend to give you more data and more say. Manufacturer roles tend to teach you syndicated data and selling a story faster. Pick the side based on whether you'd rather decide or persuade.
Plenty of analysts start supporting a sales team at a consumer goods company, pulling reports from retailer portals. Once you're the person everyone asks about the data, the move to category work is a short one.
Store managers and merchandisers who know how a shelf reset actually goes have an edge. Pair that with solid Excel and a working grasp of SQL, and a retailer's merchandising team will take you seriously.
Some manufacturers and retailers run rotational programs that drop new grads into category or shopper insights teams. Coursework in statistics or marketing helps, but the internship on your résumé counts for more.
12% of openings are fully remote.
$73,815 – $110,000
Typical range in the 13 of the newest 25 postings that list pay.
The analyst builds the evidence and the manager owns the decision and the relationship. On the retailer side, the manager usually signs off on the assortment and negotiates with vendors. On the manufacturer side, the manager presents to the buyer while you make sure every number in the deck holds up.
Not always, but it helps more each year you're in the job. Manufacturer roles lean on syndicated data tools and Excel. Retailer roles more often expect you to query point-of-sale and loyalty data yourself, so SQL shows up in a lot of those postings.
The usual next step is category manager, either at a retailer running an aisle or at a brand owning a key account's category story. Some analysts move sideways into pricing, revenue management or shopper insights instead, which suits you if you'd rather stay close to the models than the negotiations.
The data rarely agrees with itself. Syndicated data, the retailer's own sales and your shipment numbers will all tell slightly different stories, and you have to explain the gap before anyone trusts your recommendation.
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