Real account data · End-to-end Amazon store management (remote) · Dec 2025 – Apr 2026
A new brand on a brand-new seller account, with no sales history and no reviews, in a saturated home category, selling bulky, heavy products fulfilled by merchant (FBM). I ran the account end to end, remotely: brand and market positioning, product line, pricing, purchasing and supply chain, listings, advertising, account health, customer service and 3PL coordination.
Every figure comes from the account's own Seller Central reports and can be shown in an interview. Sales and traffic are indexed to the December daily average (December = 1×).
01
Most companies assign this work to separate functions or people: brand, advertising, purchasing, customer service. This store was run end to end by me alone, remotely: I was in Toronto, the store was in the US, the factory overseas and the 3PL in California. The only part not mine was the physical handling inside the warehouse.
Target customer, differentiation, Brand Story; product families under one naming system
Price tier, hero SKUs, which search terms to win first
Which sizes to keep, cut and add
Price ladder across the range, promotional pricing
Replenishment planning, factory specs, two-warehouse allocation
Image briefs and infographics, copy; titles, bullets, backend keywords; parent-child variations via flat file
Premium A+ unlocked on schedule
Campaign architecture, budgets and bid strategy, keyword harvesting and negatives; coupons and promotions planned alongside
Acted on at once; Seller Support cases followed to closure
3PL, carriers and delivery promises; returns and buyer messages
Weekly KPIs, a change log, meetings I called
Handled by the 3PL
02
7-day moving averages of daily sessions and sales, each indexed to its December daily average.
With no sales, no reviews and no brand awareness, competing for head terms would only have burned budget. I got the fundamentals right first, the content and the listing pages, then went looking for long-tail keywords: searches naming an exact size or spec, with less competition and stronger purchase intent, where a new brand can rank and convert.
Advertising started on December 19 with small, controlled budgets, and data analysis showed me which search terms actually convert. January daily sales came in at 1.9× December.
I spent much of January preparing to win business buyers, on several fronts at once. In February the orders arrived as planned. Business buyers grew into a steady source of sales and helped lift February daily sales to 4× December.
Weeks of search term analysis, budget reallocation and testing paid off: ACOS fell from 21.2% to 14.8%.
At the current sell-through rate, the #2 best seller had 14 days of stock left against a 30-day replenishment lead time. That same day I sent the restock plan and the full plan for the second purchase order (below).
This launch was in my operating plan from day one on the account. Premium A+ can't be switched on at will: it requires five approved A+ projects and a Brand Story across the whole catalog. Before launch I iterated through many versions, ran A/B tests, called the meetings to align on direction, and started preparing for Vine. Main images, A+ and keywords went live together, and March traffic reached 2.1× December, the high for the project.
The engagement ended in mid-April. The rankings, reviews and ad structure built during the launch stayed in place, and the line kept selling on that momentum in the months that followed.
03
*April 1–18.
04
This was a difficult kind of launch: a saturated category, no sales, no reviews, no brand awareness, and in 2026, not 2016, when the odds are far longer. It worked because several things came together: years of understanding customers, a feel for the product, an ear for copy, and a disciplined promotion plan. Brand Analytics records every search the store appeared in. Over 19 weeks, shoppers who saw it clicked through at 2.0× the category average.
×14in two weeks
Brand impressions grew 14-fold from the week ending December 20 to the week ending January 3, and peaked at 17.6× in early March.
Source: Brand Analytics, Search Query Performance (brand view). Click-through rate: 19 weeks to April 25, 2026. Impressions: December–March, indexed to the week ending December 20.
05
Advertising is never set-and-forget. I put every ad format the account had access to to work: Sponsored Products (automatic, keyword and product targeting), Sponsored Brands and brand video, then filtered and kept tuning, often online more than ten hours a day. Each week I pulled the search term report, decided term by term whether to raise, lower or negate, and tested the result against the playbook, round after round. By February 20 the data showed one group of scene keywords taking 38% of spend for 8% of orders. After reallocating that budget and changing the approach, ACOS fell from 21.2% to 14.8%.
Auto campaignscost per order $16
Core keywordscost per order $56
Scene keywordscost per order $180
06
I spent ten years supplying IT equipment to companies, so I know what a business buyer needs to see: precise specs, easy bulk ordering, reliable delivery dates and a seller who responds. The listings were built for that from day one. Almost a quarter of sales came from Amazon Business accounts. Once on the page, they ordered at 4.5× the rate of consumers, usually in bulk. In my experience, business buyers also reorder at a high rate. This is a customer group I know how to win and keep.
23%of sales came from business buyers
Business15.5
Consumers3.5
Business Report, December 1, 2025 to April 18, 2026.
07
I mapped the ten SKUs on a depth-by-width matrix and judged each by its contribution. Three findings shaped the second purchase order. First, the deep series drove three quarters of revenue, so inventory and ad budget went there. Second, only the three fast movers were reordered; the slow sizes already had hundreds of days of cover, and cash tied up there earns nothing. Third, search data showed shoppers buy by the industry's standard sizes: most converting searches were long-tail terms naming an exact size. The weak deep size sat one inch off the most popular standard, so it missed the biggest search. The second order moved every width to a standard size and added the larger sizes the market buys and the range lacked.
Deep
Shallow
1
2
3
4
5
Width, narrow to wide
Settled orders, January 20 to April 14, 2026.
Reorder only what turns: the three fast movers were restocked, the slow sizes were not.
08
New products weren't priced on gut feel. Across the ten existing sizes, factory cost tracked footprint almost exactly (R² = 0.97). Using that line, I estimated the cost of three new sizes before the factory quoted, then modeled each against a range of target prices for margin. Specs, quantities and pricing for the second order were settled before any money was committed.
Every order shipped from a single warehouse in California, so East Coast customers waited longest and cost the most to reach. The second order was split between a West Coast and a Northeast 3PL to cut shipping zones and carrier cost.
09
Bulky, heavy products fulfilled by merchant (FBM) have a very different cost structure from small FBA items. The two big lines are landed cost and carrier shipping; the Amazon referral fee is third, and ads take only 5.3% because most orders came from organic search. After these five costs, about $25 of every $100 remains as contribution margin. Shipping is the most flexible line: the two-warehouse plan cuts shipping zones, and carrier rates can be renegotiated as volume grows.
$100
Landed cost of goods
Carrier shipping (variable)
Amazon referral fee
Advertising
Refunds
Contribution margin per $100
Settled orders, January 20 – April 14; shipping from my monthly cost model; referral fee at the standard 15%. Excludes storage and fixed costs.
10
For an FBM account, everything rides on delivery promises and policy compliance. I checked Account Health daily, acted on any flag the moment it appeared, and kept after it: opening Seller Support cases, submitting documents and following up until the issue was fully resolved.
Account health page, late April 2026; A-to-z claims from the March seller performance report.
Just as the store gained traction, it was hit by a cluster of returns and complaints, a common tactic rivals use to kill a new listing early. I investigated every case, appealed with evidence and kept records. The account metrics held, and by late April every one was in the green.
A lifestyle image had an AI-generated prop resembling a well-known tool brand, and the listing was deactivated. My plan of action set out the root cause, the corrective actions and a preventive rule: every prop in every image is checked for trademarks before upload. Every image in the store was then audited. The appeal was accepted: the listing was reinstated and the search suppression lifted.
In March a carrier delay pushed a handful of orders past their promised delivery date. I pulled the On-Time Delivery report daily and tracked each order until it was delivered. By late April, on-time delivery was back to 100%.
11
One change at a time, each logged against a baseline. Weekly reports built by script, not copy-pasted. A threshold on every metric, and action the moment one is crossed. A launch has no shortcuts. It takes patience, reliability and staying power: numbers every day, a review every week, every issue closed the day it appears. I ran someone else's store as if it were my own, and that is the standard I bring to any company.
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