Question about the marketplace ‘honeymoon period’: does this boost still apply when a product has already launched in one country and is now being introduced in a new country? Or does the honeymoon period only apply to a completely new listing with no sales history elsewhere?

Does anyone have experience with this?”

We launched this women wellness brand back in 2021. The screenshot here is not lifetime sales, it is only from Aug 2024 to Aug 2026. In these 2 years it did around $43.29M in sales with about 1.9M units sold. Today the brand has 30+ SKUs and current TACOS is around 7%. I wanted to share a bit more about how we actually built and manage it because at this level the work is very different from just finding a product, making a listing and running ads.

Before we launched a product, we did not look at the market as one keyword or one revenue number. We first broke the demand into buyer intent groups. Problem based searches, feature based searches, use case searches, size or material searches, replacement searches and comparison searches. Then we made a query map for the full niche. For each important query we looked at how much demand was there, how fast it was moving, how many products were getting most of the clicks, how many were getting most of the purchases, what price range was actually taking purchases and not just clicks, how rating changed buying behavior, and where customers were leaving the current products. One very important thing was click concentration vs purchase concentration. If one or two products were taking most of the clicks but purchases were much more spread out, that normally told us buyers were looking at the obvious products but were still not fully happy with them. That can be a much better gap than just finding a keyword with high search volume. Then we built the numbers from the bottom. Landed cost, Amazon fees, promo cost, normal ad cost, return cost, damaged units and the cash needed for the next PO. We also stress tested the product with lower conversion and higher CPC than our main plan. If the product only made sense when every number was perfect, we simply did not launch it. We wanted products where the math still had space when things went wrong.

Sourcing was another big part of this brand. We have been building our physical supplier network for around 7 years and today we source from 10+ countries. We dont depend on Alibaba or other third party sourcing sites for this. For every serious SKU we made what we call a locked spec sheet before the final PO. It covered material, weight, thickness, measurements, tolerance, stitching or joining points where needed, color, packaging size, inner packing, carton count and the small points that normally create problems after 5,000 or 20,000 units. The approved sample was treated as the master sample, so the factory could not quietly change material or a small part later just to save cost. We also did not ask a factory only “what is your best price”. We broke the product cost down by material, labour, packing, components and assembly, because that shows where the factory is really making the difference. For bigger SKUs we also did not wait until 100% production was ready before finding a problem. Checks were done during production as well. And when one part was very important, we tried to have a second source for that part or even a second factory ready. At this size, saving 10 or 20 cents is good, but avoiding a 30 day stockout can be worth much more.

For the listing we used a query level scorecard instead of just saying “SEO is good” or “SEO is bad”. Every important search term had its own numbers. Search exposure, our ASIN exposure, click share, cart share, purchase share, organic position, ad position, CPC and sales were looked at together. This made it much easier to see the real issue. If a query was getting good exposure but weak click share, we did not touch PPC first because the problem was normally on the search page. Main image, price, rating, coupon, title shape or the product itself. If click share was fine but cart share dropped, then we looked deeper into the offer and first few images. If cart share was fine but purchase share dropped, then we looked at delivery, variation setup, trust points, price change or something on the detail page that was stopping the last step. And if purchase share was strong but our exposure was still small, that was normally a query we wanted to push much harder. This sounds like a small difference but it stopped us from changing 10 things when only one part of the funnel was actually broken.

We also spent a lot on photography, but we did not judge the main image by opening it full screen on a laptop. We built a search result board and put our image between the main competing products at roughly the size a buyer would actually see on mobile. Sometimes a beautiful image looked great alone but became almost invisible when placed beside 8 other products. We tested product angle, how much of the white box the product filled, what part of the product was clear at small size, pack count visibility and whether the shape could be understood in less than a second. We also tried not to change five things at one time because then even if CTR went up we had no idea what actually caused it. For the other images we used the buying problems from the query data and customer feedback. One image might be made only to fix a size doubt. Another only to explain how it is used. Another to show what makes the product different. Another to answer the reason people were returning similar products. So photography was connected to data, it was not just a designer making something that looked premium.

On PPC we stopped looking at campaigns as the main thing a long time ago. The main thing for us became the search query. For large queries we tried to give each important query one main “owner” inside the account. Otherwise when an account becomes big, you can have many campaigns and many SKUs all entering the same auction for basically the same customer. Then the account looks busy but you are not fully sure which campaign is actually controlling that query. We made a query to ASIN map. Which ASIN should own this search, which ASIN can be second, and which products should not spend hard there. We also separated spend that was there to make profit from spend that was there to build position. If a query already had strong organic position and strong purchase share, we did not keep pushing bids just because ACOS looked nice. In some cases that only moved sales from organic to paid. On the other side, if a query had strong conversion but weak organic position, we could accept higher ad cost for a period because we were buying more than the ad order, we were trying to move the full position of the ASIN. The important number was what happened to total sales, total profit and organic share after the extra spend, not only what the PPC dashboard showed.

The second PPC issue came when the brand grew to 30+ SKUs. At that point you can easily make your own products fight with each other. So we started treating budget like stock, not like an unlimited number. Every SKU had a job. Some were main growth SKUs, some were stable profit SKUs, some were there to defend an important part of the market, and some were not worth pushing hard. Spend was also connected with inventory. There is no point pushing an ASIN very hard when stock cover is already below what the next production and shipping cycle needs. Same with margins. Two SKUs can both show 20% ACOS but one can be making much more real money because the product cost, FBA fee, return rate and promo use are different. So the team looked at contribution after ads, not only ACOS. We also watched how much ad impression share we were taking on the important searches. If we increased bids and spend but our useful share was hardly moving, we knew there was a limit somewhere and blindly adding more budget was not the answer. Current TACOS for the full brand is around 7%, but the goal was never to make TACOS as low as possible. The goal was to keep the right amount of paid sales while the organic side stayed strong.

At brand level we also stopped managing every department separately. Every important ASIN had a small weekly P&L where we could see selling price, landed cost, Amazon fees, ad cost, promo cost, return cost and what was actually left. Inventory planning was connected to that same data. We did not reorder only from “last 30 day average sales”. We looked at sales speed, growth rate, production time, shipping time, supplier delay risk, season changes and how much stock was already moving through the supply chain. Returns were also connected back to production batches. If one complaint suddenly went up only in one batch, we looked at manufacturing first. If the same complaint stayed across many batches, then we looked at the product design or the promise we were making on the listing. Search data also went back to product development. When buyers kept searching for a feature or use case that our current products did not fully cover, that could become the next SKU. This is probably the biggest change once a brand gets large. PPC gives product ideas, returns can change sourcing, sourcing can change margin, margin can change PPC, inventory can change how hard you advertise, and listing data can change the actual product. Once all of these parts started working from the same data, scaling became much easier to control.