Interview with Jimi Patel, Director, eStore Factory LLC

This interview is with Jimi Patel, Director, eStore Factory LLC.

To get us started, how do you describe your role today as a Director in computer software and co‑founder of an Amazon‑focused agency serving ecommerce brands?

I’m Jimi, co‑founder of eStore Factory, an Amazon agency I started in 2014 that now works with sellers across the US, UK, Australia, and Canada. We’re SPN- and SPP-certified, so we work directly inside Amazon’s partner ecosystem, not just from the outside looking in.

Alongside the agency, I’ve built a few SaaS tools born out of problems I kept hitting with clients: SellerQI for account health and reimbursements, BidBison for Sponsored Ads automation, and Refunzo for FBA reimbursement recovery.

I’m happy to talk Amazon PPC, account health, reimbursements, marketplace strategy, or the software side of ecommerce tooling. Ten-plus years in the trenches, so ask me the practical stuff, not just theory.

Looking back, what pivotal decisions took you from SEO and web development into building SellerQI and BidBison and leading eStore Factory?

I started out doing SEO and web development, which is really problem solving with a technical lens. When I moved into the Amazon space and started eStore Factory in 2014, that background ended up mattering more than I expected. Amazon’s ecosystem runs on data and APIs, and having a dev mindset meant I wasn’t just executing the playbooks other agencies used. I could see where the actual bottlenecks were.

The shift to building SellerQI and BidBison wasn’t a single decision. It was more that I kept hitting the same wall. We’d find account health issues or PPC inefficiencies for clients, and doing that analysis by hand for every account didn’t scale beyond a certain point. So I started building internal scripts to speed it up. At some point, I realized the tools I was building for my own team were solving a problem every Amazon seller has, not just my clients. That’s when it stopped being an internal shortcut and became a product.

Looking back, the real pivot was deciding not to keep it as one agency doing one thing well, but to split my time between service work and building software that could scale beyond what any single agency could do manually.

From there, when you onboard a new Amazon client, what is the first revenue‑leakage check you run that most sellers miss?

The one that almost every seller misses is FBA reimbursements. Amazon owes sellers money constantly—for inventory lost or damaged in their warehouses, for weight and dimension fees charged incorrectly, and for customer returns that were refunded but never actually put back into sellable inventory. Most sellers have no idea this is happening because Amazon doesn’t proactively tell them. You have to go dig for it.

So the first thing I run on any new account is a reimbursement audit going back as far as Amazon’s data allows. It’s almost never zero. I’ve seen accounts leaking five figures a year in unclaimed money, just sitting there because no one looked.

This is actually why Refunzo exists. I got tired of doing this audit by hand for every client, cross-checking inventory reconciliation reports, removal orders, and returns data that don’t talk to each other. At agency scale, across hundreds of SKUs per account, manual reimbursement hunting just doesn’t work. So I built the tool to do what my team was doing by hand—faster and without human error.

SellerQI came from the same instinct, but for account health broadly. Reimbursements are one leak, but listing errors, suppressed ASINs, and policy violations sitting quietly in the background all bleed revenue too, and most sellers only notice once sales have already dropped. SellerQI runs that full diagnostic automatically instead of waiting for something to break first.

Both tools started the same way. They were not products I set out to build, but internal fixes for a problem I kept hitting with clients that turned out to be universal.

Focusing on rankings, what are your three most reliable levers for moving a product from page two to page one on Amazon while protecting margin?

Three levers, ranked in this order because of how compounding they are.

1. First, keyword indexing done properly — not just stuffed. Most sellers treat backend search terms and bullet points as an afterthought, but Amazon’s A9 algorithm ranks you on relevance before it ranks you on sales velocity. If you’re not indexed for the exact terms buyers search, none of the other levers matter. I’ve seen listings jump from page three to page one just from fixing indexing gaps nobody had checked in over a year.

2. Second, conversion rate on the listing itself. This is the one people skip because it feels like design work, not SEO work. But Amazon rewards listings that convert, because a sale is the strongest ranking signal there is. Images that actually answer buyer questions; A+ content that closes hesitation; review velocity that doesn’t look manipulated. Fix conversion rate and you often see organic rank move even before you touch ad spend.

3. Third, and this is where margin protection actually comes in: targeted PPC used as a ranking accelerant, not a permanent crutch. Run tight, high-intent campaigns to push a product into the top of page one for its money keywords, hold that position long enough for organic sales velocity to catch up, then pull spend back once the algorithm has “learned” the product belongs there. The mistake sellers make is running ads forever to prop up a rank that never becomes organic. Done right, PPC is temporary scaffolding, not a subsidy you pay indefinitely.

The order matters. Ads without indexing waste spend. Ads without conversion waste spend twice over: once on the click and again when the algorithm deprioritizes you for low CVR. Fix the first two, and the third lever costs a fraction of what it would otherwise.

Turning to profitability, how do you model ROI on Amazon at the SKU level using contribution margin and TACoS in a way that drives weekly bidding and catalog decisions?

TACoS alone doesn’t tell you if a SKU is actually profitable. It only indicates spend efficiency. I pair it with contribution margin: revenue minus COGS, referral fees, FBA fees, storage, and prep costs. That’s the real number.

Every week, I look at both together. High-margin SKUs can absorb a higher TACoS, so I let bids run hotter there. Thin-margin SKUs get a hard TACoS ceiling, because, past a point, you’re paying to lose money per unit.

For catalog decisions, if a SKU always needs a high TACoS just to hold rank and margin never improves, that’s not a bidding fix; it’s a sign that the SKU or its price point needs to change. SKUs with strong margins and falling TACoS receive more investment because that’s organic momentum building on its own.

It’s less about hitting one ACoS target and more about ensuring every ad dollar is buying rank that eventually pays for itself.

Expanding beyond ads, how do you structure profitable external‑traffic programs into Amazon with Attribution and the Brand Referral Bonus?

External traffic only makes sense if youre capturing the data and getting paid twice: once on the sale and once via the fee rebate. Thats where Attribution and the Brand Referral Bonus come in.

The first step is to tag everything through Amazon Attribution before a single dollar of external spend goes out. Influencer links, social posts, affiliate placements, email campaigns—all of it gets an Attribution tag. Without this, youre driving traffic blind and you lose the ability to prove whats actually working, which means you cant optimize and you cant qualify for the rebate.

Second, structure the channels around what the Brand Referral Bonus (BRB) actually rewards. The program gives back a percentage of the referral fee on sales driven by qualifying external sources, so the math changes depending on channel. Influencer and affiliate traffic tends to convert well because theres already trust built in, so thats usually where I put the first dollars. Paid social works too, but it needs a landing experience thats tight, because cold traffic converts worse and the rebate doesnt offset a bad conversion rate.

Third, track it like a separate P&L line, not folded into regular ad spend. Contribution margin per SKU still applies here, but now you subtract the external spend and add back the referral rebate before deciding if the channel is worth scaling. A channel can look expensive on the surface and still be profitable once the rebate hits, or look cheap and still be a loser if attribution shows most of that traffic wasnt incremental, meaning it would have converted anyway through organic or ads.

The mistake most sellers make is running external traffic as a branding exercise without tagging it properly. Then they cant prove incrementality, cant claim the rebate reliably, and end up treating it as a cost center instead of what it actually is: a second revenue stream sitting on top of the same catalog.

With AI‑mediated discovery (e.g., Rufus) reshaping search, how have you changed your use of attributes, copy, and A+ content to win recommendations?

Rufus doesn’t shop like a keyword search does. It reads and reasons, so a listing that used to rank fine with stuffed keywords can now be skipped entirely because the AI can’t confidently answer a buyer’s actual question from it.

So the shift for me has been writing for comprehension first, keywords second. Attributes need to be complete and specific, not just filled in to check a box. If Rufus is answering “which of these is good for sensitive skin?” or “which one works for a small kitchen?”, the listing needs that answer sitting in structured data, not buried in a paragraph the algorithm has to guess at.

The copy changed, too. Bullet points used to be written for skimming humans. Now I write them so they hold up as a direct answer if Rufus quotes or paraphrases them back to a shopper. That means specific claims, not vague ones. “Reduces prep time by half” beats “convenient and easy to use,” because the AI has something concrete to relay.

A+ content is where this matters most, because that’s often the richest source of unstructured detail Rufus can pull from. I’ve shifted A+ modules to directly answer comparison and use-case questions, since that’s exactly the kind of question shoppers now ask Rufus instead of typing a search term. If a buyer would ask “does this fit a gas or electric stove?”, that answer needs to live somewhere Rufus can find it, not assumed as obvious.

The bigger change in mindset is that we’re not just optimizing for a search algorithm anymore. We’re optimizing to be the source an AI trusts enough to recommend. That’s a different bar than ranking on page one used to be.

Bringing in your photography interest, what image testing or storytelling technique has most improved click‑through and conversion on your listings?

The biggest lift I’ve seen isn’t from one clever image; it’s from testing image sequence—meaning what a buyer sees first, second, third as they scroll. Most sellers front-load a clean product shot and save context for later. I’ve had better luck flipping that: lead with the shot that answers the biggest hesitation for that category, then follow with the clean hero shot, then lifestyle and scale context.

For conversion specifically, the single change that’s moved numbers most consistently is adding a true scale reference image—something showing the product next to a hand, a common object, or in the actual use environment. Buyers hesitate about size more than almost anything else on a page, and many return complaints trace back to size assumptions that a listing never corrected.

For click-through, it’s less about the image being pretty and more about it answering the search intent instantly. If someone searched a specific use case, the thumbnail needs to visually signal that use case in under a second, not just show the product in isolation. That’s the test I run most: swap the primary thumbnail to reflect intent rather than just aesthetics, and measure CTR before touching anything else on the listing.

Finally, for brands hiring an Amazon consultant or agency, what one vetting question best predicts whether that partner will protect margin and grow rank?

Ask them to walk you through a time when they told a client not to spend more, or not to launch something, because the math didn’t work. Not a win story — a restraint story.

Anyone can show you a case study where sales went up. That tells you they know how to spend a budget. What actually predicts whether they’ll protect your margin is whether they’ve ever pushed back on a client who wanted to chase rank at a loss, or scale a SKU that was quietly bleeding money. If they can’t answer that, or the answer is vague, it usually means they’re optimizing for looking active rather than for your P&L.

The agencies and consultants who protect margin long-term are the ones who’ll tell you no early, before you’ve spent the money to find out the hard way.

Thanks for sharing your knowledge and expertise. Is there anything else you'd like to add?

Just that most of what I’ve talked about here didn’t come from theory; it came from running an agency and hitting the same walls over and over, until building a tool made more sense than doing it by hand again. That’s true for SellerQI, BidBison, and Refunzo, and it’s true of how I think about ranking, PPC, and margin protection generally.

Happy to go deeper on any of these topics for an article, whether that’s Amazon algorithm changes, reimbursement recovery, PPC efficiency, or what AI-driven discovery like Rufus means for sellers going forward. I’d rather get into specifics with real numbers than stay high-level, so if a question needs an actual example or data point, just ask.