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RecipeBuilt with Wilift11 chapters

The Amazon Job

Finding French DNVB founders leaving Amazon money on the table

Built with Wilift to reach the founders of established French DNVB brands — the ones with a strong Shopify storefront who are either fumbling Amazon or ignoring it entirely. Here is the full plan, step by step, dead ends included.

01The target

Who the campaign is aimed at

French DNVB brands selling physical products. Established businesses only — no early-stage.

Revenue
€250k–5M (sweet spot €300k–3M)
Team size
1–15 people
Tech stack
Shopify + Klaviyo + Meta Ads
Contact
Founder or CEO — nobody else
02The two profiles

The segments it splits into

A

On Amazon, badly

Already selling on Amazon but under-exploiting it: weak listings, unprofitable PPC.

B

Not on Amazon at all

Strong Shopify brand, dependent on ads for every sale — nothing on the marketplace.

03Find companies

Start from the register, not from a list you bought

There is no clean database of French DNVBs. There is a business register with every company in the country in it, and there is the open web. So the first pass is a wide net: everything in the right revenue band and the right activity codes out of the register, plus a web sweep for brands that look the part — own storefront, own product, sold direct.

Wide is deliberate. Anything filtered out here is invisible for the rest of the campaign, and a brand missing from the first pull can never be recovered by a smarter step six. Precision comes later and it is cheap later. Coverage is only available now.

Dead end

Scraping Ankorstore and Maison&Objet automatically didn't survive contact with reality — both fight back hard. Rather than sink a week into it, 20 real brands were seeded by hand from Ankorstore to prime the pipeline and give the enrichment something to pattern-match against.

04Research each one

A legal name is not a brand

The register hands over an entity: a name, a number, an address. It does not hand over the storefront, and half the time the legal name has nothing to do with the brand on the packaging. So each candidate gets resolved — find the real website, confirm it is live, confirm the company behind it is the one the register named.

Then the question that decides everything downstream: is there an Amazon presence, yes or no. Not inferred from the brand's marketing, proven — the listings, the seller behind them. This single fact is what splits the campaign in two a few steps later, so it is worth being slow about.

The trap

A brand whose products are on Amazon via a distributor looks identical to a brand selling there itself, until you read the seller name. Same listing, opposite conversation.

05Check the tech stack

Shopify plus Klaviyo is the qualifier

The campaign is not aimed at French e-commerce. It is aimed at brands built the DNVB way: Shopify for the store, Klaviyo for the retention, Meta for the acquisition. That trio is a proxy for a whole way of operating — one that leans on paid social for nearly every order and feels it in the margin every month.

A stack detection pass reads the site and confirms it. A brand on a custom stack, or one that was marketplace-first from day one, is not a bad company. It is just not this campaign, and it drops out here rather than absorbing research budget for three more steps.

06Pull registry data

Headcount and directors, straight from the source

Back to the register, this time for the details that decide fit and reachability: legal identity, headcount, filing history, and the list of company directors.

Headcount is the real filter. One to fifteen people means the founder still runs growth and still reads their own inbox. Past that there is a marketing team, the founder delegates the channel, and a founder-to-founder email lands with someone who never asked for it.

The director list matters for a different reason: it is the only trustworthy answer to who actually runs this company. LinkedIn will cheerfully offer three candidates. The register names one.

07Sort into Profile A or B

One question, two campaigns

Everything so far was qualification. This is segmentation, and it is where the recipe earns its keep. Profile A already sells on Amazon and does it badly — thin listings, PPC that burns more than it brings back. Profile B is not there at all: a strong Shopify brand paying Meta for every single order.

Same size, same stack, completely different conversation. A gets shown the money already on the table and left there. B gets shown a channel they don't have. Send the wrong one and the email isn't merely ignored, it is wrong in a way the founder notices.

Ambiguous cases don't get a coin flip. They go back for a second and a third look, and if they are still unclear they are dropped. An unclear segment can only produce an unclear email.

08Reject the bad fits

Say no while it's still cheap

An explicit rejection gate. Everything off-target — wrong size, wrong model, wrong country, a retailer wearing a brand's website — drops out here, before it costs anything downstream.

The reason this is its own step and not a filter buried in the last one: the rejections are the part worth reading. If a whole category is being cut for the same reason every time, the sourcing query was wrong, not the companies. That's a fix at step one, and you only see it if the discards are kept somewhere you can look at them.

09Match against the ICP

Durable fit, not a lucky snapshot

A final pass, run per profile, asking whether the fit actually holds. A brand that crossed €300k once on the back of one good Black Friday is not a €300k brand. A company that looks like a DNVB but makes its living from wholesale is not the target, however good the website is.

This is the most expensive check per company, and it runs on the smallest list. That is not an accident, it is the whole reason for the order of the steps: run it first and it costs fifty times more to answer the same question.

10Find the founder

Founder or CEO, nobody else

Only now does a human being get attached to a company. LinkedIn search turns up the candidates, an AI judge picks the profile whose title and tenure genuinely mean founder, and the register's list of directors is the referee — when the two disagree, the register wins.

No heads of growth, no marketing managers, no info@ addresses. The offer is a founder-level decision about a sales channel, so a founder-level inbox is the only place it makes sense. A campaign that quietly loosens this rule to hit volume is a different campaign, with worse numbers.

11The funnel

Where the list thins out

Worked example — modelled figures

The numbers below are a worked example, not the Wilift counts: a 12,000-company pull put through the same eight steps. The ratios are the argument, not the digits. Every step throws work away, and that is the point — the survival rate is what to watch, never the volume.

StageInKeptSurvival
Registry + web sweep12,0003,24027%
Real DNVB?3,2401,51047%
Stack detect1,51069046%
Profile A / B69040258%
ICP match40226867%
Founder found26821480%
Registry + web sweep
27%
Real DNVB?
47%
Stack detect
46%
Profile A / B
58%
ICP match
67%
Founder found
80%
What a wide pull gives you
12,000
companies to research
What the recipe hands over
214
founders worth an email

That ratio is the whole recipe: about 56 companies discarded for every founder contacted. On a real market the digits move, the shape doesn't. Do that by hand and it is a quarter of somebody's year; do it once as a pipeline and it reruns every month on the same market for the cost of the enrichment.

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