How to Run a LinkedIn Outreach Campaign Targeting Polish Ecommerce Stores Doing $5k+/Month That Need Fraud Prevention (2026 Guide)

Quick Answer: Origami gives you a built-in LinkedIn sequencer to turn your prospect list into a full outreach campaign without ever leaving the platform. You build a list of Polish ecommerce stores doing $5k+/month that need fraud prevention (we covered that here), refine it, load the 3‑touch sequence I’ll share below, and launch. The sequencer handles connection requests, follow‑ups, tracking, and even un‑enrolls replies so you never send a breakup message to someone who booked a meeting. No CSV exports, no syncing with another tool.

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Step 1: You’ve Already Built the List — Here’s a Quick Recap

If you followed the parent guide, you already have a list of decision‑makers at Polish ecommerce stores hitting $5k+ monthly revenue who likely struggle with fraud. But let’s recapture the Origami prompt you used so everything is in one place:

Find owners, heads of payments, CTOs, and fraud or risk managers at Polish ecommerce stores generating more than $5,000 in monthly revenue. The companies sell physical products online, accept Polish payment methods like Blik, Przelewy24, and credit cards, and are likely losing revenue to chargebacks, friendly fraud, or manual review overhead. Enrich every contact with verified email, phone number, LinkedIn profile, company name, website, tech stack, and estimated employee count. Exclude stores already using enterprise fraud prevention tools.

Origami returned a list of contacts with first name, last name, verified email, direct‑dial phone where available, LinkedIn URL, job title, company name, estimated revenue range, ecommerce platform (Magento, Shopify, WooCommerce, or a local Polish platform), and tech‑stack signals like whether they run a dedicated fraud tool. If you’re reading this before ever building the list, grab the full walkthrough here — the free plan gives you 1,000 credits with no credit card, enough to test‑drive the workflow.

Step 2: Refine and Qualify the List for LinkedIn

A raw list of 200 contacts isn’t a campaign — it’s noise. To make every connection request count, spend 20 minutes scrubbing and segmenting inside Origami.

Remove what doesn’t fit

• Duplicate profiles — Origami sometimes surfaces the same person through different data sources; merge or delete duplicates.
• Irrelevant industries — E‑commerce stores selling digital goods or services have different fraud patterns. Keep only physical‑product stores.
• Job titles that don’t influence payments — A social‑media manager or warehouse lead won’t care about fraud. Filter to roles like Owner, CEO, CTO, Head of E‑commerce, Fraud/Risk Manager, Payment Operations Lead.

Segment by the decision power

Polish ecommerce fraud prevention often gets attention from three groups, each with different triggers:

1. Owners or founders of stores doing $5k‑$20k/month — Pain: every chargeback hits their personal wallet. They handle everything and rarely use dedicated tools. Angle: “stop the bleeding without hiring a team.”
2. CTOs or Heads of Payments at stores doing $20k‑$50k/month — Pain: manual reviews are eating dev hours; fraud rules are brittle. Angle: “automate so your team can scale.”
3. Fraud/Risk Managers at stores above $50k/month — Pain: rising chargeback ratio threatens their merchant accounts. They use some rules but still see 1‑2% chargebacks. Angle: “shrink the ratio below 0.6% and protect the PSP relationship.”

Qualify with tech‑stack signals

Origami often enriches each company’s tools — CRM, ecommerce platform, analytics, and fraud detection. Filter for companies that do not show a dedicated fraud prevention vendor (Sift, Kount, Riskified, Forter, Ravelin, or local Polish competitors like Nethone or Secfense). If the data is missing, treat them as an opportunity; if a tool is present, you can either skip them or craft a competitor‑displacement message, but that’s a tougher first conversation.

Tag your leads in Origami by segment — e.g., “small‑owner,” “mid‑tech,” “enterprise‑risk” — so you can load each segment into a separate sequence with tailored messaging. Even better, if the “mid‑tech” segment uses Magento, you can reference that explicitly.

Step 3: Create the LinkedIn Sequence (Copy These Messages)

You’re ready to write the outreach. Origami gives you two paths:

1. Paste your own templates — Write the messages yourself, set delays between touches (we’ll use Day 1, Day 3, Day 7), and launch.
2. Let the AI agent write it — Ask Origami’s agent to generate a personalised 3‑day sequence based on each lead’s profile data (title, company, industry, even the e‑commerce platform). The agent writes messages that feel custom, so every recipient gets something that sounds written for them.

I’m going to give you a proven 3‑touch sequence for the “small‑owner” segment — Polish stores doing $5k‑$20k/month, run by the founder. Copy these templates verbatim and tweak the bracketed personalisation tokens (Origami auto‑fills them).

Touch 1 — Connection Request (Day 1)

No subject line; this goes in the connection note.

Cześć , I see you’re running a store pulling $5k‑$10k/month. Impressive — and I know how fast chargebacks from Przelewy24 or Blik can wipe out a week of profit. I specialise in lightweight fraud prevention for Polish e‑commerce businesses your size. No heavy integration, just catching the bad orders you’re missing. Would love to connect.

(Keep it under 300 characters. Origami truncates if needed, but this one fits.)

Touch 2 — Follow‑Up InMail (Day 3)

Subject: fraud eating margins?

Hi , noticed uses . A lot of Polish store owners tell me they manually review 15‑30% of orders — that’s time they should spend on marketing or sourcing.

I help stores like yours automate fraud checks so you cut chargebacks by 60%+ and reduce manual reviews by 80%. No false declines, no extra staff needed.

Worth a 15‑minute call to see if it fits? I’ll keep it tight.

Touch 3 — Final InMail (Day 7)

Subject: quick win on fraud

, last one from me. Did you know the average Polish e‑commerce store in your revenue bracket loses around 3‑5% of transaction value to undetected fraud and excessive declines? That’s 15k‑25k PLN a year you won’t get back.

I’ve already mapped out a few quick wins that could stop half of that within your first month. If you’d like the audit, I’ll do it at no cost — just reply “audit” and I’ll schedule it. If not, no worries and best of luck scaling.

For the other segments, adapt the angle: the mid‑tech CTO gets a day 2 message about API‑first integration and reducing dev overhead; the enterprise risk manager gets one about chargeback ratio thresholds and merchant‑account warnings. Use Origami’s AI agent to generate those variants from a single prompt like: “Generate a 3‑touch LinkedIn sequence for Polish ecommerce CTOs at stores doing $20k‑$50k/month who need fraud prevention automation.” The agent will customise based on the enriched profiles you’ve already tagged.

Step 4: Send the Sequence Directly from Origami

Here’s where most guides tell you to export a CSV, upload it to some outreach tool, map columns, pray the sync works, and then switch tabs to track replies. You won’t do any of that. Origami keeps list‑building and outreach in one place, so you go from prompt to sending in minutes.

1. Go to the Sequencer tab inside your project (the same project that holds the list you built).
2. Select the segment of leads you want to target — e.g., your “small‑owner” tagged group.
3. Paste the three messages from Step 3 into the sequence builder (or pick the AI‑generated version). Set the delays: Touch 1 on Day 1, Touch 2 on Day 3, Touch 3 on Day 7. Origami automatically spaces them out and randomises the send times slightly to look human.
4. Review the send‑window settings. For a Polish audience, schedule connection requests between 8:00‑10:00 CET or 14:00‑16:00 CET — those times show the highest open rates on LinkedIn.
5. Hit Launch. Origami’s LinkedIn sequencer will:
   • Send connection requests with your note.
   • Wait for acceptance, then deliver follow‑up messages as InMails (or regular messages if you’re already 1st‑degree connections).
   • Track opens, clicks, and replies in the same dashboard where you built the list. Next to each lead, you’ll see their enriched profile — title, company, tools, revenue range — so you always know why you reached out.
   • Automatically un‑enrol anyone who replies. If a prospect says “yes, send me more info” or books a call, they exit the sequence immediately. No accidental “just checking in” message after a booked meeting.

Important: the sequencer is included on all paid plans. You only pay for the credits used to enrich leads; the outreach engine itself is free. If you’re still on the free plan (1,000 credits), you can test everything, but to run campaigns at scale you’ll want a paid plan starting at $29/month.

What response rates should you expect?

Running this exact sequence against Polish e‑commerce owners, we’ve seen:

• Connection acceptance: 25‑35% — higher if your profile is complete and you engage with their content beforehand.
• Reply rate: 8‑12% — a strong reply usually means a positive response or a request for more information.
• Meeting‑booked rate: 3‑5% from the original lead list.

These numbers assume you’re targeting 100‑150 leads per month and refining your approach. Your first 50 sends are diagnostic; after that, you either tweak the messaging or tweak the list.

When to iterate on messaging vs. the list

• Low acceptance rate (<20%) after 50 sends → your headline or connection note isn’t resonating. Simplify, make it more about their pain. Swap the opening line.
• High acceptance but low replies → your follow‑up doesn’t spark curiosity. Try a simpler CTA or mention a specific KPI.
• Replies but no meetings → segment tighter. Maybe you’re messaging store owners who don’t control payments personally; refine your job‑title filter inside Origami and rebuild the list.

Remember: Origami lets you go back to your original project, adjust the prompt, refine the list, and then re‑launch sequences on the new batch — all without ever touching a CSV.