# Arthur Duboks

Arthur Duboks works on go-to-market (GTM) engineering: building and repairing systems that connect sales and marketing tools, automate lead workflows, and improve conversion tracking. Their posts focus on making these systems work reliably with real customer data and existing CRMs.

- Profile: https://b2binfluencer.marketing/creators/arthur-duboks/
- LinkedIn: https://www.linkedin.com/in/arthur-duboks
- LinkedIn followers: 999
- YouTube: https://www.youtube.com/channel/UCM97o5LWN3f9o0r247VGf8w
- Topics: paid-ads, revenue-operations, meta-ads, marketing-automation, attribution
- Based on 19 dated posts, latest 2026-10-09

## Summary

They are most useful for practical GTM engineering explanations about tracking, CRM wiring, and automation with real data.

- Best for: This fits a B2B SaaS company whose paid-social and pipeline reporting disagrees with what sales sees.
- Not for: This does not fit consumer or DTC brands seeking high-reach sponsored video with a rate card.
- Check first: Confirm whether they accept sponsorships or paid work and how to price and book it.

### Takeaways

- A model wrote 2024 into a CRM from an undated out-of-office reply.
- A Salesforce task field only accepted preset values, so generated tasks said Other.
- Meta attribution change can lower reported ROAS without campaigns getting worse.

## Main ideas

- **Most GTM stacks are disconnected, not under-equipped**: Arthur Duboks argues that companies buy tools faster than they connect them. Procurement is easy to budget; integration work is not, so it goes undone and the company shops again. They recommend mapping what talks to what before buying anything else: the gap is usually a couple of integrations wide, not a missing platform.
- **The skill is knowing what not to automate**: Arthur Duboks argues that automating everything makes a demo look good and a system fragile. Deciding what a person still handles is a large part of the job. The useful version of AI in a GTM system is narrow: it handles research and drafting that people do by hand, does it in minutes, and leaves anything uncertain to a human.
- **The prompt is the easy part; the code around it is the job**: Arthur Duboks argues that prompts are the most copyable part of a GTM build and the least likely cause of failure. Failures live in the configuration around the model: a webhook sending POST while the endpoint expects GET, a CRM node reporting success after dropping a field, a date parsed without a year. The remedy is code that checks what the model did, not a better prompt, and testing on the client's real CRM rather than a clean demo account. That CRM carries rules nobody can redesign.
- **Ad-platform numbers undercount conversions, so own the tracking**: Arthur Duboks argues that ad-platform dashboards miss a real share of conversions, and that brands should own the tracking infrastructure rather than trust the browser pixel. Safari's tracking prevention, third-party cookie blocking and ad blockers stop browser-side events; sending them from the brand's own server through Meta's Conversions API creates a first-party path. For lead generation, they describe importing closed deals from the CRM back to the ad platform so campaigns optimise toward revenue instead of form fills. A related post explains Meta's move to count only link clicks for click-through attribution, with likes, shares, comments and saves in a separate engaged-view bucket.
- **A build stalls on people and permissions, not technology**: Arthur Duboks argues that GTM projects usually stall on procurement and ownership. Buyers should ask who can spend and what stays on their own plate. Their list of client-side work that never appears in a proposal or timeline includes half-duplicated leads already in the CRM, deciding what a good account looks like, someone answering the phone when the system is unsure, and saying no to requests that break the logic. To judge the build, they say to bring in the most technical person in the room before signing, not at handover.
- **The next market is repairs and commercial maturity**: Two posts look ahead to where the work is going. Arthur Duboks predicts the best-paid GTM engineering work next year will be repairs: building became cheap, people move on, a vendor raises prices or a CRM field is renamed, and flows break with nothing written down. They compare the work to WordPress sites and advise learning to read other people's builds rather than collecting another certificate. A second post describes private equity shifting its post-close focus from the CFO to the CRO, with B2B software judged on pipeline velocity, CAC payback, net revenue retention and sales process repeatability rather than product or TAM slides.
- **Showing more choice loses customers**: One post steps outside the GTM theme to argue that showing more options can lose customers rather than win them. Arthur Duboks describes a curve where too few choices suppress buying and too many cause people to freeze, with a working range of roughly three to ten for most products. They recommend reducing what is visible at once rather than removing products: lead with the best three and let the rest exist without making people wade through them.

## Work to read

- [“You spend $8K a month on Meta ads”](https://www.linkedin.com/posts/arthur-duboks_marketing-paidsocial-metaads-activity-7422715172059930625-0Rf8)
- [“Meta changed its attribution. Your ROAS is about to drop.”](https://www.linkedin.com/posts/arthur-duboks_marketing-paidsocial-metaads-activity-7440094677644677120-zzsg)
- [“Most GTM engineers are shipping a prompt in a trench coat”](https://www.linkedin.com/posts/arthur-duboks_most-gtm-engineers-are-shipping-a-prompt-activity-7510398712955785216-4mnB)
- [“You can spot a junior GTM engineer by what they automate”](https://www.linkedin.com/posts/arthur-duboks_you-can-spot-a-junior-gtm-engineer-by-what-activity-7510760771442647040-sgBL)
- [“The CRM is where most GTM builds go to die”](https://www.linkedin.com/posts/arthur-duboks_the-crm-is-where-most-gtm-builds-go-to-die-activity-7512934895975825409-eEz2)

## Brand relationships

- **Meta** (mention)
- **Salesforce** (mention)
- **Apollo** (mention)
- **Anthropic API** (mention)
- **PredictLeads** (mention)

## Partnerships

No partnership, sponsorship, or brand-collaboration formats are described in Arthur Duboks's posts. The 13 posts are practical write-ups about go-to-market engineering — attribution, CRM wiring, and automation failure modes — with no media kit, rate card, sponsored-post offering, or way for brands to inquire about working together. The only contact surface shown is the LinkedIn profile itself. Before planning a paid engagement, confirm directly whether they accept sponsorships or paid work and what terms apply.

## Alternatives

- Samuel Adeitan: A better choice when the hard part is data plumbing at agency or store scale, rather than CRM pipeline work. Samuel Adeitan's two posts work through server-side event routing for a holiday peak: making browser and server events share the same event IDs so purchases are not counted twice, passing new-versus-returning customer tags into the bidding payload, and matching settled orders against ad-platform click data to see actual cash collected. They then address the economics of that work: pay-per-task platforms burning six figures of tasks on a bulk import versus a self-hosted workflow engine, small scripts for string and deduplication work, and a queue to keep spikes from crashing downstream limits. For a team whose pipeline cannot scale past prototyping, or planning a pre-holiday audit of tracking layers, that is closer to the job than the CRM and automation failure modes Arthur Duboks writes about.
- Bivas Kumar Roy: A better choice when the symptom is a numbers disagreement and the question is whether the cause is tracking, attribution, or both. Bivas Kumar Roy sets out a diagnostic order for exactly that case—a report showing 82 purchases in the store and 103 in the ad platform—and walks through comparing attribution windows, checking whether Pixel and Conversions API sent the same purchase with matching event IDs, auditing whether Purchase fires more than once, testing payment redirects, and reviewing consent states for EU traffic. The distinction is useful to buyers: fixing duplicate events will not solve an attribution difference, and changing attribution settings will not fix broken purchase tracking. This approach helps identify the fault before paying anyone to fix it.
- Muhammad Anas Raza: A better choice when the real question is lead quality and paid-funnel economics rather than measurement. Muhammad Anas Raza's central case is a campaign producing 150 leads at $3 each that the sales team cannot use. They deliberately pushed cost per lead to $16 by switching off the one-click auto-fill form, adding mandatory qualification gates for budget and timeline, and requiring manual phone entry. Lead volume fell by around 70 percent, and the calls improved. The follow-up post states the position plainly: cheap leads ruin businesses faster than expensive ones, and a bad offer breaks a good ad. For a brand whose dashboards look fine but whose pipeline does not convert, that diagnosis is more direct than tracking repair.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/