# Mickaël Aubard

Mickaël Aubard shares practical analysis of performance marketing, including cross-channel ad benchmarks, campaign strategy, and tools that help teams manage advertising across platforms.

- Profile: https://b2binfluencer.marketing/creators/micka-l-aubard/
- LinkedIn: https://www.linkedin.com/in/mickaelaubard
- LinkedIn followers: 10,014
- Median of last 6 posts: 22 reactions, 4 comments (checked 2026-11-02)
- Topics: artificial-intelligence, paid-ads, ai-in-marketing, performance-marketing
- Based on 8 dated posts, latest 2026-10-06

## Summary

Mickaël Aubard is most useful for explaining cross-channel paid ad benchmarks and operational campaign choices.

- Best for: B2B ad tech brands commissioning paid ads benchmark reports with named performance marketing voice.
- Not for: Consumer DTC paid creator placements, lifestyle reviews, or reach-based performance guarantees.
- Check first: Confirm sponsorship offer, rates, audience analytics, and whether claims are independently verified.

### Takeaways

- Cross-channel CTR post names sources and notes Snapchat click definition changed reported clicks.
- Lower-funnel-only Snapchat spend fades because you run out of people.
- Google Performance Max may source videos from advertiser pages or social accounts by default.

## Main ideas

- **A benchmark is only useful if you name the sample and check the definition**: Aubard treats channel benchmarks as reference points that need context, not headline numbers. The CTR post gives a median for each channel alongside the sample size or data provider, and it flags where a measurement change makes a comparison misleading. The practical point for a reader is that a platform's number can shift because the platform changed what it counts, not because performance moved.
- **Lower-funnel-only spend fades because you run out of people**: Aubard's takeaway from a Snap office talk by Lars van Egmond is that the problem is not the channel but the audience: starting with lower funnel only works until the same known people have been retargeted repeatedly. Van Egmond's reframe, relayed in the post, is that full funnel is not brand versus performance but brand fuelling performance, because incremental reach comes from the algorithm going after new people. Aubard passes on a starting budget split and a peak-season checklist built on that idea. The proof points are attributed to outside research and the talk, not to Aubard's own controlled tests.
- **Read the platform defaults before you trust them**: Aubard's method on platform automation is to explain what the feature actually does, say where it is switched on, and then give a concrete action. The argument is that a default setting can expose assets a brand or creator never intended to run as paid creative, so the check belongs before scaling, not after.
- **AI pays off when it encodes your own rules into repeatable commands**: Rather than treating AI as a general writing tool, Aubard frames it as a way to compress recurring media-buying routines into named commands that already know the account's targets and rules. The distinction he draws is between an adequate media buyer and an elite one: the elite version keeps human approval on budget moves and re-checks the numbers before anything goes to a client.
- **The blocker to testing a new channel is rebuild time, not the creative**: Aubard repeatedly argues that teams already suspect their winning ads would travel to another platform, and that what stops them is the manual work of rebuilding campaigns. The fix he describes is import automation that maps objectives, structure, targeting and budgets across, so the test can happen in minutes. The adoption numbers he cites are activity signals rather than performance comparisons.
- **In-person partner conversations are a working research method**: Several posts treat a coffee or an office visit as the point of the trip rather than a courtesy. Aubard uses those meetings to pick up what agencies and platform teams are seeing, and says plainly that sitting across the table beats a video call. This is the least metric-driven of the recurring habits in the material, but it explains how the other posts get their field examples.

## Work to read

- [What a good CTR looks like in 2026 across 8 channels](https://www.linkedin.com/posts/mickaelaubard_what-a-good-ctr-looks-like-in-2026-across-activity-7511364754326876161-fAh3)
- [Your Snap campaigns didn't stop working. You ran out of people.](https://www.linkedin.com/posts/mickaelaubard_your-snap-campaigns-didnt-stop-working-activity-7513176285351870464-TtZ7)
- [PMax can now run videos you never uploaded. But it's on by default.](https://www.linkedin.com/posts/mickaelaubard_pmax-can-now-run-videos-you-never-uploaded-activity-7520801869112348672-yXqu)
- [Claude can make you a decent media buyer. But these 11 skills make you elite.](https://www.linkedin.com/posts/mickaelaubard_claude-can-make-you-a-decent-media-buyer-activity-7521893905051611136-3aSl)
- [Just left the Datashake office in Paris](https://www.linkedin.com/posts/mickaelaubard_just-left-the-datashake-office-in-paris-activity-7521539720544239616-rCg8)
- [Bonjour from Snap Inc.'s Paris office](https://www.linkedin.com/posts/mickaelaubard_bonjour-from-snap-incs-paris-office-activity-7522995803414794240-3x3g)

## Brand relationships

- **AdManage** (disclosed_partnership)
- **Snap Inc.** (disclosed_partnership)
- **Datashake** (client)
- **ASight** (mention)

## Partnerships

The available material contains no sponsorship offer, media kit, rate card, or paid-partnership listing for Mickaël Aubard. The partnership activity that does appear is company-level and platform-facing: Aubard posts about AdManage, the ad-management tool whose users and features they describe, and about its working relationships with Snap, the Paris agency Datashake, and ASight. Those posts cover automated Meta-to-Snapchat campaign import built for AdManage users, events being planned with Snap, and agencies running thousands of campaigns through the platform. For a buyer planning spend, the practical consequences are concrete. There is no published format menu, deliverable specification, or rate to build a plan around, and no audience breakdown tied to a sponsored placement. The benchmark and platform content Aubard publishes does name third-party data sources — Fospha, Triple Whale, TransUnion, Klaviyo, Google, Meta, TikTok, LinkedIn — but citing a source is not a sponsorship, and a brand appearing in that content would not by itself indicate a paid arrangement. Any paid partnership would need to be negotiated directly, and the most useful basis for a proposal is the work already visible: cross-channel ad benchmarks, platform and tool analysis, and hands-on campaign tactics across Meta, Snapchat, Google, TikTok and LinkedIn.

## Alternatives

- Nehal Kazim: Choose Kazim when the benchmark report needs a documented scaling case study rather than a median table. The reviewed posts walk a DTC health and wellness brand from $200K to $1.2M per month with the moves broken out by month: TikTok launched alongside YouTube and contributing 18% of revenue at a lower CPA than Meta within 30 days; creative volume moved from 8 to 35 new creatives a week through a modular hooks-bodies-CTAs system; audience architecture split into cold, warm and hot layers with separate creative and bid strategies; then Snapchat, AppLovin and CTV added, each assigned a funnel role. A brief that wants a practitioner narrative of how spend was added across surfaces without losing signal fits that material more directly than an eight-channel CTR comparison.
- Anthony Morgan: Choose Morgan when the report turns on platform cost pressure and funnel diagnosis rather than cross-channel CTR medians. The reviewed material tracks Meta CPMs from $9.84 to $18.01 over two years, ROAS from 2.57x to 1.56x, and CAC up 31% to $52.86, then argues that targeting moved into creative and the landing page, and that most brands still send their most expensive visitors to a generic page. A second post covers the store-as-anchor problem for 7- and 8-figure Shopify brands and message-market fit drifting away from new audiences as spend scales. A report section on why efficiency is eroding and what to change structurally fits that more closely than a channel benchmark table.
- Skylar Kraatz: Choose Kraatz when the deliverable is a seasonal Meta playbook rather than a measurement report. The reviewed posts give a BFCM mistakes list with what to do instead — start campaigns early and cite Particl's finding that 28% of 5,000 DTC companies launched by 24 October, skip new creative testing in favour of updating proven ads, and shift budget to retargeting — plus an argument for testimonial ads that starts with static ads on a small budget to find which reviews convert, then scales into new formats. A brief that wants platform-specific seasonal execution detail fits that material more directly.

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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/