I’m best where creative judgment and systems thinking have to live in the same head. Find the pain point, write the hook, build the thing that tells you whether the hook worked, then test it ten other ways to be sure.
Most of the useful stuff comes from being curious about things that have nothing to do with advertising. A creator that only draws on their own taste ends up making ads for themself, which is how you get a campaign everyone in the room loves and nobody outside it clicks. I don’t run a team from the top down. The best work comes from a mix of perspectives, data you can trust, and a direct line to the audience.
Our roster runs from classical concert pianists to a Spanish metal drummer with an NSFW personality. You can’t hand those two the same script, so I write to the performer: vocabulary, pacing, format, hook, all of it shifts with what their audience already expects. Threading our brand voice through theirs is how the trust gets built.
My strengths and my limits: I own our statics. I’m a close collaborator on video through script writing and some directing. Our copy team reviews what I write and our department head owns the budget and the final call. My paid depth is Meta, not TikTok. I’m a photographer and not a video editor, although I have some experience with simple animations and on-screen graphics. I wouldn’t call myself a copywriter, but I have the ideas to get a script built, and the taste to edit it to sound human.
Selected work
Musora · 2021–2026
01 · Audience
Meta kept handing us people twice our target age
For as long as we’ve tracked it, our Meta-determined audience has skewed old: 45 to 54 was the sweet spot and 55 plus was right behind it. Great for the business so far, but we couldn’t turn our younger YouTube audience into paying customers. Meta kept delivering our ads to who it thought would buy, and that kept being Gen X and Boomers.
My read was that the polished, branded, obviously-an-ad variants were doing the sorting for us. A 58-year-old will happily sit through a nicely made commercial. A 28-year-old scrolls past one.
I pushed the video slate toward comedy, skits, and lo-fi cuts, things that look like they belong in the feed instead of interrupting it. The statics stayed polished and on-brand, because that’s where the brand actually needs to hold.
Share of delivery by age
18 to 44
40 → 53%
45 to 54
21 → 21%
55 and over
39 → 26%
Where Meta chose to spend the money. The tick is the two months before, the bar is the most recent 30 days. 13 points moved out of 55 plus and 13 points landed in 18 to 44. Scale 0–60%.
August 2026 vs August 2025
Trial starts per 1,000 impressions
2.43×
Thumbstop rate
1.34×
Trial starts
1.32×
Cost per trial start
0.59×
Last year = 1.00, scale 0–2.5×. Same month, same place in the year, no sale in either one. We spent 22% less, bought 45% fewer impressions, and got 32% more trial starts out of it. Up is better on the first three and down is better on the last one.
Trials per thousand impressions more than doubled, and that one doesn’t care how big the budget was. Click-through went from 2.10% to 2.68% over the same comparison.
02 · Test design
One pain point, six angles
Bar chords are where beginner guitarists quit. It’s not a metaphor, it’s a specific thing your hand won’t do yet, and a lot of people quietly decide at that point that they’re not a guitar person after all.
This static went out in July and beat everything else in its set. On its own that means nothing, but it made me curious enough to point the next testing round at that one barrier and nothing else.
Origin. July 2026. The whole round came out of this one ad.
Six angles went out as 11 statics and 10 video cuts.
“Learning bar chords SUCKS” made the best static of the lot, and it nearly didn’t run. Our CMO thought the language was too harsh. I pushed to keep it because it’s how the instructor actually talks, and because a campaign from years back had already told us this register works and we’d just forgotten.
“Learning bar chords SUCKS.” The one that nearly got cut. 3.01% click-through and 1.13× trials against spend, the best static of the 11.Get it free, point. 1.29% click-through · 1.07× trials per spend · 34.9% share of spend.No grip of death. 1.95% click-through · 0.98× trials per spend · 4.7% share of spend.Four-week chord. 2.14% click-through · 1.68× trials per spend · 0.7% share of spend.Press play. 1.84% click-through · test spend only · 0.3% share of spend.Fix the buzz. 1.60% click-through · test spend only · 0.1% share of spend.
Video
Look at my hand. 46.6% thumbstop · 0.55× trials per spend · 8.5% share of spend.Repair shop. 41.4% thumbstop · 1.10× trials per spend · 15.8% share of spend.The F chord. 35.8% thumbstop · 0.87× trials per spend · 12.0% share of spend.This is the sound. 35.4% thumbstop · no trials · 1.2% share of spend.We talking bout practice. 31.6% thumbstop · no trials · 1.5% share of spend.Progress halter. 31.0% thumbstop · 1.31× trials per spend · 5.3% share of spend.The fly. 30.2% thumbstop · 2.17× trials per spend · 2.7% share of spend.Excuses. 26.0% thumbstop · no trials · 0.6% share of spend.We talking bout practice, short cut. The short version of the same hook.
Video
All 10 cuts, built by our video team to angles I set. Thumbstop is 3-second plays over impressions. Trials over spend is an ad’s share of trial starts divided by its share of the money, so anything above 1.00 paid for itself.
Naming the exact technical failure, the buzz, the F chord, the grip of death, made the strongest hooks by a distance.
Attention and trials came apart almost entirely. The cut that held the most people returned the least of anything we funded; the one that returned the most sits seventh on attention. I don’t have a tidy explanation for that yet and I’d rather say so than invent one.
And the angle my team normally bets on, the demographically precise one aimed at stuck intermediates in their forties, did nothing. It described a person instead of a moment they’d recognise.
Video and statics split the money nearly evenly and came back nearly in proportion, so I can’t tell you one format beat the other. What mattered was which angle you picked.
This launched in early September and is still under our significance floor, so treat all of it as an early read. Ratios instead of counts to protect Musora’s numbers.
03 · Reading the signal
When the variable turns out to be a person
A static was beating everything around it in both spend and trial starts. Same template, same offer, same angle as the rest of the set. The only thing actually different was the guy in the photo.
Not knowing if it was a fluke or something about the model, we built a video from the b-roll of the same shoot. It’s now the biggest creative by spend in that portfolio.
The static.The b-roll cut. Repurposed b-roll video ad featuring the same model as the static.
Against the ads running beside it
Thumbstop, against the median of 22 video ads
1.58×
Installs as a share of spend
1.25×
Peer set = 1.00, scale 0–2×. Holding the largest share of the spend and still coming back ahead of it is the harder half of that. These are attention and installs, not revenue, because it ran as an app-install campaign.
He’s booked for an upcoming shoot so we can pull it apart properly: his face, his energy, or just the room he happened to be sitting in. We’ll run our best hooks, angles, and formats across instruments (he’s a multi-instrumentalist). If it turns out to be him, that’s a better thing to own than a concept. We’ll burn through concepts forever; he’ll still have that face.
The read and the argument were mine. Our video team cut the footage.
04 · Customer voice
750,000+ student comments, finally readable
Every lesson on the platform has a comment thread, some going back to 2012. There’s an enormous amount in there about our customers and none of us could read all of it. We’d occasionally dig through a few videos for a nice sales-page quote, but it was slow and not particularly effective.
So I pulled the lot into a database and built a tool that queries it. 750,000+ comments, five brands, fourteen years.
It isn’t only the size, it’s what I threw out. Our student experience team comments constantly, warm and encouraging, because that’s literally their job. Leave them in and your “student voice” is your own brand talking back to you. The working set strips staff, moderators, and deleted threads.
Now I can find the real language students use, the pain points they’re struggling with, and the wins they’re experiencing. It’s been a game changer for headlines and scripts.
05 · Creative systems
The boring layer everything else sits on
We could always tell you which ad won. But as a data set, we couldn’t tell you why, because ad names were whatever the person building the thing felt like typing that day.
So I built the naming convention, then the taxonomy underneath it: every ad coded for hook tactic, messaging angle, visual format, audience, pain point, offer, and model. Now we know whether “Olivia” is a coach, a student, or a celebrity, which hooks work for which audience, and whether an ad hit because the offer was great or failed because the pain point wasn’t relevant.
06 · Creative engine
Ten times the output, no AI-generated photos
Testing at volume turns into a production problem long before it’s a creative one. Dozens of our top of funnel campaigns sat untouched for years because building variants by hand cost more than the variants were worth.
I’ve been leading the build of an asset manager holding the whole library, 200,000-odd photographs. Filenames stay as they are; every image gets AI-tagged on upload for shot angle, emotion, demographic, instrument, location, face recognition, and quality. The catalogue feeds an automated Figma template, so one set of our best-performing static designs became our entire course catalogue, with AI-generated headlines and hooks.
Two templates, three instruments. The layout and the structure stay put, the photograph and the palette and the line change. Same two people doing the work, roughly 10 times as much of it.
If you can query what’s in the library you can query what isn’t. What came back was an uncomfortably specific list of the people we can’t currently cast, and that list is now the brief for a full day of shooting. We’re filling holes the data found instead of holes we’d have guessed at.
The one thing I won’t move on is AI-generated imagery as the subject. We cast real students and represent real human creativity. Using AI to get real photographs into templates quickly is a completely different thing from using it to fake the photograph.
In build
A creator programme made of actual students
Most UGC programmes buy strangers who’ve never used the product. That works for a skin care company, not for a music education app, where the before-and-after takes real time investment.
The people who already use and love Musora are the best people to talk about it, so I’m building this out of our own student base instead of a generic marketplace.
I’ll select strong candidates from customer data inside the community and onboard them with audio and video capture tips. Pay per post, performance bonuses, extra perks, and a weekly brief with hooks, prompts, and formats: direction without a script.
Everything lands in the asset manager and gets tagged on arrival, which is what makes it a programme and not a pile of content. I can trace performance back to an individual hook and give a creator actionable feedback.
This program hasn’t launched yet.
Resume
Laura Scotten
Creative Strategist · Performance Creative · Vancouver, BC
I own the static ads on a five-brand music-education platform and build the systems the rest of the team briefs from: the creative taxonomy, the asset infrastructure, and a queryable store of 750,000+ student comments that defines our customer voice from the source. This year I diagnosed an audience skew as a creative format problem, moved delivery thirteen points younger, and watched trial starts rise. Twenty-two years in the creative seat, five inside a paid ad account.
Experience
Musora Media2021 – Present
Creative Services Manager, previously Brand Designer; joined as Staff Photographer
Diagnosed an old-skewing Meta audience as a creative format problem, not a targeting one, and pushed the video slate toward comedy, skit, and lo-fi while keeping statics on brand. Delivery to 18–44 went from 40% to 53%. August year on year: trial starts up 32% on 22% less spend, trials per thousand impressions more than doubled.
Own the static ad output across five brands, from concept and hook through art direction, build, and final review. Closely involved in video: setting angles, contributing hooks and scripts, occasionally producing a shoot end to end. Hooks are written to the performer, across a roster from concert pianists to high-velocity YouTube drummers.
Built a queryable store of 750,000+ platform comments (2012–2026, all five brands) plus the AI skill that reads it, with staff and moderator voice stripped out. Journey friction is now measured from what students write, not from instinct.
Identified bar chords as an under-attacked beginner drop-off point and designed the test around it: six angles across eleven statics and nine video cuts in one production cycle, all against one pain point. Attention and conversion came apart almost entirely, which changed how we brief.
Spotted that an overperforming static was carried by the model, not the concept. Had unused b-roll recut as a standalone video ad at no new production cost: about 1.5× the median thumbstop of everything running beside it, now the largest creative by spend in that portfolio. Cast him in an upcoming shoot to isolate whether the performance is the person.
Built the ad naming convention and creative taxonomy from scratch: every ad coded for hook tactic, messaging angle, visual format, audience, pain point, offer, and casting model, so creative is analysed by attribute rather than filename.
Helped build a custom asset manager with the IT team: 200,000+ photographs and b-roll, AI-tagged on ingest, so search becomes a query and statics assemble programmatically in Figma from real photography. Roughly ten times the output, same headcount, no AI-generated imagery as a subject. Querying the library for who is missing now briefs shoots against the demographic gaps the data finds.
Building the creator programme, currently pre-launch: sourced from our own student base, onboarding in lighting and sound capture, pay per post plus bonuses, weekly briefs, and tagging on arrival so performance is attributable.
Run concepting as a team process, pressure-testing with the designer, copywriters, and media buyers before production. Built Musora’s brand design alongside our UI designers; manage a graphic designer and run the creative services function.
Two Peas Photography2010 – 2022
Owner-Operator
Ran a wedding, event and commercial photography business for twelve years: client acquisition, pitching, art direction, shooting, post-production and delivery.
Pixilink Solutions2016 – 2021
Photographer
Professional real estate photography at daily volume, to fixed turnaround and consistent output standards.
Michael Tourigny Photography2004 – 2011
Commercial Photographer
High-production product, food, and lifestyle photography in a commercial studio, working to agency briefs and art direction. Seven years of interpreting somebody else’s brand to a standard the client would sign off.
Education and training
Electronic Media Design Certificate, Langara College, Vancouver
Motion Creative Strategist Bootcamp · Motion AI Strategy Training Club, completed 2026
Capabilities
Strategy. Paid social creative strategy, creative testing frameworks, awareness-stage and funnel mapping, creative taxonomy and naming systems, performance analysis and creative reporting.
Craft. Creative direction, art direction, graphic design, briefing, hook and script writing, brand systems, set design, styling, photography, creator direction, editing AI-generated copy to brand, prompting for high-volume asset creation with human-made creative.
Tools. Meta, Motion, Figma, Illustrator, Photoshop, Lightroom, Jitter, Asana. AI-assisted creative workflows with Claude and MCP integrations, Runneth and Gemini.
