✦case_study · spec work

Mid-Day Squares: from 2,669 ads to one afternoon story

How I researched a Montreal functional snack brand, found where its creative was strong and where it was thin, and wrote a creator script for the 3 p.m. snack moment. Below is each step, what AI did for me, and the calls I made myself.

SHEETOpen the full research workbook↗

brand

Mid-Day Squares

product

PB&J square, 130 cal

deliverable

Research + creator script

format

UGC video, 9:16

Spec project, September 2026. Not commissioned by or affiliated with Mid-Day Squares. All ad data is public (Meta Ad Library via Foreplay).

The process

~/mid-day-squares/process

Seven steps. Click through them in order: each step shows what the evidence said and who did what.

process · 7 steps

Pull the ad library

Foreplay indexes 2,669 Mid-Day Squares ads going back to 2021. On the day I pulled, 48 were live: 25 on Meta and 23 on TikTok. I sorted the Meta ads by days live and read every transcript and caption.

The first finding came before any counting. The TikTok account is a founder reality show (the Hershey lawsuit, the $400,000 machine, getting into Costco). The paid workhorse on Meta is something else: a customer sentence in a static, live for 441 days.

days live	format	opening line
441	static	"Stops me from over snacking. 1 square is enough."
241	creator	"POV you finally broke the binge restrict cycle with one simple swap."
148	creator	"I used to be the pantry person every single night. Blamed my willpower..."
113	static	"Your desk snack just leveled up."
109	static	"PB&J... without the bread?"
50	video	"Tastes like dessert. Scores like a smarter snack."

ai didPulled all 48 live ads through the Foreplay API, computed days live, attached transcripts, filtered Meta from TikTok.

i decidedDays live measures how long an ad stayed in the library, not how much it spent. I use it as a signal of what the brand keeps paying for, never as proof of performance.

Count the angles

I tagged each of the 25 live Meta ads by the angle it leads with. One ad can carry more than one angle, so counts overlap.

Angles across the 25 live Meta ads

ads carrying each angle · hover for an example

ai didFirst-pass tagging of every ad, the counts, and a search across a 221-ad sample back to 2021 to confirm that no ad is built on a workout.

i decidedSatiety is proven and crowded. The only video format that survives is a woman confessing her own snacking pattern. That is the territory to iterate on, not to avoid.

Listen to customers

Amazon US carries 8,631 ratings at 4.1 stars. I read the reviews that render publicly, two Reddit threads (including a gestational diabetes community) and fan blogs. These lines came back again and again, and they became the copy bank.

"Stops me from over snacking. 1 square is enough."

"quell a hungry stomach in the afternoon or are a yummy treat before bed"

"just one square is pretty effective when it comes to killing those cravings"

"Feels like I'm eating a little dessert (even though it's healthy)"

"not super sweet and don't spike me"

"tastes great, fills me up, and is good for me"

Sources: Amazon US and CA, r/GestationalDiabetes, r/LovedItOrSnubbedIt2, Costco fan blogs.

ai didScraped what was reachable, logged which sources were blocked, and grouped quotes into pain points, desires and objections with a source on every line.

i decidedThe afternoon shows up unprompted. People are not describing a protein bar. They are describing a craving that a "healthy" snack failed to fix.

Check the claims

My favourite flavour is Cookie Dough, so I checked the nutrition panels before choosing. Protein and fibre are identical across all seven flavours. Only fat and carbs move.

flavour	calories	fat	carbs	% cal from fat
PB&J Strawberry	130	6 g	16 g	42%
Brownie Batter	150	10 g	12 g	60%
Peanut Butta	160	11 g	12 g	62%
Cookie Dough	170	12 g	11 g	64%

PB&J won on two counts: it is the lightest square for a calorie-aware snacker, and 10 of the 25 live ads already push it, so the brand is putting its money there. I also tested a pre-workout angle, since no ad is built on fitness. The panel showed about 12 g of usable carbs, too little to say "fuels your workout" honestly, so I parked it.

ai didRead all seven US nutrition panels off the product pages and computed the ratios.

i decidedThe flavour, against my own preference. And I dropped an open lane because the product could not back the claim.

Write the hypothesis

Before writing any lines, I wrote down why the ad should work and what evidence supports it.

Hypothesis

A first-person story about repeatedly choosing "healthy" snacks that don't satisfy what she actually wants should resonate with a calorie-conscious afternoon snacker. The research keeps placing Mid-Day Squares where three needs meet: something sweet, something filling enough that one square is enough, and nutrition that feels worth the calories. The "I thought it was my willpower" framing adds tension, in a narrative territory the brand's own longest-running creator ads already prove.

ai didPulled the supporting evidence into one place: matching review lines, the 148-day and 241-day creator ads, the angle counts.

i decidedWho is watching: a 9-to-5 office worker who is mindful of calories, hungry again by 3 p.m., and has learned that the smallest snack is useless if she is still hungry after it.

Get reviewed, rewrite

My second draft went to my creative strategy coach for review. The research held up. The script did not: I had gone from research straight to sentences and skipped the story.

v2 · before review

Hook gave the answer away: "This is what I've been eating basically every afternoon."
One hook read as an ad on sight: "I found a snack you need to try asap."
Low-stakes problem: sweet versus filling at 3 p.m.

v3 · after

Story first: a weekly cycle of buying snacks she thinks she should want.
Open-loop hooks that hold the answer back.
Higher stakes: she thinks she has no willpower. The ad tells her it was never willpower.
The fridge stops being a caveat and becomes proof: no preservatives.

ai didTranscribed the 70-minute review call locally and turned it into a checklist of notes.

i decidedI rewrote the lines myself. AI drafts kept sounding like ads; the story and the final wording are mine.

Lock the script

Three hooks feed one lead, base and CTA, so each hook can be tested on its own. Hook 2 deliberately echoes the brand's 148-day creator ad ("Blamed my willpower"): it iterates on proven territory instead of starting cold.

Read the script ↓

ai didWrites finished drafts into the research workbook in Google Sheets through its API, so the brief a creator receives is always the current one.

i decidedEvery word, and which hook goes first in a test.

The script

~/mid-day-squares/script_v3

Creator to camera, about 45 seconds. The angle is a relatable 3 p.m. snack dilemma, casual and observational: she's sharing something she actually eats, not explaining the benefits of a product.

creator_brief.doc

HOOKS · three variants, same body

Why did nobody tell me my afternoon snack could basically taste like dessert?

I genuinely thought I had no willpower around food. Turns out, that wasn't the problem.

I thought picking the "healthy" snack would help me stay on track. It was actually doing the opposite.

LEAD · the story, the tension

Because every Sunday I'd go to the grocery store and get all the snacks that sound healthy… protein bars, almonds, things like that.
Then whenever I'm thinking about a snack, like at 3 p.m. on a Wednesday, I don't want any of it… I'd rather just have something sweet.
So I'd eat like almonds or something… but then an hour later I'd end up grabbing something way more calorie-dense because I'm still hungry and thinking about food instead of focusing.
And every single time, it felt like I'd failed some test… like I just had no willpower.
Then I found these, and they were basically what I'd been looking for… a snack that still felt like the sweet thing I actually wanted.

BASE · the product, the proof

They're called Mid-Day Squares. This flavour is PB&J, and it literally tastes like you'd expect PB&J, but without the bread.
It's 6 grams of protein, 4 grams of fibre, 130 calories, and one square is filling for me.
And that's what I like about them… it actually feels like I'm having the sweet thing I wanted in the first place, not the "healthy" alternative I'm trying to convince myself I want.
And they're still healthy: the ingredients are clean, no palm oil, nothing artificial. You actually have to keep these in the fridge because there are no preservatives.
So now when I want something sweet in the afternoon, I'm not eating something else first and then going looking for chocolate anyway.
Not because I suddenly have more willpower. I just finally have a snack I actually want to eat.

CTA

So if you also keep buying "healthy" snacks just to end up wanting something else anyway, these Mid-Day Squares are definitely worth trying. The 12-pack is linked below.

Customer language"One square is filling for me" comes from the 441-day review static and the Amazon reviews, said in a creator's voice.

Proof, not claimsThe fridge turns a shipping complaint into evidence that there are no preservatives.

Same angle end to endHook, lead, base and CTA all stay on willpower versus the wrong snack. The CTA hands the viewer her own pattern back.

The system behind it

~/medina/tools

I built these tools with Claude Code so the research runs the same way for every brand, and so what I learn stays in one place instead of scattered across docs.

Ad library pull

Any brand or competitor: live ads, transcripts, days live, sorted and filtered by platform.

fp.py · Foreplay API

Customer mining

Reviews and Reddit threads turned into pain points, desires and objections, each with its source.

rd.py · scrapers

Workbook sync

Research and the final script written straight into the research workbook in Google Sheets, formatted and backed up before every write.

gs.py · Sheets API

Brand brain

One working doc per brand: research, rulings, hooks tried, feedback. The next brief starts from it.

build-doc.py · Notion API

↻ with live performance data, results feed back in: tag every ad by hook, angle, awareness level and format, join the tags to spend, hook rate and CPA, and test next whatever is working but not yet saturated

What AI does

Pulls and counts at a volume I can't read by hand
First-pass tagging and grouping
Keeps every source attached to every claim
Moves finished work into the client's tools

What I keep

Which angle, which audience, which flavour
What the product can honestly claim
The story, the hooks and every final line
Reading why an ad won, and what to test next

Open the research workbook ↗ Their Meta Ad Library ↗ ← All projects