Words that convert.
Numbers that tell the truth.

I'm Manhal, a writer and data analyst. I help brands write copy that performs because I can actually measure what does.

[ Headshot of Manhal goes here ]
↑ Replace with real headshot (portrait orientation, 4:5 ratio works best)

Most copywriters write what sounds good and hope it works. I write what I have reason to believe will work.

I have six years of experience in data analysis. SQL, Python, AWS Athena. I spend my days reading queries, running models, and pulling patterns out of customer behavior data at scale.

I also have a degree in psychological and health sciences from the University of Toronto, and a freelance writing practice across DTC, healthcare, B2B, real estate, and parenting media.

The two halves don't compete. They feed each other. The data tells me what to write about. The writing tests what the data implied. Then I read the numbers again. That loop is the whole job.

Trusted by brands that watch the numbers.

A small selection from across DTC, healthcare, B2B, real estate, and parenting media.

Boosted Commerce
DTC holding co. for beauty & wellness
Motherly
Parenting media & sponsored content
Manhattan Associates
Supply chain technology, B2B
MeasuringU
UX research firm
Eyes On Eyecare
Clinical news for eye care professionals
Luxely Marketing
Luxury real estate social media
Digital Standout
Healthcare practice marketing
Media Scaling
Short-form video script production

Three pieces I'm most proud of.

MOTHERLY
Parenting · Sponsored Content

[ Headline goes here, e.g. "Writing in Motherly's first-person mom voice" ]

[ Open with the brief: "Motherly's sponsored content has to read like Motherly editors wrote it, never like ad copy. The voice is specific, first-person, vulnerable, and grounded in real motherhood." ]

[ Your contribution: "I write paid social, dedicated emails, and IG stories for Motherly's sponsor brands. Each campaign starts with reading 30+ existing Motherly pieces to lock in the voice before writing a single line." ]

↑ Replace with real campaign details + screenshot of one of your Motherly emails or IG stories
[X]+
Campaigns delivered
[X]%
Open rate lift
Read the full case study →
"[ Pull a real headline from one of your Glance articles ]"
Healthcare · Clinical News

[ Headline, e.g. "Translating dense clinical research for busy optometrists" ]

[ The audience and need: "Eyes On Eyecare's audience is clinicians who don't have time to read full peer-reviewed studies. They need clinical news they can act on between patients." ]

[ Your contribution: "I take peer-reviewed trials and translate them into Q&A-format stories that surface what's clinically actionable, in under 400 words. I read the studies. I pull the stat. I write what matters and cut the rest." ]

↑ Replace with link to one published article + real engagement or editorial feedback
25+
Stories published
Ongoing
Weekly cadence
Read the full case study →
"[ A viral hook line from one of your scripts ]"
Direct Response · Short-Form Video

[ Headline, e.g. "Weekly viral script batches for short-form video" ]

[ The brief: "Media Scaling produces short-form video scripts at volume for DTC and creator brands. Hooks that stop the scroll, structures that hold attention, CTAs that move." ]

[ Your contribution: "I deliver weekly batches of viral scripts using a slot-machine approach: multiple variations per format. Each script is informed by what's currently going viral on the platform, then customized to the client's voice." ]

↑ Replace with real script examples or video thumbnails + view counts
[X]k+
Top script views
Weekly
Batch cadence
Read the full case study →

How data informs the writing.

01
The data signal

What the numbers were saying

[ Example placeholder: "Looking at 247 Foxybae product reviews, one objection appeared in 60% of low-rated comments: 'Will this damage my hair?' That fear was killing conversion at the consideration stage." ]

SELECT keyword,
COUNT(*) AS mentions
FROM reviews
WHERE rating < 4
GROUP BY 1
ORDER BY 2 DESC;
"damage" — 142
"break" — 89
"frizz" — 34
02
The writing decision

What I wrote, and why

[ Example placeholder: "The hook had to lead with the exact fear, not skip past it. So instead of opening with the demo, I opened with the objection: 'I was scared this would fry my hair, too.' Then the script earned permission to keep going." ]

"I was scared this would
fry my hair, too. Then I
tried it for two weeks."
03
The result

What happened in the numbers

[ Example placeholder: "The script outperformed the previous batch by 4.2x in average view duration. CTR doubled. The hook line got pulled into three follow-up scripts because it kept working. The pattern, addressing the top-mentioned objection by name, became a template I now run for every Foxybae launch." ]

4.2x
View duration lift
↑ This is a placeholder example. Replace with one real walkthrough from your work: the data you pulled → the writing decision you made → the actual result.

The stack behind the writing.

I lead with data tools because the data shapes the writing. AI tools sit in here too, used the way you'd use any other tool. They don't write the copy. I do.

Data

Query, model, analyze
  • SQL
  • Python
  • AWS Athena
  • Excel / Sheets

Research

Voice of customer, market
  • SparkToro
  • Reddit / forums
  • Customer interviews
  • Review mining

AI

As a tool, not the writer
  • Claude
  • ChatGPT
  • Custom workflows
  • Topic modeling

Writing & Delivery

Drafting, editing, handoff
  • Google Docs
  • Notion
  • Asana
  • Airtable

Let's work together.

If you've got a project, a brand voice question, or you just want to know if I'm available, drop a line. I usually respond within a day.

Email

hello@manhalsultana.com

LinkedIn

linkedin.com/in/manhalsultana

Based In

New York metropolitan area