Your competitor's GMV tells you one thing: they're winning. Their product reviews tell you why — and more importantly, who is handing them money. Most TikTok Shop sellers ignore the second number. That's the gap you exploit.
TikTok creator @imamsmail made this argument with data. Stop tracking competitor revenue. Start reading their reviews. What he found inside two batik shirt listings should change how you think about listing optimization, content strategy, and who you're actually writing copy for.
The Problem With GMV Obsession
GMV is an outcome. It tells you a competitor is doing something right — not what that thing is. Chasing GMV without understanding the underlying buyer behavior is like trying to replicate a dish by reading the restaurant's revenue report.
Reviews are different. Reviews are customers telling you, unprompted and in their own words, who they are, why they bought, and what context the product actually gets used in. That data sits openly on your competitor's product page. Most sellers scroll past it.
The methodology has a name in marketing: proxy persona research. Instead of running expensive surveys or focus groups, you mine existing secondary data — reviews, Q&A, unboxing comments — to build an accurate buyer persona. It's faster, cheaper, and often more honest than primary research, because customers writing reviews aren't performing for a survey. They're writing for themselves.
What the Batik Case Study Found
@imamsmail pulled two top-10 men's batik shirts from TikTok Shop and ran their reviews through Kalodata, a TikTok Shop analytics tool. Both products were listed as men's batik shirts. Neither was marketed to women.
Here's what the review data showed:
| Product | Primary Use Context | Share of Reviews |
|---|---|---|
| Batik Shirt #1 | Bought by wives/mothers for husband (family events) | 62.5% |
| Batik Shirt #1 | Worn by fathers and teachers at school | 12% |
| Batik Shirt #2 | Ordered for family members | 57% |
| Batik Shirt #2 | Used for daily wear and formal occasions | Remaining |
The pattern is consistent across both products: the buyer is female, the user is male. Wives buying for husbands. Mothers buying for sons and fathers. The listing says kemeja batik pria. The checkout is done, overwhelmingly, by women.
This isn't a quirk of batik. Statista data on TikTok Shop Indonesia shows the platform's buyer base skews 70-75% female. The top categories by GMV — womenswear, health and beauty — confirm the demographic. But the insight @imamsmail is surfacing is sharper than raw platform demographics: even in a category explicitly labeled for men, the decision-maker is almost certainly a woman. That has direct consequences for how you write your listing.
Buyer ≠ User: Why the Gap Exists and Why It Matters
Marketing strategy has distinguished buyer personas from user personas for a long time. The buyer is the person who makes the purchase decision and pays. The user is the person who consumes the product. They're often different people — and most sellers optimize their listings for the wrong one.
Academic research backs the pattern. A study published in PLOS ONE (available via NIH) found that women consistently outperform men at gift selection across relationship types and receiver genders. Gift-buying — which structurally creates buyer-user separation — skews female. This is documented purchasing behavior, not assumption.
For batik specifically, the use contexts are revealing: school uniforms, family occasions, Lebaran, formal workplace events. These are recurring, predictable purchase moments where someone — usually a wife or mother — is organizing what the family wears. The batik shirt is a household or gift purchase, not an impulse buy by the end user.
If your listing is written to appeal to a 35-year-old man who wants a comfortable batik for Friday prayers, you've optimized it for someone who isn't at the checkout screen. The person at checkout is his wife, deciding whether this looks worth the price and will arrive before the event.
What Changes When You Know the Actual Buyer
Once you have the buyer profile, every element of your listing shifts. Here's what the buyer-user gap means in practice for a men's fashion category:
Product Title
Titles optimized for male buyers tend toward specs: fabric weight, collar type, motif name. Titles optimized for female gift buyers lean toward occasion and recipient signal: "Kemeja Batik Pria Ayah Suami Acara Formal." If your competitor's reviews show family gifting patterns, their title almost certainly contains family occasion keywords. That's conversion copy written for the actual buyer, not the user.
Product Imagery
Most men's fashion listings default to a male model wearing the shirt. If women are buying, reconsider the image sequence. Context shots showing gift-giving occasions, family events, or even fabric close-ups that emphasize quality for gifting — these speak to the actual buyer. Same product, different visual frame.
Description Copy
A description written for the male user talks about fit and personal comfort. A description written for the female buyer talks about the occasion it's appropriate for, whether it wrinkles (relevant when you're buying for someone else and can't inspect it in person), and size guidance so she can buy the right size for the right person. The angle changes completely.
Content Hooks
If you're running TikTok content for a men's batik shop, the default hook is a man putting on the shirt. Review data suggests a different hook performs better: a woman selecting the right batik for her husband's office trip, or a mother choosing a formal shirt for her son's graduation. The emotional trigger is pride in taking care of family — not personal style.
How to Do This Without Kalodata
@imamsmail used Kalodata for structured review analysis. It's a valid tool — it aggregates review data at scale and surfaces themes that manual reading misses. But Kalodata isn't the only path. A manual workflow works, especially when you're validating before committing to a tool subscription.
Step 1: Find the right competitor products
Sort by units sold or review count, not GMV. You want products with enough review volume to spot patterns — 50+ reviews minimum to get reliable signal. Top-10 products in your category are the right targets.
Step 2: Read reviews for context, not sentiment
Don't skim for good/bad. Read for who and why. Look for phrases that reveal buyer-user separation: "untuk suami," "untuk bapak," "hadiahnya bagus," "cocok untuk lebaran," "dipakai anak saya." These phrases tell you the buyer is not the end user.
Step 3: Tag by use context
A simple spreadsheet: Column A for the review excerpt, Column B for buyer signal (self/gift/family), Column C for use occasion (formal/casual/school/event). After 30-40 reviews, patterns become undeniable.
Step 4: Cross-reference with the Q&A section
Questions buyers ask before purchasing reveal their decision logic. "Apakah lengannya bisa dilipat?" from someone buying a gift tells you they're imagining someone else wearing it in a specific situation. That's buyer behavior, not user behavior — and it signals what copy should address.
Step 5: Apply to your listing surgically
Rewrite one element at a time — title first, then main image, then description. Test before overhauling everything simultaneously. You want to know what moved the needle, not just that something did.
One Limit Worth Naming
Two batik products is a thin sample. The buyer-user gap @imamsmail found is consistent with platform demographics and published gift-buying research, but don't extrapolate it to every men's category without running your own review read. A men's gym supplement, a gaming peripheral, a grooming tool — the buyer profile might look entirely different. Review mining gives you a starting hypothesis, not a universal law.
Also: knowing "women buy men's batik" is step one. Knowing which women — what age bracket, which occasions drive the purchase, how price-sensitive the decision is — is the step most sellers skip after finding the top-line gap. Review mining surfaces the gap. Closing it well requires more specific follow-on research.
And one more: @imamsmail is using this video to promote Kalodata (the discount code "MTT" is his affiliate code). The insight is valid. The tool is one option among several. Don't let the promotional frame dismiss the underlying methodology — proxy persona research via review mining is a documented and effective technique, whether you use Kalodata, a manual spreadsheet, or another analytics platform.
The Actual Competitive Edge
Your competitors' reviews are an open dataset. Every seller in your category has access to the same raw material. The edge isn't the data — it's reading it with the right question.
Most sellers read reviews for sentiment: do people like this product? The sharper question is: who is actually buying this, and why? That second question is where listing optimization, content strategy, and ad targeting should start. GMV tells you where money ended up. Reviews tell you who sent it — and that's the number worth chasing.
The immediate action is concrete: open your top three competitors, read 50 reviews each, tag who's buying. You'll either confirm your assumptions — in which case your current strategy is already calibrated — or you'll find a buyer-user gap that explains why traffic hasn't converted the way it should. Either result is worth 30 minutes of your time.
For more on building a TikTok Shop strategy from data rather than assumption, see our breakdown of TikTok Shop listing optimization and how we approach buyer persona research for e-commerce clients. If you're running competitor analysis for a TikTok Shop category and want a structured audit framework, our content strategy guide for TikTok Shop sellers covers how review data connects to content production at scale.
