Mining Scent Feedback from Online Reviews: Practical Methods for Product Managers

Online reviews are noisy.
One person says “too strong”, another says “love the scent”, a third just writes “smells weird lol”.

If you manage scented products, it’s tempting to ignore all that chaos. But buried inside those messy comments is real data: how people describe your fragrance, how long it lasts in real bathrooms and bedrooms, and when it pushes people away.

This article walks through practical ways to mine scent feedback from online reviews, using simple language and real product scenarios. We’ll also show where a partner like I’SCENT fits into the loop, from insight to new fragrance oils.

Why Scent Feedback in Online Reviews Matters for Product Managers

Most teams already track ratings, returns and basic NPS. But smell comments carry a different kind of signal:

• Customers talk about sensory details: “powdery”, “chemical”, “fresh laundry”.
• They share usage context: “great in small bathroom”, “too heavy for open office”.
• They reveal emotional impact: “comforting”, “gives me headache”, “feels cheap”.

When you look at a large batch of reviews, a few patterns show up again and again:

1. Only a fraction of reviews really help scent work. In many projects we see something like 15–25% of comments that mention smell in a concrete way. The rest are noise.
2. Comments with sensory words get more “helpful” votes. People trust reviews that talk about real experience, not just “nice product”.
3. The same smell issues appear across channels: “scent fades fast”, “too strong in small rooms”, “doesn’t smell clean”.

As a product manager, that means you can treat online reviews like a free, always-on sensory panel. Not perfect, but very, very useful.

Collecting and Cleaning Scent Feedback from Online Product Reviews

Before any text mining, you need a clean set of reviews. Otherwise you’ll just build fancy charts on top of garbage.

Simple filtering steps

1. Gather reviews from multiple channels
   • Your own site
   • Retailer platforms
   • Marketplaces
   • Screenshots saved by sales or CS teams
2. Keep only scent-relevant comments
   Search for smell-related words first. For example:
   • “smell”, “scent”, “fragrance”, “perfume”, “odor”, “aroma”
   • “too strong”, “too weak”, “headache”, “cloying”, “like nothing”
   • “lasts”, “fades”, “lingers”, “stays on clothes”
   If a comment doesn’t mention scent at all, drop it from this analysis. You’re not doing a full UX study here, just aroma.
3. Remove obvious noise
   • One-word reviews like “good” or “ok”.
   • Pure shipping complaints.
   • Obvious bots.

After this quick clean-up, you may end up with, say, 300 meaningful scent reviews for a single SKU. That’s already plenty to see patterns.

Text Mining Scent Descriptions into Structured Fragrance Data

Once you have the right comments, the goal is simple:
turn free text into a few clear tags that perfumers and marketers understand.

Note Families, Performance and Context Tags from Reviews

You can start with three tag types:

1. Note family tags – what the customer nose feels Examples:
   • Powdery, musky, “baby smell” → soft floral + musk
   • Fresh, citrus, “clean laundry” → citrus + clean musk
   • Warm, amber, woody → amber-woody
   • Sweet, vanilla, gourmand → gourmand
2. Performance tags – how the scent behaves
   • Longevity: “gone after shower”, “lasts all day”, “stays on pillow”
   • Diffusion / throw: “no smell in room”, “fills the whole bathroom”
   • Stability: “turns weird in candle”, “smells burnt in soap”, “goes plastic”
3. Context tags – where and how they use it
   • Category: skin, hair, laundry, candle, room spray, car, food & beverage
   • Space: small bathroom, hotel lobby, living room, office, kid’s room

You can tag by hand at the start. It’s a bit boring, but after a few hundred reviews your eye gets very fast. Later, you can automate parts of it with simple scripts.

Example Table: Scent Feedback Dashboard for Product Managers

Here’s a small, imaginary data slice for one body wash product:

This simple table already tells you a story:
people like the powdery baby vibe, but a lot of them want better longevity.

Using Scent Feedback in Fragrance Development and Reformulation

Now the important part: what do you do with all this feedback?

Priority grid for scent issues

You can build a small 2×2 grid:

• Axis 1: How much people care – how emotional or strong the language is
• Axis 2: How badly you perform – how often the comment is negative

Then each scent issue lands in one quadrant:

This little matrix stops you from chasing every random reviewer. You focus on the things that really move repeat purchase.

Turn tags into clear fragrance briefs

Instead of writing “make it stronger”, you can brief like this:

• “Most reviews call the scent ‘powdery baby clean’, which is good. Around a quarter say it disappears during towel dry. Please keep powdery signature, push base notes a bit more, avoid extra sweetness.”
• “For our kitchen cleaner, many comments say ‘smells chemical’ and only a few say ‘smells natural’. Need a greener, less harsh top with same clean cue.”

Fragrance houses love this level of clarity. This is where I’SCENT can do real work for you, not just send catalog samples.

Baby Care and Personal Care Fragrance Oil Case

I’SCENT’s site has clear ranges for different categories. For baby and gentle products you can look at their Baby-Care Soft Powder Personal Care Fragrance Oil.

Imagine your review dashboard for a baby bath line looks like this:

• Many parents say “love the baby smell”.
• Quite a few also say “it’s gone once baby is dry”.
• A small group worry that the scent is a bit sharp when first poured.

A practical move:

1. Use the existing baby-care oil as a benchmark for powdery comfort and smooth top.
2. Compare your product vs this benchmark in internal panel and in reviews.
3. Ask I’SCENT to mod the base toward the profile your reviews actually describe:
   • Keep soft musk and powder accord.
   • Adjust top-note brightness to reduce sharpness.
   • Add a bit more base weight for towel-stage perception.

Because I’SCENT holds a 40,000+ formula library and works with multiple personal care brands, they can often find a ready base and tweak, instead of building from scratch. With sample lead times in the 1–3 day range and low 5 kg MOQ for stock profiles, you can test new scent directions fast without huge risk.

Soap, Candle and Detergent Fragrance Oil Use Cases

Different categories throw up different pain points in reviews. The same text-mining method still works.

Soap fragrance oil and cold process issues

Handmade soap and bar soap reviews often talk about:

• “Smell disappears after cure”
• “Turns weird in cold process”
• “Colour change from fragrance”

If your tags show many comments like that, you don’t just have a taste problem. You have a stability and compatibility issue.

An OEM partner like I’SCENT already works as a dedicated soap fragrance oil manufacturer. They understand CP, HP, syndet and other bases. You can brief them in real industry slang:

• “We see morphing in CP at our usual load, need CP-stable accord with low discolouration.”
• “Goal is no burnt notes after 30 days cure and decent scent carry on dry bar.”

They can match your review data with tested bases from their library and then tune.

Candle fragrance oil and hot throw complaints

Candle buyers are brutal in comments:

• “No hot throw”
• “Too strong in bedroom”
• “Weird plasticy smell when burning”

If your review tags show “no smell when lit” or “too much in small room”, you know you need help with hot throw, diffusion and dose.

I’SCENT has a focused candle fragrance manufacturer OEM custom oils offer. Those oils are designed for different waxes and burn behaviors. You feed in:

• Room size patterns from reviews.
• Burn-time comments (“after 1 hour I smell nothing”).

They respond with base choices and scent structures that solve the real problem, not just smell nice in blotter testing.

Detergent fragrance and “clean but not chemical”

Laundry and home care reviews often sit on a knife edge:

• Some users want “hotel-level” scent.
• Others want almost no smell, just “clean, not perfumy”.

Your tags may show two clusters:

• High-intensity fans using phrases like “love the strong scent on clothes”.
• Low-intensity seekers using “too perfumed”, “overpowering in small flat”.

You might decide to split into two SKUs and brief I’SCENT’s detergent fragrance manufacturer service for each:

• One high-sillage, long-lasting profile for “scent seekers”.
• One softer, airier profile for “clean minimalists”.

Because I’SCENT works across detergents, softeners and other home care bases, they can manage IFRA categories, dosage, and batch consistency through their ERP system and certified production.

Cosmetic, Food & Beverage Aroma: Extended Scent Feedback Scenarios

It’s not just soap and candles. Many of I’SCENT’s clients work in other spaces too.

Cosmetic fragrance and skin comfort

Cosmetics reviews mix smell and skin feel in one sentence: “nice light scent”, “gives me headache”, “OK smell but too heavy for face cream”.

You can tag:

• “light / fresh / subtle” vs “strong / heavy / cloying”
• “good for sensitive skin” vs “irritating”

If lots of users say “nice cream but scent too strong for face”, you can stay in the same scent family but move to a lighter cosmetic profile from a supplier like I’SCENT’s cosmetic fragrance IFRA-certified custom scents.

Food & beverage aroma expectations

For food and drink, smell sets the first expectation when people open the pack. Reviews often read:

• “Smells artificial”
• “Smell doesn’t match taste”
• “Nice bakery smell, taste is flat”

Here the feedback guides not only the flavour house but also the aroma system. I’SCENT offers food & beverage fragrance oil options that line up with real usage: bakeries, drinks, confectionery, and so on.

If your review tags show “great smell, weak taste”, you discuss with both your flavour partner and I’SCENT how to balance aroma intensity and flavour perception, not just pump one side.

Working with I’SCENT as OEM/ODM Fragrance Oil Partner

Mining reviews only pays off if you can act on the findings fast. This is where an OEM/ODM partner like I’SCENT is useful:

• Deep library – 40,000+ fragrance formulas across personal care, home care, candles, F&B and more.
• Senior perfumer team – 20+ experienced perfumers able to read your review tags and translate them into technical mods.
• High replication accuracy – around 98% match when copying an existing scent direction, helpful if you want “same but better” based on feedback.
• Speed – sample lead time often in the 1–3 day range; production in roughly 3–7 days after approval, with low starting volumes for many categories.
• Compliance – IFRA, ISO, GMP, Halal and full traceability with ERP, so you don’t have to worry about documentation each time.

You bring:

• Cleaned review data and simple tags.
• Your categories, base systems and market position.

They bring:

• The fragrance engine and regulatory brain.

Together you close the loop from “people complain about smell online” to “new batch ships with fixed scent”.

Checklist for Product Managers Using Online Scent Feedback

To wrap up, here’s a short checklist you can copy into your doc. It’s not perfect English on purpose, but it works:

1. Pick one category first (baby wash, laundry, candles…).
2. Pull recent reviews from all channels, keep only scent-related ones.
3. Tag each review with note family, performance, context and sentiment.
4. Count patterns, not just nice quotes. Look for “too strong”, “too weak”, “fades fast”, “smells cheap”.
5. Place each issue into a priority grid: high or low impact, high or low performance.
6. Turn top issues into clear fragrance briefs using the same words customers use.
7. Share briefs and tags with a partner like I’SCENT at customfragranceoil.com. Don’t just ask for “a nice scent”, ask for “solution to real review pain”.
8. After launch, go back to reviews and check if those pain words drop. If not, tweak again. It’s fine, iteration is the game.

Do this and your online reviews stop being a source of stress.
They become a practical tool that guides your fragrance roadmaps, helps your perfumers, and keeps your scented products closer to what people actually want in their homes, on their skin, and even in their food.