Consumer Feedback

More about consumer feedback like efficacy effects, olfactory, etc.

Consumer Feedback

CosmetiQ-MCP ingests reviews in English, Spanish, and other languages.

A multilingual Gen-AI pipeline then enriches every record with structured fields across 6+ signal groups, giving teams an X-ray view of how products perform, smell, feel and are perceived - down to each consumer segment and region.

Signal group	Example of review	Example of AI-generated data field
Cosmetic Effects	“After two weeks my hormonal acne calmed down and my cheeks feel more hydrated.”	Effect_reduced_acne = increased  Effect_increased_hydration = increased
Frangrance Intelligence	“Smells like fresh peach at first, then a soft woody dry-down. Not overpowering at all.”	scent_family = fruity  secondary_family = woody  pleasantness = 1.3  scent_intensity = 0.8  off_notes[] = [ ]
Texture Analysis	“Absorbs in seconds, leaves zero tackiness—perfect under make-up.”	absorption_speed = fast  afterfeel = weightless  tackiness = none
Usage & Rituals	“I apply it every morning after spin class—it keeps the post-workout redness away.”	use_context[] = [post-workout, morning routine]  step_order = serum
Packaging perception	“Love the frosted glass but the dropper leaks if I travel.”	material_kudos = glass  pack_critique = dropper_leak  sustainability_mentions = 0
Value for Money	“Got it at 30 % off—still worth full price; a little goes a long way!”	discount_pct = 30  promo_flag = true  value_for_money_score = high

---

Cosmetic Effects

Example of what we capture

• Effect_reduced_acne, Effect_increased_hydration, Effect_reduced_redness, etc.

• Direction (increased, reduced, no change)

Why it matters

• Quantify real-world outcomes for every skin type or concern.

• Verify whether a hero claim (e.g., anti-blemish) truly resonates.

• Identify sleeper benefits - effects consumers mention even if the brand never claimed them.

---

Fragrance Intelligence

Example of what we capture

• scent_family (fruity, woody, marine, …)

• scent_intensity (-1 … 3 scale) & pleasantness (-2 … 2)

• off_notes[] — specific negative descriptors (chemical, medicinal, overpowering).

Why it matters

• Optimise fragrance accord and strength for each persona or region.

• Detect early if an off-note starts hurting star ratings.

• Align scent direction with regulatory limits (IFRA allergens).

---

Texture Analysis

Example of what we capture

• absorption_speed (slow · medium · fast)

• afterfeel (tacky, silky, matte, …)

• weight (light · medium · rich)

Why it matters

• Match formula sensoriality to skin-type preferences (e.g., lightweight gels for oily skin).

• Pinpoint texture adjustments that could lift satisfaction without changing actives.

• Support marketing copy with consumer-validated texture language.

---

Usage & Rituals

Example of what we capture

• use_context[] — recommended moments (morning routine, post-workout, office, …)

• avoid_context[] — situations to skip (formal events, humid climate, …)

• step_order — where the product sits in multi-step routines

Why it matters

• Build occasion-based messaging and bundling strategies.

• Time ad spend to the daypart when the target cohort actually applies the product.

• Spot contexts that trigger negative experiences and pre-empt them in guidance.

---

Packaging Perception

Example of what we capture

• material_kudos — praise for glass, recycled plastic, etc

• sustainability_mentions — eco keywords, refill interest

• pack_critique — pump failures, leaks, difficult droppers

Why it matters

• Prioritise packaging tweaks that will move satisfaction fastest.

• Support CSR goals with data-backed eco claims.

• Avoid post-launch reputation hits from known pack issues.

---

Value for Money

What we capture

• value_for_money_score (low · fair · high)

• Auto-benchmarked against local price-tier (€ / ml) and current promo

Why it matters

• Gauge elasticity before price changes or promo campaigns.

• Detect regions where perceived value lags despite strong efficacy - clue to adjust pack size or comms.

• Combine with Packaging perception to see if eco upgrades justify a premium.

---

Why consumer feedback is a force-multiplier:

• Bridges soft & hard data – marry scent_family = woody with IFRA allergen limits to confirm both delight and compliance in one step.

• Feeds persona insight – cross-tab scent_intensity with age_tag = teen and skin_type = oily to see what Gen-Z really wants.

• Closes the claim loop – prove “reduces redness in 7 days” by counting Effect_reduced_redness = increased among sensitive-skin reviews.

• Signals reformulation early – rising off_notes or pack_critique scores alert teams months before star ratings collapse.

Because every one of these AI-enriched consumer feedback data module is connected to ingredients, regions and people profile, you can pivot instantly - from a single complaint to the formula’s ingredient list, regional pricing, and target persona's concerns - without leaving CosmetiQ-MCP.