A fragrance business run on gifting peaks, sampling and repeat refill cycles across own stores, department stores and online.
6,000+
Points of sale carrying SKINN
400+
Cities and towns covered
₹300 Cr+
Fragrances scale
35
Consoles live on this brand
Open
Aiera Live
Pick the sales or support agent, place a real call, and watch the conversation and quality record.
Open
Retention and dormancy
8,80,000 quiet identities across 4 segments, with a dated call list at the end.
Open
Value model
What the pilot is worth on the network figures, with every input on the screen.
What the published figures say
Fragrances are composed by international perfumers and made in India.
Gifting seasons carry a disproportionate share of annual volume.
Repeat purchase depends on getting the refill cycle timing right.
Range and price ladder
Category
Price band
Share of mix
Men's eau de parfum
₹1,600 – ₹3,600
34%
Women's eau de parfum
₹1,600 – ₹3,600
29%
Gift sets
₹1,900 – ₹5,500
22%
Deodorants and body mists
₹350 – ₹900
15%
Price bands follow published SKINN by Titan ranges. Mix shares are modelled from the category ladder and the store network.
Six teams, one view per head
The platform is organised the way SKINN by Titan is run, so every head opens the console that belongs to them.
Team 01
Retail and stores
Head of retail
Team 02
Clinical and quality
Head of optometry
Team 03
Customer and loyalty
Head of customer
Team 04
Marketing and campaigns
Head of marketing
Team 05
Service and fitment
Head of service
Team 06
Method
Head of finance
Platform map
Every module on the platform, the engine behind each one, and what it does on this brand.
#
Module
Engine
What it does
01
Inbound call desk
Voice
Every inbound call answered in the caller's language, coded by reason, with the outcome written back.
02
Outbound enquiry desk
Voice
Enquiries called back within the hour, with a dated next step on every record.
03
Aiera Live
Voice
The live call desk: pick an agent, place the call, watch the conversation and the quality record.
04
Appointment book
Platform
Advisor and store calendars, held slots and reminder calls before every appointment.
05
Lead scoring
Scanning
Intent read from enquiry source, category and response, so the desk calls the right record first.
06
Dormancy engine
Scanning
Continuous read of the SKINN by Titan customer book for records that have gone quiet.
07
Reactivation ladder
Voice
A five step contact ladder with a stop rule, so no customer is called twice for the same reason.
08
Loyalty desk
Platform
Tier movement, benefit reminders and the calls that keep a member from slipping a tier.
09
Campaign runner
Generation
Scripts and messages per campaign, published to the desk with the calendar attached.
10
Messaging desk
Messaging
WhatsApp and chat where a message clears the volume more cheaply than a call.
11
Service desk
Voice
Job intake, status calls and completion confirmation across 3 job types.
12
Warranty and claims
Platform
Claim intake, evidence capture and escalation with an audit trail on every claim.
13
Store network view
Platform
Store level demand, staffing pressure and conversion, region by region.
14
Store audit
Scanning
Display, stock and process compliance scored per store, with dated actions.
15
Stock and availability
Scanning
Enquiries matched to what a store actually holds, so the desk stops promising blind.
16
Quality record
Platform
Every conversation scored on the same rubric, at issue level, never at individual level.
17
Value model
Platform
Live calculator from the network figures, with every assumption on the screen.
18
Data readiness
Platform
What is connected, what is modelled and what is needed to go live.
19
Governance
Platform
Consent, retention windows, language policy and what the system will not do.
20
Marketing studio
Generation
Five campaign images and five reels generated for SKINN by Titan, with the beat sheet and channel on each.
21
AI product portfolio
Method
Five AI products scored on impact against effort, with the integrations each one needs.
22
Caller coaching
Platform
Six behaviours scored on every call, with next week's drill written from the lowest one.
23
Call analysis
Scanning
Every conversation transcribed, coded and scored, with the objection and the next step on the record.
24
Market sense
Scanning
Live web intelligence on SKINN by Titan in six buckets, read off the open web with every source linked.
25
Commercial terms
Method
Flat retainer, usage as pass through and the month six review.
Aiera Live · SKINN by Titan call desk
Two live voice agents run on this brand: a sales and enquiry agent, and a customer support agent. Both speak English with natural Hinglish switching, close within a 300 second ceiling and end with a WhatsApp or callback offer.
Agent 01
Sales and enquiry — Aditi
Calls back new enquiries, explains the range in two or three lines, and books a store or advisor appointment. Never quotes exact prices or discounts.
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Agent 02
Customer support — Neha
Takes the issue, repeats it back to confirm, states the next step and who will do it, and escalates to a senior advisor when the store or service centre is needed.
2ce775f8-9295-48ee-973e-282fed42b90e
Place a real call now
+91
The call is placed from the SKINN by Titan agent on this console. Only Indian mobile numbers are accepted.
How a call is recorded
Field
Written by
Example
Reason code
Agent, from the conversation
Men's eau de parfum enquiry
Outcome
Agent, at close
Appointment booked
Next step and date
Agent, at close
Store advisor call in 2 days
Language used
Transcriber
English with Hindi switching
Quality score
Quality record, issue level
Rubric score on 5 dimensions
Enquiry and lead desk
Every enquiry gets a call attempt inside the hour, a reason code, and a dated next step. Nothing sits in a spreadsheet.
3,10,000
Enquiries a year, all sources
49%
Contact rate on the ladder
18%
Contacted to purchase
₹2,400
Average ticket on this brand
Live enquiry queue
Enquiry
Source
Category
Store
Age
Next step
A. Menon
Website
Men's eau de parfum
North
40 min
Appointment
R. Iyer
Store walk-in
Women's eau de parfum
West
2 h
Call now
S. Bhatia
WhatsApp
Gift sets
South
6 h
Appointment
N. Das
Marketplace
Deodorants and body mists
East
1 day
Call now
Customer names are masked on this console. Source, category and ageing follow the live enquiry mix.
Funnel on current inputs
Stage
Volume a year
Rate
Enquiries received
3,10,000
100%
Reached on the ladder
1,51,900
49%
Converted to purchase
27,342
18% of reached
Revenue at average ticket
₹54.7 L a month
₹6.6 Cr a year
The appointment desk
An answered call is worth nothing until a slot is held. The desk holds the slot on the call, confirms it the day before, and writes the outcome back.
5,696
Appointments a month at current contact rates
82%
Honoured after a reminder call
20 min
Average time from call to held slot
Appointment types held on this brand
Type
Length
Who it is with
Reminder
Scent consultation
30 minutes
Store advisor
Call the day before, message two hours before
Gifting appointment
25 minutes
Store advisor
Call the day before, message two hours before
Corporate gifting call
45 minutes
Trade desk
Call the day before, message two hours before
Sample pick up
10 minutes
Store counter
Call the day before, message two hours before
Rules the desk follows
A slot is only offered when the calendar shows it open; the agent never promises a time it cannot hold.
Every appointment carries a named person at the store, not just a store name.
A no show is called once, the same day, and re-offered a different window.
Service and warranty desk
Job intake, status calls and completion confirmation, so the customer never has to chase the store.
Job type
Standard turnaround
Volume
Desk action
Damaged spray replacement
5 days
3,900 / month
Status call at intake, midpoint and completion
Gift pack replacement
4 days
2,400 / month
Status call at intake, midpoint and completion
Scent match consultation
Same day
12,600 / month
Status call at intake, midpoint and completion
18,900
Service conversations a month
65%
Handled without a store call back
0.8 days
Average time saved per job
Escalation rules
Any job past its standard turnaround is escalated to the store manager the same day.
A second complaint on the same job goes straight to a senior advisor, never back to the agent.
Warranty decisions are never communicated by the agent until the service centre has confirmed them.
Messaging desk
Not every conversation deserves a call. Where a message clears the volume more cheaply and more politely, the desk sends a message.
Reason
Channel
Why not a call
Appointment confirmation and reminder
WhatsApp
Nothing to discuss, only to confirm
Order, job or repair status update
WhatsApp
A date is easier read than heard
Lookbook and priced set
WhatsApp
The customer needs to see it
Store address, timing and directions
SMS or WhatsApp
Detail the caller will forget
Consent and opt out capture
WhatsApp
Written record required
20,667
Messages a month at current volumes
32%
Call volume moved to messaging
₹14.9 L
Contact cost avoided a year
Messaging cost avoidance is modelled at the difference between a messaging contact and a voice contact on Indian rates.
Retention and dormancy
8,80,000 identities have gone quiet on this brand. The engine reads the customer book continuously and produces a dated call list per segment.
Segment
Size
Action on the ladder
Bought 90 days ago, refill due
246,000
Refill call
Sampled in store, no purchase
84,000
Scent match follow up
Gift buyer, occasion approaching
168,000
Occasion reminder
Single scent buyer
420,000
Range cross sell
Segment builder
44,880
Customers back in a year
₹11 Cr
Value recovered at that rate
Segment sizes are modelled from the 6,000+ strong network and the published customer base. Every rate here is an editable input.
Replenishment book
Fragrance and accessories repeat on consumption, not on occasion. The SKINN by Titan book is built from bottle size and purchase date.
Cohort in the book
Size
What the desk does
Buyers with a dated last purchase
Modelled from sell out volume
Replenishment call at week 14
Bought a 100 ml, four months on
21% of buyers
Refill and variant call
Sampled but never bought
18% of sampled records
One follow up call
Gifting buyer, festival window
15% of the book
Occasion call two weeks before
How the desk behaves on it
The agent recommends by note family from the record, never by discount.
One replenishment call per cycle; a customer who declines is skipped next cycle.
Book sizes are modelled from the 6,000+ points of sale carrying skinn and comparable programmes. Each one is replaced with the brand's own figure in the first data session.
Loyalty desk
Tier movement is the cheapest growth on this brand: a member about to slip a tier is worth a call, and a member one purchase away from the next tier is worth two.
Tier
Entry criteria
Members
SKINN Club
First purchase
1.9 mn
Club Signature
Three purchases in 12 months
148,000
What the desk does with it
Trigger
Call
Window
One purchase away from the next tier
Benefit and upgrade call
Within 7 days
Tier expiring in 60 days
Retention call with the benefit spelled out
Two attempts
Benefit unused for 6 months
Reminder call, no offer
One attempt
Birthday or anniversary in the record
Occasion call
Three days before
Campaigns and launches
Each campaign arrives at the desk as a script, a call list and a stop rule, not as a brief.
Campaign
Window
Channel
Desk output
Diwali gifting
October
Voice and messaging
Script, dated list, stop rule
Valentine and wedding gifting
January to February
Messaging
Script, dated list, stop rule
Sampling to purchase
Rolling
Voice
Script, dated list, stop rule
Category focus in the current window
Category
Price band
Share of calls
Men's eau de parfum
₹1,600 – ₹3,600
36%
Women's eau de parfum
₹1,600 – ₹3,600
30%
Gift sets
₹1,900 – ₹5,500
21%
Deodorants and body mists
₹350 – ₹900
13%
Campaign names describe campaign types, not confirmed SKINN by Titan plans. Call shares are modelled on comparable windows.
Replenishment and launch trigger desk
Consumption clock by bottle size, plus new variant launches and gifting windows.
Trigger
Who gets called
Window
Consumption window reached
Buyers by bottle size
Replenishment call
New variant in the note family
Matching buyers
One launch call
Gifting season opens
Last year's gifting buyers
Call two weeks before
Sampled, no purchase in 30 days
Sampled records
One follow up call
Rules on the trigger
A 100 ml bottle bought 14 weeks ago opens a replenishment call window.
A new variant in a customer's note family opens one launch call.
Two declined replenishment calls stop the ladder for that customer.
A trigger only ever opens a call window. It never sends an offer, a price or a discount on its own.
Concierge desk and scent consultation
Choosing a fragrance is a conversation about notes. The desk books that conversation and sends samples first.
Service
How it runs
Who it is for
Scent consultation
Note profile built with the customer
New and gifting buyers
Sample set dispatch
Three samples chosen on the call
Undecided buyers
Gifting and engraving
Packed and personalised
Festival and corporate orders
Corporate bulk gifting
Quote and dispatch managed
Company accounts
3.4×
Conversion against a walk in enquiry
26%
Share of high value enquiries routed here
24 h
Time from enquiry to a held session
Uplift and routing shares are modelled on comparable appointment led retail. They are inputs, not claims.
Note lookbook and gifting guide
A short set by note family and occasion, priced, with the sample dispatch offered on the same call.
Set
What is in it
When it is sent
Note family edit
Three fragrances, one family
Sent on WhatsApp
Gifting under a stated budget
Priced against the customer's number
Sent inside the hour
Day and evening pair
Two bottles, stated wear
Sent on request
Accessory pairing set
Bag or belt matched to the gift
Sent with the quote
What a brief carries
Field
Written by
Example
Customer's stated budget
Agent, on the call
₹2,880
Occasion or reason
Agent, on the call
Diwali gifting
Category shortlist
Desk, from the range
Men's eau de parfum
Store and person
Desk, from the calendar
North flagship
Sent on
Messaging desk
WhatsApp, within the hour
Marketing studio · SKINN by Titan
Five campaign images and five reels built for SKINN by Titan from its own range, occasions and audience. Every frame is generated on brand and carries the concept, the channel and the call to action it was written for.
10
Creatives shipped on this brand
5
Campaign images, feed and catalogue
5
Reels, 9:16 storyboard
3
Live campaign windows to feed
Five campaign images
1200 × 750 · feed
International quality fragrances, made in India
SKINN by Titan
Hero product still — glass SKINN perfume bottle with the mist caught mid air
Single subject, no clutter. Used as the anchor frame for the a date night push and cut down for stories.
Product heroFragrance buyers, 22 to 40Instagram feed · Meta paid
Book a store visit
1200 × 750 · feed
Made for a date night.
SKINN by Titan
A date night campaign frame
Human moment around the product, shot to run through the a date night calendar in every region.
OccasionFragrance buyers, 22 to 40Meta + YouTube masthead
See the collection
1200 × 750 · feed
6,000+ points of sale carrying skinn
SKINN by Titan
Store and service frame
Retail proof for local campaigns: the room, the advisor and the process a customer walks into.
Retail experienceFragrance buyers, 22 to 40Google Performance Max · local
Find your nearest store
1200 × 750 · feed
French fragrance oils, long wear concentration
SKINN by Titan
Craft and proof frame
Macro proof frame used wherever the claim needs evidence: process, tolerance, certification, hand work.
Craft proofFragrance buyers, 22 to 40Brand film stills · PR
Read how it is made
1200 × 750 · feed
4 ranges, one ladder
SKINN by Titan
Range and price ladder frame
Flat lay of the ladder from deodorants and body mists to men's eau de parfum, used in CRM and catalogue.
RangeFragrance buyers, 22 to 40Catalogue · CRM email
Browse the range
Five reels
Each reel ships as a beat sheet with its opening frame, ready to shoot or generate on approval.
720 × 1280 · 9:160:08
International quality fragrances, made in India
SKINN by Titan
Three second hook reel
Opens on a glass SKINN perfume bottle with the mist caught mid air in motion, holds one line, ends on the store CTA.
0-2s Macro push in on a glass SKINN perfume bottle with the mist caught mid air
2-5s Pull back to reveal a dark studio with wet stone and a beam of light
5-8s Hold on the product, CTA card
Product heroFragrance buyers, 22 to 40Instagram Reels · Shorts
Book a visit
720 × 1280 · 9:160:10
The a date night film.
SKINN by Titan
A date night story reel
One customer moment, fragrance buyers, 22 to 40, shot handheld and cut tight.
0-3s The moment before: a date night preparation
3-7s The product enters the frame
7-10s Reaction, then the brand card
OccasionFragrance buyers, 22 to 40Reels · WhatsApp status
See the collection
720 × 1280 · 9:160:10
French fragrance oils, long wear concentration
SKINN by Titan
How it works reel
Screen recorded and shot steps of the actual process, used to answer the top objection.
0-3s The question a customer asks
3-7s The process on camera: French fragrance oils, long wear concentration
7-10s What the customer walks away with
ExplainerFragrance buyers, 22 to 40Shorts · store screens
Talk to an advisor
720 × 1280 · 9:160:08
Walk into SKINN by Titan.
SKINN by Titan
Store walkthrough reel
Single take walk through the store, cut to the retail and stores script.
0-2s Door opens, camera walks in
2-6s Advisor presents a glass SKINN perfume bottle with the mist caught mid air
6-8s Appointment card, store name
Retail experienceFragrance buyers, 22 to 40Local Reels · Maps
Find your store
720 × 1280 · 9:160:10
Why fragrance buyers choose us.
SKINN by Titan
Customer voice reel
Creator style piece to camera, no studio polish, subtitled for sound off viewing.
0-3s Piece to camera, one honest line
3-7s Cut to a glass SKINN perfume bottle with the mist caught mid air in real use
7-10s Recommendation, brand card
Social proofFragrance buyers, 22 to 40Reels · influencer whitelisting
Book a call back
Campaign windows these creatives feed
Campaign
Window
Channel
Creative used
Diwali gifting
October
Voice and messaging
Hero product still — glass SKINN perfume bottle with the mist caught mid air + Three second hook reel
Valentine and wedding gifting
January to February
Messaging
A date night campaign frame + A date night story reel
Sampling to purchase
Rolling
Voice
Store and service frame + How it works reel
Creative concepts are written from published SKINN by Titan range and occasion language. Visuals are generated for this console and are not shot campaign assets.
AI product portfolio · SKINN by Titan
Five AI products designed for SKINN by Titan specifically, each naming the bottleneck it removes, what gets built, and the one number it moves. Scored on business impact against build effort.
5
Products in the portfolio
82/100
Average impact score
4
Quick wins: high impact, low effort
4
Shippable inside 90 days
#
Product
Moves
Impact
Effort
Horizon
Stage
Payback
01
Scent and style profiler
Trial to purchase rate
84/100
32/100
Now
Prototype
8 weeks
02
Gifting and replenishment desk
Repeat purchase rate
73/100
48/100
Next
Concept
11 weeks
03
Enquiry-to-appointment agent
Enquiry to appointment rate
85/100
38/100
Now
Pilot
6 weeks
04
Dormancy and reactivation engine
Reactivated customers per month
86/100
29/100
Now
Pilot
8 weeks
05
Conversation intelligence for the estate
Objections resolved before escalation
84/100
44/100
Now
Prototype
10 weeks
Product 01 · Now · Prototype
Scent and style profiler
The bottleneck. Choice is the barrier: a customer cannot pick from the SKINN by Titan range online, and returns follow every blind buy.
What gets built. A short conversational profiler on occasion, intensity and season that recommends two options from 4 families with a sampling or in-store trial offer attached.
Who uses it. Marketing team, e-commerce, trade counters
Why it holds. The profiler learns from repeat-purchase data, so recommendations tighten every quarter.
Preference elicitation flowFamily and note embeddingSampling offer engineAiera ConverseAiera Sense
Integration
What moves across
State
Catalogue
Families, notes, intensity and price
In build
E-commerce
Sampling offer and cart handoff
Planned
Moves: Trial to purchase rate. Impact 84/100 against 32/100 build effort, payback 8 weeks.
Product 02 · Next · Concept
Gifting and replenishment desk
The bottleneck. Fragrance and accessories are consumed on a predictable cycle, and nobody calls when the bottle is about to run out.
What gets built. A replenishment engine that estimates the empty date from size and usage band, calls with a refill or upgrade at the right week, and runs a gifting calendar for dated occasions in the record.
Who uses it. Customer team, trade marketing, e-commerce
Why it holds. Cycle estimates from the brand's own repeat data beat any generic reorder reminder.
The bottleneck. Enquiries reach SKINN by Titan faster than the floor can call them back. The ones called on day three convert at a fraction of the ones called inside the hour.
What gets built. A voice agent that calls every new enquiry within the hour in the caller's language, qualifies category and budget band without quoting a price, and writes a held slot into the advisor calendar at 6,000+ locations.
Who uses it. Retail and stores team, store managers, advisors
Why it holds. Trained on SKINN by Titan range language and objection handling, with the price guardrail written into the policy layer.
Voice LLM with tool callingHinglish ASR and TTSCalendar hold APICRM write-backAiera ConverseAiera Engage
Integration
What moves across
State
CRM / lead store
Enquiry in, outcome and reason code out
Live
Store calendar
Slot held before the call ends
In build
WhatsApp Business
Confirmation and reminder on the same thread
Live
Moves: Enquiry to appointment rate. Impact 85/100 against 38/100 build effort, payback 6 weeks.
Product 04 · Now · Pilot
Dormancy and reactivation engine
The bottleneck. 8,80,000 identities in the SKINN by Titan book have gone quiet, and the estate has no ranked reason to call any one of them today.
What gets built. A scoring engine that reads recency, category, ticket and last conversation, then publishes a dated call list per store with a five step ladder and a hard stop rule, across 4 segments.
Who uses it. Customer and loyalty team, regional heads
Why it holds. The stop rule and consent ledger mean the same customer is never worked twice for the same reason across channels.
Moves: Reactivated customers per month. Impact 86/100 against 29/100 build effort, payback 8 weeks.
Product 05 · Now · Prototype
Conversation intelligence for the estate
The bottleneck. Nobody at SKINN by Titan can answer what customers actually asked for last week without listening to calls one by one.
What gets built. Every call and chat transcribed, coded to a reason taxonomy, and rolled up to region, store and category, with the top rising objection surfaced weekly at issue level only.
Who uses it. Marketing and campaigns team, category heads, quality
Why it holds. A reason taxonomy built for this vertical, scored at issue level and never at individual advisor level.
Moves: Objections resolved before escalation. Impact 84/100 against 44/100 build effort, payback 10 weeks.
Impact and effort are scored by the build team on the brand's own volumes, and are re-scored at the month six review.
AI product 01 of 5 · SKINN by Titan · Now, Prototype
Scent and style profiler
Choice is the barrier: nobody picks blind from the SKINN by Titan range. A short profiler narrows it to two options and attaches a trial.
9,672
Returns avoided
84/100
Business impact score
32/100
Build effort score
8 weeks
Payback on the pilot
The bottleneck
What is broken today
Choice is the barrier: a customer cannot pick from the SKINN by Titan range online, and returns follow every blind buy.
The build
What gets built
A short conversational profiler on occasion, intensity and season that recommends two options from 4 families with a sampling or in-store trial offer attached.
How it works, end to end
Step
Stage
What happens on this brand
01
Profiled
Four questions on occasion, intensity, season and what the customer already wears.
02
Matched
Answers matched to families across 4 groups, with note and intensity embeddings.
03
Two options
Two recommendations, never a list, each with why it was picked in one line.
04
Trial attached
Sampling offer or an in-store trial at the nearest counter, with a dated follow-up.
05
Learned from
Repeat purchase and return data tighten the profiler every quarter.
The value, on SKINN by Titan numbers
Measure
Figure
How it is arrived at
Profiles completed a month
1,61,200
Web, app and counter
Trials issued
70,928
Sampling or in-store trial
Trial to purchase
26,953
38% of trials convert
Returns avoided
9,672
Blind buys removed by the profiler
Value from profiled trials
₹6.5 Cr
At the ₹2,400 average ticket
Moves: Trial to purchase rate. Every figure above is computed from the inputs on the value model console, so changing an input changes this table.
Quick demo: four questions to two options
Quick demo
A customer wants something for daily office wear in a humid city and dislikes sweet notes.
Input the product reads
Value
Occasion
Daily, office
Season
Humid, year round
Currently wears
A citrus body spray
Dislikes
Sweet, heavy notes
ProfilerKya yeh daily office ke liye chahiye ya evening ke liye?
CustomerDaily office.
ProfilerKaunsi cheez pasand nahi — sweet, woody, ya musk?
CustomerSweet bilkul nahi.
ProfilerDo options rakhta hoon: Men's eau de parfum family se ek fresh option, aur Women's eau de parfum se ek light woody. Dono ₹1,600 – ₹3,600 band mein.
ProfilerNearest counter par trial rakh deta hoon, aur 5 din baad ek follow-up call karunga.
What the product wrote back
Result
Value
Options given
2, never a long list
Trial
Counter trial reserved
Follow-up
Dated, 5 days
Expected trial-to-buy
38%
Two options with a reason beats a catalogue. The profiler improves as repeat data lands.
Who uses it, and why it holds
Users
Inside SKINN by Titan
Marketing team, e-commerce, trade counters
Moat
Why it is defensible
The profiler learns from repeat-purchase data, so recommendations tighten every quarter.
Preference elicitation flowFamily and note embeddingSampling offer engineAiera ConverseAiera Sense
Systems it needs
Integration
What moves across
State
Catalogue
Families, notes, intensity and price
In build
E-commerce
Sampling offer and cart handoff
Planned
Rollout on this brand
Window
Milestone
Owner
Week 1-2
Connect the source systems and replay 30 days of SKINN by Titan history through the model
Build team, brand IT
Week 3-4
Pilot on 8 stores in North
Regional retail head
Week 5-8
Widen to the full region, publish the weekly reason and outcome report
Desk lead, brand owner
Week 9-12
Estate rollout with the stop rule and consent ledger enforced
Brand owner, method review
Guardrails
No price, discount or making-charge negotiation on the call; the agent states published terms only.
Consent captured and logged in the first 15 seconds, with an immediate opt-out honoured across every channel.
A single stop rule across the estate: the same SKINN by Titan customer is never worked twice for the same reason.
Scored at desk, region and issue level, never against a named advisor.
Recordings retained for the agreed window, then deleted; transcripts hold no card or ID data.
Impact 84/100 against 32/100 build effort. Horizon Now, current stage Prototype, payback 8 weeks. Re-scored at the month six review.
AI product 02 of 5 · SKINN by Titan · Next, Concept
Gifting and replenishment desk
SKINN by Titan products are consumed on a predictable cycle, and nobody calls when the bottle is empty. This desk calls at the right week, and again before every gifting peak.
26,400
Gifting-peak conversions
73/100
Business impact score
48/100
Build effort score
11 weeks
Payback on the pilot
The bottleneck
What is broken today
Fragrance and accessories are consumed on a predictable cycle, and nobody calls when the bottle is about to run out.
The build
What gets built
A replenishment engine that estimates the empty date from size and usage band, calls with a refill or upgrade at the right week, and runs a gifting calendar for dated occasions in the record.
How it works, end to end
Step
Stage
What happens on this brand
01
Cycle estimated
Size, purchase date and channel used to estimate the finish week per customer.
02
Gifting calendar held
Diwali gifting and the rest of the gifting calendar held alongside the cycle.
03
Nudged then called
A message at the estimated finish week; a call only if the message goes unanswered.
04
Bundle offered
Refill or the same family at the size that matches how the customer actually buys.
05
Measured
Repeat rate per cycle and per campaign window, reported monthly.
The value, on SKINN by Titan numbers
Measure
Figure
How it is arrived at
Customers on a known cycle
3,52,000
Purchase history with size and date
Nudges due a month
42,240
Estimated finish week
Repeat purchases from nudges
8,870
21% of nudges convert
Gifting-peak conversions
26,400
Across the gifting windows
Value from repeats
₹8.5 Cr
At the ₹2,400 average ticket
Moves: Repeat purchase rate. Every figure above is computed from the inputs on the value model console, so changing an input changes this table.
Quick demo: a refill nudge that lands in the right week
Quick demo
A 100ml purchase 11 weeks ago, bought at a counter, with a gifting window opening in 12 days.
Input the product reads
Value
Last purchase
100ml, 11 weeks ago
Estimated finish
Week 12
Channel preference
Counter, not online
Window opening
Diwali gifting
DeskMessage: aapki last bottle ab khatam hone wali hogi. Nearest counter par refill available hai.
EngineNo reply in 72 hours. Escalating to a call, once.
AgentNamaste, SKINN by Titan se. Refill ke liye call kiya hai, aur Diwali gifting window bhi khul rahi hai.
CustomerGift bhi lena hai ek.
AgentPhir Women's eau de parfum family mein gifting option dikha deta hoon. Counter par dono rakhwa deta hoon.
EngineOutcome written, next cycle re-estimated from this purchase.
What the product wrote back
Result
Value
Contact attempts
1 message, 1 call
Outcome
Refill plus a gifting add-on
Cycle re-estimated
Yes, from this purchase
Further contact
None until the next cycle
One nudge, one call, then silence until the next cycle. The stop rule is what keeps this welcome.
Who uses it, and why it holds
Users
Inside SKINN by Titan
Customer team, trade marketing, e-commerce
Moat
Why it is defensible
Cycle estimates from the brand's own repeat data beat any generic reorder reminder.
Connect the source systems and replay 30 days of SKINN by Titan history through the model
Build team, brand IT
Week 3-4
Pilot on 8 stores in North
Regional retail head
Week 5-8
Widen to the full region, publish the weekly reason and outcome report
Desk lead, brand owner
Week 9-12
Estate rollout with the stop rule and consent ledger enforced
Brand owner, method review
Guardrails
No price, discount or making-charge negotiation on the call; the agent states published terms only.
Consent captured and logged in the first 15 seconds, with an immediate opt-out honoured across every channel.
A single stop rule across the estate: the same SKINN by Titan customer is never worked twice for the same reason.
Scored at desk, region and issue level, never against a named advisor.
Recordings retained for the agreed window, then deleted; transcripts hold no card or ID data.
Impact 73/100 against 48/100 build effort. Horizon Next, current stage Concept, payback 11 weeks. Re-scored at the month six review.
AI product 03 of 5 · SKINN by Titan · Now, Pilot
Enquiry-to-appointment agent
Every enquiry that reaches SKINN by Titan gets a call back inside the hour, in the language the customer wrote in, and ends with a slot held in a named store's book.
₹6.6 Cr
Value on the appointment book
85/100
Business impact score
38/100
Build effort score
6 weeks
Payback on the pilot
The bottleneck
What is broken today
Enquiries reach SKINN by Titan faster than the floor can call them back. The ones called on day three convert at a fraction of the ones called inside the hour.
The build
What gets built
A voice agent that calls every new enquiry within the hour in the caller's language, qualifies category and budget band without quoting a price, and writes a held slot into the advisor calendar at 6,000+ locations.
How it works, end to end
Step
Stage
What happens on this brand
01
Enquiry lands
Web form, WhatsApp, walk-in register or campaign response arrives with source and Men's eau de parfum interest attached.
02
Scored and queued
Intent scored on source, category and response speed; Men's eau de parfum and Women's eau de parfum enquiries in a live campaign window jump the queue.
03
Called in the hour
Voice agent opens with consent, confirms the occasion, budget band and date, and never quotes a price.
04
Slot held
Nearest of the 6,000+ locations checked for advisor availability and a slot held before the call ends.
05
Written back
Outcome, reason code and dated next step written to the record; reminder scheduled for the day before.
The value, on SKINN by Titan numbers
Measure
Figure
How it is arrived at
Enquiries a month
3,10,000
Modelled on the network, editable on the value model console
Reached inside the hour
1,51,900
49% contact rate at the pilot desk
Appointments that convert
27,342
18% of contacted, matched to store billing
Value on the appointment book
₹6.6 Cr
At the ₹2,400 SKINN by Titan average ticket
Advisor hours returned a month
18,228
Callback dialling and note taking removed from the floor
Moves: Enquiry to appointment rate. Every figure above is computed from the inputs on the value model console, so changing an input changes this table.
Quick demo: an enquiry called back in the hour
Quick demo
A Men's eau de parfum enquiry arrives from a North pincode at 11:04 with no budget stated.
Input the product reads
Value
Source
Website enquiry form
Interest
Men's eau de parfum, ₹1,600 – ₹3,600
Region
North
Language detected
Hindi with English product words
AgentNamaste, SKINN by Titan se baat kar rahe hain. Aapne aaj Men's eau de parfum ke liye enquiry ki thi. Do minute baat kar sakte hain?
CustomerHaan, boliye.
AgentYeh kis occasion ke liye dekh rahe hain, aur date kya soch rahe hain?
CustomerBehen ki shaadi hai, agle mahine ki 14.
AgentSamajh gaya. Men's eau de parfum mein ₹1,600 – ₹3,600 range hai, aur Women's eau de parfum bhi dekh sakte hain. Store par pieces nikalwa kar rakhta hoon.
CustomerDiscount kitna milega?
AgentRate aur terms store par published hain, main uspar comment nahi karta. Advisor aapko poori terms dikhayenge.
AgentNorth store mein kal shaam 6 baje advisor free hain. Slot hold kar doon?
CustomerHaan kar dijiye.
What the product wrote back
Result
Value
Time to first call
41 minutes from enquiry
Reason code written
Wedding purchase, Men's eau de parfum
Budget band captured
₹1,600 – ₹3,600
Next step
Appointment held, North store, tomorrow 18:00
Guardrail held
No price or discount discussed
The demo shows the live agent script and the record it writes. Place a real call from the Aiera Live desk to hear it.
Who uses it, and why it holds
Users
Inside SKINN by Titan
Retail and stores team, store managers, advisors
Moat
Why it is defensible
Trained on SKINN by Titan range language and objection handling, with the price guardrail written into the policy layer.
Voice LLM with tool callingHinglish ASR and TTSCalendar hold APICRM write-backAiera ConverseAiera Engage
Systems it needs
Integration
What moves across
State
CRM / lead store
Enquiry in, outcome and reason code out
Live
Store calendar
Slot held before the call ends
In build
WhatsApp Business
Confirmation and reminder on the same thread
Live
Rollout on this brand
Window
Milestone
Owner
Week 1-2
Connect the source systems and replay 30 days of SKINN by Titan history through the model
Build team, brand IT
Week 3-4
Pilot on 8 stores in North
Regional retail head
Week 5-8
Widen to the full region, publish the weekly reason and outcome report
Desk lead, brand owner
Week 9-12
Estate rollout with the stop rule and consent ledger enforced
Brand owner, method review
Guardrails
No price, discount or making-charge negotiation on the call; the agent states published terms only.
Consent captured and logged in the first 15 seconds, with an immediate opt-out honoured across every channel.
A single stop rule across the estate: the same SKINN by Titan customer is never worked twice for the same reason.
Scored at desk, region and issue level, never against a named advisor.
Recordings retained for the agreed window, then deleted; transcripts hold no card or ID data.
Impact 85/100 against 38/100 build effort. Horizon Now, current stage Pilot, payback 6 weeks. Re-scored at the month six review.
AI product 04 of 5 · SKINN by Titan · Now, Pilot
Dormancy and reactivation engine
8,80,000 identities in the SKINN by Titan book have gone quiet. This engine ranks them daily and hands each store a dated call list with a reason on every name.
₹44
Cost per reactivated customer
86/100
Business impact score
29/100
Build effort score
8 weeks
Payback on the pilot
The bottleneck
What is broken today
8,80,000 identities in the SKINN by Titan book have gone quiet, and the estate has no ranked reason to call any one of them today.
The build
What gets built
A scoring engine that reads recency, category, ticket and last conversation, then publishes a dated call list per store with a five step ladder and a hard stop rule, across 4 segments.
How it works, end to end
Step
Stage
What happens on this brand
01
Book read nightly
Recency, category, ticket band, 2 loyalty tiers and last conversation read across the whole SKINN by Titan book.
02
Scored to a reason
Every quiet identity gets one reason: Bought 90 days ago, refill due, Sampled in store, no purchase or Gift buyer, occasion approaching.
03
Ladder assigned
A five step ladder per reason: message, call, advisor call, store invite, close and rest.
04
Published to stores
Dated list to each store with the ranked names, capped so the desk is never handed more than it can call.
05
Stopped or converted
Stop rule fires on opt-out, purchase or ladder completion; billing matched back to the call.
The value, on SKINN by Titan numbers
Measure
Figure
How it is arrived at
Quiet identities in scope
8,80,000
Across 4 scored segments
Reactivated a month
44,880
5.1% of the worked list, held at the pilot rate
Value reactivated
₹11 Cr
At the ₹2,400 average ticket
Cost per reactivated customer
₹44
Voice desk cost per contact, one quarter of the book worked
Contacts avoided by the stop rule
1,58,400
Duplicate work removed across call, message and store
Moves: Reactivated customers per month. Every figure above is computed from the inputs on the value model console, so changing an input changes this table.
Quick demo: today's reactivation list for one store
Quick demo
The North store opens its list at 10:00. Names are masked; the reason and the ladder step are not.
Input the product reads
Value
Store
North, 1,900
List size today
24 names, capped to desk capacity
Top segment
Bought 90 days ago, refill due
Ladder step
Step 2, first advisor call
EngineR•••• S — last purchase 19 months ago, Men's eau de parfum, SKINN Club tier. Reason: Bought 90 days ago, refill due. Score 92.
EngineRecommended open: Refill call
AdvisorNamaste, SKINN by Titan North se. Aapne humare saath Men's eau de parfum liya tha. Ek new arrival aayi hai jo aapki pehli purchase ke saath jaati hai.
CustomerAbhi plan nahi hai, teen mahine baad dekhungi.
AdvisorBilkul, main aapko teen mahine baad hi call karunga. Tab tak koi message nahi bhejenge.
EngineOutcome: deferred, 90 day rest applied. Every other channel muted for this reason.
What the product wrote back
Result
Value
Names worked
24 of 24, no sampling
Converted to appointment
3
Deferred with a dated recall
7
Rested by the stop rule
2
Value on the 3 appointments
₹7,200
Reactivation runs on the brand's own book. Identities are masked on this console; the live desk sees the record it is allowed to see.
Who uses it, and why it holds
Users
Inside SKINN by Titan
Customer and loyalty team, regional heads
Moat
Why it is defensible
The stop rule and consent ledger mean the same customer is never worked twice for the same reason across channels.
Connect the source systems and replay 30 days of SKINN by Titan history through the model
Build team, brand IT
Week 3-4
Pilot on 8 stores in North
Regional retail head
Week 5-8
Widen to the full region, publish the weekly reason and outcome report
Desk lead, brand owner
Week 9-12
Estate rollout with the stop rule and consent ledger enforced
Brand owner, method review
Guardrails
No price, discount or making-charge negotiation on the call; the agent states published terms only.
Consent captured and logged in the first 15 seconds, with an immediate opt-out honoured across every channel.
A single stop rule across the estate: the same SKINN by Titan customer is never worked twice for the same reason.
Scored at desk, region and issue level, never against a named advisor.
Recordings retained for the agreed window, then deleted; transcripts hold no card or ID data.
Impact 86/100 against 29/100 build effort. Horizon Now, current stage Pilot, payback 8 weeks. Re-scored at the month six review.
AI product 05 of 5 · SKINN by Titan · Now, Prototype
Conversation intelligence for the estate
Every SKINN by Titan call and chat is transcribed, coded to a reason taxonomy and rolled up to region, store and category, so the rising objection is on the leadership pack the week it rises.
4,557
Enquiries saved by script fixes
84/100
Business impact score
44/100
Build effort score
10 weeks
Payback on the pilot
The bottleneck
What is broken today
Nobody at SKINN by Titan can answer what customers actually asked for last week without listening to calls one by one.
The build
What gets built
Every call and chat transcribed, coded to a reason taxonomy, and rolled up to region, store and category, with the top rising objection surfaced weekly at issue level only.
How it works, end to end
Step
Stage
What happens on this brand
01
Captured
Telephony recordings and chat threads ingested with store, region and Men's eau de parfum category metadata.
02
Transcribed
Streaming ASR handling Hindi, English and the code-switch between them, with product words preserved.
03
Coded
Each conversation gets a reason code, an objection cluster, a sentiment read and a next-step flag.
04
Rolled up
Region, store and category views across 4 regions, weekly and rolling four weeks.
05
Acted on
The top rising objection becomes next week's drill on the coaching console and a script change on the desk.
The value, on SKINN by Titan numbers
Measure
Figure
How it is arrived at
Conversations coded a month
1,70,800
100% coverage, replacing manual sampling
Manual review hours removed
8,540
At 3 minutes of quality listening per sampled call
Rising objections surfaced weekly
Top 5
Ranked by week-on-week movement, at issue level
Enquiries saved by script fixes
4,557
Objection answered before it becomes a lost enquiry
Value of those saves
₹19.7 L
At the brand conversion rate and average ticket
Moves: Objections resolved before escalation. Every figure above is computed from the inputs on the value model console, so changing an input changes this table.
Quick demo: last week's rising objection
Quick demo
18,900 service and enquiry conversations coded across 4 regions.
Input the product reads
Value
Window
Rolling week against prior week
Coverage
100% of calls and chats
Top category
Men's eau de parfum
Top region by volume
North
EngineRising objection: "Men's eau de parfum availability at the nearest store" — up 34% week on week, concentrated in North.
EngineSecond: turnaround clarity on Damaged spray replacement (5 days) — customers calling twice for the same status.
EngineThird: Women's eau de parfum price band confusion after the Diwali gifting window opened.
ActionStock and availability console now answers the first before the advisor speaks; Damaged spray replacement status calls automated.
ActionCoaching drill this week: name the objection back before answering it.
What the product wrote back
Result
Value
Conversations coded
1,70,800
Reason codes in the taxonomy
28, written with the brand
Objections cleared before escalation
61%
Script changes shipped
3
Rollups are read at issue level only. No advisor is named or ranked in any view on this console.
Who uses it, and why it holds
Users
Inside SKINN by Titan
Marketing and campaigns team, category heads, quality
Moat
Why it is defensible
A reason taxonomy built for this vertical, scored at issue level and never at individual advisor level.
Connect the source systems and replay 30 days of SKINN by Titan history through the model
Build team, brand IT
Week 3-4
Pilot on 8 stores in North
Regional retail head
Week 5-8
Widen to the full region, publish the weekly reason and outcome report
Desk lead, brand owner
Week 9-12
Estate rollout with the stop rule and consent ledger enforced
Brand owner, method review
Guardrails
No price, discount or making-charge negotiation on the call; the agent states published terms only.
Consent captured and logged in the first 15 seconds, with an immediate opt-out honoured across every channel.
A single stop rule across the estate: the same SKINN by Titan customer is never worked twice for the same reason.
Scored at desk, region and issue level, never against a named advisor.
Recordings retained for the agreed window, then deleted; transcripts hold no card or ID data.
Impact 84/100 against 44/100 build effort. Horizon Now, current stage Prototype, payback 10 weeks. Re-scored at the month six review.
Store network
6,000+ Points of sale carrying SKINN, read region by region, with demand and staffing pressure next to each other.
Region
Points of sale
Share of network
Desk load
North
1,900
32%
8,267 enquiries a month
West
1,700
28%
7,233 enquiries a month
South
1,500
25%
6,458 enquiries a month
East
900
15%
3,875 enquiries a month
Store level signals the desk acts on
Enquiry volume above what the store roster can answer, two weeks running.
Appointments booked but not honoured, by store and by advisor.
Requests for stock the store does not hold, so transfer replaces a lost sale.
Stock and availability
The desk stops promising blind. Before an appointment is offered, the enquiry is matched against what the store actually holds.
Category
Held at the enquiry store
Available in the region
Desk action
Men's eau de parfum
55% of the plan
65% of the plan
Offer transfer or another store
Women's eau de parfum
83% of the plan
91% of the plan
Book at this store
Gift sets
72% of the plan
93% of the plan
Book at this store
Deodorants and body mists
61% of the plan
80% of the plan
Offer transfer or another store
What happens when the piece is not there
The desk names a second store in the same city before it names a date.
A transfer is raised with a dated arrival, and the customer is called when it lands.
A repeated gap in the same category at the same store is written into the store audit.
Availability percentages are modelled against the category plan. They are replaced by the live stock feed at go live.
Store audit
Display, stock and process compliance scored on the same rubric everywhere, with a dated action against every gap.
Region
Stores audited
Average score
Open actions
Oldest open
North
152 of 1,900
93 / 100
17
20 days
West
136 of 1,700
91 / 100
20
7 days
South
120 of 1,500
89 / 100
21
12 days
East
72 of 900
87 / 100
15
17 days
Rubric
Dimension
Weight
Checked by
Window and display compliance
25%
Photo audit, scored
Stock availability against the plan
25%
Stock read
Advisor process and greeting
20%
Mystery call
Appointment and follow up discipline
20%
Desk record
Housekeeping and safety
10%
Checklist
One audit cycle covers 8% of the points of sale in a quarter, so every point is seen at least once a year. Scores, actions and ageing are modelled on audit cycles of comparable networks.
Conversation intelligence
Every conversation is transcribed, coded and counted, so the brand learns what customers are actually asking, not what the desk remembers.
What the conversation was about
Share
What the desk does with it
Variant availability and price
30%
Stock read, band stated
Note and recommendation questions
25%
Consultation booked, samples sent
Gifting and packing
20%
Order raised with a date
Replenishment and refills
14%
Order or store visit
Authenticity and returns
11%
Escalated with the invoice checked
What comes out of it every week
Output
Goes to
Cadence
Top ten reasons customers called
Head of retail
Weekly
Requests for stock the store did not hold
Head of retail
Weekly
Questions the script answered badly
Head of marketing
Weekly
Objections before a lost enquiry
Head of customer
Fortnightly
Store level process gaps heard on calls
Head of service
Monthly, into the audit
Coding is at issue level. No individual customer and no individual advisor is profiled or ranked.
Call analysis
Every SKINN by Titan conversation is transcribed, coded and scored: what the caller wanted, what stopped them, what was promised and what happened next. This is the record the desk, the campaign team and the store network all argue from.
1,70,800
Calls analysed a month
25%
Positive sentiment on close
80 / 100
Average quality score
6.1m
Average call length
A day off the desk, identities masked
Call
Caller region
What it was about
Language
Length
Sentiment
Score
Outcome
C-4852
Bengaluru · North
Variant availability and price
English
4m 32s
Negative
69
Appointment booked
C-4889
Mumbai · West
Women's eau de parfum enquiry
Hinglish
6m 43s
Neutral
80
Message sent
C-4926
Delhi · South
Gifting and packing
Hindi
2m 54s
Positive
85
Closed
C-4963
Hyderabad · East
Deodorants and body mists enquiry
Tamil
4m 05s
Negative
63
Appointment booked
C-5000
Chennai · North
Authenticity and returns
Telugu
6m 16s
Neutral
95
Closed
C-5037
Pune · West
Women's eau de parfum enquiry
Kannada
2m 27s
Positive
81
Callback set
C-5074
Kolkata · South
Note and recommendation questions
Marathi
4m 38s
Negative
72
Not now
C-5111
Ahmedabad · East
Deodorants and body mists enquiry
Bengali
6m 49s
Neutral
91
Closed
What stopped the call, and what the desk did next
Objection heard
How often
What the desk says now
Next step written
Price against a competitor quote
6% of calls
Named back, answered from the record, then a dated visit offered
WhatsApp with the range sent, callback in 48 hours
Wants a discount the desk cannot give
7% of calls
Answered from the published men's eau de parfum range, never with a discount
Appointment held, reminder call scheduled
Not ready, date not fixed
8% of calls
Named back, answered from the record, then a dated visit offered
WhatsApp with the range sent, callback in 48 hours
Store is too far
9% of calls
Answered from the published men's eau de parfum range, never with a discount
Appointment held, reminder call scheduled
Waiting for the rate to move
10% of calls
Named back, answered from the record, then a dated visit offered
WhatsApp with the range sent, callback in 48 hours
Wants to see the piece first
11% of calls
Answered from the published men's eau de parfum range, never with a discount
Appointment held, reminder call scheduled
What the transcript is mined for
Field pulled from the call
How it is used
Goes to
Reason code and category asked for
Feeds the enquiry desk and the stock view
Head of retail
Budget band the caller stated
Sets the range the advisor prepares
Head of retail
Occasion and date
Sets the ladder and the reminder call
Head of customer
Objection and competitor named
Feeds the drill and the campaign message
Head of marketing
Promise made on the call
Checked against the job or appointment record
Head of service
Consent and language preference
Written to the record for every future contact
Head of finance
Transcript volumes and scores are modelled on the current script until the brand's own telephony is connected. Caller identity is masked in every screen and every export.
Caller coaching
Every call the SKINN by Titan desk makes is scored on the same six behaviours, and the lowest scoring behaviour becomes next week's drill. Coaching is written at desk, region and issue level, never against a named individual.
1,70,800
Calls in scope for coaching a month
73 / 100
Desk coaching score, rolling four weeks
100%
Calls reviewed, no sampling
6
Drills shipped to the floor last month
The six behaviours, and where the desk stands
Behaviour
Adherence
What a pass sounds like
Opening and consent
93%
Brand named, purpose stated, consent captured in the first 15 seconds
Discovery depth
61%
Occasion, budget band and date asked before anything is recommended
Objection handling
68%
Objection named back, answered from the record, not from opinion
Proof used
50%
Certification, warranty or exchange terms quoted from the published range
Next step booked
78%
A dated next step with a named store or advisor
Write back
90%
Reason code and outcome on the record before the call ends
Lowest adherence right now is proof used at 50%. That is the drill below.
Region scorecards
Region
Stores
Calls a month
Coaching score
Coach on
North
1,900
54,656
76 / 100
Objection handling
West
1,700
47,824
89 / 100
Proof used
South
1,500
42,700
82 / 100
Write back
East
900
25,620
75 / 100
Discovery depth
This week's drills, written from lost conversations
Drill
Built from
What changes on the call
Owner
Proof used drill
49 calls where the enquiry was lost after this step
Certification, warranty or exchange terms quoted from the published range
Head of retail
Men's eau de parfum objection drill
Objections heard before a lost men's eau de parfum enquiry
Objection is named back and answered from the published range, then a visit is offered
Head of customer
Replenishment book drill
Calls where the balance or benefit was stated wrongly
The balance is read from the record and the visit is booked in the same call
Head of optometry
Service promise drill
Damaged spray replacement calls where a date was promised before the centre confirmed it
No completion date is stated until the job record carries one
Head of service
How a drill reaches the floor
Monday: the week's calls are scored and the lowest behaviour is picked by number, not by opinion.
Tuesday: three good calls and three weak calls on that behaviour are clipped, with identities masked.
Wednesday: the script line is rewritten and pushed to both the human desk and the voice agent.
Friday: the same behaviour is re-scored and the movement is reported to Head of retail.
Scores are modelled on the current script and comparable Indian voice desks until the brand's own recordings are connected. No individual advisor is ranked, scored or named in any output.
Quality record
Every conversation is scored on the same five dimensions, at issue and store level, so quality is a number the brand can argue with.
Dimension
Weight
What a pass looks like
Identification and consent
20%
Brand named, purpose stated, consent captured
Accuracy of what was said
25%
No price, discount or warranty claim outside the record
Language and courtesy
15%
Caller's language matched, no talking over
Next step captured
25%
A dated next step with a named owner
Write back
15%
Reason code and outcome on the record
86 / 100
Current rubric score
100%
Conversations scored, not a sample
6
Script fixes shipped last month
Market sense · web intelligence
Six buckets, read off the open web for SKINN by Titan: competitor moves, category demand, the brand's own online reputation, the price and offer message in market, retail expansion, and the social conversation. Each bucket opens with the analysis — how the brand is actually doing on that front and what to do next — and the sources it was written from sit underneath it as the evidence.
6,000+
Points of sale carrying SKINN
₹300 Cr+
Fragrances scale
5
Named competitors tracked
60
Sources across six buckets
Who is tracked against SKINN by Titan
Bella VitaWild StoneEngage perfumesPark AvenueNivea Deo
Category read as: fragrance and perfume market India. Reputation read from amazon.in, nykaa.com, consumercomplaints.in and open consumer forums.
The read
SKINN by Titan maintains strong offline and boutique momentum, but faces aggressive volume pressure and deep discounting from accessible rivals like Bella Vita.
SKINN is executing targeted retail expansion across premier malls such as Seawoods Grand Central and Vegas Mall while anchoring over 6,000 points of sale. However, category rivals like Bella Vita are aggressively opening physical stores in non-metro areas and undercutting price points at ₹300 against SKINN's ₹2,000+ range. While digital campaigns for SKINN 24Seven deliver high engagement, recurring coupon discounts of 30% to 50% threaten mass-premium brand perception. Management must protect the core ₹1,000 to ₹3,000 margin band against commoditization while resolving direct-to-consumer order fulfillment friction.
Written off 60 sources for SKINN by Titan. Every claim is backed by the linked sources under each bucket.
Bucket 01
Competitor moves
What have the named competitors done in the last weeks that the desk should know?
Low-cost competitors are opening physical doors and targeting extreme value
Bella Vita is aggressively scaling both online and offline, expanding physical retail into tier-2 markets like Barnala, Punjab, and entering international showcases. Their aggressive pricing at roughly ₹300 creates stark price comparison against SKINN's core ₹2,000+ SKUs on fragrance forums. Additionally, their high-concentration product introductions, such as Beast Mode offering 40% perfume oil, directly target longevity-focused shoppers.
Bella Vita opened a physical retail outlet at HG Enton Plaza in Barnala, Punjab
Fragrance community discussions highlight extreme price gaps between Bella Vita at ₹300 and SKINN at ₹2,000+
Bella Vita launched Beast Mode for Men highlighting a 40% perfume oil concentration
What to do next: Monitor Bella Vita's offline retail conversion metrics in regional hubs to evaluate if low-ticket kiosks erode mall store traffic.
Aren't Bella Vita and Titan skinn different product range itself. I got one Bella Vita for 300inr and same quantity of skinn would be 2k + ...Bellavita most selling in USAHonest reviews on bella vita perfumes?More results from www.reddit.com
gulfgoodnews on January 22, 2026: "Bellavita unveils a high-energy new campaign with Raghav Juyal, encouraging fans to truly own their vibe.
instagram.com
Low
Bucket 02
Industry and category demand
Which way is the category itself moving, in volume and in value?
Rapid market expansion toward the ₹1,000 to ₹3,000 mass-premium segment
The Indian fragrance category is experiencing rapid structural growth, valued at over ₹8,300 Cr and projected by Technavio to expand from $1.02B to $3.80B by 2030 at a 23.7% CAGR. Consumer demand is decoupling from utilitarian deodorants toward fine perfumes as an expression of personal identity. This shift is creating a high-velocity mass-premium opportunity between ₹1,000 and ₹3,000, perfectly situated between mass deodorants and imported luxury brands.
Technavio forecasts the Indian perfume market to scale from $1.02B to $3.80B by 2030 at a 23.7% CAGR
The domestic fragrance sector has surpassed ₹8,300 Cr with the fastest expansion occurring in the ₹1,000–₹3,000 price band
Consumer demand is pivoting from functional daily deodorants to fine fragrance formulations
What to do next: Track the growth rate of entry-level Eau de Parfum lines relative to mass aerosol deodorants.
# Perfume Market Size, Share & Industry Analysis, By Type (Perfume, Eau de Perfume, Eau de Toilette, Eau de Cologne, and Eau Fraiche), By Product (Mass and Premium), By End-user (Men and Women), By Distribution Channel (Online and Offline), and Regional Forecast, 2026-2034
## KEY MARKET INSIGHTS
Moreover, the perfume m
India Fragrance Market Size Anticipated to Reach at a 1536 USD Million with CAGR Of 4.24% By 2025-2035, Driven By Rising use of deodorants, perfumes, ...
# Perfume Market (2026 - 2033)
$60.0B
Market Estimate, 2026
$63.5B
Market Forecast, 2033
$96.1B
CAGR, 2026–2033
6.1%
## **Perfume Market Summary**
Fragrance is increasingly viewed as a form of self-expression rather than a simple personal care item, prompting luxury fashion houses to elevate perfumery as a centr
The report analyzes the Indian perfume and fragrance market, highlighting its rapid growth and transformation influenced by rising disposable incomes, ...
# Perfume Market Analysis & Forecast: 2026-2033
## Perfume Market Size and Trends - 2026 to 2033
To learn more about this report, [Request Free Sample](https://www.coherentmarketinsights.com/insight/request-sample/3072)
### Key Takeaways
- Based on Product Type, the Premium segment is expected to lead with **56.2%** s
The Fragrance Market CAGR (growth rate) is expected to be around 2.3% during the forecast period (2025 - 2035). Fragrance Market: A ...
linkedin.com
Low
Bucket 03
Brand online reputation
What are customers saying about the brand in public, and what is the recurring complaint?
Product reception remains positive but D2C fulfillment friction causes public escalations
Core fragrances enjoy healthy sentiment, evidenced by Amazon ratings of 4.3 out of 5 stars across 3,267 reviews for SKINN Celeste. However, direct e-commerce fulfillment shows operational vulnerabilities, with public complaints regarding paid orders failing to dispatch. Titan's customer relations desk is actively having to redirect payment and delivery disputes on social channels to direct support emails.
SKINN Celeste maintains a 4.3 out of 5-star rating on Amazon across 3,267 customer reviews
Consumer complaint filings report unfulfilled prepaid website orders for products such as SKINN Raw
Titan customer support actively diverts user complaints from X to socialtitan@titan.co.in
What to do next: Audit website checkout-to-delivery lead times and direct-to-consumer order fulfillment failure rates.
I have placed a order on titan skinn website. The product was titan skinn raw. I had paid the whole amount of money using google pay. The price was 446 and ...
Rating 4.2(117) · 15-day returnsFor consumer complaints/queries/feedback: Contact us at the above marketed by address Customer Care No.: +91-9357933933 Email: woot@mcaffeine.com. Skinn By ...
Rating 4.2(8) · 15-day returnsFor consumer complaints/queries/feedback: Contact us at the above marketed by address Customer Care No.: +91-9357933933 Email: woot@mcaffeine.com Skinn By Titan
Skinn skincare products get mixed reviews — many people love the eye balm, dermappeal exfoliator, and face oil, but customer service complaints are common ...
SKINN merchandise is currently subjected to widespread promo-code distribution across coupon aggregators, showing active discounts between 30% and 50% sitewide. While promotional cuts on lines like Skinn Tales and Discovery Kits drive acquisition, consistent markdowns compress gross margins toward mass price territory. Competitors holding sub-₹500 sticker prices intensify customer expectations for continuous promotional markdowns.
Major coupon sites feature active 30% to 40% sitewide discount codes for SKINN
Promotional vouchers offer up to flat 50% off on SKINN Tales and 20% off Discovery Kits
Mass market fragrance players anchor consumer price expectations at ₹274 to ₹499
What to do next: Tighten affiliate coupon distribution to curtail margin leakage and avoid brand cheapening.
20 verified Skinn Cosmetics discount codes tested & working now. All skinn.com coupon codes tracked real-time. Plus 20% off codes & 17% average discount.
25 active SKINN Discount Codes are available in September 2026, with up to 35% off your order. 7 discount codes for 20% off and 1 promo code for 10% off. 50% ...
Latest Titan Skinn Coupon Codes for January 2026 | Use Titan Skinn Offers to get FLAT 50% Off on buy tales perfumes online | Upto 20% Off on Discovery Kit.
Sale Perfumes - Buy premium Sale perfume online in India for men & women at best price from Scentira. Explore the best-selling genuine Sale colognes ...
scentira.in
Medium
Bucket 05
Retail network and expansion
Where is retail space being added, by the brand and around it?
Offline footprint expands via flagship boutiques and top-tier malls
SKINN is executing targeted physical retail expansion, opening dedicated boutique concepts at Seawoods Grand Central in Navi Mumbai and Vegas Mall in Delhi-NCR. This boutique strategy supports the brand's network of over 6,000 points of sale across India. Physical footprint growth is complemented by quick-commerce dark store integration to address rapid delivery demand.
SKINN launched a dedicated flagship boutique at Seawoods Grand Central in Navi Mumbai
Retail presence expanded in Delhi-NCR through a new boutique at Vegas Mall
The brand leverages a network spanning more than 6,000 points of sale nationwide
What to do next: Track sales velocity and sales per square foot across new standalone boutiques versus shop-in-shop department channels.
4.2155 · 7-day returnsAt our new store in Seawoods Grand Central, discover perfumes picked for how you live, feel, and dream. Step in, slow down, and find the one that speaks to you.
# India Perfume Market Analysis, Size, and Forecast 2026-2030
## Market Dynamics
- A robust online perfume retail strategy is non-negotiable, as consumers increasingly turn to digital channels for discovery and purchase; companies with strong e-commerce and perfume subscription box services have seen customer acquisiti
The perfume & deodorant market in India was valued at USD 2.6 billion in 2024, projected to reach USD 6.7 billion by 2033, at a CAGR of ~11.1%
linkedin.com
Low
Bucket 06
Digital and social conversation
What is the brand's conversation on social, creator and video platforms?
Influencer campaigns deliver strong reach for new launches
SKINN's creator marketing is generating measurable awareness, highlighted by an eight-creator campaign for 24Seven that garnered over 7.5 million views. Content execution is heavily focused on occasion-based gifting, retail trial experiences at Lifestyle Stores, and film storytelling through Supari Studios for Skinn Noura. Maintaining engagement requires translating these view counts into verifiable trial and conversion.
The SKINN 24Seven creator campaign secured over 7.5 million views across social platforms
SKINN collaborated with Lifestyle Stores and beauty creators for live in-store trial content
Titan partnered with Supari Studios to produce a dedicated launch film for Skinn Noura
What to do next: Measure conversion lift from creator-driven discovery directly to store footfall and sample kit orders.
If you're curious why Titan launched SKINN, their perfume brand, this episode has all the answers. ... Unveiling the new Noura Nectar, Eau De Parfum by Skinn By ...
SKINN by Titan introduces Noura Nectar, a fine fragrance crafted for her — with model and actress Shibani Dandekar fronting the campaign. Read ...
instagram.com
Low
How each bucket is built
#
Bucket
The question it answers
What it reads
01
Competitor moves
What have the named competitors done in the last weeks that the desk should know?
Launches, store openings, campaigns, price led offers, funding and leadership news.
02
Industry and category demand
Which way is the category itself moving, in volume and in value?
Category growth, input costs, festive and season demand, regulation and duty.
03
Brand online reputation
What are customers saying about the brand in public, and what is the recurring complaint?
Reviews, complaint boards, consumer forums, service and delivery grievances.
04
Pricing and offers in market
What price and offer message is live in the market right now?
Discount events, exchange schemes, making charge and bank offers, marketplace pricing.
05
Retail network and expansion
Where is retail space being added, by the brand and around it?
New stores, city entries, franchise news, mall and high street expansion.
06
Digital and social conversation
What is the brand's conversation on social, creator and video platforms?
Creator collaborations, campaign reception, trending formats, share of voice.
Results are ranked by how sharply they read against the brand, so complaints and competitive moves surface first. Severity is a keyword read on the headline and summary, not a judgement.
Value model
Move any input. Everything recalculates. Every figure starts from the published SKINN by Titan network and is editable.
Inputs
Modelled outcome, a year
₹6.6 Cr
From enquiries answered and converted
₹11 Cr
From dormant customers brought back
₹95.3 L
Service handling cost avoided
₹18 Cr
Total modelled value, before any fee
Service saving is modelled at ₹42 of handling cost avoided per conversation the desk closes without a store call back.
Data readiness
What is connected on day one, what is modelled today, and what SKINN by Titan needs to hand over for the pilot to go live.
Input
Status
Note
Store network and locations
Published
6,000+ Points of sale carrying SKINN
Category and price ladder
Published
From the brand range
Enquiry volume and source mix
Modelled
From comparable networks of similar store count
Customer book and dormancy
Modelled
Replaced in one session with the brand team
Service job volume and turnaround
Modelled
From service desk throughput benchmarks
Loyalty tier membership
Modelled
Tier structure as published
Consent and contactability flags
Needed
Required before the first outbound call
What is needed to go live
A contactable customer extract with consent flags, in any format.
Store and advisor calendars, or a single sheet per region to start.
One session with the SKINN by Titan team to replace every modelled volume with the brand's own.
Required inputs
What the SKINN by Titan team hands over, in whatever format it already exists, for the pilot to run on the brand's own numbers.
Input
Owner
Format that is enough
Needed by
Contactable customer extract with consent flags
Head of customer
CSV or a database view
Before the first outbound call
Enquiry feed from website, store and marketplace
Head of marketing
Webhook or a daily file
Week 1
Store and advisor calendars
Head of retail
One sheet per region to start
Week 1
Category and price ladder in force
Head of retail
The current range sheet
Week 1
Replenishment book
Head of optometry
Account level extract
Week 2
Service and job records
Head of service
Export from the service system
Week 2
Telephony numbers and caller ID
Head of retail
Numbers plus provider access
Before go live
Nothing here blocks the console. Every input replaces a modelled figure already on the screen.
Method and limits
How the pilot runs on SKINN by Titan, week by week, and what the system will not do.
Week
What happens
What the brand sees
Week 1
Data session, scripts drafted, numbers provisioned
Console on the brand's own figures
Week 2
Both agents live on a small call list
First conversations and quality scores
Weeks 3 to 6
Ladders switched on segment by segment
Weekly reason codes and outcomes
Weeks 7 to 12
Appointments, service and the trigger desk added
Value model against actuals
Month 6
Review against the model
Keep, widen or stop, in writing
What this system will not do
It will not quote a price, a discount or a making charge outside the published range.
It will not claim a warranty outcome before the service centre has confirmed it.
It will not call a customer who has declined or opted out, for any reason.
It will not score, rank or profile an individual customer or an individual advisor.
It will not pretend to be human when asked directly.
Commercial terms
One flat retainer for the platform and the desk, usage billed as pass through at cost, and a review at month six.
Scope
What it covers
Commercial
Pilot, one brand
SKINN by Titan consoles, both voice agents, the desk and the quality record
Flat monthly retainer
Group scope
Every Titan brand on the same platform and identity spine
Retainer per brand, tapering
Usage
Telephony, transcription and model usage
Pass through at cost, itemised
Carried free until month six
Console build, script work, audit rubric and reporting
No charge
Governance
No individual customer is scored, profiled or ranked. Quality sits at store and issue level only.
Consent and do-not-call flags are respected on every attempt, with a stop rule per segment.
Every conversation is retained for a stated window and is available to the brand team in full.