Author: Gaurang Govind
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Our real time case study-When E-commerce Orders Were Failing, the Problem Was Inside the Stores
An international retail client came to us with a simple but serious problem:
Their e-commerce business was growing, but they were not able to fulfil around 30% of the orders.
The business had around 400 hypermarkets and supermarkets across the country.
Customers could place orders through the company’s own e-commerce website as well as through other marketplace platforms.
The stores were connected to these platforms and were expected to fulfil customer orders from their existing inventory.
On paper, the model looked simple.
In reality, it was creating a major operational problem.
The same inventory was serving two customers:
the customer standing inside the store and the customer ordering online.
And the fulfilment rate was only around 70%.
That meant roughly 3 out of every 10 orders were facing a fulfilment problem.
The first question was not:
“How do we increase inventory?”
The first question was:
“Where exactly is the problem happening?”
We started with the data.
We looked at four areas.
1. Order fulfilment lead time
We studied how much time the stores actually had to pick and service an online order.
2. Demand forecast
We checked the demand quantity being generated by the application and whether it reflected actual store-level demand.
3. Inventory update frequency
We examined the time gap between the physical inventory available in the store and the inventory quantity visible on the e-commerce platform.
4. Store-level fulfilment
We mapped which stores were servicing which platforms and where orders were getting missed.
That analysis started showing us something important.
The problem was not simply a lack of inventory.
The bigger problem was inventory visibility and inventory accuracy.
Different platforms were generating orders at different times.
The company’s own website was generating orders.
Customers were simultaneously buying from the physical stores.
The store inventory was changing continuously.
But the e-commerce platforms were not receiving inventory updates quickly enough.
So the system could show a product as available online even when the store could no longer fulfil the order.
That created an out-of-stock situation after the customer had already placed the order.
Then we applied Pareto.
We analysed the SKU-level data.
The result was clear.
Around 60–70% of the fulfilment problem was concentrated in the top-selling SKUs.
That changed our approach.
We did not try to fix hundreds or thousands of SKUs at once.
We went after the SKUs that were creating the biggest impact.
We worked with the stores to identify and correct ambiguous inventory quantities.
We looked at the actual stock on the ground.
We compared it with the system stock.
And we focused on improving the connection between the store inventory and the e-commerce platforms.
One important recommendation was technology-related.
The ideal solution was real-time inventory synchronisation.
But implementing a completely real-time system would have required additional technology changes and investment.
So we looked at what was commercially practical.
Our recommendation was:
If real-time synchronisation is not immediately possible, reduce the inventory update cycle to around 10 minutes.
That small change could significantly reduce the gap between:
Physical Stock → System Stock → Online Availability
The technology alone, however, was not enough.
We went to the stores.
This is where Gemba became important.
Data can tell you where the problem is.
But going to the actual workplace helps you understand why it is happening.
We checked what was happening at store level.
We looked at the actual inventory.
We understood how orders were coming from different platforms.
We checked how the store teams were responding.
We compared system inventory with physical inventory.
And we connected those observations back to the data.
That combination gave us a much clearer picture.
Technology showed us the symptom.
Data showed us the concentration.
Pareto showed us where to focus.
Gemba showed us the operational reason.
The result?
The out-of-stock problem reduced from around 30% to approximately 11%.
That took the fulfilment rate from around:
70% → 89%
in a relatively short period.
More importantly, the client did not need to simply keep adding inventory to solve the problem.
We improved the way existing inventory was being seen, updated and allocated for e-commerce fulfilment.
The client subsequently took the process forward and continued following the improvements internally.
What did we actually change?
It was not one big technology project.
It was a combination of several practical interventions:
• Analysed fulfilment lead time
• Studied demand forecasting logic
• Mapped inventory update frequency
• Identified store-level fulfilment gaps
• Created SKU-level Pareto analysis
• Focused on high-impact SKUs
• Checked physical inventory against system inventory
• Identified ambiguous inventory quantities
• Recommended faster inventory synchronisation
• Recommended approximately 10-minute inventory updates where real-time integration was not immediately practical
• Conducted Gemba-level validation
• Connected technology data with store operations
And that is what made the difference.
The bigger lesson for retailers
Many retailers assume that e-commerce fulfilment problems can be solved by:
“Keep more stock.”
I don’t agree.
Sometimes the inventory is already sitting inside the store.
The problem is that the system does not know the right quantity at the right time.
And when that happens, the customer sees:
“Available”
places the order,
and then receives:
“Sorry, this item is unavailable.”
That is not just an inventory problem.
It is a technology + data + process + store execution problem.
For retailers operating an omnichannel model, the real question is not simply:
“How much inventory do we have?”
It is:
“Do we know exactly where that inventory is, whether it is actually available, and how quickly can we make that information visible to the customer?”
That is where e-commerce, store operations, data analytics and Gemba have to work together.
And sometimes, a relatively simple change in the way inventory is updated can create a much bigger impact than simply adding more stock.
From 70% fulfilment to 89%.
Not by throwing more inventory at the problem.
By finding the real problem first.
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Quick Commerce Is No Longer About 10-Minute Delivery — It Is Becoming a New Retail Format
When quick commerce started gaining traction in India, the big attraction was simple: get your order in 10 minutes.
Milk, bread, eggs, snacks, cold drinks, fruits and vegetables — things people needed quickly could suddenly reach their homes without a trip to the neighbourhood store.
For a while, the entire conversation was about speed.
Who can deliver faster?
10 minutes or 15 minutes?
But I think we are now looking at something much bigger.
Quick commerce is slowly becoming a new retail format.The reason is quite simple. These platforms are no longer trying to solve only an urgent shopping need. They are trying to become part of the customer’s regular shopping behaviour.
Think about how people actually shop.
They don’t always wake up thinking, “Today I will visit a grocery store.”
They think, “We are out of milk.”
“I need snacks for tonight.”
“I forgot toothpaste.”
“Guests are coming.”
“I need a small gift.”
These are shopping missions.
Quick commerce is trying to capture these missions one by one.
And that changes the game.
The first generation of quick commerce was largely about groceries and everyday essentials. Today, the assortment is moving into many other categories — beauty, personal care, electronics, gifting, home products and more.
Once the customer starts buying these categories regularly, the platform is no longer just delivering products.
It is competing for the customer's basket.That is where I believe traditional retailers need to pay attention.
Because the biggest change is not happening in delivery.
It is happening in inventory.
A 10-minute delivery promise sounds impressive to the customer. But behind that promise sits a much more complicated operation.
The product has to be available close to the customer.
The forecast has to be reasonably accurate.
The right quantity has to be stocked.
The order has to be picked quickly.
The product has to be packed correctly.
And someone still has to deliver it profitably.
If the retailer keeps too little stock, the customer sees “out of stock.”
Keep too much stock and the business starts carrying unnecessary working capital. Products age. Discounts increase. Fresh products get damaged. Wastage goes up.
This becomes particularly important in fruits and vegetables and other perishables.
A retailer cannot solve an availability problem simply by putting more stock into a dark store.
The quality of the forecast matters.
This is something I have seen repeatedly in retail. Inventory problems are often treated as a technology problem. In reality, the technology can only work as well as the demand data, processes and discipline behind it.
That is why I think the next competitive battle in quick commerce will not simply be about who delivers in 10 minutes.
It will be about who can maintain better availability without carrying excessive inventory.That is a very different game.
Traditional retailers already understand many parts of this equation.
They have spent decades managing assortment, inventory, margins, store productivity, shrinkage, wastage and working capital.
What is changing is the operating model.
A traditional store waits for the customer to walk in.
A dark store waits for the order to come through the app.
The traditional store uses shelves to present products.
The quick-commerce platform uses the app to present products.
The traditional retailer measures sales per square foot.
The quick-commerce operator needs to think about sales, orders and contribution from each fulfilment location.
The customer sees a simple app screen.
Behind that screen is a fairly complex retail operation.
And this is where I think the conversation around quick commerce needs to become more mature.
Sales growth alone is not enough.
Quick Commerce Retailers need to ask harder questions.
What is the inventory turn?
What percentage of orders get fulfilled completely?
How often does the customer find the product unavailable?
What does picking cost?
What does delivery cost?
How much inventory gets damaged?
How much money is tied up in local inventory?
And perhaps the most important question:
Are customers coming back because the platform is genuinely useful, or only because someone is subsidising the order?These questions will become increasingly important as the sector matures.
There is another change that I find particularly interesting.
The traditional argument used to be:
Online versus offline.
I don’t think that is the right argument anymore.
A physical store can also become a digital fulfilment point.
A customer can walk into the store and buy something.
Another customer, sitting two kilometres away, can order from the same inventory.
The same store can therefore serve both customers.
This changes the role of the store.
It can become a place for shopping, product discovery, experience, fulfilment and local inventory — all at the same time.
So, rather than asking whether quick commerce will replace traditional retail, I would ask a different question:
How quickly will traditional retailers learn to operate with the same level of convenience? and How efficiently QUick Commerce companies can run like a Retailer to ensure backend excellence and inventory availability at optimium costs
Because the customer has already changed.
Once someone gets used to receiving a product within minutes, asking that customer to wait two days for a basic product can feel very different.
Convenience changes expectations.
And expectations eventually change retail.
I don’t think the future of quick commerce will be decided by the fastest delivery time.
The 10-minute promise helped create the market.
But speed alone won’t build a sustainable retail business.
The winners will need to get several things right at the same time:
Right product. Right location. Right inventory. Right price. Right availability. Right cost.
That is retail.
Quick commerce has simply put that retail equation on a much smaller clock.
And perhaps that is the real story.
Quick commerce started by promising to deliver faster.
It is now trying to change the way we shop.
The next phase will tell us whether it can also change the economics of retail.
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UPI Charges: What Retailers Actually Need to Watch
My take on the whole issue: Off late the income of Visa and Master card has dropped due to high usage of all kinds of transactions through UPI. The merchants are having multiple benefits- instant cash, no charges as merchant. Easy to maintain no need to keep enough cash and change for transaction closure. To run the UPI- UPI companies ,VIZA / Master card and banks also have to make money. Hence it is levied. When you read you will understand , how it is structured.
But one point here merchants have already inbuilt their margins assuming debit card and credit card transactions. So cash and UPI was helpful in negating the losses out of credit card and debit card purchases.
In my view , here the US companies are going to get more benefits like Phone Pe and Google pay along with Banks, both are US backed companies like Walmart and Google combined have a market share of 78% appx. Ultimately this charge will come to customers only in some form. Ideally impact is very negligible when it comes to few transactions but in large transactions in numbers may be very high.
UPI Charges: What Has Changed, Who Pays and What It Means for RetailersFor years, UPI has been associated with one simple proposition: pay digitally without paying a transaction fee.
That model is now changing for certain merchant transactions.
MDR is a fee associated with processing a digital payment. It is a cost within the payment ecosystem rather than a separate charge that a customer necessarily sees on the bill.
For example, if a customer makes a ₹10,000 UPI payment to a merchant and the applicable MDR is 0.4%, the MDR works out to:
₹10,000 × 0.4% = ₹40
The customer does not automatically pay an additional ₹40. The cost is handled within the payment ecosystem and, depending on the applicable rules and commercial arrangements, becomes a cost for the merchant/payment participants.
That distinction is important.
Scenario 1: Customer buys groceries for ₹1,500
A customer purchases groceries from a local retailer.
Bill: ₹1,500 Payment: UPI MDR: Zero under the ₹2,000 threshold.
For the customer, nothing changes.
For the retailer, there is no MDR on this transaction.
This is particularly relevant for kirana stores and small-ticket retail where most transactions are below ₹2,000.
Scenario 2: Customer buys a television for ₹45,000
Now consider a consumer-durable purchase.
Bill: ₹45,000 Payment: UPI Applicable MDR: 0.4%
The MDR would be:
₹45,000 × 0.4% = ₹180
The customer still pays ₹45,000, assuming the merchant does not separately pass on the payment cost.
But for the retailer, ₹180 becomes an additional payment-processing cost.
This is where retailers need to start looking carefully at their payment economics.
Scenario 3: A customer buys jewellery for ₹1.5 lakh
Consider a jewellery transaction.
Purchase: ₹1,50,000 Payment: UPI Rate: 0.4% Calculated MDR: ₹600
However, where the transaction falls under the applicable ₹300 cap for transactions of ₹75,000 or more, the MDR would be capped at:
₹300
This makes the effective rate much lower than 0.4% for a high-value transaction.
For retailers handling large-ticket purchases, the cap becomes commercially significant.
Scenario 4: Customer transfers ₹25,000 to a friend
This is completely different.
Person A → Person B Amount: ₹25,000 Payment: UPI
There is no merchant involved.
Therefore, this is a P2P transaction, and the MDR framework for merchant payments does not mean that the customer suddenly pays a percentage on this transfer.
₹25,000 sent = ₹25,000 received.
This distinction between P2P and P2M is one of the most important points to understand.
Scenario 5: Restaurant bill of ₹3,500
Four people have dinner and the restaurant bill is:
₹3,500
They pay the restaurant through UPI.
If the applicable merchant MDR is 0.4%:
₹3,500 × 0.4% = ₹14
The customer does not necessarily see a ₹14 “UPI charge”.
But the restaurant now has to consider payment-processing cost when calculating its margins.
For a restaurant operating at a 5–10% operating margin, even relatively small payment costs can matter when multiplied across thousands of transactions.
Scenario 6: Fuel purchase
A customer buys fuel worth:
₹4,000
The transaction falls into an essential-sector category covered by a special fixed MDR structure.
Instead of applying a percentage in the same way as a normal merchant transaction, the applicable charge is specified at ₹5 per transaction.
So:
Fuel purchase = ₹4,000 Applicable MDR = ₹5
This creates a very different economics from a normal 0.4% merchant transaction.
Scenario 7: Telecom payment
Suppose a customer makes a telecom-related payment of:
₹10,000
Telecom is among the specified essential sectors for which the framework provides a special ₹5 per transaction structure.
So the merchant’s payment cost is not simply:
₹10,000 × 0.4% = ₹40
Instead, the specified category-based charge applies.
This illustrates why retailers and businesses should not assume that one MDR rate applies to every UPI transaction.
Scenario 8: Mutual fund or securities transaction
Financial-market transactions have another structure.
For specified capital-market transactions, including mutual funds and securities-related transactions, the rate is 0.02%, subject to the applicable ₹300 cap.
For example:
Transaction = ₹5,00,000
At 0.02%:
₹5,00,000 × 0.02% = ₹100
So the applicable charge would be ₹100, assuming the transaction qualifies for this category.
Again, the category matters.
What does this mean for retailers?
This is where the issue becomes commercially interesting.
Retailers have traditionally looked at payment methods mainly from the customer’s convenience perspective.
That will increasingly need to change.
A retailer should now look at:
Sales value → Payment mix → MDR → Payment cost → Gross margin → Net contribution
Imagine a retailer doing:
₹10 crore annual sales through UPI
If the average effective payment cost were 0.4%, the theoretical annual payment cost would be:
₹10 crore × 0.4% = ₹4 lakh
That is not necessarily the actual cost because the effective MDR depends on transaction size, category, caps, merchant classification and applicable arrangements.
But the example shows the scale of the issue.
Who could gain?
The change creates a clearer revenue mechanism for participants in the payment ecosystem.
Banks, payment service providers and other participants can potentially earn from qualifying merchant transactions.
The broader objective stated by the Government is to make the UPI ecosystem financially sustainable as transaction volumes continue to grow.
That matters because UPI is no longer a small payment system. It has become a major part of India’s retail-payment infrastructure.
Who could feel the pressure?
The pressure is more likely to be felt by businesses with:
- High-value UPI transactions
- Large transaction volumes
- Low gross margins
- Heavy dependence on UPI
- Limited ability to absorb payment-processing costs
A ₹10 or ₹20 payment-processing cost may look insignificant on one transaction.
Multiply it by 10 lakh transactions and the number becomes commercially meaningful.
The bigger retail question
The interesting question for retailers is not:
“Will customers stop using UPI?”
The more relevant question is:
“What will UPI cost my business after the new MDR structure?”
Retailers should start tracking payment costs just as they track:
- Card charges
- Marketplace commissions
- Delivery costs
- Discounts
- Shrinkage
- Banking charges
- Logistics costs
Payment cost is becoming another operating-cost variable.
For a retailer, the right dashboard could therefore include:
UPI sales % | Average UPI transaction value | Number of UPI transactions | MDR paid | MDR as % of sales | Net margin impact
This will tell management whether the cost is material or negligible.
One simple way to remember the change
Think of UPI in three buckets:
P2P: Person → Person Generally free.
Small-ticket P2M: Customer → Merchant Up to ₹2,000 → Zero MDR under the framework.
Specified higher-value P2M: Customer → Merchant Above the threshold → MDR may apply depending on transaction category.
So the headline “UPI will now be charged” is too simplistic.
The real story is:
UPI remains free for ordinary person-to-person transfers, while specified merchant transactions can carry a regulated payment-processing cost.
For retailers, banks, fintech companies and consumers, the real impact will depend on transaction value, category, payment mix and how businesses ultimately manage the cost.
Note: The examples above are illustrations based on the notified rates and categories. Actual merchant charges can depend on the merchant’s classification, transaction type and applicable commercial arrangements.
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India Hair Care 2030: Where Will the Next ₹25,000–35,000 Crore Come From?
India’s hair-care market is entering its next phase. The next five years will not be about selling more shampoo. They will be about solving more hair problems.
India’s broad hair-care market is currently around ₹40,000 crore, depending on the definition used. The important story, however, is not only market size. It is the change in consumer behaviour. Hair care is moving from basic cleansing and grooming towards treatment, scalp health, repair, styling, personalisation and professional care. Consumers increasingly want solutions for hair fall, dandruff, damage, frizz, thinning, premature greying and chemically treated hair.
Where are we today?
The market remains concentrated among large companies, but competition is becoming more fragmented. Euromonitor’s latest 2025 data shows Hindustan Unilever and L’Oréal India each at 17% value share, followed by Marico at 13%. Over the review period, L’Oréal gained four percentage points, while Marico declined three points. This is an important signal: the market is gradually shifting from traditional mass hair care towards premium, specialised and professional propositions.
Overall company market share — 2025
The five-year movement is even more interesting. HUL moved from 16% to 17%. L’Oréal moved from 13% to 17%. Marico moved from 16% to 13%. This suggests that premiumisation and specialised hair care are changing the competitive balance.
Where is the money today?
Different research firms use different category definitions. In one comparable Euromonitor dataset, 2025 hair-care sales were approximately ₹28,485 crore, covering colourants, conditioners/treatments, salon professional hair care, shampoos and styling agents. This narrower dataset should not be confused with the broader ~₹40,000 crore market estimate.
Category structure — comparable 2025 dataset
This table is particularly useful because it shows something that is easy to miss: treatment-led categories are already extremely important. In this particular market definition, conditioners and treatments are substantially larger than shampoo.
Other market definitions produce a different mix. For example, IMARC estimates shampoo at 45% of its 2025 market definition, while another market estimate puts hair oil at roughly 35–38% and shampoo at around 30%. These differences are mainly methodological, not necessarily contradictory.
What has changed in five years?
The old Indian hair-care model was simple: oil + shampoo + colour. The new model is becoming much broader: shampoo + conditioner + mask + serum + scalp treatment + styling + professional care.
The growth rates show the direction. In the comparable Euromonitor dataset, salon professional hair care was forecast to grow at 7.2% CAGR, styling at 6.7%, colourants at 5.6%, while standard shampoo was expected to grow at only 0.5%.
This tells us where incremental spending is likely to come from.
The consumer is therefore becoming more demanding. “Good for hair” is no longer enough. Consumers want to know what the product does, why it works and whether it will solve their particular problem.
What does 2030 look like?
Now work backward.
If the broad market is approximately ₹40,000 crore today, a reasonable base case of roughly 10–13% annual growth would take it to around ₹65,000–75,000 crore by 2030. A stronger scenario could take it towards ₹80,000–90,000 crore or more.
India hair-care outlook: 2030
These are strategic scenarios, not a single published forecast. Published estimates vary because market definitions differ. MarketLine, for example, reports a 2025 market of US$4.1bn and a 2020–25 CAGR of 5.5%, while current Euromonitor research explicitly forecasts value sales through 2030 and highlights personalisation, diagnostics and premiumisation as key themes.
Where will the incremental ₹25,000–35,000 crore go?
This is the most important question.
Indicative 2030 category-share scenario
Strategic scenario ranges; categories overlap across different market definitions and therefore should not be treated as audited market shares.
The key point is simple: traditional categories can continue growing in rupee terms while losing percentage share. The market is expanding because consumers are adding products to their routines.
Which category should win?
My strongest bet for 2030 is scalp care and hair-loss solutions.
Why?
Because it solves a problem rather than simply providing grooming. Hair loss creates high emotional involvement. Consumers search for information. They compare solutions. They are willing to experiment. And, if they find something that works, the purchase can become highly recurring.
The opportunity is therefore bigger than an anti-hairfall shampoo. It is a scalp-health ecosystem: diagnosis, scalp treatment, specialised shampoo, serum, conditioner, professional consultation and maintenance.
The second major opportunity is repair and treatment. Colouring, heat, pollution, straightening and other chemical treatments increase the need for masks, leave-ins, bond repair, heat protection and professional treatments.
The third is professional hair care. The salon is becoming a discovery and recommendation engine, while e-commerce allows consumers to continue the professional routine at home. Euromonitor also identifies professional hair care as an important contributor to premium beauty in India.
What will the consumer expect by 2030?
This is where technology will matter. Euromonitor expects advanced diagnostics and personalised care solutions to become more important as AI and diagnostic capabilities mature.
What about imports and exports?
India is not simply a consumption market. It is also a manufacturing and export base. Under the broad HSN 3305 category for hair preparations, India exported approximately ₹1,772 crore in FY2025 against imports of around ₹880 crore, leaving India with a positive trade balance.
The future could therefore work in both directions. Global brands will bring premium and specialised products into India, while Indian manufacturing and Indian brands can increasingly serve international markets.
The strategic conclusion
The Indian hair-care market is not moving from shampoo to another single category. It is moving from products to routines.
That distinction is critical.
The next ₹25,000–35,000 crore of opportunity will not be distributed equally. Basic shampoo and traditional hair oil will remain large. But treatments, scalp care, hair-loss solutions, serums, styling and professional care can grow faster and capture a larger share of the incremental wallet.
For brands, the strategic priorities are therefore clear: defend the core, premiumise the routine and solve higher-value problems.
The biggest opportunity is likely to sit where consumer anxiety, willingness to pay and repeat purchase intersect.
That makes scalp health and hair-loss solutions one of the most attractive categories to build for 2030, followed by repair/treatment, professional care and serums/styling.
The question for the industry is no longer:
“How do we sell more shampoo?”
It is:
“Which hair problem can we own for the next five years?”
That question could determine who captures the next ₹25,000–35,000 crore of India’s hair-care opportunity.
Final recap
Market today: ~₹40,000 crore broad-market estimate.
2030 base case: ₹65,000–75,000 crore.
Potential incremental opportunity: ₹25,000–35,000 crore.
Biggest structural shift: From cleansing to treatment.
Fastest strategic opportunities: Scalp care, hair-loss solutions, repair, professional care and serums/styling.
Core categories: Shampoo and hair oil remain essential, but their percentage share may decline.
Winning model: Product → problem → routine → personalisation → repeat purchase.
The future of Indian hair care will not be defined by how many products consumers buy. It will be defined by how many problems brands can solve.
India’s hair-care market is entering its next phase. The next five years will not be about selling more shampoo. They will be about solving more hair problems.
India’s broad hair-care market is currently around ₹40,000 crore, depending on the definition used. The important story, however, is not only market size. It is the change in consumer behaviour. Hair care is moving from basic cleansing and grooming towards treatment, scalp health, repair, styling, personalisation and professional care. Consumers increasingly want solutions for hair fall, dandruff, damage, frizz, thinning, premature greying and chemically treated hair.
Where are we today?
The market remains concentrated among large companies, but competition is becoming more fragmented. Euromonitor’s latest 2025 data shows Hindustan Unilever and L’Oréal India each at 17% value share, followed by Marico at 13%. Over the review period, L’Oréal gained four percentage points, while Marico declined three points. This is an important signal: the market is gradually shifting from traditional mass hair care towards premium, specialised and professional propositions.
Overall company market share — 2025
CompanyValue shareStrategic positionHindustan Unilever17%Mass + premium, very strong distributionL’Oréal India17%Premium + professional + innovationMarico13%Hair oils + premiumisationOthers53%Highly fragmented marketTotal100%
The five-year movement is even more interesting. HUL moved from 16% to 17%. L’Oréal moved from 13% to 17%. Marico moved from 16% to 13%. This suggests that premiumisation and specialised hair care are changing the competitive balance.
Where is the money today?
Different research firms use different category definitions. In one comparable Euromonitor dataset, 2025 hair-care sales were approximately ₹28,485 crore, covering colourants, conditioners/treatments, salon professional hair care, shampoos and styling agents. This narrower dataset should not be confused with the broader ~₹40,000 crore market estimate.
Category structure — comparable 2025 dataset
Category2025 valueApprox. shareConditioners & treatments₹12,623 cr44.3%Shampoos₹7,216 cr25.3%Colourants₹6,232 cr21.9%Salon professional₹1,670 cr5.9%Styling agents₹744 cr2.6%Total₹28,485 cr100%
This table is particularly useful because it shows something that is easy to miss: treatment-led categories are already extremely important. In this particular market definition, conditioners and treatments are substantially larger than shampoo.
Other market definitions produce a different mix. For example, IMARC estimates shampoo at 45% of its 2025 market definition, while another market estimate puts hair oil at roughly 35–38% and shampoo at around 30%. These differences are mainly methodological, not necessarily contradictory.
What has changed in five years?
The old Indian hair-care model was simple: oil + shampoo + colour. The new model is becoming much broader: shampoo + conditioner + mask + serum + scalp treatment + styling + professional care.
The growth rates show the direction. In the comparable Euromonitor dataset, salon professional hair care was forecast to grow at 7.2% CAGR, styling at 6.7%, colourants at 5.6%, while standard shampoo was expected to grow at only 0.5%.
This tells us where incremental spending is likely to come from.
The challenge with today’s categories
CategoryCurrent challengeWhat the consumer now expectsHair oilCommoditisation and low differentiationNon-sticky, specialised, Ayurvedic + scienceShampooMature and highly competitiveSpecific solutions, better ingredients, visible resultsConditioner/treatmentLower penetration outside urban consumersRepair, smoothness, nourishment and routine-based careHair colourDamage concerns and competitionSafer, convenient, natural-looking, premium colourStyling/serumSmall base and fragmented brandsInstant visible results, frizz control, heat protectionScalp/hair lossTrust and efficacy concernsEvidence, diagnosis, personalisation and resultsProfessional careHistorically salon-dependentProfessional results available at home
The consumer is therefore becoming more demanding. “Good for hair” is no longer enough. Consumers want to know what the product does, why it works and whether it will solve their particular problem.
What does 2030 look like?
Now work backward.
If the broad market is approximately ₹40,000 crore today, a reasonable base case of roughly 10–13% annual growth would take it to around ₹65,000–75,000 crore by 2030. A stronger scenario could take it towards ₹80,000–90,000 crore or more.
India hair-care outlook: 2030
Scenario20252030Incremental opportunityCurrent₹40,000 cr——Base case₹40,000 cr₹65,000–75,000 cr+₹25,000–35,000 crHigh-growth case₹40,000 cr₹80,000–90,000+ cr+₹40,000–50,000+ cr
These are strategic scenarios, not a single published forecast. Published estimates vary because market definitions differ. MarketLine, for example, reports a 2025 market of US$4.1bn and a 2020–25 CAGR of 5.5%, while current Euromonitor research explicitly forecasts value sales through 2030 and highlights personalisation, diagnostics and premiumisation as key themes.
Where will the incremental ₹25,000–35,000 crore go?
This is the most important question.
Indicative 2030 category-share scenario
CategoryToday*2030 scenario*DirectionHair oil30–35%23–28%↓ ShareShampoo18–22%15–19%↓ ShareConditioners / treatments25–30%27–32%↑Hair colour14–16%13–16%→Styling / serums3–5%6–9%↑↑Scalp / hair-loss3–5%6–9%↑↑Professional hair care4–6%7–10%↑↑
Strategic scenario ranges; categories overlap across different market definitions and therefore should not be treated as audited market shares.
The key point is simple: traditional categories can continue growing in rupee terms while losing percentage share. The market is expanding because consumers are adding products to their routines.
Which category should win?
My strongest bet for 2030 is scalp care and hair-loss solutions.
Why?
Because it solves a problem rather than simply providing grooming. Hair loss creates high emotional involvement. Consumers search for information. They compare solutions. They are willing to experiment. And, if they find something that works, the purchase can become highly recurring.
The opportunity is therefore bigger than an anti-hairfall shampoo. It is a scalp-health ecosystem: diagnosis, scalp treatment, specialised shampoo, serum, conditioner, professional consultation and maintenance.
The second major opportunity is repair and treatment. Colouring, heat, pollution, straightening and other chemical treatments increase the need for masks, leave-ins, bond repair, heat protection and professional treatments.
The third is professional hair care. The salon is becoming a discovery and recommendation engine, while e-commerce allows consumers to continue the professional routine at home. Euromonitor also identifies professional hair care as an important contributor to premium beauty in India.
What will the consumer expect by 2030?
TodayBy 2030″Does it clean my hair?””Does it solve my problem?”One or two productsComplete routineGeneric claimsEvidence and efficacyMass formulationsMass + premium choicesBrand-led communicationExpert + creator + community-led discoveryStandard productsPersonalised solutionsSalon as occasional serviceSalon as ongoing expert relationshipOffline discoveryOmnichannel discovery and purchase
This is where technology will matter. Euromonitor expects advanced diagnostics and personalised care solutions to become more important as AI and diagnostic capabilities mature.
What about imports and exports?
India is not simply a consumption market. It is also a manufacturing and export base. Under the broad HSN 3305 category for hair preparations, India exported approximately ₹1,772 crore in FY2025 against imports of around ₹880 crore, leaving India with a positive trade balance.
The future could therefore work in both directions. Global brands will bring premium and specialised products into India, while Indian manufacturing and Indian brands can increasingly serve international markets.
The strategic conclusion
The Indian hair-care market is not moving from shampoo to another single category. It is moving from products to routines.
That distinction is critical.
The next ₹25,000–35,000 crore of opportunity will not be distributed equally. Basic shampoo and traditional hair oil will remain large. But treatments, scalp care, hair-loss solutions, serums, styling and professional care can grow faster and capture a larger share of the incremental wallet.
For brands, the strategic priorities are therefore clear: defend the core, premiumise the routine and solve higher-value problems.
The biggest opportunity is likely to sit where consumer anxiety, willingness to pay and repeat purchase intersect.
That makes scalp health and hair-loss solutions one of the most attractive categories to build for 2030, followed by repair/treatment, professional care and serums/styling.
The question for the industry is no longer:
“How do we sell more shampoo?”
It is:
“Which hair problem can we own for the next five years?”
That question could determine who captures the next ₹25,000–35,000 crore of India’s hair-care opportunity.
Final recap
Market today: ~₹40,000 crore broad-market estimate.
2030 base case: ₹65,000–75,000 crore.
Potential incremental opportunity: ₹25,000–35,000 crore.
Biggest structural shift: From cleansing to treatment.
Fastest strategic opportunities: Scalp care, hair-loss solutions, repair, professional care and serums/styling.
Core categories: Shampoo and hair oil remain essential, but their percentage share may decline.
Winning model: Product → problem → routine → personalisation → repeat purchase.
The future of Indian hair care will not be defined by how many products consumers buy. It will be defined by how many problems brands can solve.
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India’s Skincare Market: Who Is Winning, Who Is Struggling and What Comes Next?
India’s Skincare Market: Who Is Winning, Who Is Struggling and What Comes Next?
India’s skincare market is growing. But the bigger story is not growth. It is the redistribution of growth.
Consumers are moving away from generic skincare and demanding ingredients, efficacy, transparency and convenience. At the same time, digital-first brands are moving offline, legacy FMCG brands are upgrading their portfolios, and global companies are acquiring successful Indian brands.
So, who actually dominates the market today?
And more importantly — which brands are building the strongest position for 2026 and beyond?
This article looks at the current state of India’s skincare industry, the leaders, the challengers, their key problems, what is driving growth and where the market is heading.
What you should learn from this article
- Who currently dominates Indian skincare?
- Which brands are gaining momentum?
- What is driving consumer demand?
- Why are some D2C brands struggling despite high sales?
- What will shape the skincare market from 2026 onwards?
1. First, understand the market
Estimates put India’s skincare market at roughly $2.7–3.2 billion in 2025, depending on how skincare is defined. Facial care represents approximately 85% of the category.
The market is growing at roughly 7–10% annually, but growth is becoming increasingly fragmented.
The structural picture
HUL remains the clear company-level leader with around 28% retail value share in 2025.
But there is an important warning:
HUL’s share has fallen from around 37% in 2020 to 28% in 2025.
That tells us something important.
The leader is still leading — but the market is becoming much more competitive.
Market leadership snapshot
HUL ████████████████████████████ 28% Top 5 players combined ██████████████████████████████████████████████████ ~50% Others █████████████████████████████████████████████████Note: Brand-level market shares are not consistently publicly disclosed. The 28% figure refers to HUL’s company-level skincare value share, not Ponds alone.
The competitive battlefield now includes HUL, Beiersdorf, L’Oréal, P&G, Honasa and fast-growing independent brands such as Plum, alongside strong Ayurvedic and prestige players.
2. Who is winning?
The market can broadly be divided into four groups:
Legacy FMCG: Ponds, Lakmé, Vaseline, Garnier Global premium-mass: Nivea, Olay, L’Oréal Paris Digital-first challengers: Mamaearth, The Derma Co, Minimalist, Plum Ayurvedic/premium heritage: Himalaya, Forest Essentials, Biotique, Kama Ayurveda
The interesting part?
The fastest growth is increasingly coming from brands that combine science + digital discovery + omnichannel distribution.
3. Brand-by-brand: Current State
Ponds — Strong leader, but facing disruption
Ponds remains one of HUL’s biggest skincare engines and benefits from enormous distribution across general trade, modern trade, e-commerce and quick commerce.
HUL has been repositioning Ponds away from its traditional tone/fairness associations toward science-led premium skincare.
Current state: Strong scale and distribution.
Key challenge: Defend leadership while younger consumers shift toward ingredient-led brands.
What Ponds needs to do: Convert its enormous reach into stronger relevance among Gen Z and premium skincare consumers.
Lakmé — Powerful ecosystem, but skincare must keep evolving
Lakmé benefits from its strong brand recognition, modern trade presence, salons, e-commerce and mass distribution.
Its expansion into products such as retinol shows the shift toward science-backed skincare.
Current state: Strong legacy brand with a powerful beauty ecosystem.
Key challenge: Maintain relevance as consumers increasingly evaluate products by ingredients and efficacy rather than brand heritage alone.
Nivea — Mature growth, excellent profitability
Nivea generated approximately ₹1,540 crore revenue in India in FY25, while net profit jumped sharply.
That is an important signal.
Revenue was almost flat, but profitability improved dramatically.
Current state: Mature but financially strong.
Key challenge: Reignite volume/value growth in a slowing mass market.
Its Sanand manufacturing facility also shows how localization can reduce import dependence and improve supply-chain resilience.
Olay — Strong premium positioning
Olay remains an important premium-mass skincare player, particularly in anti-ageing and brightening.
Its challenge is not brand awareness. It is winning the modern Indian skincare conversation against highly visible serum and active-based challengers.
Current state: Strong premium positioning.
Key challenge: Deliver enough innovation and local relevance to justify its premium.
Garnier & L’Oréal Paris — Winning through innovation and accessibility
L’Oréal is benefiting from strong skincare momentum, particularly through Vitamin C, UV protection, glycolic acid and brightening products.
Garnier has another advantage: it can take ingredient-led skincare into mass markets through affordable formats.
Current state: One of the strongest combinations of science, marketing and distribution.
Key challenge: Maintain innovation while protecting affordability.
Mamaearth — From growth-at-any-cost to profitable growth
Mamaearth is perhaps the most important D2C case study.
Honasa’s FY25 revenue reached approximately ₹2,067 crore, but PAT declined 34% to ₹73 crore while advertising remained extremely high.
Then came FY26.
Revenue increased approximately 16% to ₹2,392 crore, while PAT jumped 175% to ₹200 crore.
This is a major lesson:
The D2C model is not dead. Undisciplined D2C economics are.
Current state: Recovery and restructuring.
Key challenge: Sustain growth without excessive advertising and channel costs.
The Derma Co — One of the strongest challengers
The Derma Co represents the new Indian skincare consumer better than almost any other brand.
Its proposition is simple: actives, efficacy and dermatological credibility.
It crossed approximately ₹750 crore ARR and was recognized as India’s No. 1 sunscreen brand by Euromonitor for CY24.
Current state: High-growth challenger.
Key challenge: Scale offline without losing its science-led identity and profitability.
Minimalist — A strategic success story
Minimalist has become one of India’s strongest examples of ingredient-led skincare.
Founded in 2020, it reached more than ₹500 crore ARR and remained profitable before HUL acquired a 90.5% stake for approximately ₹2,706 crore.
The acquisition says a lot about the future.
Global-scale FMCG distribution + D2C innovation is becoming a powerful combination.
Current state: High-quality challenger backed by HUL.
Key challenge: Scale dramatically without diluting consumer trust and brand authenticity.
Plum — Quietly building a healthier model
Plum grew approximately 28% to ₹515 crore in FY26 and had already turned profitable in FY25.
Its model is increasingly omnichannel, with offline contributing around 30% of revenue.
Current state: One of the more balanced D2C success stories.
Key challenge: Scale while maintaining unit economics.
Himalaya — Heritage remains a competitive advantage
Himalaya continues to benefit from strong pharmacy and general-trade distribution, Ayurvedic credibility and international presence.
Unlike many newer brands, its heritage itself is an asset.
Current state: Strong mass and international presence.
Key challenge: Make traditional herbal positioning relevant to a younger consumer increasingly asking for clinical evidence and active ingredients.
4. What is actually driving the market?
Five forces are reshaping skincare:
1. Ingredient awareness Consumers now actively search for niacinamide, Vitamin C, retinol, salicylic acid and SPF.
2. Premiumization Consumers are moving from basic creams toward serums, sunscreens, targeted treatments and multi-step routines.
3. Social discovery Influencers, dermatologists and social media increasingly shape product discovery.
4. Quick commerce Beauty sales through quick commerce are growing dramatically. Face wash, sunscreen and other repeat-use products fit the model particularly well.
5. Omnichannel expansion The winning model is no longer pure D2C.
It is:
D2C + marketplaces + modern trade + general trade + quick commerce.
5. The biggest challenges ahead
The industry has six major pressure points:
CAC inflation: Advertising-led growth is becoming expensive.
Margin pressure: Quick-commerce commissions and visibility costs can erode profitability.
Channel conflict: Different prices across websites, marketplaces and stores can damage brand economics.
Generic natural positioning: “Natural” alone is no longer enough.
Import dependency: Specialty actives, UV filters and other ingredients remain vulnerable to global supply chains.
Trust: Counterfeits, grey-market inventory and poor storage can damage consumer confidence.
The next phase will therefore reward operational discipline as much as marketing creativity.
6. What changes from 2026 onwards?
I see six major trends.
1. Science will beat vague claims
“Natural” will not disappear.
But natural + evidence will outperform natural alone.
2. Sunscreen becomes a major battlefield
Daily SPF adoption is still expanding, creating a large opportunity for brands that can combine efficacy, texture, affordability and education.
3. Quick commerce becomes a strategic channel
Brands will stop treating Blinkit, Zepto and Instamart only as distribution channels.
They will increasingly treat them as media + retail + convenience platforms.
4. D2C brands will consolidate
The market will favour brands with strong repeat purchase, healthy contribution margins and differentiated products.
Revenue alone will no longer impress investors.
Profitable growth will.
5. M&A will accelerate
The HUL–Minimalist transaction and global interest in premium Indian beauty demonstrate the strategic value of Indian skincare brands.
Expect more consolidation.
6. India can become a skincare manufacturing and export hub
Ayurvedic heritage, local manufacturing and cost-efficient formulation can create an export opportunity — particularly in GCC, Africa and other emerging markets.
The final takeaway
India’s skincare market is not simply growing. It is changing shape.
HUL still dominates structurally.
Nivea remains financially strong.
L’Oréal is winning through innovation.
The Derma Co is building serious momentum.
Minimalist has already attracted strategic acquisition.
Mamaearth is demonstrating that disciplined D2C economics can work.
Plum is proving profitability can coexist with growth.
Himalaya continues to leverage heritage and distribution.
The real winner of 2026 and beyond, however, will not necessarily be the brand with the biggest advertising budget.
It will be the brand that can combine:
Efficacy + Trust + Innovation + Distribution + Repeat Purchase + Profitable Growth.
That is where India’s next skincare battle will be fought.
This study is an assessment of a writer. Please do your own research and decide way forward. Don’t depend alone what we are writing and expressing our views.
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What 5,200 Customer Reviews Reveal About Process Failures in Quick Commerce
What Customer Complaints Can Tell Us About Quick Commerce Operations
A customer complaint rarely starts where the customer experiences it.
A late delivery may not be a delivery problem.
A delayed refund may not be a customer-support problem.
A cancelled order may not be a cancellation problem.
And that is exactly where many organisations get improvement wrong.
They respond to the visible complaint instead of investigating the process that created it.
Quick commerce makes this challenge even more interesting.
The customer sees a simple promise: fast, convenient and reliable delivery.
Behind that promise, however, sits a complex chain of inventory, store operations, picking, packing, technology, rider allocation, delivery, payments, refunds and customer support.
When that chain breaks, the customer does not see the process.
They see the outcome. And they complain about it.
That makes customer complaints far more than customer-service data.
They can be early signals of process variation, waste and defects.
With that perspective, we analysed customer reviews across five major quick-commerce platforms to understand what recurring complaints could tell us about the underlying customer journey.
The Objective
The purpose was not to rank companies or identify winners and losers.
Instead, we wanted to answer three questions:
What are customers complaining about most frequently?
What patterns emerge when all complaints are analysed together?
Which recurring problems may require a structured Lean Six Sigma investigation?
The study analysed 5,200 recent customer reviews across the five platforms.
Of these, 1,401 reviews—or 26.9%—contained identifiable complaints.
The remaining reviews were positive or did not contain a clearly identifiable complaint.
In other words, roughly one in every four reviews contained a signal that something in the customer journey had not worked as expected.
What Did the Combined Data Reveal?
When all five platforms were analysed together, complaints were not concentrated around one single operational problem.
The major themes included:
Refund and customer-support issues
General dissatisfaction
Unclassified complaints
Delivery delays
Product quality issues
Missing or incorrect items
Pricing and charge-related concerns
Order cancellations
Trust-related concerns
Technical and app-related problems
One finding stood out immediately.
A significant number of complaints were broad, emotional or difficult to classify into one specific defect.
When a customer says, “Worst experience”, the dissatisfaction is clear. The root cause is not.
Was the problem delivery?
Was it the product?
Was it the refund?
Was it customer support?
Or did several process failures combine to create one poor experience?
This is where complaint analysis alone reaches its limit.
It can tell us where customers feel the pain.
It cannot always tell us why the process created it.
The Pattern Was Not the Same for Every Company
The most interesting insight was not simply the overall number of complaints.
It was how differently the complaint patterns appeared across the platforms.
One platform showed a significantly higher complaint rate, with concerns concentrated around:
Refunds
Customer support
Product quality
Another showed a stronger delivery-related pattern.
Another had relatively greater concerns around pricing and charges.
Another showed a combination of delivery delays and order cancellations.
This distinction matters.
These companies operate in the same industry and compete for similar customers.
But the customer-facing defects are not identical.
That means a generic “customer experience improvement programme” may be too broad to solve the real problem.
Each complaint pattern points towards a different part of the process that needs investigation.
Company Comparison
What Does This Mean from a Lean Six Sigma Perspective?
The data does not tell us what solution each company should implement.
And it should not.
Jumping directly from a complaint to a solution is one of the most common mistakes in improvement work.
A late delivery does not automatically mean more riders are required.
A refund delay does not automatically mean more customer-service employees are needed.
A cancellation does not automatically mean inventory is the problem.
The visible complaint is the symptom.
The real question is:
Where did the process begin to fail?
Based on the patterns identified in this study, organisations may need structured Lean Six Sigma projects around five broad areas.
1. Refund and Customer Support
Key questions include:
Where does waiting occur?
Why do some cases take longer to resolve?
Why are repeat customer interactions required?
Where do handoffs and escalations create variation?
2. Delivery Reliability
The customer sees a late delivery.
The investigation needs to trace the entire journey:
Order → Picking → Packing → Rider Allocation → Dispatch → Delivery
The critical question is:
At which point did the variation enter the process?
3. Product and Order Quality
For missing, incorrect, damaged or poor-quality items:
Where did the defect originate?
Inventory?
Picking?
Packing?
Storage?
Delivery?
4. Order Cancellations
A cancellation is often the final visible outcome of an earlier failure.
The investigation needs to understand what happened before the order reached that point.
5. Pricing, Transparency and Trust
Some complaints may reflect an operational defect.
Others may reflect a gap between:
What the customer expected and What the process delivered or communicated.
Both require investigation.
The Bigger Lesson
The strongest takeaway from this study is simple:
Customer complaints are process signals.
A customer sees the final failure.
The organisation needs to trace that failure backwards.
A delivery complaint may have started long before dispatch.
A refund complaint may have nothing to do with the person speaking to the customer.
A missing product may have entered the process before the order was packed.
That is why these findings should not immediately lead to solutions.
They should lead to better questions.
Why are refunds delayed?
Why do customers need repeated support interactions?
Why does delivery performance vary?
Why do orders get cancelled?
Why do incorrect or damaged products reach customers?
Why do customers perceive some charges or transactions as unclear?
These are not simply customer-service questions.
They are process questions.
And process questions require structured root-cause investigation.
The Way Forward
This study is a starting point, not a diagnosis.
It identifies where customers experience recurring problems.
The next step is to investigate why those problems occur.
That is where Lean Six Sigma can provide a structured path:
Customer Complaint → Problem Definition → Measurement → Root-Cause Analysis → Improvement
The biggest mistake would be to assume that the visible complaint is also the root cause.
It rarely is.
Quick commerce will continue to compete on speed.
But the next competitive advantage may not come from delivering a few minutes faster.
It may come from something much harder to build:
A process that works consistently—even when things do not go as planned.
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The Real Challenge in Fashion Retail: Turning Inventory into Profitable Sales
In fashion retail, the biggest problem is rarely buying merchandise.
The real problem is what happens after the merchandise arrives.
A retailer can have experienced buyers, strong vendor relationships, detailed market research, carefully planned ranges and significant seasonal budgets. Yet, at the end of the season, the same retailer may still find itself sitting on unsold inventory and offering deep discounts to clear it.
That is the fundamental challenge of fashion retail.
The question is not simply:
“Did we buy the right merchandise?”
The more important question is:
“How efficiently did we convert the merchandise we bought into profitable sales?”
Because in fashion retail, inventory does not just represent stock. It represents committed capital, planned gross margin, future markdown risk and, ultimately, the quality of merchandising decisions.
Fashion Buying Is a High-Stakes Commitment
A fashion buyer or merchandiser does not wake up every morning and purchase inventory based on yesterday’s sales.
Fashion buying works very differently.
Merchandising teams make commitments well in advance of the season. They study trends, review vendor presentations, analyse previous performance, understand customer preferences and build product ranges across categories.
Typically, the merchandise portfolio contains three broad layers.
The first is core merchandise. These are products that customers expect to find throughout the year. They may not always create excitement, but they provide consistency and predictable demand.
The second is seasonal or festival merchandise. These products are designed around specific periods—festivals, weather changes, weddings or other demand peaks.
The third and most challenging category is fashion merchandise.
Fashion moves quickly. Consumer preferences shift. Colours, silhouettes, fabrics and styles can suddenly become desirable—or lose relevance.
Retailers cannot ignore fashion because customers expect freshness. However, fashion buying involves significantly higher risk.
And then comes another complex variable: size.
The same product may perform differently across geographies because customer body profiles and size preferences differ. A size mix that works extremely well in Punjab may not work in the Northeast. A retailer operating across multiple regions cannot simply apply one universal size curve everywhere.
Therefore, before the season even begins, the merchandising team has already made a series of critical decisions:
What categories should we buy?
Which designs should we select?
How many options should we carry?
How much inventory should we commit?
What should be the size ratio?
When should deliveries arrive?
And how much of the budget should we allocate to each product category?
Everything may appear perfectly planned.
But fashion retail rarely follows the plan exactly.
The Problem Is Not Always Demand Forecasting
When inventory does not sell, the first solution many people suggest is demand forecasting.
Better forecasting is certainly useful—but it does not solve the entire problem.
Fashion retailers often place orders months before the merchandise reaches the store. Production has lead times. Vendors require advance commitments. Seasonal products may only be available during a specific manufacturing window.
A retailer cannot simply wait until the season begins, observe customer demand and then immediately replenish the winning products.
By the time the demand becomes clear, the buying decision may already have been made.
Vendor capability also plays a major role.
If a vendor can manufacture and replenish products frequently, the retailer can operate with greater flexibility. The retailer can test the market, identify winning styles and reorder them.
But many seasonal and fashion products do not offer that luxury.
They are produced for a particular season, ordered in advance and supplied according to a pre-decided delivery schedule.
That means fashion retail is not merely a forecasting problem.
It is a commitment and inventory-risk management problem.
The quality of the buying decision depends not only on predicting what customers will buy, but also on understanding:
How much should we commit before demand becomes visible?
How much flexibility does our supply chain provide?
How quickly can we react if the product performs differently than expected?
These questions often matter more than improving the forecasting model by a few percentage points.
The ₹100 Inventory Question
Let us simplify the entire challenge.
Imagine a retailer purchases merchandise worth ₹100.
The most important question is not simply whether that ₹100 eventually sells.
The important question is:
At what price did it sell?
That ₹100 worth of inventory can move through multiple stages.
The first portion sells at full price.
This is the most profitable outcome. The retailer captures the planned margin and validates the quality of the buying and merchandising decisions.
The second portion sells during a planned festival or promotional period.
The retailer may offer discounts, but these discounts are already part of the commercial plan. The retailer expects lower percentage margins but anticipates significantly higher sales volumes.
This is not necessarily a problem.
In fact, a well-planned festival period can generate substantial gross margin in absolute value because of the higher sales volume.
The real problem begins with the third layer.
This is the inventory that remains after the main selling opportunity has passed.
Perhaps the design did not work.
Perhaps the colour was wrong.
Perhaps the fashion trend moved in another direction.
Perhaps customers bought only certain sizes.
The retailer may have sold Medium and Large but still be left with significant quantities of Small and Extra Large.
Or the retailer may have sold one option within a product line while other options remained untouched.
At this stage, inventory stops behaving like an asset.
It begins behaving like a liability.
Unsold Inventory Destroys Gross Margin
Once merchandise misses its planned selling window, the retailer faces difficult choices.
The first option is to offer deeper discounts.
The second is to carry the inventory into the next period.
The third is to attempt alternative channels to liquidate the stock.
Each option carries a cost.
Deep discounting directly affects gross margin.
Carrying inventory forward blocks working capital and creates additional risk.
And alternative liquidation channels require careful execution to avoid damaging the brand or cannibalising regular store sales.
This is why gross-margin performance should never be evaluated only through sales growth.
A retailer can achieve strong sales and still destroy profitability if too much inventory eventually moves through deep markdowns.
The merchandising team’s real efficiency lies in its ability to convert committed inventory into sales at the right margin levels.
A buyer who purchases ₹10 crore worth of merchandise is not necessarily successful because ₹10 crore eventually sells.
The buyer becomes successful when a high proportion of that merchandise sells at full price or within planned promotional margins—and only a limited portion falls into distress liquidation.
That is the difference between buying merchandise and managing inventory productivity.
Every Fashion Retailer Needs a Margin Waterfall
Retailers should stop looking at inventory as one large number.
Instead, they should create a weekly inventory and margin waterfall.
Every week, the merchandising and leadership teams should be able to answer four simple questions.
How much inventory have we sold at full price?
How much have we sold at planned festival or promotional prices?
How much have we sold through additional markdowns?
How much inventory is still left and at risk?
This simple framework can dramatically improve decision-making.
Because the problem becomes visible before the end of the season.
If the retailer waits until the annual review to discover that gross margin has missed the budget, it is already too late.
By then, the unsold inventory has accumulated, markdowns have accelerated and the organisation has lost its ability to take corrective action.
Weekly visibility changes the conversation.
The buyer no longer says, “Sales are looking good.”
The organisation asks:
Are we selling the right inventory at the right margin?
That is a far more powerful question.
Predict Gross Margin Before the Quarter Ends
Once retailers track inventory through these different selling layers, they can begin predicting gross-margin performance much earlier.
For example, if the retailer notices that a growing proportion of inventory is moving into deeper markdowns, management can immediately estimate the impact on future gross margin.
Similarly, if full-price sell-through remains strong, the retailer can protect margin expectations even before the season ends.
This creates a much more dynamic approach to gross-margin management.
Instead of reviewing profitability after the quarter closes, the retailer can manage profitability while the season is still active.
The merchandising team can identify slow-moving categories.
Stores can rebalance inventory.
Winning products can receive greater visibility.
Slow-moving sizes can move to geographies where demand is stronger.
Promotional activity can become targeted rather than broad.
And management can intervene before the stock reaches the stage of distress liquidation.
Alternative Channels Can Protect Inventory Value
Today, fashion retailers also have an advantage that did not exist at the same scale a few years ago.
They have direct access to customers through digital channels.
Video commerce and live product selling have become particularly effective tools for inventory liquidation.
For example, a retailer operating in the Northeast may find itself with merchandise that is not performing strongly in its local stores. Instead of immediately applying deep in-store discounts, the retailer can showcase the merchandise through video-led selling and reach customers beyond the physical catchment area.
The objective is not simply to clear stock.
The objective is to clear stock intelligently.
A retailer should always try to find the highest-value channel available for unsold merchandise before moving to aggressive markdowns.
Digital selling, live commerce, targeted customer databases and inter-store inventory transfers can all provide additional opportunities to recover value.
The key is to act early.
The longer a retailer waits, the fewer options remain.
The KPI That Fashion Retailers Should Watch Every Week
Fashion retail leaders need to move beyond traditional sales reporting.
The most important weekly KPI framework should connect inventory, sell-through and realised margin.
A merchandising team should know how much of its inventory has moved through:
- Full-price sales
- Planned festival or promotional sales
- Additional markdown sales
- Unsold and ageing inventory
This should not remain a year-end analysis.
It should become a weekly management discipline.
Because the moment a retailer understands the composition of its sales, it also begins to understand the future composition of its gross margin.
And that is where better decisions begin.
The Bottom Line
Fashion retail will always involve uncertainty.
Trends will change.
Customers will surprise buyers.
Sizes will behave differently across regions.
Some designs will outperform expectations while others will fail completely.
No forecasting system can eliminate these realities.
But retailers can control how quickly they identify risk and how intelligently they respond to it.
The strongest fashion retailers will not necessarily be the ones that predict every trend perfectly.
They will be the ones that build a more efficient system for converting inventory into profitable sales.
Because ultimately, the real measure of merchandising success is not how much merchandise a buyer purchased.
It is not even how much merchandise the retailer sold.
The real measure is much more powerful:
How much of the inventory sold at full price, how much sold within planned promotional margins, how much required additional discounting—and how much was left behind?
When a retailer starts reviewing these numbers every week, inventory stops being a year-end problem.
It becomes a business variable that management can actively control.
And in fashion retail, where every buying decision carries risk, that visibility can be the difference between meeting the gross-margin budget and watching it disappear into markdowns.
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The Retail Expansion Trap: Why Growth Between 10 and 30 Stores Matters Most
Expansion is exciting. It gives a company the feeling of spreading its wings into new geographies, reaching new customers and building a stronger brand footprint.
And that is exactly where the danger often begins.
A retailer opens its first few stores outside its original market. The initial response is encouraging. Footfalls look healthy, customers appreciate the brand, and a few locations perform better than expected. Confidence rises. Suddenly, the organisation starts thinking bigger: If five stores can work, why not 20? If 20 can work, why not 50?
The expansion engine accelerates.
New locations are signed. Store openings become frequent. Teams become busy. The number of stores becomes a symbol of growth.
Then, three to six months later, reality begins to surface.
Some stores are performing well. Others are struggling. A few are nowhere close to the business case that justified their opening. Rentals are high, competition is stronger than anticipated, merchandise is not moving as expected, and catchments are behaving very differently from the original assumptions.
Now imagine this happening not to one store, but to ten or fifteen stores simultaneously.
The impact on the bottom line can be enormous.
Expansion Is Not About Opening Stores. It Is About Repeating Success.
Retail expansion is often mistaken for a real estate exercise. Find a location, negotiate the rent, sign the property and open the store.
In reality, successful expansion is a far more complex process.
Every new store represents a combination of several variables:
- The right geography
- The right micro-market
- The right catchment
- The right customer demographic
- The right competition landscape
- The right store size
- The right merchandise mix
- The right price points
- And, of course, the right rental economics
A mistake in any one of these variables can affect store performance. A mistake in several variables can turn a promising expansion plan into a financial burden.
Consider a fashion retailer entering a new city. The company may identify a busy market and assume that high footfall will automatically translate into sales. But what if that footfall consists largely of students with lower spending power? What if three established fashion retailers already dominate the market? What if the retailer has selected a 15,000-square-foot store when the catchment can realistically support only an 8,000-square-foot format?
The store may look impressive. The location may appear busy.
But the economics may never work.
That is why expansion should not focus only on where to open. It must focus equally on why that particular store should succeed.
Catchments Have Changed Dramatically
Retail catchment behaviour has also changed significantly.
Earlier, retailers often considered a three- to five-kilometre radius while assessing a store’s potential. Today, particularly in dense urban markets, the effective catchment can be much smaller.
In many large cities, customers do not want to travel unnecessarily.
Why should they?
There are competing brands nearby. Shopping malls are nearby. Local high streets offer alternatives. Online shopping provides another option altogether.
In many urban markets, the practical catchment for a convenience-oriented fashion or lifestyle store may shrink to one or two kilometres.
However, this logic cannot simply be copied across India.
The situation changes dramatically in Tier 2, Tier 3 and Tier 4 towns.
Many of these markets operate as self-contained ecosystems. Customers may willingly travel three, four or even five kilometres to visit a strong destination store. If the store offers the right combination of assortment, pricing, brand experience and product availability, distance becomes less of a barrier.
For example, a well-designed value-fashion store in a smaller town may attract customers from several surrounding neighbourhoods because there are limited comparable options. In such a market, the retailer can build a much wider catchment than it could in a highly competitive metropolitan area.
This is why a standard expansion formula is dangerous.
The same catchment assumptions cannot work everywhere.
The First Six Months Can Make or Break the Expansion Strategy
The real challenge often starts after the store opens.
The first three to six months are critical.
During this period, the retailer discovers whether its assumptions were correct. Is the expected customer actually coming? Is the conversion rate healthy? Are customers buying the right categories? Is the average transaction value sufficient? Is the store attracting repeat customers?
If one new store struggles, the organisation can usually manage the situation.
But expansion creates a multiplier effect.
Suppose a retailer opens 15 stores within six months. If ten of those stores underperform during their initial months, the company faces multiple pressures simultaneously: higher fixed costs, inventory requirements, employee expenses, rentals and working capital pressure.
By the time the management fully understands what went wrong, the financial impact may already be substantial.
This is one of the biggest differences between opening five stores and opening 30 stores.
At five stores, mistakes are manageable.
At 30 stores, mistakes become expensive.
Retail History Has Already Taught Us This Lesson
India has seen this pattern before.
During the retail boom of the mid-to-late 2000s, there was enormous enthusiasm around expansion. Retailers were opening stores across cities and regions at a rapid pace.
Growth itself became the objective.
Stores opened everywhere.
But not every store worked.
Many retailers eventually had to close stores, withdraw from regions or rethink their formats. The problem was not that expansion was wrong. The problem was that companies expanded faster than they learned.
A retailer would assume that a particular catchment would perform well. It would not.
Another store would struggle because the customer profile was different from what the company expected. A third location would suffer because competition changed after the store opened.
The most successful retailers were not necessarily those who made the fewest mistakes.
They were the ones who learned from mistakes faster.
That distinction is crucial.
East India Is Entering a More Competitive Retail Phase
This lesson is particularly relevant for East India today.
The region has witnessed a significant increase in fashion and apparel retail activity. Numerous local and regional retailers have emerged alongside larger national players. Many of these companies have already expanded to 10, 20 or 30 stores, while some have grown much larger.
Competition is no longer limited to a handful of established brands.
A retailer entering a new market today must understand that the customer has more choices than ever before.
This makes processes extremely important.
A company cannot depend only on the intuition of its promoters or expansion team. It needs a disciplined operating system that continuously evaluates store performance.
Regular retail audits become essential.
Merchandise quality must remain consistent.
Price points must match local purchasing behaviour.
Data must reveal what is working and what is not.
Management needs to ask sharper questions:
Why is one store converting better than another?
Why is the same merchandise selling in one town but not another?
Why has footfall increased without a corresponding increase in sales?
Is the problem the catchment, the merchandise, the price or the store experience?
The faster a retailer identifies the real issue, the faster it can act.
The Most Dangerous Phase Is Often Between 10 and 30 Stores
Moving from 10 stores to 30 stores is one of the most critical stages in a retailer’s journey.
The company is no longer small enough to manage everything informally.
But it may not yet be large enough to have mature systems, strong data infrastructure and specialised leadership across every function.
This is the risky zone.
The promoter can no longer personally understand every market.
The expansion team can no longer rely purely on experience.
Store managers cannot operate without standardised processes.
And small operational weaknesses begin to multiply.
Yet this phase is also the gateway to larger scale.
If a retailer cannot build strong processes between 10 and 30 stores, scaling from 30 to 100 stores becomes extremely difficult.
The objective, therefore, should not simply be to open more stores.
The objective should be to build the capability to operate more stores successfully.
Growth Requires Better Metrics and Faster Action
The future of retail expansion will belong to companies that combine ambition with discipline.
Retailers need to track the right metrics, not just top-line sales. They need to understand store productivity, conversion, repeat customers, category performance, inventory movement, rent-to-sales ratios and local competitive changes.
More importantly, they need to act quickly.
A problem identified after six months is expensive.
The same problem identified after four weeks may still be manageable.
Expansion will always involve uncertainty. No retailer can predict every catchment perfectly. Not every store will succeed exactly as planned.
But the goal is not to eliminate mistakes.
The goal is to reduce the cost of mistakes and increase the speed of learning.
Because in retail, expansion is not ultimately a test of how fast you can open stores.
It is a test of how fast you can learn, correct and improve while opening them.
