Tag: retail strategy

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.
September 22, 2026
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
1. Who currently dominates Indian skincare?
2. Which brands are gaining momentum?
3. What is driving consumer demand?
4. Why are some D2C brands struggling despite high sales?
5. 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.
September 10, 2026
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.
Comments

Comments settings

Like

Comment

Sharerce

September 8, 2026
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.

Welcome
September 6, 2026
Hiring talent will not help if business processes undefined – build processes

Before you blame your people for poor performance, ask yourself one question:
Have you given them a process that allows them to succeed?
Most business performance problems are not people problems. They are process problems.
You may recruit highly skilled, capable people. But without clear processes, even the best talent cannot perform at its full potential.
People need clarity: what to do, when to act, who owns the decision, and which framework should guide that decision. When these are unclear, decisions slow down. Employees naturally fall back on personal judgment, past experiences, and practices learned in previous organizations.
The result is inconsistent execution.
And inconsistency eventually reaches the customer.
Different employees create different experiences, send different signals, and shape different perceptions of the same brand. You may invest heavily in customer acquisition, Customer Lifetime Value (CLV), retention, or Net Promoter Score (NPS), yet customers may still leave through gaps created by poorly defined processes.
While one part of the business brings customers in, another may be pushing them out.
This is why scalable organizations do not grow simply by adding more people. They grow by creating clear operating processes, defined decision-making frameworks, accountability, and consistency across the customer journey.
People perform better when expectations are clear and decisions follow a structured path. A chaotic environment creates delays, confusion, inconsistent outcomes, and unnecessary pressure.
Before questioning whether your people are capable, evaluate the system in which they are expected to perform.
The right people create results. The right processes make those results repeatable, scalable, and sustainable.
Fix the process before blaming the people.

September 2, 2026
The ₹100 Crore Retailer: How Much Inventory Should It Actually Carry?
A retailer tells me:

“We are now a ₹100 crore business.”

My next question is usually not about sales.

I ask:

“How much inventory are you carrying?”

The answer can tell us a lot more about the health of the business than the ₹100 crore revenue number itself.

One retailer may be doing ₹100 crore with ₹6 crore of inventory.

Another may need ₹18 crore.

Both can be perfectly healthy.

Or both can have a serious inventory problem.

The difference lies in the business model, category, gross margin, inventory velocity, supplier lead time, assortment and, importantly, how much of that inventory is actually saleable.

So, if you run a ₹100 crore retail business, what should your inventory really look like?

There is no single answer.

But let’s look at the economics category by category.

Start with the basic calculation

A simple way to think about inventory is:

Inventory = Cost of Goods Sold × Inventory Days ÷ 365

So, if a retailer has ₹100 crore of sales, a 50% gross margin means its annual COGS is approximately ₹50 crore.

If the business carries 75 days of inventory:

₹50 crore × 75 ÷ 365 = ₹10.3 crore

That gives us a starting point.

But the interesting part begins when we apply this to different retail businesses.

Fashion: ₹9–10 crore may not be unusual

Consider a ₹100 crore fashion retailer.

Suppose:
- Gross margin: 55%
- COGS: ₹45 crore
- Inventory: 75 days
The inventory requirement works out to approximately ₹9.2 crore.

At first glance, that may look reasonable.

But I would immediately ask another question:

How much of that ₹9.2 crore is current and how much is ageing?

Imagine the inventory looks like this:

₹5.5 crore — fast-moving/current ₹1.5 crore — normal-moving ₹1 crore — slow-moving ₹700 lakh — ageing ₹500 lakh — effectively dead

The balance sheet still says ₹9.2 crore inventory.

But commercially, it is a very different story.

This is why fashion retailers should not look only at inventory value.

They need to look at inventory age, sell-through and expected realisation.

A ₹10 crore inventory number can be healthy.

It can also be the beginning of a ₹3 crore markdown problem.

Grocery: ₹5 crore can support ₹100 crore of sales

Now take grocery.

Suppose:
- Gross margin: 25%
- COGS: ₹75 crore
- Inventory: 25 days
Inventory requirement:

₹75 crore × 25 ÷ 365 = ₹5.1 crore

That’s a completely different inventory structure.

Why?

Because grocery moves much faster.

The retailer buys, sells and replenishes continuously.

Now imagine this retailer carries 60 days instead of 25.

Inventory becomes:

₹75 crore × 60 ÷ 365 = ₹12.3 crore

The retailer has not generated one additional rupee of sales.

But another ₹7.2 crore has become tied up in inventory.

That is the part of inventory management that often gets missed.

Inventory is not free.

Every additional rupee sitting on the shelf is a rupee that cannot be used somewhere else.

Beauty: the ageing problem is different

Beauty is another interesting example.

Take:
- ₹100 crore sales
- 55% gross margin
- ₹45 crore COGS
- 60 inventory days
Inventory:

₹45 crore × 60 ÷ 365 = ₹7.4 crore

So perhaps ₹7–8 crore of inventory looks reasonable as an illustrative starting point.

But beauty has another risk:

expiry and product relevance.

A product sitting in the warehouse for six months is not necessarily the same economic asset as a product that sells every week.

And then there are:
- New formulations
- Packaging changes
- Seasonal products
- Shade variations
- New launches
- Consumer preference changes
So the question isn’t simply:

“How much inventory do we have?”

It is:

“How much of our inventory is likely to convert into cash at the expected margin?”

That is a much more useful question.

Electronics: inventory can become obsolete before it becomes slow-moving

Electronics is even more interesting.

Assume:
- ₹100 crore sales
- 20% gross margin
- ₹80 crore COGS
- 60 inventory days
Inventory:

₹80 crore × 60 ÷ 365 = ₹13.2 crore

So an electronics retailer could be carrying ₹13 crore or more.

But electronics has a unique risk.

Technology moves faster than inventory.

A product can be perfectly saleable today and commercially unattractive six months later.

A new model arrives.

The manufacturer reduces prices.

A competitor launches a better product.

Customers wait for the next version.

Suddenly, ₹2 crore of inventory may require discounting.

This is why I would rather see an electronics retailer carrying ₹14 crore of highly productive inventory than ₹10 crore containing ₹3 crore of ageing stock.

The lowest inventory number is not necessarily the best inventory position.

Furniture: ₹18 crore may actually be reasonable

Furniture creates another interesting contrast.

Suppose:
- Gross margin: 45%
- COGS: ₹55 crore
- Inventory: 120 days
Inventory:

₹55 crore × 120 ÷ 365 = ₹18.1 crore

That sounds huge compared with grocery.

But should we immediately call it inefficient?

No.

Furniture has:
- Longer buying cycles
- Longer supplier lead times
- Larger ticket sizes
- Large display requirements
- Lower purchase frequency
- Higher logistics complexity
So 120 days of inventory may be perfectly understandable for the business.

This is why comparing a furniture retailer’s inventory days with a grocery retailer’s inventory days is meaningless.

Inventory benchmarks have to be category-specific.

Footwear: having inventory doesn’t mean having availability

Now consider footwear.

Suppose:
- ₹100 crore sales
- 50% gross margin
- ₹50 crore COGS
- 75 inventory days
Inventory:

₹50 crore × 75 ÷ 365 = ₹10.3 crore

Again, roughly ₹10 crore.

But here’s the problem.

Footwear doesn’t just have SKU-level inventory.

It has size-level inventory.

You may have:

100 pairs of a particular style.

But if your customers predominantly want sizes 8, 9 and 10, having 40 pairs in sizes 6 and 7 doesn’t solve the problem.

You can have:

₹10 crore of inventory

and still tell customers:

“Sorry, your size is unavailable.”

This is where inventory productivity becomes much more important than inventory value.

D2C: the problem often starts with the buying decision

D2C businesses have another challenge.

Suppose a ₹100 crore D2C brand has:
- 60% gross margin
- ₹40 crore COGS
- 60 inventory days
Inventory:

₹40 crore × 60 ÷ 365 = ₹6.6 crore

That looks efficient.

But now look inside the number.

Suppose the brand has 100 SKUs.

10 SKUs generate most of the demand.

30 are average.

60 are struggling.

The overall inventory number may still look perfectly acceptable.

But the problem is sitting at the SKU level.

This is where I believe many D2C businesses need to move from:

“How much inventory do we have?”

to:

“Which SKUs are consuming our working capital?”

Because one bad buying decision repeated across 50 SKUs can quietly lock up crores.

So, what does a ₹100 crore retailer really need?

Using the illustrative assumptions above, the picture could look something like this:

These are illustrative calculations, not universal industry targets.

And that’s exactly the point.

There is no magic number called:

“Ideal inventory for a ₹100 crore retailer.”

The bigger question is inventory productivity

Let’s say two retailers both have ₹100 crore revenue.

Retailer A carries:

₹8 crore inventory

Retailer B carries:

₹16 crore inventory

It is tempting to say Retailer A is twice as efficient.

But we need to know more.

What if Retailer A is constantly out of stock?

What if it is losing sales because its best-selling SKUs aren’t available?

What if Retailer B has higher availability and faster growth?

Now the answer changes.

The objective isn’t:

Minimum inventory.

The objective is:

Optimum inventory.

Enough inventory to maintain availability and support growth.

But not so much that working capital gets trapped in slow-moving or obsolete stock.

And this is where I would ask a ₹100 crore retailer five questions

1. How many inventory days are we carrying?

Not just at company level, but by category and channel.

2. What percentage of inventory is older than 90/120/180 days?

Ageing tells us much more than the total number.

3. Which SKUs are consuming working capital without producing sufficient sales?

This is where SKU-level analysis becomes powerful.

4. Where are we overstocked while another store/channel is out of stock?

Inventory in the wrong location is almost as problematic as not having inventory.

5. If sales grow from ₹100 crore to ₹150 crore, how much additional inventory will we need?

This may be the most important question of all.

The ₹100 crore → ₹150 crore test

Suppose a retailer grows from ₹100 crore to ₹150 crore.

The conventional approach says:

Sales +50% → Inventory +50%

So:

₹10 crore inventory → ₹15 crore.

But what if better forecasting, replenishment, transfers and assortment management allow the retailer to reach ₹150 crore with ₹12 crore inventory?

The business has effectively released ₹3 crore of capital.

No new store.

No additional borrowing.

No additional investor capital.

Just better inventory productivity.

That is why inventory management should not sit only with the supply-chain team.

Inventory is a finance issue.

Inventory is a merchandising issue.

Inventory is a buying issue.

Inventory is a customer-experience issue.

And ultimately, inventory is a CEO issue.

The number I would like every retailer to know

Not:

“Our inventory is ₹12 crore.”

But:

“Every ₹1 of inventory in our business generates ₹X of annual sales and ₹Y of gross margin, and we know exactly where the unproductive inventory is.”

That is a much more meaningful measure of retail health.

Because the objective of inventory is not to sit in a warehouse.

It is not to fill a store.

It is not even to make the balance sheet look healthy.

Inventory has only one job: to be converted into sales, margin and ultimately cash.

And the faster and more profitably you can do that—without compromising availability—the stronger your retail business becomes.

₹100 crore of sales is a milestone.

But ₹100 crore of sales with disciplined inventory is a much stronger business.

What is your view?

For a ₹100 crore retailer, what is more dangerous:

₹5 crore of excess inventory—or ₹5 crore of lost sales because of stockouts?

I’d be interested in hearing how retailers and D2C businesses are looking at this today.

Welcome
August 30, 2026
India What a Decade of Borrowing Actually Bought Us. Are we in a Debt Trap?
India’s Public Debt Story (2015–2025): What a Decade of Borrowing Actually Bought Us. Are we in a Debt Trap?

India’s combined government debt has roughly doubled over the past decade. Central government debt alone climbed from about ₹121 trillion in FY2020 to an estimated ₹197 trillion by FY2026. State liabilities nearly tripled, from ~₹32.6 trillion in FY2016 to ~₹94 trillion by FY2025.

Numbers like these trigger one of two reactions: alarm or indifference. Neither is entirely right.

The better question is: What drove this rise, where did the money go, and where does it leave India today?

Here’s the decade, unpacked.

The pattern: steady drift, then a shock, then a slow climb-down

Pre-2020, India’s fiscal deficit sat in a fairly disciplined 3–4% of GDP band, broadly consistent with FRBM targets — even if actual numbers occasionally missed the mark (FY2019 came in around 3.4% against a 3.3% target).

Then COVID hit. The central fiscal deficit spiked to roughly 9.5% of GDP in FY2021 as revenue collapsed and emergency spending — cash transfers, free food grain, MSME credit guarantees, health spending — went out the door. Debt-to-GDP jumped by an estimated 8–10 percentage points in a single year.

Since then, the story has been consolidation: the deficit has been brought down to around 5–6% of GDP, with a budgeted target of 4.4% for FY2026. It’s real progress, even if the debt base it’s working off is now structurally higher than it was five years ago.

Three episodes that explain most of the swings Debt to GDP ratio %

Demonetisation (Nov 2016): Intended to curb black money and push digital transactions, it also slowed activity and tax collections in the short run — GDP growth cooled and the fiscal deficit widened modestly (to around 3.5% of GDP against a 3% target) in FY2017.

GST rollout (Jul 2017): A genuine structural reform with long-term revenue upside, but the transition wasn’t free. Collections initially undershot expectations, and the Centre had to borrow to compensate states during the changeover — nudging FY2018’s deficit above target.

COVID-19 (2020–21): By far the biggest driver. A relief package of ₹20 trillion, layered onto a collapsing revenue base, took the deficit from roughly 3.5% to 9.5% of GDP in one year. Was it worth it? Most independent analyses — including from the IMF and RBI — suggest yes: the counterfactual (a sharper austerity response) likely meant a materially deeper contraction. The trade-off was a permanently higher debt stock in exchange for a shallower recession.

Add to this two smaller but persistent pressures: periodic PSU bank recapitalisation (roughly ₹2.2 trillion across FY2018 and FY2020, needed to clean up bad loans) and an expanding footprint of welfare and housing schemes (PMAY, Ujjwala, MGNREGA) that keep revenue spending elevated even as capital expenditure has also been rising.

The quiet number that matters most: interest payments

Debt totals get the headlines, but the number that actually constrains policy choices is the interest bill. Central government interest payments rose from about ₹6.6 trillion in FY2020 to roughly ₹8.1 trillion by FY2022 and now consume somewhere in the range of 20–25% of central revenue receipts. That’s revenue that isn’t available for capex, subsidies, or further consolidation — it’s simply the cost of past borrowing. As global and domestic interest rates moved up through 2022–23, this burden has only gotten heavier.

Where did the borrowed money go?

It helps to separate how much India borrowed from what it was spent on. The story changed significantly after COVID:
- FY2020–21: Borrowing largely supported emergency needs — food distribution, welfare support, MSMEs and bank recapitalisation. Necessary at the time, but with limited long-term growth impact.
- FY2022–24: The focus shifted toward capital expenditure — roads, railways, healthcare and infrastructure. Capex as a share of GDP steadily increased.
The positive shift: India increasingly moved from borrowing to support consumption toward borrowing to build productive assets.

The road ahead

The government’s own glide path targets a fiscal deficit closer to 4% of GDP, with debt-to-GDP expected to ease from its COVID-era peak (above 60%) toward the high-50s by FY2026. Getting there sustainably will likely depend on a few things playing out together: continued nominal GDP growth outpacing debt growth, disciplined targeting of subsidies rather than blanket cuts, tighter oversight of contingent liabilities and off-budget borrowing (state guarantees, PPP obligations), and a continued shift of the expenditure mix toward capital rather than revenue spending.

None of this is dramatic. Fiscal consolidation rarely is — it’s mostly the unglamorous work of holding a line over several budget cycles. But the direction, so far, has been the right one.

The takeaway

The last decade brought demonetisation, GST, COVID-19 and a major infrastructure push. As a result, India’s public debt is roughly twice what it was in 2016.

But debt is only one side of the story. India also built a stronger digital payments ecosystem, a unified tax system and significantly better infrastructure, while managing a strong post-COVID recovery.

The key question now isn’t how much India owes, but whether economic growth can continue to outpace the growth in debt.

What’s your read — was the post-COVID borrowing surge the right call, or did it set a debt base that will constrain policy for years to come? Would like to hear other perspectives on this.

#IndianEconomy #PublicFinance #FiscalPolicy #Macroeconomics #Debt #Budget2026 #EconomicPolicy #India
August 28, 2026