How Enterprise Analytics Scaled SleepyHug from ₹0 to ₹100CR

What does it really take to build a ₹100 Cr brand in under 13 months, without burning cash or cutting corners?

For SleepyHug, the answer wasn’t celebrity ads or discount wars. It was enterprise analytics, ruthless execution, and the GrowthJockey team running point from day one.

With GrowthJockey at the helm, we reverse-engineered demand: analyzed 300+ competitor SKUs, mined thousands of reviews to find the “why” behind purchases, and monitored 22,000 live prices every day.

Those signals fed our business analytics stack, steering everything from product mix and packaging to pricing, channel bets, and promotions. No guesswork. No vanity dashboards; just decisions tied to revenue.

If you’ve ever wondered what business analytics looks like beyond dashboards and slides, this playbook is for you.

In this blog, we'll break down how GrowthJockey used business analytics and intelligence to turn a sleepy category into a ₹100 Cr rocketship, plus what you can steal from the journey.

Cracking the white space: How enterprise analytics uncovered a sleepy industry gap

Before it became a ₹100 Cr brand, SleepyHug started with a simple question: Why is buying a mattress in India still such a mess?

You walk into a store, face a wall of lookalike options, and get pitched with confusing jargon. In 10 minutes, you’ve made a 10-year decision, usually without any clarity on what you're actually paying for.

SleepyHug was launched to change that. A direct-to-consumer mattress brand focused on better sleep. So, to bring that vision to life, they partnered with GrowthJockey: a full stack venture builder.

With deep expertise in venture building, data intelligence, and execution, GrowthJockey helped turn SleepyHug from a simple hypothesis into a fast-scaling business.

But none of it was guesswork. Our team used enterprise analytics to understand what the market truly lacked and where the opportunity lay.

Here’s what the data uncovered:

1. Prices that change depending on where you live

Dealer markups and hidden margins made it hard for customers to know what they were actually paying for. A mattress could cost ₹25,000 in one city and ₹18,000 in another for the exact same model.

2. Buying in-store felt overwhelming and rushed

Most showrooms stocked dozens of options with barely any real difference between them. Sales reps pushed what was in stock, not what was right for the customer. And a five-minute lie-down wasn’t enough to know if the mattress actually worked.

3. No room to change your mind

Once a mattress was bought, that was it. No trial period. No return. If it didn’t work for your back or your sleep, tough luck.

4. Delivery took forever, and came with zero visibility

People often waited 10-15 days for delivery. Tracking was unclear, support was patchy, and the last-mile experience felt like an afterthought.

5. Sleep science rarely entered the picture

Most brands were still selling basic foam or coir with fancy names. Hardly anyone was building around spinal alignment, cooling tech, or real orthopaedic needs.

6. Supply chains were bloated and inefficient

By the time a mattress got to the customer, it had passed through too many hands, like factories, distributors, dealers, and warehouses, each adding time, cost, and complexity.

7. Warranty claims were a nightmare

Even for high-ticket purchases, there was no clear way to raise an issue. The process was slow, offline, and often designed to make customers give up halfway through.

How business analytics guided SleepyHug before product-market fit

Although SleepyHug now had a clear handle on the market’s pain points, solving them was anything but easy.

It’s one thing to spot broken systems like confusing pricing, poor fulfilment, or outdated foam tech. But turning those insights into a scalable business meant wrestling with tough questions around product, pricing, positioning, and trust.

This phase called for more than instinct. It demanded structured testing, cross-functional research, and serious business analytics skills.

Let’s walk through the early-stage challenges SleepyHug faced and how they used enterprise analytics to begin solving them one by one:

1. Breaking into a crowded category with no legacy pull

The mattress space wasn't underserved; it was overserved with sameness. Between legacy brands flooding the retail space and D2C foam brands battling over discounts, standing out felt nearly impossible. SleepyHug didn’t have decades of credibility or ad budgets to match.

To move forward, the team needed hard data, not gut feel.

With GrowthJockey’s help, they conducted teardown audits on over 300 mattresses, scraped 2,700 customer reviews, and created a white space matrix that identified real gaps. This pointed them toward the sweet spot: orthopaedic cooling support with transparent pricing.

2. Picking a hero product without falling for guesswork

Every founder talks about the MVP, but in a category like mattresses, where returns are painful and inventory costs run high, picking the wrong product to start with could sink everything.

Should we go mass-market or premium? Focus on comfort or orthopaedics? There were hundreds of directions and no obvious answer.

GrowthJockey leveraged Intellsys.ai, an AI copilot designed for serious business analytics, and used it to map comfort scores against pricing expectations.

That’s how we identified the ideal launch point, which was a ₹14,000 queen mattress that balanced quality, demand, and profit without needing massive volume to break even.

3. Decoding what people actually wanted from a mattress

Sleep, as it turns out, is deeply personal. The team faced wildly conflicting feedback across reviews. Some wanted firmer back support, others complained about heat retention, while a third group just wanted something that didn’t sag in 6 months.

It was hard to separate edge cases from real trends. Without a clear pattern, every product decision felt risky.

Instead, SleepyHug filtered noise using structured review mining and 24 in-home sleep interviews, tagging feedback by sleeping style, weight class, and geography.

Insights like motion isolation and pressure relief kept surfacing, which led to the development of their product market fit, AirCell™ technology, and differentiated zoned support layers.

4. Choosing where to launch without wasting budget

Going D2C meant setting up logistics, support, and customer trust from scratch. Marketplaces offered reach but came with razor-thin margins and limited brand control. The team debated endlessly over where to start, knowing a wrong first move could derail momentum.

SleepyHug came to the solution of choosing Flipkart as their testing ground. It served as an ecosystem with sleep-category traffic and price-sensitive buyers. It gave them buyer behaviour data, review traction, and a low-risk space to refine SKUs before expanding to Amazon and D2C.

5. Convincing people to trust a brand they'd never heard of

Mattresses are high-investment purchases, and people don’t switch brands easily. With no track record, SleepyHug had to earn trust before a single order was placed.

The team led with transparency: same price across cities, a 100-night trial, and verified reviews from early Flipkart buyers. By launching on marketplaces first, they leveraged platform trust before transitioning to a D2C funnel.

6. Building something high-quality without pricing themselves out

The team agreed: quality had to be non-negotiable. But how do you build a product that competes with ₹25K+ legacy models and still price it under ₹15K?

The more features they added, the tighter the margins got. On top of that, without volume, production costs stayed stubbornly high. So they partnered with Prime Comfort, a top-tier foam manufacturer, to build their custom SKUs in-house.

This gave them control over inputs, while enterprise analytics helped with data-driven decision making through monitoring cost, performance, and customer satisfaction. Eventually, they launched 39 carefully crafted options, each backed by data, not guesswork.

Enterprise analytics in action: The systems that scaled SleepyHug to ₹100 Cr

Once the core questions were answered, it was time to execute, and that's where GrowthJockey came in.

It was now about velocity, systems, and scalable decisions powered by real-time business analytics and intelligence. Our team integrated cross-functional operators, proprietary tech platforms like Intellsys.ai and OttoPilot, and a structured go-to-market strategy that turned SleepyHug into a ₹100 Cr category leader.

Here’s how it all came together.

1. Tackling price confusion and markups

SleepyHug killed the confusion around mattress pricing by going fully D2C. With GrowthJockey’s support, they bypassed showrooms, supply chains distributors completely. No more middlemen marking up prices at random.

Every product had a clear, transparent price tag on the website, backed by factory-direct costs and city-level inventory optimisation using Intellsys.ai. Whether you were in Delhi or Durgapur, you paid the same fair price.

2. Making buying feel easy, not pushy

No more wandering showrooms with 15 foam beds and zero guidance. SleepyHug’s site, built with GrowthJockey’s UX team, gave people real info: mattress comparisons, sleep needs, spinal support benefits, and easy product selectors.

The 100-night trial added breathing room. Try it at home, and return it if it’s not right.

3. Giving people room to change their mind

SleepyHug didn’t just stop at this 100-night trial, though. They made it frictionless. Thanks to GrowthJockey’s backend workflows with OttoPilot, trial tracking, returns, and replacements became seamless. No awkward calls. No “fill this PDF form.” Just clear steps, clear support.

Returns don’t have to be a headache. GrowthJockey’s OttoPilot handles workflows so you keep customers happy (without chasing tickets).

4. Speeding up delivery and improving tracking

Nobody wants to wait 2 weeks for a mattress. So GrowthJockey set up dynamic logistics using Intellsys Copilot, syncing SleepyHug’s inventory with multiple fulfilment partners. Orders got routed smartly by pin code, and real-time delivery updates kept customers in the loop.

5. Utilising tech-led operational clarity

Running a D2C brand is chaos unless you have OttoPilot. SleepyHug used GrowthJockey’s business automation system to automate lead capture, follow-ups, and sales tracking.

Custom workflows were set up to auto-segment leads, book demos, and keep every stakeholder updated in real time. Now, there were faster sales cycles, 100% lead visibility, and no manual reminders.

6. Having real-time business intelligence

Most startups fly blind between marketing, inventory, and cash flow. Not SleepyHug. With Intellsys.ai, they tracked over 1,000 metrics in real-time: SKU-level P&L, funnel drop-offs, ad ROAS, and even daily cash flow.

Notable use cases:

• Dynamic pricing based on sales + ad trends

• Live order monitoring across Flipkart, Amazon, and D2C

• AI-generated board-ready performance decks

7. Making warranties actually usable

SleepyHug knew they had to make their warranty process easy and realistic. So now, thanks to OttoPilot’s automation, customers could raise claims digitally, get responses fast, and track status without chasing. No gatekeeping. No excuses.

8. Running growth marketing that converts

GrowthJockey ran all performance marketing in-house. Armed with creative testing and ROAS dashboards from Intellsys, the team ran Meta and Google ads with weekly A/B loops. They scaled only what converted, drastically reducing CAC.

Business analytics lessons founders can steal from GrowthJockey’s playbook

GrowthJockey helped SleepyHug scale to ₹100 Cr+ by using enterprise-grade intelligence. They treated the brand like a living organism: tracking every move, predicting outcomes, and building for durability from day one.

For founders eyeing sustainable growth, here’s what you can steal from the playbook (no shame, we’d do the same):

1. Run a 2-week diagnosis before you write a single ad

Instead of jumping into paid marketing or building out a huge team, GrowthJockey started with a sprint. They scraped 2,700+ reviews, benchmarked 300 mattresses, and mapped pricing vs comfort using enterprise analytics tools. That’s how they knew exactly where the market gap was.

Founder takeaway: You don’t need a 6-month research phase. You need the right questions and fast data to back your instincts. Use business analytics to build right from day zero.

2. Don’t hire five analysts; build a smarter dashboard

SleepyHug didn’t hire an analytics team. They ran lean, with Intellsys.ai showing real-time SKU-level P&Ls, ad ROAS, and cash flow. Meanwhile, OttoPilot automated lead follow-ups and CRM ops. The stack did what 6 team members normally would.

Founder takeaway: With the right business analytics software, you can get the firepower of a much larger team, without the overhead or the chaos. Invest in tools that pay for themselves.

3. Set CAC tripwires (and actually stick to them)

Forget “spend more to grow.” SleepyHug set CAC limits by channel, then tracked them daily with live dashboards. The moment costs crossed a threshold, the system alerted the team before budgets spiralled.

Founder takeaway: Build breach alarms. Your enterprise analytics stack should tell you when to scale and when to pause so you don’t find out after your bank balance does.

4. Don’t just optimise the funnel - optimise the fulfilment

Founders obsess over front-end metrics. But GrowthJockey used business analytics and intelligence to tighten backend ops, too. Such as routing orders based on real-time pincode demand, working with multiple 3PLs to avoid outages, and cutting delivery time by half.

Founder takeaway: Reliable fulfilment builds trust. Use analytics to forecast demand, manage warehouses, and avoid putting all your eggs in one delivery basket.

5. Use add-on behaviour to increase LTV, not guesswork

SleepyHug noticed buyers often returned for pillows and protectors within 30 days. Instead of hoping for repeat purchases, they turned those insights into bundled offers, post-purchase flows, and retargeting that felt like service, not spam.

Founder takeaway: What business analytics tells you about buyer behaviour can fund your growth. Study what happens after checkout and design systems that double revenue from the same customers.

6. Leverage cross-venture knowledge like a cheat code

Because SleepyHug was built inside a venture studio, they didn’t start from scratch. They used what worked from brands like Livpure, Polycab, and Automate; copying pricing logic, funnel frameworks, and even team rituals.

Founder takeaway: You don’t have to figure everything out yourself. If you want that kind of backing for your next big idea, GrowthJockey's incubator is one click away.

What enterprise analytics can teach every founder

The biggest unlock in SleepyHug’s ₹100 Cr journey? It wasn’t luck or a viral campaign. It was systems. Real enterprise-grade systems that helped the team move faster, test smarter, and spend wiser.

Tools like Intellsys.ai gave real-time visibility into what was working (and what wasn’t). OttoPilot handled the boring-but-critical stuff so teams could focus on building. Plus, at every step, business analytics, not gut instinct, drove decisions, from pricing to product to P&L.

If you’re a founder navigating 10 fires a day, here’s the real lesson: having the right business analytics and intelligence infrastructure can save you from months of wasted energy and dead-end pivots.

At GrowthJockey, that’s exactly what we do, as your startup incubator, accelerator, and venture studio. We plug in experienced cross-functional teams, deploy proven systems like Intellsys.ai and OttoPilot, and engineer ventures with speed, structure, and full-stack support.

When you are ready to build ventures with velocity and strategic clarity, GrowthJockey is your unfair advantage.

FAQs on Enterprise Analytics

What do you mean by enterprise analytics?

Enterprise analytics refers to the use of advanced data analysis tools and systems to make smarter, faster decisions across large organisations.

It blends business analytics and intelligence to uncover patterns, optimise processes, and drive growth at scale. Platforms like Intellsys.ai help companies monitor thousands of metrics in real time, from P&L to customer behavior.

What are the 5 benefits of data analytics?

When done right, data analytics is like having X-ray vision for your business. You can:

• See what’s driving growth (and what’s dragging it down)

• Catch trends before your competitors do

• Save time on things that don’t need manual effort

• Personalise how you sell and serve

• And avoid expensive guesswork

What is the end goal of using business analytics?

To take smarter, faster actions. Business analytics helps you answer questions like “Where’s my margin leaking?” or “Which product should I double down on?” It’s this clarity that helps you move with conviction, not confusion.

What are the pillars of business analytics success?

Business analytics success rests on four key pillars:

1. Clean, reliable data,

2. The right analytics tools,

3. Skilled teams with business analytics skills, and

4. Clear decision-making frameworks.

Together, these enable organisations to move from reactive reporting to proactive, data-driven growth.