At a Glance
| Metric | Before Kyros | End of Pilot Season |
|---|---|---|
| Dead stock as % of closing inventory | 29% | 19% |
| Full-price sell-through | 58% | 71% |
| Planning cycle time | 4–5 weeks | 2.5 weeks |
| Mid-season transfer volume | High — reactive | Reduced by ~40% |
| OTB visibility | Updated weekly, manually | Live, updated per PO |
| Post-season learnings applied to next season | Manual, inconsistent | Structured, system-led |
The Brand
Rasa Studio is a 34-store ethnic and occasion wear brand operating primarily in Gujarat and Rajasthan, with a small presence in Pune and Bengaluru. They had been growing at a steady clip — adding 6 to 8 stores a year — and were at the point where the founder and a two-person buying team could no longer carry the planning process in their heads.
The product was well-loved. Repeat customer rates were strong. The brand knew its customer. What it didn't have was a system that could hold all of that knowledge and turn it into structured buying decisions at the scale they were now operating.
The Problem
Rasa Studio's planning process before Kyros looked like this: the founder and head buyer would sit down six months before a season, pull last season's sales exports from the ERP, build a rough category split in Excel, and start vendor conversations. The OTB existed as a number in a spreadsheet that got updated whenever someone remembered to update it.
It wasn't chaos. For a 12-store brand, it had been fine. At 34 stores — across four states, with different climatic profiles and meaningfully different customer preferences by region — it was producing the same frustrating outcomes every season.
Overbuying in the wrong places. Stock was bought at the brand level and distributed evenly. A style that sold through in two weeks in Ahmedabad would sit unsold in Pune. By the time the team noticed, the markdown window had either passed or the transfer logistics weren't worth it. Underbuying the winners. The styles that ran fastest were also the ones most likely to have stocked out the previous season — which meant the sales history the team was planning from quietly understated their actual demand. They were consistently buying less than they needed of the products customers most wanted, and had no way to see it. No early warning on slow movers. If a style was aging badly, the team found out when someone visited the store or when the monthly ERP report landed. By then, the right markdown moment had usually passed. The plan lived in one person. The head buyer knew which clusters preferred which silhouettes, which vendors could be trusted with new styles, which categories needed more OTB buffer. That knowledge wasn't written down anywhere. When she was out for two weeks during the pilot, the team realised how exposed they were.The Pilot
Kyros was brought in at the start of the brand's Summer season. Setup — connecting their POS and ingesting two seasons of historical data — took just under a week. The team running it was lean: one buyer, one junior planner, and the founder checking in on key decisions.
The pilot focused on the three areas the team had flagged as their biggest pain points: knowing their real OTB position, getting stock to the right stores from day one, and finding out about problems before it was too late to act.
OTB stopped being a guess
Within the first two weeks, the buying team had quietly stopped maintaining their Excel OTB tracker. The Kyros position updated automatically whenever a PO was raised or changed. For the first time, the buyer and the founder were working from the same number in the same moment — not two slightly different versions of a spreadsheet last saved at different times.
It also showed them something they hadn't clearly seen before: one category was burning through OTB much faster than planned while another was barely touched. The Excel tracker had smoothed over that gap because it was updated too infrequently to catch it in time. Seeing it live let them rebalance before the buy window closed.
Stock went to the stores that would actually sell it
The allocation engine considered each store's recent performance on the specific attributes being sent — occasion type, silhouette, fabric — rather than spreading inventory evenly across all 34 locations. For Rasa Studio, this meant the occasion wear that moved fastest in their Rajasthan stores got more depth there from the first delivery, rather than being diluted across locations where it historically sat.
The team overrode the engine on a few lines — they knew things about specific stores that weren't in the data yet. But the overrides were deliberate decisions, not the default.
Mid-season transfers dropped. Not to zero, but the reactive scramble that had previously eaten up the third and fourth week of every season was much smaller.
Problems surfaced before the window closed
The team set a simple threshold: any style with more than 21 days of aging and sell-through below 30% would appear in the daily action queue. In the first six weeks of the pilot, this caught three styles early enough to markdown at 20% and move them cleanly. Caught two weeks later, those same styles would likely have needed 35–40% to clear.
Two styles running well ahead of their stock cover were also flagged in time to arrange small top-ups from the vendor. Both had been missed in previous seasons.
What Changed by Season End
Dead stock as a share of closing inventory fell from 29% to 19%. Full-price sell-through went from 58% to 71%. The planning cycle for the next season — started immediately after close — ran in two and a half weeks, because the post-season analysis had already structured the key learnings and translated them into starting defaults for the next plan.
The founder put it simply: they had always known roughly what was working. Kyros made it possible to act on that knowledge while it still mattered.
What the Team Said
"I used to dread the mid-season check because I knew I'd find things we should have caught earlier. This season, most of what needed action had already been flagged. That's a different way to work." — Head Buyer, Rasa Studio
"We were up and running in under two weeks. I expected it to be slower and need more from our side. It didn't." — Founder, Rasa Studio
Where They Are Now
Rasa Studio is in their second season on Kyros. The pilot validated enough that they've expanded into the range planning tools and are using the prior-season comparison to build a more intentional range architecture than they've had before.
They're a ₹9 crore brand with 34 stores. The problems they needed to solve were the right size for where they are — not enterprise challenges, but real ones with real consequences. The goal going into Season 2 was simple: start with what last season already taught them. They did.
Figures reflect platform data from one full pilot season. Brand name has been changed at the client's request.