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How Rustorange reduced discount burn by 10–12%
Industry:
Apparel
Products Live:
Cart
Checkout
Features:
No items found.
+25%
Increase in Prepaid orders
12%
ROAS lift
12%
Increase in Conversion rate
Meet the brand
Founded in 2018 and headquartered in Noida, Rustorange was built on a bold vision: Indian wear should evolve with Indian women. As women became more independent, ambitious, and dynamic, fashion remained rooted in outdated silhouettes and occasion-only dressing. Rustorange set out to modernize Indian wear designing for confidence, comfort, and everyday life.
Meet the brand
Founded in 2018 and headquartered in Noida, Rustorange was built on a bold vision: Indian wear should evolve with Indian women. As women became more independent, ambitious, and dynamic, fashion remained rooted in outdated silhouettes and occasion-only dressing. Rustorange set out to modernize Indian wear designing for confidence, comfort, and everyday life.
Challenge
Rustorange operates on Shopify. At the time, Shopify’s native checkout in India did not offer
- Rich Indian address autofill capabilities (similar to US/Canada markets)
- Mobile-number-first login
- Strong discount controls
This created two core issues for the brand.
First, uncontrolled discount burn. Percentage-based discounts applied to high-value carts could escalate beyond what the brand had budgeted. Without proper capping, acquisition campaigns risked eating into margins.
Second, data inaccuracies.
While Shopify did validate pin codes against cities, the address fields were not as rich or intelligent as markets like the US or Canada where autofill systems ensure more structured and accurate address capture.
Customers manually entered addresses and phone numbers, often leading to mismatches or incomplete details. This increased operational friction from delivery coordination to higher risk of RTOs and support overhead.
As Founder Shashank explains:
“Because we are on Shopify and Shopify natively does not have a rich database of Indian addresses… you had to manually fill in everything. The customer might put in a city of Bombay and give a pin code of Delhi… and phone numbers could be of nine digits and they were getting saved.”
– Shashank Agnihotri, Co-founder, Rustorange
They began looking for a checkout solution to fix margin leakage and data inefficiencies at the source. The goal was tighter control over discounts, prepaid orders, and checkout accuracy.
Evaluation criteria
“When we made the decision to onboard to Shopflo, it was primarily because of the discount engine… we wanted a better discount engine where we could customize discounts based on multiple parameters. Compared to other solutions at that time, we felt this was stronger.”
– Shashank Agnihotri, Co-founder, Rustorange
1. Discount engine with capping logic
For Rustorange, this was the decisive factor.
The team wasn’t trying to reduce discounts. They were trying to control them intelligently.
In fashion, especially during new customer acquisition, percentage discounts work. But without proper guardrails, they distort contribution margins on high-value carts.
They specifically needed:
- Percentage-based discounts
- The ability to cap maximum discount value
- Flexibility to tweak campaigns collection-wise\
This wasn’t about offering deeper discounts. It was about maintaining acquisition efficiency without allowing discount burn to spiral.
For a brand launching 2–3 drops per month, that flexibility directly impacts profitability.
2. Accurate data capture for the Indian market
Shopify’s native checkout previously lacked deep Indian address enrichment and autofill sophistication, particularly when compared to Western markets.
It was not about complete absence of validation; it was about the lack of intelligent autofill and structured address assistance.
For a brand shipping pre-orders with 3–4 week lead times, even small inaccuracies meant:
- Higher RTO risk
- Operational friction
- Customer dissatisfaction
They needed cleaner, mobile-first checkout flows that reduced manual errors.
The real decision filter
The decision wasn’t about which checkout increases conversion the most?
It came down to this:
- Does this solution protect margins?
- Does it improve operational control?
- Does it reduce preventable errors?
- Does it align with our product-first philosophy?
For Rustorange, checkout was positioned as a control layer; designed to make a strong product business more efficient.
Solution and strategies
In July 2025 Rustorange onboarded on Shopflo and the first thing that Shashank restructured was the discount logic. Here’s what happened:
Structured discount control
1. Percentage-based offers with hard caps
Instead of removing percentage-led campaigns, they introduced capped logic:
- 20% off
- But capped at a fixed maximum value (e.g., ₹1,000)
This ensured:
- Customer perception of strong value remained intact
- High AOV carts no longer disproportionately eroded margins
- Discount burn became measurable and controlled
The brand could now forecast acquisition costs more reliably.
2. Multi-parameter customization
Rustorange launches 2–3 drops every month.
That velocity demands flexibility. The discount engine allowed:
- Collection-specific campaigns
- Rule-based configurations
- Quick tweaks without operational delays
This mattered because each drop could carry different commercial intent: new customer acquisition, prepaid push, or campaign-specific incentives.
With the new discount engine Rustorange did not:
- Reduce conversion
- Increase discount depth
- Change its acquisition philosophy
Instead, they added guardrails.
Post implementation, Rustorange observed a 10–12% reduction in discount burn.
Prepaid vs COD optimization (without over-burning discounts)
Rustorange operates with 3–4 week lead times on many collections due to its pre-order model.
Naturally, customers hesitate to prepay for products that won’t ship immediately. This caused prepaid percentages to dip at one point to around 36–37%.
But Shashank did not want to:
- Eliminate COD
- Force prepaid through heavy discounting
- Inflate acquisition costs
Instead, he used checkout as a behavioral lever by introducing clear COD signalling at checkout.
Firstly, customers were made aware that:
- COD carries an added charge
- Prepaid options (UPI, etc.) could offer better value
Second, prepaid incentives were tweaked strategically. Instead of introducing blanket, deeper discounts, the team adjusted discount logic thoughtfully — ensuring any prepaid encouragement did not escalate overall discount exposure.
Importantly, Rustorange chose not to implement partial COD. Because of their pre-order model, they did not want to block small upfront amounts from customers who would still wait weeks for delivery. This decision reflected operational alignment, not feature experimentation.
Prepaid percentage is now trending around 40–45%, up from lows of ~36–37%.
And crucially, this improvement was achieved without significantly increasing discount burn.
Impact
Shashank is clear-eyed about checkout tools:
“Checkout solutions are tools that can be empolyed to give incremental benefits.”
From his estimate, Shopflo checkout contributed roughly a 10–12% improvement in ROAS.
Bottom line
As Shashank puts it,
“Ultimately, your business is your product and your customer. These are tools to make this journey easier.”
For Rustorange, Shopflo became exactly that; a control layer that added a real 10–12% lift without distorting the brand’s fundamentals.
Curious how this could work for you?
Let’s make it real. Book a call with our team, and we’ll show you exactly how Shopflo can drive real impact for your brand.
