Libas has been selling ethnic wear in India since 1985 — four decades as an offline-first retail brand before it ever needed a serious digital storefront. When it finally built one, on Adobe Commerce, the platform choice became the thing holding the DTC business back rather than the thing enabling it. The rebuild on Shopify, completed in phases from 2019 through 2022, is now behind a DTC business that scaled from zero to ₹300 crore in annual recurring revenue, growing 80% year over year between 2018 and 2025.
What a 39-year-old retailer actually needed from a digital platform
Most Shopify migration stories involve a brand that started online and outgrew its first platform. Libas is a different, arguably harder case: a brand with decades of offline retail infrastructure — physical stores, wholesale relationships, an established customer base that had never bought from them digitally — needed a platform that could become the primary system for a genuinely new channel, not just digitize an existing catalogue.
That distinction matters because it changes what "platform limitations" actually cost. For a pure digital-native brand, a clunky platform slows down marketing experiments. For a brand like Libas, it also means the online business can never really talk to the 35+ physical stores that already exist — two completely separate views of the same customer, with no way to reconcile them.
Where Adobe Commerce actually broke down
The specific failures Shopify's case study documents are worth listing precisely, because they're the same category of problem a lot of scaling Indian D2C brands hit on custom or heavily-customised platforms: basic updates — a new collection, a campaign landing page, a promotional banner — required heavy developer involvement, which meant marketing timelines were hostage to engineering bandwidth. There was no built-in customer analytics, so the team had no real visibility into behaviour patterns or customer lifetime value beyond raw transaction counts. Online and offline data lived in separate systems with no path to combine them. And scaling itself — adding markets, adding stores, adding complexity — was resource-intensive in a way that compounded every other problem.
None of these are single catastrophic failures — they're the kind of chronic friction that quietly caps growth for years before a brand notices the platform is the bottleneck, not the market or the product.
A phased migration, not a single leap
Libas didn't jump straight to Shopify Plus. The brand moved to standard Shopify in 2019 to launch its DTC journey, then upgraded to Shopify Plus in early 2022 once the business had validated the channel and needed the additional scale, customisation, and enterprise tooling Plus provides. That sequencing is itself a useful data point: a brand this size didn't need to over-commit to the most expensive tier on day one to get the platform benefits that mattered first.
The three-year gap between the two moves also matters. It gave the team time to learn what a well-run Shopify store actually required operationally — catalogue structure, app stack, checkout flow — before layering on Plus-specific capability like Scripts, Flow automation, and higher API rate limits. Brands that skip straight to Plus without that groundwork often end up paying for enterprise features they don't yet have the operational maturity to use; Libas's sequencing avoided that by letting real usage data, not a sales pitch, decide when the upgrade was actually necessary.
Why RTO filtering specifically mattered for an ethnic wear brand
One detail in the checkout rebuild deserves more attention than a single mention: RTO (return-to-origin) filtering. Indian ecommerce carries structurally higher RTO rates than most Western markets — cash-on-delivery remains common, address quality is inconsistent, and impulse COD orders get refused at the door far more often than prepaid ones. For a category like ethnic wear, where fit, colour accuracy, and occasion-specific buying (a wedding, a festival) drive a meaningful share of orders, RTO isn't a minor operational nuisance — it's a direct hit to margin on exactly the orders a brand most wants to fulfil.
Building RTO risk-flagging directly into the checkout layer — rather than discovering a bad order after it's already been packed and shipped — is the kind of fix that pays for itself quickly but is easy to deprioritise on a platform where checkout customisation itself is the hard part. On Shopify, where checkout extensibility is a native capability rather than a custom build, RTO filtering becomes a policy decision (which signals to flag, what to do with flagged orders) rather than an engineering project.
What actually changed operationally
The Shopify Plus upgrade unlocked several specific, concrete changes rather than a vague "better platform" improvement. Non-technical marketing and merchandising staff could manage catalogues and campaigns independently — the exact bottleneck that had been the biggest complaint about Adobe Commerce. Given that over 80% of Libas's traffic comes through mobile, the team optimised specifically for mobile-first browsing and checkout rather than treating mobile as a scaled-down desktop experience. App Store integrations covered loyalty, reviews, checkout enhancements, and marketing automation — capability that would have required custom development on the old platform.
The checkout itself was rebuilt using Shopify Extensions, adding address autocomplete (a meaningful friction-reducer in Indian addresses, which are notoriously inconsistent in format), RTO (return-to-origin) filters to flag high-risk orders before they ship, and multiple payment options tuned to Indian buyer preferences. None of these are exotic features — they're exactly the kind of checkout-layer work that's straightforward on Shopify's native checkout and expensive custom engineering on almost anything else.
The part that mattered most for a retailer this size: unifying online and offline
The single most structurally important change was integrating a customer data platform (CleverTap) to unify customer insights across ecommerce, the mobile app, and Libas's 35+ physical stores. For a brand with decades of offline retail history, this is the difference between "we have a website" and "we actually know who our customer is across every channel they use." A customer who first discovered Libas in a physical store and later completed a purchase on the app or website could, for the first time, be recognised as the same person — enabling retention and personalisation strategies that were structurally impossible when online and offline data lived in separate systems.
The results
The headline number is stark: Libas scaled its DTC channel from ₹0 to ₹300 crore in annual recurring revenue, sustaining 80% year-over-year growth from 2018 through 2025. Average order value rose 24% — a meaningful lift that's consistent with better mobile checkout and more relevant merchandising rather than one-off promotional spikes. And mobile now accounts for over 50% of DTC sales specifically through the Libas app, not just the mobile web — evidence that the mobile-first investment paid off in a durable, owned channel rather than just improving the mobile website experience.
What this means for a brand with a similar offline-to-online story
Libas's story is a useful reference point for any legacy or offline-first Indian brand building or rebuilding its digital channel: the platform decision isn't just about storefront speed or checkout conversion — for a brand with real physical retail history, it's about whether the digital system can actually see the same customer the physical stores already know. A migration that only solves for storefront performance and skips the customer-data unification piece captures maybe half the available value. For the mechanics of what a migration like this involves, see our guide to migrating to Shopify, and for the mobile-specific checkout work, our guide to Shopify checkout optimisation.
About this case study.
Did Carryup work with this brand?
No — Carryup did not work with Libas. This is independent analysis of publicly available information (official case studies, press coverage, and reported figures — see the sources cited on this page), written to extract lessons transferable to other Shopify D2C brands. Our own client work lives on the Work page, with real, attributable results.
Does this apply if my brand is a different size or category?
The underlying mechanics — infrastructure readiness, retention systems, platform fit — are largely category-agnostic. The specific numbers will differ, but the diagnostic approach transfers.
How do I know if this problem applies to my store?
The fastest way is a direct diagnostic of your own store, tracking, and infrastructure — we can tell you within a week whether the same pattern shows up.
Related reading.
Want results like these for your brand?
Tell us about your store — we'll review your setup and tell you exactly where the opportunity is.