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Fastrr Checkout Helps D2C Brands Tackle Conversion, RTO Challenges

Fastrr Checkout Helps D2C Brands Tackle Conversion, RTO Challenges

VMPL
New Delhi [India], August 17: Shiprocket has rolled out 20 features under Fastrr Checkout for D2C brands in India, spanning conversion, payments, delivery-risk management, and post-purchase engagement. The company says Fastrr can enable checkout speeds up to 70% faster, reduce RTOs by up to 30%, and lower cart abandonment by up to 25%.
The platform brings key parts of the buying journey into one place, including customer login, address entry, payments, delivery updates, order recovery, and post-purchase engagement. These features help D2C sellers reduce friction during checkout and improve the overall customer experience.
A key feature is Fastrr's AI-powered address pre-fill, which gives brands access to a pool of 150 million pre-filled addresses. The platform also includes an address correction interface that detects potentially incomplete entries, such as missing house numbers, and prompts shoppers to make corrections before placing an order.
According to the company, these capabilities can help improve address accuracy and reduce issues linked to delivery failures.
On the login and payment side, Fastrr integrates Truecaller, single sign-on, and OTP-less login to reduce manual steps during checkout. Its QuickPay feature can recommend a shopper's preferred payment mode based on previous payment behaviour, while the platform supports payment options including UPI, cards, and net banking.
Fastrr also addresses return-to-origin (RTO) risk through a prediction engine that provides tiered risk levels instead of a binary assessment. Drawing on delivery history from the Shiprocket network, the engine allows brands to apply different rules based on the risk associated with an order. For instance, a seller can restrict COD for higher-risk orders while offering a prepaid discount as an alternative.
The platform's Smart Cart uses progress bars and tiered rewards to encourage shoppers to add more products. Fastrr says many retailers using the feature have recorded an average 15-20% increase in average order value. Post-purchase capabilities include Scratch Cards and Shipcoins, its loyalty and wallet system, which can provide discounts or points towards future purchases.
Cart recovery is supported through WhatsApp, SMS, and email. Recovery messages can include one-click checkout links that retain relevant cart and address information, allowing shoppers to return to an incomplete purchase.
Fastrr's Discount Engine also allows brands to create personalised coupons and payment links for recovery and promotional use.
For brands looking to understand checkout performance, Fastrr provides funnel analytics across stages from phone-number entry to payment initiation. This allows sellers to identify where shoppers drop off and assess specific points in the checkout journey. The platform also provides finance and settlement tracking, including visibility into payment gateway commissions, payout cycles, and wallet balances.
Fastrr supports custom-built storefronts through headless checkout APIs for technologies including React, Next.js, and Vue. Other capabilities include GSTIN collection and validation, recurring orders and subscriptions, configurable charging rules, real-time estimated delivery dates, and support for hyperlocal and split-shipment scenarios.
As part of the Shiprocket ecosystem, Fastrr Checkout connects with its order management and shipping services. This enables capabilities such as delivery-date estimates, post-purchase order tracking, address pre-fill, and RTO risk management to operate within the wider checkout and fulfilment journey.
About Fastrr Checkout
Fastrr Checkout is a checkout platform for D2C eCommerce brands, built by Shiprocket. It brings together checkout, payments, customer login, address pre-fill, risk management, cart recovery, analytics, and post-purchase capabilities.
For more information, visit Fastrr Checkout
(ADVERTORIAL DISCLAIMER: The above press release has been provided by VMPL. ANI will not be responsible in any way for the content of the same.)

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