Founded in 1987 as a physical retailer, Matches Fashion transitioned online in 2007. Today, the company has 130 million+ site visits yearly, with 95% of sales made online. The Matches Fashion app has also been downloaded by two million customers. With so much revenue dependent on its online business, optimising authorisation rates was critical. With its clients located across 176 countries, Matches Fashion identified that extending its payment method offering would be vital to capturing more revenue and providing a superior customer experience.
Together with Stripe, Matches Fashion implemented the six global card schemes (Visa, Mastercard, Amex, JCB, CUP, Discover) as well as a local card network (Cartes Bancaires), providing its customers with a streamlined payment experience optimised for different preferences. The company also began using Stripe’s Card Account Updater feature and Adaptive Acceptance tool, the latter of which uses machine learning to optimise authorisation messages.
Finally, Matches Fashion benefits from Stripe’s robust offering of multi-currency settlements which protects its margins and mitigates risks involving currencies and exchange rates.
In one year, Matches Fashion has experienced a 2.47% uplift in authorisation rates, generating an additional £6 million+ in annual revenue. The effect was further emphasised in certain markets. In France, Matches Fashion’s early adoption of the Carte Bancaires local card network resulted in double-digit uplift in its authorisation rates.
“Through one integration, we were able to increase our acceptance rates by creating a bespoke approach for each market, offering relevant local payment methods, optimising payments, and navigating 3D Secure in a user friendly way for our customers” said Lauren Mirynowski, Product Manager of Matches Fashion.
Here is a closer look at the key Stripe features Matches Fashion leveraged to gain a 2.47% uplift:
Adaptive Acceptance optimises authorisation rates with machine learning
Adaptive Acceptance uses machine learning to optimise authorisation messages and make smarter retry decisions instantly. Stripe’s machine learning models leverage Stripe’s extensive historical data, such as transaction type, issuer and merchant type, from processing hundreds of billions in payments annually. Stripe’s data scientists and engineers continuously improve these models to help merchants accept as many legitimate transactions as possible and generate additional revenue.
Card Account Updater prevents future card declines
Stripe works with card networks to automatically update saved card details whenever a customer receives a new card (e.g. replacing an expired, lost or stolen card). This matters for Matches Fashion because, for its customers, manually updating their previously-saved credit card information can be tedious and time-consuming. This results in cart abandonment and/or credit card declines, and lost revenue for the company.
Custom fraud solutions built together with Stripe
For Matches Fashion it’s critical to balance high authorisation rates with low fraud rates. To do this, the company leverages Stripe Radar, which helps detect and block fraud using machine learning that trains on data across millions of global companies. Stripe and Matches Fashion worked closely together to optimise their results, and feedback from Matches Fashion was built directly into the Stripe Radar product. This included new features such as risk insights, an increased Radar rule limit, and the ability to assign manual reviews to an internal agent.
Stripe helps Matches Fashion drive superior customer experience
As a luxury brand, Matches Fashion aims to provide an optimal customer experience at every stage of the shopper’s transaction. “With an average order value upwards of £500, our clients expect to have a seamless interaction with our site regardless of where in the world they are or which device they are transacting from,” Mirynowski said. Stripe has supported Matches Fashion not only with payments but also in analysing customer behaviours across all merchants in the Stripe ecosystem. The company plans to utilise this data to build a more personalised checkout experience for customers.
We aspire to provide users with an exceptional payment and seamless checkout experience by offering the payment methods most relevant to them. Stripe has made it easy to offer popular local payment methods within a single integration, which has resulted in a significant uplift in revenue.