Block’s Q1 results included news of solid progress at Square, the merchant service provider as its focus on selling vertical solutions to larger merchants pays off. Gross payment volume was up 17% to $46.2 billion, with international volume rising 33%.
Continuing Square’s remorseless progress towards what it calls the mid-market, the strongest growth was again among larger sellers processing over $500K annually, which saw volume up 25%. Square is still making comparatively little progress with eCommerce. CNP volumes were up just 10%, compared to a 21% increase at POS.
Square’s revenue was up 16% to $1.7 billion, with strong growth from its new banking products, including $1.1 billion lent via Square Loans in Q1.
Gross profit rose a healthy 16% to $770 million, although the result is even better (up 26%) when excluding a one-off bump from PPP loan forgiveness in 2022. The transaction mix has not been helpful as Square has seen fewer low-cost debit transactions and more high-cost credit ones.
Going forward, Square’s management has four priorities:
• Enabling omnichannel – eCommerce has been a weak spot as Square is very focused on face-to-face sellers. Management says it has introduced approximately 100 new products, features, and partnerships to help its merchants diversify their revenue streams. For example, Square for restaurants is now integrated with OpenTable.
• Growing upmarket – Management is pleased with a redesigned homepage that is more appealing to larger sellers and includes more vertical content. Amrita Ahuja, Square’s CFO, said, “We’ve seen strong momentum from our vertical point of sale offering across retail, restaurants, and appointments, where gross profit was up 42% year over year in aggregate.”
• Global expansion – International operations again outperformed, with gross profit up 28% (excluding BNPL) to $74 million, representing 10% of the total. Square has launched loyalty and restaurants in Japan, including a new Kitchen Display System.
• Integrating generative AI – Square has launched “Suggested Actions in Square Messages” which uses a large language model to predict the next actions a merchant could take in a customer conversation. This might include sending a coupon or requesting a payment. The model is also suggesting product descriptions to merchants, and approximately 75% are accepted without edit.
