Every Business Needs to Be Global — Here’s How to Make It a Lot Easier
Businesses have struggled with the problem of localization for decades — how does a company take its success in one market someplace new? Connecting authentically with the new market is crucial, which is why globalization today is a $6B market. Yet so often products we know and love in English are simply terrible in other languages.
That’s because teams still haven’t evolved to scale global product development. Companies use the same process to translate their app as they do their website and white paper case study. And these processes have been the same for decades, relying largely on a human process that needs to plug into development workflows. As a result, companies considering international expansion are faced with two equally unpalatable options:
- Build a brand new local product at a high price tag that consumes resources across multiple functions in an incredibly slow and inefficient process
- Forego lucrative opportunities and cede ground to competitors due to internal fear for a big globalization effort.
It’s often a lose-lose scenario.
I witnessed both of these use cases in my time at IAC — companies like Match internationalized rapidly by building large local teams who created entirely separate code and language bases. This was successful, but also expensive and inefficient, especially working between markets. In the case of Citysearch, we had a meaningful Spanish-speaking user base domestically, and our head of product was motivated to launch a Spanish version of the site — but ultimately the project never made it past an initial assessment due to significant upfront costs and a cumbersome change in the engineering workflow. Most of my partners at Upfront are also ex-operators who lived the problems of globalizing e-commerce and SaaS platforms. No one ever found a truly scalable solution. Everyone felt the pain.
In the next 5–10 years, another billion people come online, and they are coming with their credit cards. These are not English-speaking users - in just three short years, non-native-English speaking markets like Russia, Brazil, Indonesia, and Malaysia are projected to be worth 4.6x more than their English speaking counterparts. The brands who will thrive in this environment are the ones that are natively global — their engineering, product, and marketing teams think across +30 markets simultaneously.
When we met with Qordoba, we saw a product, a vision and a team that could do exactly that. Enabling companies to build digital businesses that don’t simply exist in global markets but win natively in global markets is a huge challenge — one that requires a unique team to rethink the solution from the ground up. Qordoba’s product brings together the best of natural language machine intelligence trained by human intelligence to attack this problem, and we are thrilled to have co-led their Series A funding alongside Rincon Venture Partners.
Virtually everyone who meets Qordoba co-founders May Habib (CEO) and Waseem AlShikh (CTO) are blown away by their understanding of the globalization problem and mastery of the solution they’ve created.
I think that’s because the company’s mission of “every product in every language” comes out of deep personal experience. Prior to founding Qordoba, May, who was born in Lebanon and immigrated to Canada, was helping to manage the $4B global portfolio of UAE-based Mubadala (one of the world’s largest sovereign wealth funds.) Working with a portfolio that spanned dozens of countries brought home the challenge of market expansion. Waseem grew up in Syria and taught himself computer programming (and English!) He has since become one of the world’s experts in natural language processing and machine learning, solving problems he had himself when trying to understand difficult concepts in a foreign language. He holds multiple patents in the field and led a 100+ person technical team at iMENA, where he lived the localization challenge from the engineering side.
From their experiences, May and Waseem saw two problems with localization-as-usual: engineers needed to do too much work, and the content people — country managers, translators, editors, local agencies, designers, etc — didn’t have the opportunity to do enough work.
The solution they built dramatically changes the game for engineering: they don’t have to worry about natural language at all, as text in all languages now gets supplied by Qordoba as an API-driven service. It’s so powerful a collaboration tool that customers are using Qordoba to draft all of their languages, including English.
On the content side, Qordoba built a collaborative editing platform that is powered by machine learning and people — usually the company’s product managers and in-house or in-country teams — who can make any edit, any time, independent of engineers. It’s a powerful engine enabling an easy, user-friendly interface.
When content writers are constantly teaching an algorithm their company’s tone, voice and brand preferences across multiple languages, companies become more efficient at producing content in a scalable way. The resulting ROI is tremendous, as customers can dramatically increase conversion rates across geographies and languages (shoppers are 10x more likely to buy from fully localized sites.) The real “a-ha moment” for Qordoba customers comes when the platform turns localization from a cost center into a profit center.
When we did our diligence with their customers (which include Cartier, Sephora, UberEATS and WorldRemit), no one used the words “machine learning” or “NLP.” Rather, we heard praise for the ease of use of the product, and how enthusiastically engineering, product and in-country marketing teams embraced Qordoba, fueling a positive cycle of success in new markets.
I am so impressed by Qordoba’s early traction and am thrilled to support them on their journey in solving this massive problem. Please join me in congratulating May and Waseem on their success so far. And if you’re a company struggling with localization, please reach out to the Qordoba team — you won’t be sorry.
