3 Insights I Took Away from Launchpad Accelerator Class 4

Alyssa Maharani
Jul 31, 2017 · 3 min read
The Launchpad Accelerator Startup Success Managers for Class 4 at our Googlers’ Open House

Disclaimer: I am an employee at Google. The opinions stated here are my own, not those of my company.

Hey everyone, I just finished an exhausting, yet fulfilling two weeks working on Launchpad Accelerator (LPA) Class 4. LPA is a 6-month program for growth-stage startups from emerging markets. First and foremost, LPA is focused on supporting startups where they need help. During the program, the startup founders fly out to SF for an intense 2-weeks of mentorship, talks, and the best Google has to offer. Along the way, we we hope to learn a lot about how to help startups in emerging markets and how Google products are helping (or not helping) them.

Now that I got the intro out of the way, it was really fantastic to be able to work with our top mentors and 33 startups from 16 different countries. Here are some general insights that I gained from working at LPA.

  1. At growth stage, most startups in emerging markets struggle with how to scale their operations

As I sat shadowing startup mentorships, one theme that continuously occurs is that many startups know how to get from a few founders to 20–30 employees, but struggle to get from 20–30 employees to 100 employees. When you are part of 20–30 employee startup, you still know everyone in the team. But once you grow large enough, you start losing track of everyone — and you are no longer interviewing and choosing your employees. Add on the issue of ensuring your system architecture is able handle the increased load from a larger user base, as well as increasing competition and regulation in the market, and you can see why startups struggle with scaling. Particularly for emerging markets, there aren’t too many people who are experienced in scaling in the ecosystem. This is why mentorship is valuable for these startups, and hopefully these later-stage startups can in return mentor others in their own ecosystems.

2. User experience (UX) and user research is not placed as importantly in emerging markets, when it should be

While in Silicon Valley we’ve seen a lot of companies putting significant emphasis on user experience, this is not necessarily true in emerging markets. This is due to partially the lack of UX and user research resources in the emerging markets in comparison to more mature ecosystems. Additionally, when companies are still at seed stage, it’s normal to see user research and UX take a backseat as founders are busy coding and crafting a product. Building in user research and UX early on before even developing a product is key to reducing startup failures. Through the bootcamp, startups learn how user research and UX can help them to improve their conversion funnel with minimal effort, and even locate clearly their customers’ pain points.

3. We are seeing an increase of startups in emerging markets using artificial intelligence and machine learning in their products

According to TechCrunch, we’re seeing the democratization of artificial intelligence and machine learning to everyone stemming “… from a series of rapid technological advances over the last few decades: widespread internet connectivity and proliferation of online data, faster/cheaper computers (per Moore’s Law), variable-cost cloud computing, R&D investments from large technology companies and a vibrant open-source software community” Startups are now utilizing open source libraries like TensorFlow and cloud computing platforms like Amazon Web Services and Google Cloud Platform at increasingly cheaper pricing than before. Some interesting use cases that startups from emerging markets are using include: image recognition for health of a plantation in Kenya, diagnosis from limited visual medical data in India, temperature of water in a fish tank in Indonesia, and even creditworthiness in severely underserved banking markets in South Africa. One thing that strikes me is how creative founders from emerging markets are in leveraging AI and ML to make their product superior to the market. Some of these use cases are definitely not thought of by more mature ecosystems, simply as these data sets and market problems do not exist in mature ecosystems.

Hope these insights might be helpful to those of you thinking about startups in emerging markets.

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