Taiwan Unicorn Appier bets on AI-based software for digital marketing
Academics who have become Yu Chih-han and Winnie Lee led Appier to become Taiwan’s first listed unicorn. Now they are betting on global demand for the marketing company’s AI insights.
s Yu Chih-han found a spot in a Boston parking lot in 2010, he knew there was a better way. Years earlier, the computer science student had designed AI software for a self-driving car for a college competition. “That’s when I felt we needed to make AI not just a thing in academia, but more widely available to businesses,” says co-founder and CEO of SaaS company Appier. , 43 years old.
Together with his wife, COO Winnie Lee, they have done just that: represent a new generation of tech talent in Taiwan who have found success outside of the island’s hardware industry. The couple turned Appier into a billion-dollar software company (the only others to achieve unicorn status in Taiwan are electric scooter developer Gogoro and software company 91App). A public offering on the Tokyo Stock Exchange raised $270 million last year, valuing the company at around $1.4 billion.
Now business leaders are eyeing further growth in the United States and new ways to expand their product portfolio, says Yu, who spoke with Lee from their office in Taipei. The company specializes in combining machine learning and big data to establish a presence in digital marketing, using AI to predict customer behavior and personalize messaging across devices.
The company’s finances have kept pace with growing demand for digital marketing services, touted as a high-value approach to improving advertising ROI and reducing customer turnover. Revenue rose 41% in 2021 to 12.7 billion yen ($111 million) from a year earlier, marking its second consecutive year of growth. Its operating loss narrowed to 1.1 billion yen and Ebitda turned positive for the first time at 42 million yen. And there is huge potential for further growth: the digital marketing software market reached $57 billion in 2021 and is expected to grow at a CAGR of 19% over the next decade, according to US researcher Grand View Research.
Still, it’s been a bumpy ride for investors. After a strong start – Appier’s shares closed up 19% on their first day of trading in March last year – the stock has fallen 43% over the past year to a market capitalization of 108 billion yen (as of April 8), much more than the Nikkei 225 index’s 8% decline over the same period. Yu attributes the drop to six-month “corrections”, while Brady Wang, a Taipei-based analyst for trading information firm Counterpoint Research, notes that tech stocks around the world are under pressure from swings in financial markets. Lee shrugs. “Whether or not [Appier] is a unicorn doesn’t matter,” she said. You better be a “dragon,” she adds, because “when investors invest in you, they’re looking for a company that can bring them returns.”
Appier was advanced from the start, Wang said. He was an early mover in AI marketing in Asia and developed what the analyst calls a coveted database of behavior patterns. It’s essential to help businesses find new sales, predict how customers will act, and automate digital campaigns with relevant messaging and purchase incentives across all devices and multiple channels, including social media. and apps. Turning data into information is important, but turning that information into action will be essential for most businesses, Lee said in a media interview last year.
“Advertisers desperately need new ways to target their advertising in the face of cookie removal,” says Wang, who are now increasingly blocked by technology products. “These days, consumers often use different devices, such as PCs, smartphones and tablets to access information. However, many precision marketing companies tend to only scan one device, so it’s not easy to take advantage of it,” he says. This advantage gives Appier leverage in an increasingly crowded market using AI to generate advertising that includes competition from software giants Adobe and Salesforce.
Yu says the company’s high-tech software helps it reach 15 billion daily users on nearly 2 billion mobile devices in Asia, and the company’s technology generates 51 billion predictions per day. day. Its biggest markets are Japan, Singapore and Taiwan, with a list of 1,088 customers that includes Carrefour and Google, as well as online travel agencies, digital game companies and others. Its growth reflects broader trends in the Taiwanese startup scene. Last year, artificial intelligence and big data companies accounted for nearly 12% of all startups (retail and wholesale led with 22%), according to PwC’s 2021 Business Survey. startup ecosystem in Taiwan. Appier had only 700 customers in 2019.
Appier got his start 12 years ago in Malden, Massachusetts, a short drive from Harvard University where Yu was studying for his doctorate in computer science. He shared an apartment with Lee (they had met at Stanford several years earlier while pursuing master’s degrees) and Joe Su, also a graduate computer science student at Harvard. All three are from Taiwan, Lee says, and were inspired by American startup culture.
Led by Yu, the trio brainstormed at their dinner table on ways to bring AI to a mass market. They had nine ideas in total and started a game company called Plaxie in 2010 that used AI to control an avatar when the player logged out. But the trio struggled to monetize Plaxie’s technology. “We don’t give up easily,” recalls Lee. They turned to digital marketing and the integration of AI into big data to help companies better understand their customers. After graduating, Yu returned to Taiwan and established Appier in 2012, joined by Su as co-founder and CTO and Lee who had just completed her PhD. in immunology at Washington University in St. Louis. For start-up capital, each invests between $100,000 and $150,000 of their own money in the business.
Lee, 41, who made his debut on Forbes Asia‘s Power Businesswomen list last year initially did “random things” for Appier, including recruiting. Her studies had nothing to do with AI, but she found a synergy. “Coming from a research background where I was constantly studying new genes, I have the ability to be resilient,” she says. “It’s okay if your guess goes wrong, because that’s part of the experiment.”
Fueled by venture capital raised over the next seven years, Appier expanded outside of Asia, going deeper and deeper into AI. Sequoia Capital India became its first investor with $6 million in 2014, Yu says, and it was notably the fund’s first investment in Taiwan. Several other funding rounds followed which attracted companies like Jafco, SoftBank and UMC Capital, among others. In total, the company racked up $162 million in funding ahead of its Japanese IPO, following its aggressive expansion there. It was the first Taiwanese company to register in over 20 years.
The company specializes in combining machine learning and big data to establish a presence in digital marketing.
The capital increase was used to develop new products and invest in talent. Nearly a fifth of its roughly 570 employees are in sales, Yu says, and they spend six weeks to six months introducing clients, including those who manage marketing budgets. “All of these decisions and stakeholders need to be satisfied in order to move forward,” he says. Appier aims to increase revenue by 38% to 17.5 billion yen this year, while Ebitda is expected to rise nearly 1,270% to 575 million yen. The company is seeing higher demand in the United States and is also targeting investments there to prepare servers and inventory capacity. While the U.S. only contributes about 4% to Appier’s revenue, it has seen 50% quarter-over-quarter growth over the past three quarters, Yu said.
Last May, the company acquired the Taiwan-based conversational Chatbox AI BotBonnie for an undisclosed amount, following its purchase of Japanese AI startup Emotion Intelligence in 2019 and Indian content marketing company QGraph a year earlier. Yet Yu does not view mergers and acquisitions as a major driver of future business, but rather as the exploitation of new technologies that reflect the ability of the human brain to learn from experience. “If we can achieve that, then I think [artificial] intelligence can evolve on its own,” he says. “We don’t have to do a lot of programming for different tasks.”