transformed academics Yu Chih Han and winnie lee led to Appier becoming the first unicorn listed in Taiwan. Now they’re betting on the global demand for the marketing firm’s AI insights.
s Yu Chih-han navigated a parking space in a Boston garage 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 university competition. “It was at this point that I felt we had to make AI not just a thing in academia, but more widely available to business,” says the 43-year-old co-founder and CEO of Taipei-based SaaS company Appier.
Together with his wife, COO Winnie Lee, they did just that – representing a new generation of tech talent in Taiwan that found success outside the island’s main hardware industry. The duo 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 about $1.4 billion.
Now, the company’s bosses are eyeing growth in the US and new ways to expand its product portfolio, says Yu, who spoke to Lee from his Taipei office. The company specializes in combining machine learning with big data to build a digital marketing presence, using AI to predict customer behavior and personalize messages across devices.
The company’s finances have kept pace with growing demand for digital marketing services, touted as a high-value approach to improving return on ad investment and reducing customer churn. Revenue increased 41% in 2021 to ¥12.7 billion ($111 million) year-on-year, marking its second consecutive year of growth. Its operating loss shrank to ¥1.1 billion and Ebitda turned positive for the first time at ¥42 million. And there is huge potential for further growth: The digital marketing software market reached $57 billion in 2021 and is expected to expand at a CAGR of 19% over the next decade, according to US researcher Grand View Research.
Still, it has been a bumpy road for investors. After a strong start – Appier shares closed up 19% on their first day of trading in March last year – the shares fell 43% last year to have a market cap of ¥108 billion (as of April 8 ), much more than the 8% drop in the Nikkei 225 index over the same period. Yu attributes the drop to “corrections” over the course of six months, while Brady Wang, a Taipei analyst at market intelligence firm Counterpoint Research, notes that tech stocks around the world are under pressure from financial market fluctuations. Lee shrugs. “Whether or not [Appier] it’s a unicorn doesn’t matter,” she says. It’s better to be a “dragon,” she adds, because “when investors invest in you, they look for a company that can bring in returns.”
Appier was forward from the start, says Wang. He was a pioneer in AI marketing in Asia and developed what the analyst calls a coveted database of behavioral patterns. This is critical to helping companies find new sales, predict how customers will act, and automate digital campaigns with relevant messages and purchase incentives across multiple devices and multiple channels, including social media and apps. Turning data into insights is important, but turning that insight into action will be critical for most companies, Lee said during a media interview last year.
“Advertisers desperately need new ways to target their advertising in the face of cookie retirement,” says Wang, who is now increasingly being blocked by tech products. “Today, consumers often use different devices such as PCs, smartphones and tablets to access information. However, many precision marketing companies tend to only look at one device, so it’s not easy to reap the benefits,” he says. That advantage gives Appier leverage in an increasingly crowded market, using AI to drive advertising that includes competition from software giants Adobe and Salesforce.
Yu says the company’s deep technology software helps it reach 15 billion users daily on nearly 2 billion mobile devices in Asia, and the company’s technology generates 51 billion predictions daily. Its biggest markets are Japan, Singapore and Taiwan, with a customer list of 1,088 that includes Carrefour and Google, as well as online travel agencies, digital game companies and others. Its growth reflects broader trends in Taiwan’s startup landscape. Last year, AI and big data companies accounted for nearly 12% of all startups (retail and wholesale topped the list with 22%), according to PwC’s 2021 Taiwan Startup Ecosystem Survey. Appier had just 700 customers in 2019.
Appier started 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 their master’s degrees) and Joe Su, also a graduate student in computer science at Harvard. All three are from Taiwan, says Lee, and were inspired by American startup culture.
Led by Yu, the trio brainstormed at their dinner table about ways to commercialize AI in a mass market. They came up with nine ideas in all and started a game company called Plaxie in 2010 that used AI to control an avatar when the player went offline. But the trio found it difficult to monetize Plaxie’s technology. “We don’t give up easily,” recalls Lee. They turned to digital marketing and AI in big data to help companies better understand customers. After graduating, Yu returned to Taiwan and set up Appier in 2012, joined by Su as co-founder and chief technology officer and Lee, who had just completed her PhD. in immunology at Washington University in St. Louis. For start-up capital, each put between $100,000 and $150,000 of their own money into the venture.
Lee, 41, who debuted in Forbes AsiaLast year’s Power Businesswomen initially did “random stuff” for Appier, including recruiting. Her studies had nothing to do with AI, but she found synergy. “Coming from a research experience where I was constantly studying new genes, I have the ability to be resilient,” she says. “It’s okay when your hypothesis goes wrong, because that’s part of the experiment.”
Fueled by venture capital raised over the next seven years, Appier expanded outside of Asia, delving deeper and deeper into AI. Sequoia Capital India became its first investor with $6 million in 2014, says Yu, and it was notably the fund’s first investment in Taiwan. Several other funding rounds followed that attracted the likes of Jafco, SoftBank and UMC Capital, among others. In total, the company amassed $162 million in funding ahead of its IPO in Japan, following its aggressive expansion in the country. It was the first Taiwanese company to list there in over 20 years.
The company specializes in combining machine learning with big data to build a presence in digital marketing.
The capital increase was directed towards the development of new products and investment in talent. Nearly a fifth of its roughly 570 employees are in sales, says Yu, and they spend anywhere from six weeks to six months introducing clients, including those who manage marketing budgets. “All these decisions and stakeholders need to be satisfied to move forward,” he says. Appier aims to increase revenue by 38% to 17.5 billion yen this year, while Ebitda is expected to increase nearly 1,270% to 575 million yen. The company sees increased demand in the US and is also directing investment to prepare servers and storage capacity. While the US only contributes about 4% to Appier’s revenue, there has been a 50% quarter-on-quarter growth over the past three quarters, Yu says.
Last May, the company acquired Taiwan-based conversational AI chatbox BotBonnie for an undisclosed amount, following its purchase of Japanese AI startup Emotion Intelligence in 2019 and Indian content marketing firm QGraph a year earlier. Still, Yu doesn’t see mergers and acquisitions as a major driver of future business, but rather taking advantage of new technologies that reflect the human brain’s ability to learn from experience. “If we can do that, I think [artificial] intelligence can evolve by itself,” he says. “We don’t need to do a lot of programming on different tasks.”