3 AI trends that are changing the Asian tech scene

Photo credit: Unsplash

Southeast Asia’s adoption of artificial intelligence has been especially evident in the areas of “high tech and telecom, transportation and logistics, financial services, and healthcare,” according to a 2017 report from management consulting firm McKinsey & Company. But the technology’s applications are not just limited to industries – they can be seen in personal activities such as shopping and television viewing as well.

This reality was showcased at the Startup Runway Demodays in Bangkok and Ho Chi Minh City in October by Korean startup accelerator Roa Invention Lab. At the events held in October, eight startups highlighted how they use AI in different products and services.

A startup called Things, for example, uses AI in its MonthlyThing app to help female users track their menstrual cycles and receive a delivery of sanitary products at the exact time they need it.

Another participant, Okxe, zeroes in on a common but time-consuming activity in Vietnam: buying used motorcycles. Okxe’s online marketplace taps AI to pinpoint trustworthy sellers and verified items, as well as to look at data related to buyers, vendors, and markets. It’s also the first app-based used motorcycle transaction platform in the country.

These applications and other offerings that were presented at Roa’s demo days reflect three AI trends that are changing the Asian tech scene.

1. Recommendation algorithms for ecommerce

Photo credit: Pixabay

From Chinese tech titan Alibaba to French beauty retailer Sephora, ecommerce businesses are turning to recommendation algorithms to offer personalized suggestions to users. Things’ app, for instance, proposes products relevant to different stages of its users’ monthly cycles.

“With AI, customers can use recommended products to find the product that best suits their physiological health,” says Wonyuep Lee, CEO of Things.

Another ecommerce startup, Star Secret Korea (SSK), is dedicated to bringing Korean beauty products and reviews to Southeast Asia, beginning with Myanmar. SSK uses a recommendation algorithm to offer products to users based on Southeast Asian consumer data through a chatbot on its platform.

Using an AI-powered recommendation engine benefits clients by creating a more pleasant and efficient shopping experience and helps e-tailers by giving them higher purchase conversion rates. In fact, it’s estimated that 35 percent of all sales on Amazon are generated by the US giant’s recommendation engine.

During Alibaba’s 24-hour online sale in 2016, merchants on the platform generated around 6.7 billion personalized shopping pages, which delivered 20 percent higher conversion rates compared to non-personalized pages.

2. Machine learning cloud solutions

Photo credit: Jeferrb

Machine learning enables a company to understand, categorize, and analyze data. It assists in identifying patterns and – given sufficient time as well as data volume and quality – predicting possible outcomes or user responses in different situations.

That’s what Favvrs hopes to do with its platform, which lets social media influencers connect Instagram photos with links to shopping sites. With the help of machine learning, Favvrs’ service provides insight into how much influencers drive product sales. It also examines user acquisition rates and gauges user response to specific products.

Taggers, a marketing solutions provider, operates on the same premise. Its AI focuses on data involving products, marketing campaigns, and user behavior to enhance the machine learning capabilities of product recommendation ads on Facebook, Instagram, and Google.

In the long run, Taggers’ AI will be able to anticipate how customers will respond to ad creatives and which products they’ll prefer. It’ll also optimize ad creatives and landing pages.

Startups are combining this machine learning ability to assess outcomes and user behavior with cloud solutions so that people and companies won’t need to store data on physical drives with limited space. This allows teams in separate countries to work on the same data sets.

Delivering machine learning solutions over the cloud also enables subscription-based services such as platform-as-a-service (PaaS) and software-as-a-service (SaaS) solutions.

Neoflat, a portal for property rental, offers a PaaS solution for customers to develop location-based services in an easy, modular manner. To help users secure a house to rent, they will get access to chatbot functions that are tailored and refined via machine learning to suit their needs.

3. Recommendation algorithms for community and content

Photo credit: Alex Read

AI can also enhance the experiences of people who like to consume online content or interact with a virtual community. For instance, an audiobook platform might scrutinize users’ topic and genre preferences, and then propose similar titles.

A 2017 study of news publishers worldwide showed that content recommendation was the most common use of AI in publishing. In China, reading apps are using AI to offer interest-based subscriptions.

In the same way, a dating app might intelligently match users based on their interests, the people they tend to connect with, and their behavioral history on the platform.

“AI technology helps […] generate closer relationships,” explains Junhwan Moon, the CEO of Doongle, a community startup that links people from over 180 countries to ask one another for local information and assistance.

With the help of natural language processing, the Doongle app lowers language barriers to communication. It also uses AI to provide tips for generating further conversation between users.

Intelligent matching may also be applied in recruitment platforms. For instance, in a study of 800 talent acquisition professionals worldwide, 69 percent said that AI helps them find candidates of higher quality.

The same concept is used by Tkit, which allows users to create digital and trackable tickets. By analyzing the data and behavior of ticket buyers and sellers, Tkit allows for intelligent matching and suggestion of events to potential attendees.

Changing Asia’s tech landscape

China leads the way in AI innovation and adoption in Asia, but the rest of the region is following suit.

South Korea has promised to invest US$2 billion in AI-related research and development by 2022, while Singapore has formed an AI ethics advisory council.

As the startup ecosystem becomes more vibrant and regulatory support goes up, industry players and pundits alike claim that Asia will be the “next frontier for AI development.”

Venture capital firms, incubators, and accelerator programs like Roa Invention Lab’s Rising X – as well as initiatives from the government, academe, and private sectors – are all contributing to turn this potential into a reality.

These startups joined Roa Invention Lab’s Startup Runway Demodays in Bangkok and Ho Chi Minh City in October 2018.

Find out more about the eight startups and Rising X, Roa’s accelerator program, here.