Getting a customer onto a company’s website to check out its products is challenging enough in itself, but getting them to complete a transaction is a whole different ball game. Last year, 75% of all ecommerce orders were abandoned before finalizing the purchase — reasons include an overly complicated checkout process, limited payment options, hidden costs, registration friction, security concerns, and more.
This is one of the problems that Taiwan-based Appier is looking to help online retailers solve. Its AI-powered platform tracks customers’ activities across a website to improve the chances of them completing a transaction.
Today, Appier announced it had raised $80 million in a series D round of funding from Insignia Venture Partners, HOPU-Arm Innovation Fund, TGVest Capital, Temasek’s Pavilion Capital, JAFCO Investment, and UMC Capital. This takes the company’s total funding to around $160 million, following its 2017 series C round of funding which saw big names including SoftBank join the fray.
Tracking
Founded in 2012, Appier uses machine learning to crunch myriad data points in real time, such as the cursor position of a mouse, how the customer taps or swipes a screen, the amount of scrolling, and more — this is then used to determine their purchase intent. In tandem, Appier can also A/B test different campaigns to determine which ones are more effective in converting a casual window-shopper into a customer, which may include customized promotional offers. This is part of a product offering it calls AiDeal, which recently launched as a result of Appier’s acquisition of Japan-based Emin.
Above: AiDeal allows companies to A/B test different promotions.
It’s worth noting here that the platform isn’t exclusively about getting people to buy physical goods once they’re on a website. It can also be used to proactively target people who already have an app installed on their phone. For example, a video-streaming company that offers some free shows could use Appier’s AiQua platform to test and issue push notifications or in-app messages to drive subscription signups. Similar to AiDeal, Appier’s AiQua product was the result of its acquisition of an Indian startup called QGraph last year.
Elsewhere, Appier has long offered a product it calls CrossX Advertising, which can be used by retailers to, say, deliver better-targeted ad exposures to those most likely to convert — Audi, for example, used the platform to target test-drive ads at people aged 30 and over who had previously searched online for luxury cars.
In-house
Appier said that it develops its machine learning algorithms entirely in-house rather than using an “off-the-shelf” solution, and its models are trained through ingesting data from websites, apps, customer relationship management (CRM) software, and so on, which helps improve the machine learning model over time.
“The real-world environment — unlike that of a lab — is dynamic and diverse and ‘off-the-shelf’ algorithms don’t always cope with it well,” Appier CEO and cofounder Chih-Han Yu told VentureBeat. “Our clients need to be able to use our solutions to manage many different and fast-moving scenarios — different KPIs, varying data sources, etc. This means that our scientists spend a lot of time making sure our deep learning solutions can deliver optimal performance in any situation that our clients might face.”
With another $80 million in the bank, Appier said that it will push ahead with global market expansion and target its technology at new industries “beyond digital marketing.”
“Our latest investment brings with it new shareholders whose growth-stage experience will help us to scale faster towards our ultimate goal of revolutionizing the way enterprises adopt and leverage AI to grow, remain competitive, and manage continuous business transformation,” Yu added.
One example Yu provided was products to help companies automate the process of building AI models, enabling them to bolster their data science capabilities without having to hire a “complete data science team,” he said.
Data decisions
Appier’s methods of tracking customers on digital properties isn’t entirely a unique approach, with the likes of Contentsquare adopting similar techniques to tell companies why their customers may be abandoning their carts before completing a purchase.
Moreover, another thing Appier and Contentsquare have in common is that they’re both tapping a growing demand for platforms that crunch large amounts of data to improve decision making — this spans far beyond retail and marketing, and into areas such as insurance and even cities’ infrastructure projects.
“Appier is riding a strong long-term trend for enterprises leveraging data to make smarter decisions,” added TGVest Capital chairman DC Cheng. “Thanks to its unique use of AI technology in the digital marketing space, Appier has been a category leader since its inception and has the opportunity to expand into new corporate functions where data-based decisions are made.”
Including its Taiwan headquarters, Appier claims 400 employees across 14 offices in 12 markets in Asia Pacific (APAC), and Yu said the company is currently looking to expand to new markets, though it wouldn’t confirm whether one of those would be the U.S.
“We are planning to look beyond our current markets and explore opportunities in other parts of the world,” Yu said. “We look forward to sharing more news on this in the coming months.”
Author: Paul Sawers
Source: Venturebeat
