AsianScientist (Aug. 24, 2018) – By Sim Shuzhen – When Mr Anshul Gupta was working in Kolkata, India, he used to eat at a café which had replaced its menus with iPads. But the introduction of technology had changed nothing about the dining experience, he observed—the tablets merely held a PDF version of the menu, and diners still interacted with the waitstaff to place orders and pay.
Eating at the café got Mr Gupta thinking about how restaurants could take full advantage of technology to improve the entire experience of dining out—whether it is helping customers get food and drink recommendations, giving them a heads-up about how long a wait to expect, or allowing them to order and pay without having to flag down a waiter. In 2012, these ideas became Tabsquare, an F&B software solutions company Mr Gupta co-founded with fellow INSEAD alumni Mr Chirag Tejuja and Mr Sankaran Sreeraman.
An SGInnovate-invested company headquartered in Singapore, TabSquare builds artificial intelligence (AI)-driven digital ordering systems that not only make eating out more personalised and convenient for diners, but also help restaurants boost their profitability.
“The concept is about leveraging technology to improve the interactions between customers and a brick-and-mortar store,” explains Mr Gupta.
Eating like you shop
Online retailers like Lazada and Amazon personalise their landing pages to the extent that no two customers see the same items. In contrast, the customer experience in most restaurants is highly standardised, with everyone—meat-lover or vegetarian, beer guzzler or teetotaler—being offered the same menu.
This uniform, plain-vanilla experience is what TabSquare has set out to change. “We really felt that [the personalisation] happening in the online retail industry can be made to happen in the restaurant industry as well,” says Mr Gupta.
To offer a restaurant experience that Mr Gupta likens to browsing Netflix, TabSquare built an AI engine that learns customers’ preferences based on their past choices, and then recommends food items, pairing suggestions, discounts and promotions tailored to each individual. Now embedded into TabSquare’s tablet and kiosk ordering systems, the engine identifies individuals when they input their mobile phone numbers or does so using facial recognition.
It turns out that our preferences are more complicated that you might imagine. The same customer might have a different taste profile at lunch and at dinner, or on a weekday versus a weekend. TabSquare’s goal is to predict these behaviours accurately, providing a level of personalisation that no human waiter can offer.
“Like any AI engine, the more data it has to learn, the more intelligent it becomes. So the more a customer dines at any restaurant using TabSquare, the better we know them,” explains Mr Gupta.
Selling like hotcakes
TabSquare now counts major restaurant chains in Singapore, Malaysia, Thailand, Indonesia and Australia among its clients. In cities where F&B margins are tight and manpower crunches have hit the industry hard, TabSquare’s systems help restaurants save on labour costs, streamline operations and reduce wastage. But these are not the only advantages, says the company’s co-founder Mr Tejuja.
By presenting customers with the right suggestions at the right time in a quick, convenient manner, TabSquare also helps restaurants upsell their offerings and boost revenues.
“The restaurant can sell more because the customer is more informed about the entire offering,” says Mr Tejuja.
This approach not only addresses modern-day customers’ growing need for immediacy—getting what they want fast—but also gives them the content they need to make effective decisions, adds Mr Tejuja.
“Twenty years ago, when you wanted to buy something, the staff in a store were the single point of communication, providing you with all the information,” he says. “Today, before you go to the store, you’ve had access to pictures and descriptions, and your decisions are very content-driven. We’re now giving the same content to customers in restaurants, which helps them make much more effective decisions.”
The tip of the iceberg
Alongside investing in TabSquare, SGInnovate has also supported the company in several other important ways, say the founders.
“The team at SGInnovate has always been very helpful in keeping us abreast of other technological developments—even in other industries—that they feel could be useful for TabSquare. This helps us create a better story,” said Mr. Gupta.
Like many other startups, TabSquare competes for scarce technical talent.
“Getting high-quality talent to work for a startup is really difficult in Singapore, and SGInnovate has been really helpful in terms of the recruitment process. They do a lot of events which are attended by interested, competent people who could be good team members for TabSquare,” says Mr Gupta.
Over the next few years, TabSquare plans to expand its overseas operations in markets such as Southeast Asia, Australia and New Zealand, with the aim of replicating the success it has seen in Singapore. Technology-wise, the company is focusing on enhancing the capabilities of its AI engine, so that it can provide recommendations that are even better optimised for each customer.
One way it is doing this is through the incorporation of external information such as weather data, explains Mr Gupta. On a scorching day, for example, the AI engine might offer a coffee-loving customer an ice-cold frappuccino; a chilly day, on the other hand, would see it proffering a hot latte.
“We are really getting down into the details so that we can personalise the experience for the customer to the highest level, where we can understand what they want even before they want it,” says Mr Gupta. “We are really just at the tip of the iceberg with respect to the capabilities of AI in the F&B industry—a lot more can be done.”
Asian Scientist Magazine is a content partner of SGInnovate.
Copyright: SGInnovate. Read the original article here. Photo credit: Cyril Ng/SGInnovate.
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