The software company explains how AI could drive a "personalised" in-restaurant customer experience.
Infusing artificial intelligence (AI) in restaurant services will grow its revenues by up to 30%, according to TabSquare co-founder and head of global sales and partnerships Chirag Tejuja.
Speaking at the 2018 QSR Media Detpak Conference and Awards' multi-site technology stream, he said that acquiring data "is not enough" and stressed the importance of utilising such information.
AI, he says, is “turning the world around.”
“When a machine does something, as compared to human beings, they do it more consistently. There aren't any breaks, no more sick leaves, no more holidays,” he commented. “They do it more accurately, there aren't any mistakes. It does work efficiently, faster than any human being could, and more importantly - deliver quickly.”
However, he argues that unlike in entertainment and retail – the restaurant industry as a whole is far from an “AI revolution” due to limited available data, limited transaction data from POS systems, and customer behaviour data that “remains” in a restaurant’s staff.
“When it comes to consumers, restaurants have been trying to mount their data to CRM (customer relationship management) solutions. But in most cases, the data is limited to what is there in the mind of the staff, and when the staff leaves the restaurant the data goes with him,” Tejuja explained.
Seeing AI’s potential in enhancing the customer’s restaurant experience, Tejuja proposed the use of table-ordering tablets, self-order kiosks, and mobile web ordering to collect big data in real-time.
“You’ll be given the same data that Amazon has access to. [Restaurants] will have data around transactions, what customers are ordering, customer interaction [and] where they are navigating,” he explains.
An infographic of how big data can be utilised to understand customer behaviour. Photo credit: TabSquare
Big data, Tejuja says, will be able to drive AI in the restaurant industry through menu engineering, where the menu itself is able to get data and modify itself for customers due to earlier transactions made. Another is personalised recommendations where a person ordering more vegetarian options in the past will likely to see a list of suggested menu items or related options. Burger enthusiasts, meanwhile, will see more burgers or related items based on earlier information. The third example, he cited, is on smarter and targeted promotions where brands can provide specific promos for customers based on acquired data.
Such acquisition, Tejuja surmises, would allow restaurants to further understand the customer’s taste, preferences and choices.
A restaurant chain that is using TabSquare’s SmartKiosk, for example, discovered that more than 70% of their returning customers are selecting personalised dishes that were recommended by the AI-driven machine. The restaurant has captured close to 90% of its sales from all transactions, over 25% higher average bill value from orders placed on the integrated point-of-sale system and almost 50% orders placed by customers registered.
Do you know more about this story? Contact us anonymously through this link.