Technology is increasingly playing a role in what we order from a restaurant menu and what we eat. Most of us don't realise that artificial intelligence (AI) can be used to cross-sell dishes - in fact, it can possibly nudge you into ordering the higher-priced grilled Atlantic Salmon versus the moderately priced Vietnamese Basa.
Restaurants in India are gradually warming up to the idea of harvesting data and using restaurant-tech. Partly because they have seen the power of data science. Online food aggregator platforms, who use quite a bit of exponential technologies, have walked away with every customer insight. Aggregators now wield a tight grip on all restaurants using their platforms.
Business Today recently caught up with Ankit Malhotra, Co-founder, and CEO of Dineout. The company runs a table reservation business and also a successful restaurant-tech portfolio. Through an inorganic strategy, Dineout has beefed up products rapidly since 2015. The company's software, today, processes the data of roughly six million restaurant visitors every month. Over a Chinese lunch, Malhotra spoke passionately about technology that is shaping the future of restaurants.
One of the company's newest products converts the physical printed menu into a digital one. That seems simple but has far reaching consequences. Prices in the hospitality business, whether it is hotels, cabs or even airlines are a factor of supply and demand. Restaurants, nevertheless, typically, carry the same printed menu for six months to a year and can't take advantage of demand cycles.
"It is impossible to print physical menus at scale and have multiple versions of the menu," says Malhotra. "We launched a product called DineIn. We are now replacing physical menus with QR codes on every table. As a consumer, one can scan the QR code and see the menu on our phone," he adds.
Prices can now be customised to demand cycles. Not only this, algorithms can help up-sell - an act that was left to the persuasive powers of a human waiter, thus far.
"The system can suggest intelligent combos. For example, if you ordered a soup, it can push a dim sum to you. You can order a pizza, and it can push you a beer. The system, therefore, does intelligent combos that help in up-selling," Malhotra says.
The machine is fed with tons of data to be able to suggest what humans must eat. This includes thousands of menus and the usual combinations people look for or have ordered in the past. "We are using a lot of AI. At the back-end, there is continuous machine-learning. We are not doing this at a restaurant level; we are doing this at an aggregate 10,000 restaurants level, across hundreds of thousands of bills every day," the founder explains.
This sort of data can potentially also help restaurants do targeted discounting in the future. A higher-priced item can carry a 10 per cent discount instead of a blanket discount on the entire bill. "More than a higher-priced item, what matters to the restaurant is which item has a bigger margin. Dim Sums, for example, have crazy margins inside the restaurant, but maybe noodles do not. At the dish level, I know what the margin on each dish is. Hence, I am able to push or recommend them," Malhotra says.
The reason Dineout's algorithms know the margin structures of a dish is because of an acquisition. In 2018, Dineout acquired Torqus, a company that sells supply-chain management software among other products. It optimises supply-chains through menu-level mapping of ingredients required for a dish.
There is probably no name to describe the phenomena of machines influencing eating decisions. 'Algorithmic gastronomy', perhaps.