Below is a reprint of an article published on Nation's Restaurant News. View original article here.
New machine learning algorithm enhances average check size and cross-sell conversions.
In the rapidly evolving restaurant landscape, digital and online ordering are vital components of a robust sales strategy. That's because customer experience is increasingly multifaceted. What used to be largely on-premise interactions between guests and restaurant staff now happens hundreds, or even thousands, of times a day, through the web and various apps. It is not simply a matter of having an online or digital presence, but instead having those digital outlets seamlessly integrated with digital payment gateways, loyalty programs, tools for analytics and metrics, and CMS systems.
This reality is why Koala created an industry-leading platform that unites the customer experience across multiple digital channels, both on-premise and off-premise, with a single cloud-based system and easily customizable front-end interfaces.
"What's important to today's restaurant industry is insight," explains Brett Spiegel, Co-Founder and Chief Operating Officer at Koala. "The Koala Platform is designed to create opportunities to collect meaningful data while also maximizing transactional efficiency and profitability. It's the next generation of smart ordering and customer satisfaction."
While Koala's key features, such as premium online ordering with built-in analytics and real-time optimization, have made a measurable difference for major restaurant brands, including MOD Pizza, Pei Wei, and TGI Fridays to name a few, Koala's latest launch promises to raise the bar once again on what's possible with digital ordering integration.
The new feature is designed to increase check size and guest satisfaction by utilizing machine learning to make tailor-made recommendations for cross-sell suggestions. At the core of the feature is an affinity mapping engine that tracks sales of popular items then cross-references it against specific menu items that are most often purchased together. Then, it applies those recommendations to each individual's existing order experience—resulting in a digital recommendation tool that outperforms traditional if/then ordering systems by a ratio of 2 to 1.
For instance, if a customer orders a sandwich, the traditional if/then system may automatically recommend a side of fries. When that same initial sandwich order is placed, Koala instantly analyzes the most-popular side items ordered with that sandwich by local/regional/national users, and makes a tailored recommendation reflecting real-time data. The customer may be presented with a recommendation for a side salad, a vegetable, or other items based on other customers' buying. Koala may even recommend an up-charged or premium side and exhibit more conversions than the traditional if/then recommendation of fries. The system can even go further with follow-up recommendations of beverages, desserts, appetizers, and more. The restaurant makes a stronger sale, and the customer feels as though the digital interaction was more personalized to their preferences.
"Consider what positive digital upsells can mean for profitability," continued Spiegel. "Now, instead of selling just a single sandwich and, perhaps, a side of fries that doesn't excite a customer, Koala suggests a side salad with signature dressing, a craft lemonade, and a gluten-free dessert, all of which are more reflective of local tastes. Just like that, the $12 check becomes a $20+ check, all driven by menu items with higher margins than center-of-plate items."
Koala has been piloting its new machine learning algorithm with WaBa Grill, the fast-growing Southern California-based brand specializing in health-conscious grilled protein bowls, with dozens of options for customers to build unique combinations. The new machine learning feature is ideal for the brand's wide variety of menu customizations because it can distinguish between different upsell trends for customers ordering chicken – or even WaBa Grill's popular plant-based steak option.
"We're really impressed by the initial performance of the recommendation algorithm and how it can automatically learn and respond to consumer behavior. The pilot with WaBa Grill has provided us with some great insights which will serve to reinforce the recommendation system," explains Walter Beller-Morales, the Head of Engineering at Koala. "The team is already working on some major enhancements, and as we roll those out, it's going to be exciting to watch this powerful system evolve."
The machine-learning algorithm has analyzed more than 115,000 orders, collecting approximately 260,000 individual data points. That data was then used to make smart, informed recommendations on future orders. With the new feature in place, the Koala platform successfully doubled the revenue generated by cross-sells.
"This is a game-changer for the digital ordering experience," added Spiegel. "And it's not simply limited to app or web orders. If Koala-powered digital menus are used on-premise, restaurants gain the same increased revenue benefit."
Below is a reprint of an article published on Nation's Restaurant News. View original article here.
New machine learning algorithm enhances average check size and cross-sell conversions.
In the rapidly evolving restaurant landscape, digital and online ordering are vital components of a robust sales strategy. That's because customer experience is increasingly multifaceted. What used to be largely on-premise interactions between guests and restaurant staff now happens hundreds, or even thousands, of times a day, through the web and various apps. It is not simply a matter of having an online or digital presence, but instead having those digital outlets seamlessly integrated with digital payment gateways, loyalty programs, tools for analytics and metrics, and CMS systems.
This reality is why Koala created an industry-leading platform that unites the customer experience across multiple digital channels, both on-premise and off-premise, with a single cloud-based system and easily customizable front-end interfaces.
"What's important to today's restaurant industry is insight," explains Brett Spiegel, Co-Founder and Chief Operating Officer at Koala. "The Koala Platform is designed to create opportunities to collect meaningful data while also maximizing transactional efficiency and profitability. It's the next generation of smart ordering and customer satisfaction."
While Koala's key features, such as premium online ordering with built-in analytics and real-time optimization, have made a measurable difference for major restaurant brands, including MOD Pizza, Pei Wei, and TGI Fridays to name a few, Koala's latest launch promises to raise the bar once again on what's possible with digital ordering integration.
The new feature is designed to increase check size and guest satisfaction by utilizing machine learning to make tailor-made recommendations for cross-sell suggestions. At the core of the feature is an affinity mapping engine that tracks sales of popular items then cross-references it against specific menu items that are most often purchased together. Then, it applies those recommendations to each individual's existing order experience—resulting in a digital recommendation tool that outperforms traditional if/then ordering systems by a ratio of 2 to 1.
For instance, if a customer orders a sandwich, the traditional if/then system may automatically recommend a side of fries. When that same initial sandwich order is placed, Koala instantly analyzes the most-popular side items ordered with that sandwich by local/regional/national users, and makes a tailored recommendation reflecting real-time data. The customer may be presented with a recommendation for a side salad, a vegetable, or other items based on other customers' buying. Koala may even recommend an up-charged or premium side and exhibit more conversions than the traditional if/then recommendation of fries. The system can even go further with follow-up recommendations of beverages, desserts, appetizers, and more. The restaurant makes a stronger sale, and the customer feels as though the digital interaction was more personalized to their preferences.
"Consider what positive digital upsells can mean for profitability," continued Spiegel. "Now, instead of selling just a single sandwich and, perhaps, a side of fries that doesn't excite a customer, Koala suggests a side salad with signature dressing, a craft lemonade, and a gluten-free dessert, all of which are more reflective of local tastes. Just like that, the $12 check becomes a $20+ check, all driven by menu items with higher margins than center-of-plate items."
Koala has been piloting its new machine learning algorithm with WaBa Grill, the fast-growing Southern California-based brand specializing in health-conscious grilled protein bowls, with dozens of options for customers to build unique combinations. The new machine learning feature is ideal for the brand's wide variety of menu customizations because it can distinguish between different upsell trends for customers ordering chicken – or even WaBa Grill's popular plant-based steak option.
"We're really impressed by the initial performance of the recommendation algorithm and how it can automatically learn and respond to consumer behavior. The pilot with WaBa Grill has provided us with some great insights which will serve to reinforce the recommendation system," explains Walter Beller-Morales, the Head of Engineering at Koala. "The team is already working on some major enhancements, and as we roll those out, it's going to be exciting to watch this powerful system evolve."
The machine-learning algorithm has analyzed more than 115,000 orders, collecting approximately 260,000 individual data points. That data was then used to make smart, informed recommendations on future orders. With the new feature in place, the Koala platform successfully doubled the revenue generated by cross-sells.
"This is a game-changer for the digital ordering experience," added Spiegel. "And it's not simply limited to app or web orders. If Koala-powered digital menus are used on-premise, restaurants gain the same increased revenue benefit."
