In the last few years, several QSR brands have started piloting AI-powered recommendation engines in their kiosks and apps. What are the benefits of using recommendation engines in self-service kiosks? And how do they boost profit? In this article, we’ll answer those questions.
Using Recommendation Engines in QSR Kiosks can …
1. Provide Personalized Recommendations and Drive Higher Purchase Amounts
One of the subdisciplines of Artificial Intelligence is machine learning (ML) – which allows the algorithm powering the recommendation engine to analyze its own performance and improve over time. ML can gather customer feedback and use it to create better results.
ML also happens to be very good at analyzing customer and restaurant data. For customers, this could include purchase history, food preferences, and behaviors (i.e. ordering at a certain time of day). For restaurants, this could include order volumes at various dayparts, weather, and other factors.
You can put these two areas together to identify patterns, make personalized recommendations for each customer, and even tailor each location’s menu. Over time, each customer should get recommendations that closely match their tastes and preferences, which leads to larger ticket sizes, happier customers, and increased revenue.
2. Enhance the Customer Experience
As we discussed in A Quick Guide To Kiosk User Interface Design, creating a customer-friendly user experience is tremendously important. Without it, guest satisfaction goes down – and people may stop using kiosks altogether if the experience is bad enough.
In addition to powering the data analytics we describe later in this article, including a recommendation engine in your kiosk can improve engagement. It can provide real-time menu updates so that customers aren’t disappointed by ordering something that’s not available. It can use that customer’s order history (and current location/weather information) to highlight special offers or interesting new items.
In a related note, some restaurants are using other aspects of AI like voice recognition to make the ordering process even easier. Customers can order from kiosks as they would from a human employee, which eliminates some of the barriers associated with using tech-forward solutions like kiosks. Sophisticated ordering experiences like these – including at-table ordering, online ordering, and mobile ordering – are a core part of Tillster’s offering; our research has shown that these can boost digital sales by up to 15% and increase the average check size by up to 30%.
3. Optimize Pricing Strategies
AI powered recommendation engines also enable kiosks to employ dynamic pricing strategies, which adjust prices in real time in response to demand, seasonality, and inventory levels. This allows restaurants to offer discounts on certain items or during certain times of the day. In the big picture, it also allows location managers to create pricing strategies that increase revenue without decreasing other parameters.
4. Increase Order Management Efficiency
Another way that personalization engines can increase revenue is by handling orders more efficiently. Because AI can ‘recall’ customers’ details, it makes it easier to handle dietary restrictions or preferences. It can also make customizing menu items much more convenient for the guest. This streamlines the ordering process on the guest side and reduces the number of order errors in the kitchen. And because ML-driven personalization engines are self-learning, performance improves over time. You can learn more about Tillster’s expertise in order management optimization here.
5. Provide Upsell and Cross-Sell Opportunities
We’ve already discussed how personalization increases restaurant revenue. To this, we’ll just add that AI not only makes it easier to deliver personalized recommendations and offers, it also makes it easier to cross-sell and upsell to guests – and in a very natural way. For customers, these upsell/cross-sell opportunities are as simple as a recommendation that ‘B pairs well with A” or “Other customers also ordered X, Y, and Z”.
What makes these recommendations more actionable? They can be tailored to each guest’s known preferences and order history. The same goes for creating special offers and promotions; if you adapt an offer to that customer’s tastes, it’s more likely to receive a positive response. And this leads to happier customers who submit larger-value orders. So, instead of an one-offer-for-all approach, you can add some real-time strategy to your recommendations.
6. Improve Inventory Management
Recommendation engines are built on data analytics – it’s the insights harvested from analytics programs that become the customer recommendations. But these analytics can also power change in the back of the house.
By analyzing historical data and aggregated customer demands, your analytics system can identify patterns in when and sometimes why certain items are in demand. In turn, this can be used to forecast future demand levels for different stock items. This insight lets managers order enough inventory to meet projected demand without spilling over into over-ordering (and wasting food). This also leads to an optimized budget, since less money is spent on unused food products.
7. Drive Better Decision Making and Competitive Advantage
Other insights can be derived from restaurant data analytics programs. We’ve covered many of these elsewhere in our blog, so we’ll just summarize them now:
Insights on customer behavior lead to better menu and pricing decisions.
Understanding food trends helps shape menu changes.
Data analytics makes it easier to target and refine marketing campaigns.
Most large QSR brands know all of this already. It’s the early adopters who set themselves up for the strongest competitive advantage. For example, 65% of people like to use restaurants with kiosks; brands that have adopted this already have an advantage in speed, flexibility, and customer satisfaction over those that are slow to take up this trend.
Personalization is also an appealing target; 71% of customers across industries expect it. When you combine these two – kiosks and personalization – you have an experience that appeals to tech-savvy customers and a brand that projects a forward-thinking, customer-centric image. That’s a definite advantage in today’s world.
The Benefits of AI-Powered Recommendation Engines in Kiosks
Adding recommendation engines to self-serve kiosks can help QSR brands provide a better, more personal customer experience. It can also drive a lot of other improvements: reducing waste, optimizing ordering processes, and increasing revenue. Ultimately, the data analytics that power recommendation engines can help brands make smarter, faster decisions and stand out from the crowd.
If you’re interested in learning more about deploying recommendation engines in your kiosk, app, or online ordering, contact Tillster today. We have years of experience helping restaurant chains leverage the latest technology to improve profit margins and guest satisfaction. We’d be happy to answer your questions and help explore your options!
