Be it hectic work-life schedules, preferred past time activity or for any celebration, online food ordering apps are now the ”Go-to” for almost every individual. They don’t have to make the same conventional choice of meals from regular restaurants. … Continue reading
Be it hectic work-life schedules, preferred past time activity or for any celebration, online food ordering apps are now the ”Go-to” for almost every individual.
They don’t have to make the same conventional choice of meals from regular restaurants. Rather, they can order from a wide range of food varieties that are delivered at their doorsteps. And the delivery time is almost the same, sometimes less too!
However, it is only when these choices from a plethora of restaurants are brought to your users’ notice that they can explore and get them all ordered.
And that’s where a recommendation system steps in!
In this blog, we will discuss how a recommendation engine can boost your revenue for online food delivery app.
For that, we need to consider some categories that a recommendation engine, when trained accordingly, can get more orders placed.
This is one of the key parameters that users consider while ordering food. Most restaurants close at night. However, the hunger pangs of your users might not follow the same protocol always!
As your online food ordering website or app is active 24/7, they will undoubtedly resort to it to get their food ordered.
Hence, you can integrate a recommendation engine and train it to consider all restaurants in the user’s city that are open during the day, night and/or 24/7 and make appropriate recommendations.
As your users will go bonkers over their hunger, they might find it difficult to be patient enough and wait for an hour or more to get their food delivered. Not to forget the fact that delayed delivery time also happens to have a negative impact on user experience.
You can discuss the average meal preparation and travel time with the restaurants first. And then draw your own delivery zones(upto 20 or more zones) or use your own delivery radius. You can then set limits for minimum orders and delivery charges as per the zones.
A recommender system can then be trained as per these parameters to recommend food choices to users.
There’s absolutely no point in suggesting fried chicken to a vegan! Rather, recommending lemon vegetable salad and roasted spinach with nuts would be more meaningful. After having multiple food choices in menu templates that aligns with their diet, chances are that the user would place orders for more number of dishes.
Thus, you can recommend cuisines to your users as per their dietary preferences.
Additionally, for individuals seeking structured nourishment tailored to personal health goals or dietary needs, exploring diverse meal planning options that cater from calorie-smart to keto, vegan, and high-protein diets can significantly enrich your menu.
Once you have an AI-based recommendation engine integrated into your food ordering app, you can train it to suggest dishes and restaurants as per users’ preferences.
Users do not always make a location-based or cuisine-based search. Some users also have a clear way of looking for discounts, offers and promo codes for ordering food online. And in most cases, they decide their orders based on the offers available.
Using this feature as a parameter will also help your users to have search results showing food discount coupons and offers provided by restaurants.
This will further assist your users to keep their food budget in check. Moreover, it will grab more users to your online food delivery app, ultimately adding to your revenue.
With the content-based filtering method, you can recommend restaurants or food based on your users’ previous orders. Every user’s profile is analyzed as per the categories chosen, searched or browsed, liked or rated. The algorithm will work on the present data and recommend dishes that are similar to those orders made in the past.
A powerful recommendation engine, when trained accordingly, can also suggest food items from different restaurants that the users have never tried before. This kind of personalized recommendation will help build a good rapport with the users and guide them to try something new.
Basically, it would be like a food buddy that your users can rely on to experiment with new cuisines and dishes.
Wrapping up!
When your online food ordering app is powered by an artificial intelligence based recommendation system, the possibilities of getting more orders increases. Suggesting food dishes as per the above categories will help your app to gradually grow into a personalized community for foodies. This will keep both the user engagement and loyalty high. Eventually, your online food delivery app will have its revenue growing.
Alie, an AI-powered recommendation engine can personalize user experience for your online food delivery app.
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Written by: Ishita Banik
Ishita is a Content Writer with Muvi Marketing Team. Apart from business writing, she is also an acclaimed author of three best seller romantic thriller novels. In 2020, she got featured in The Hindustan Times, a leading news portal as an inspirational Indian author.
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