The idea of food ordering and food delivery was neither modern nor conventional. But its processes kept evolving using modern methods for better user experiences. Digitalization was a breakthrough for the industry. Till then orderings were placed using phones and delivery was made accordingly. With the introduction of smartphones and web apps, things changed. Rather than ordering food from one or two available options, users were able to opt a service provider from a list of options. Artificial intelligence and machine learning took the case to an even better level. With these technologies now on the run, users are able to make the perfect choices. Here we do not discuss the history of food delivery services. Rather know more about the impact of AI in online food delivery systems.
How AI is Creating Difference?
As we know, the online food delivery industry is at its peak, especially during the lockdown phase. Even before AI introduced, the concept of online food delivery was in the markets. Simply host a web app for your delivery services and then users were able to make orderings from it, easy-peasy right? So what is the impact of AI in online food delivery systems? Before moving further, if you are new to the concept of AI it would be better if you know what is Artificial Intelligence?
Consider yourself as a vegan and an end-user of such an app. Every time you enter a food delivery app, you are given a list of food varieties and restaurant options. In earlier the list of options used to be static and sometimes you have to surf the whole app for finding the right choice. Well, things have changed. Now just as you enter the app it lists a variety of food that aligns your taste. Bingo! Now you don’t have to search for the list of foods that you were looking for. AI has made the app more intelligent to analyze your likes and dislikes and make a list of recommended items from it. In simple terms, AI has improved user experience as well as the productivity of these services. Let us dive more deeply into areas where AI has made its impact in the food delivery industry.
AI in Online Food Delivery
1. Quarterly Plannings
The food delivery industry is very dynamic as its trends keep on changing. Companies must always be prepared for the upcoming quarters in order to get the best results. So this planning should be very accurate, a trait which is also hard to achieve through human interpretations. With AI this is quite simple. It helps companies to gain better insights from the previously generated data and decide the best action plan for the upcoming months. This might include listing the most chosen food item or restaurant picked by the customer, etc.
2. Faster Deliveries
Food delivery is all about delivering food fresh and hot. AI algorithms can help companies achieve this in the most efficient way. Earlier algorithms used to assign a nearby delivery person to its end-user. The system even didn’t take in account the state of his/her previous order or even his/her lifetime track record. This eventually led to poor services and unwanted delays.
AI on the other hand takes in all such factors and lists the most eligible delivery persons for an end-user rather than listing nearby ones. These algorithms consider the hotel conditions, stages of food processing, the track record of delivery persons, state of his/her previous delivery, etc. So that both consumer and delivery person need not wait at any stage of the food delivery. A better understanding of this can be made when we analyze the greedy algorithm of Uber eats in the later sections of this blog.
3. Recommended Eats
This is the most common application of AI in the industry. Once a user enters an app, it starts to generate tons of data relating to his/her likes and dislikes. AI-enabled apps can make use of this data to create a dynamic food catalog for the user based on his/her taste. A far better approach than previous algorithms where it creates the same static list for all its users. According to reports, the recommended eats feature has improved user experience and has helped companies widen their range of customers.
4. Chatbots and Voice Orderings
NLP has been a breakthrough in both the AI and food delivery industry. Now customers are able to place their order through a voice request or simply using the chatbot facility. Not simply placing orders, companies also found this as a resource of tons of data that used to customize their services.
5. Restaurant Planning and Listings
Implementation of AI in the system has also helped restaurants to make timely decisions and do some efficient planning. Now restaurants can know the real trend of the market and get prepared accordingly. This also helps food delivery companies to choose the restaurants to be included in the lists. All these decisions are made from user-generated data and AI algorithms.
This section might seem repetitive for the readers. But it’s always better to understand things in the light of examples. So for the readers, I have included cases of 2 food delivery giants in the Indian industry and how they have used AI in their services.
3-way marketplace of Swiggy
Swiggy has so far disclosed to have used AI both in vendor and user fronts. Their algorithms match customer demands, supply from vendors, and delivery executives, creating a 3-way marketplace to deliver a wow customer experience in the food delivery industry.
On the vendor front, Swiggy uses AI for time-based prediction models that help restaurant partners to plan ahead of the demand. On the consumer side, they use AI for better user experience and are achieved through AI roadmaps like Catalogue intelligence (classifying products as veg/non-veg/egg), Customer intelligence (segmenting customers), Restaurant discovery (listing the best restaurants in the neighborhood), Simple order tracking, etc. Click here to find a detailed blog on various AI roadmaps of Swiggy.
Greedy algorithm of Uber Eats
Similar to Swiggy, Uber eats has also inculcated AI in online food delivery systems to improve user experience. However, upgradation of their “Greedy Algorithm” is the most notable one. Greedy algorithms used to be their method of finding the delivery person for a particular order. What the algorithm basically did was finding the nearby delivery person for that order. This estimation was more locale and didn’t consider other available delivery persons. So they wanted the estimation to be more global. AI was capable of it.
Let me describe this with an example. Consider, an order placed by a user. And the time for cooking that food is 30 minutes. According to the Greedy algorithm, the nearby person to that order will be assigned the delivery task. Now he/she may or may not reach the hotel in 30 minutes. Either if he reaches there early or lately, it affects the efficiency of food delivery. And once the food cooked and dispatched, it should be delivered as soon as possible. Again, consider that our delivery person is the kind of guy who usually delays his orders. This again affects the service efficiency.
Using AI and ML Uber eats could solve all such problems. Here the approach is a more global one. It considers all the delivery persons under the hood during the estimation. So again if we consider the above example, the algorithm will not rely on that single person and search for other candidates who can make it to the hotel in exactly 30 minutes. Once the food dispatched, then it looks for the person who could do the delivery in the least time. For such an estimation, the algorithm learns the previous records of all delivery persons and makes the best choice out of it. This is just one among their AI roadmaps. Read here to know more about the Greedy algorithm and other ML-driven investments of Uber eats.
Who We Are?
Every AI or ML application are introduced into the market after thorough training. It should give desired results when acted upon unknown data. For training these modules large sets of data are required, and the interesting fact is tons of data are generated every day. But these data cannot be fed as such for training purposes and need to be made smarter. We smarter these training datasets through accurate data labeling and annotation services. We label all kinds of data using various techniques. Visit us to learn more about our services. Got to power your AI model? Let’s discuss your data labeling requirements.