Machine learning enables derive patterns that help businesses to enhance their customer experience and subsequently increase their market share. Rishabh Software blends this business knowledge with technology to help food delivery companies optimize operations, accelerate delivery times & enhance the customer experience to outperform the competition.
An on-demand food delivery model connects merchants, riders, and end customers likewise. It helps collate the data from past orders and user consumption patterns. Using Big Data & Analytics, the food delivery companies can track several operational aspects like traffic, climatic conditions, obstacles in the delivery route, and analyze how food can be delivered faster. The AI & ML systems offer insights about the route status in real-time to the delivery person for ensuring that the food gets delivered on time, every time!
Still on the fence about how to leverage Analytics & Machine Learning in food delivery? Then this article will show you how these emerging technologies like Machine Learning can help optimize delivery cost & time for profitable business outcomes. Further, you can even expect to get inputs on how to go about building a fully functional predictive model that is sure to satisfy more customers with quicker & quality service!
Let’s dive right in!
Faster delivery time is one of the most important factors that ensure success for the food delivery app. It most certainly offers a front-line edge to your business over the competition.
Rishabh Software team specializes in developing advanced analytics solutions that enable food aggregators, cloud kitchens, and other businesses in this domain to seamlessly optimize delivery cycles across the board while ensuring customer delight!
With our experience, we support businesses to better address the below scenarios during the delivery phase:
Leverage our specialization in analytics & data science to craft result-driven models for your food business
Below are the five actionable elements that we put to practice while deriving a food delivery time prediction model:
It starts with taking into consideration the delivery information as the input with the desired pick-up time as the output.
Some crucial factors for consideration include:
We leverage your food delivery dataset to enable you to stay competitive with a top-of-the-mind recall. For this, we implement an ML-based architecture that helps with:
And as an input to our predictive model, we divide the data into three broad categories:
These data pointers are combined with other variables to execute one-hot-encoding. It helps with the creation of several binary variables for every possible data set. The variables are then fed into the predictive model for training.
We divide the food delivery optimization cycle into three components:
1. Collection of food from the vendor (restaurant and others)
2. Drive from the vendor’s location to the customer’s address
3. Deliver the food package
The time taken for every component is then added to arrive at the final delivery time. Other aspects that are taken into consideration while considering the food delivery route optimization model would include;
To feed the trip-level data into the model, we assemble the dataset as suggested below:
(restaurant1, rider1, timestamp1, parked)
(restaurant1, rider1, timestamp2, waiting_at_restaurant)
(restaurant1, rider1, timestamp3, walking_to_bike)
(restaurant1, rider1, timestamp4, enroute_to_eater)
The model further analyzes activity data such as routes, parking status, vehicle type, traffic conditions, food preparation time, & delivery time, and more from the previous orders & draws useful insights.
Break pick-up & drop-off in stages like On a trip – walk – service time – walk back
The detailed breakdown of trip state helps predict food delivery time using Machine Learning and optimize performance across stages, including:
Right from predicting the need for inventory to food prep time, this startup takes a data-driven approach to decision-making. They leverage capacity planning algorithms to accurately predict the need for manpower on a given day.
With an autonomous dispatch system that combines ML & Big Data technologies, they can address the constantly fluctuating customer preferences and demands. They also use the insights to optimize their food preparation and delivery time. This helps ensure that the drivers arrive right on time and food is delivered hot. Further with a logistics software system that provides a real-time view of deliveries, they can even ensure that the drivers are fast & efficient.
To conclude, our food delivery data analytics model helps businesses reduce delivery time by dividing the on-demand delivery cycle into granular stages.
Rishabh Software’s team continues to create smart solutions that cater to the varied needs of food delivery businesses. We assist them with scalable, robust, reliable architecture to improve the experience for restaurant & delivery partners and end customers. For a US-based organization, we helped automate the food delivery mechanism with back-office operations for payment and invoicing management.
To help you make the most of your customers’ data, we can design a custom on-demand app powered with food delivery data analytics and big data algorithms. It will enable you to get real-time insights into food prep time, GPS, delivery location, and traffic updates. Using this data, you can dispatch drivers on the most efficient route, reduce the wait time for customers, improve satisfaction rates & boost your ROI!
Partner with us to unlock the true potential of data analytics in food delivery & drive efficiency gains across delivery cycles.