Accurate Food Delivery Time Forecasting
Big Data analytics, driving the food industry in profitable growth.
Businesses are becoming more and more competitive day after day. The food industry is no better. Getting our foods delivered at the doorstep anytime anywhere is easier than ever. Thanks to the modern incredible food delivery apps. All we need to do is just download the app, choose our favorite food, place an order and make the payment right from our mobile phones.
Now it’s time for the delivery team to deliver the piping hot food as quickly as possible. Late or lukewarm food leads to bad order. Even small misconceptions of 5 to 10 minutes delay can make a big difference. So it’s imperative to predict the accurate delivery time to stay ahead of the competition.
With this objective, a U.S based client who acts as a leading player in the food service sector approached Smackcoders. They wanted us to offer an accurate solution to predict the delivery time that improves the customer satisfaction and reduce churn rates.
Through analyzation of the client requirements, our experts decided to go with today’s leading technology, BIG DATA. As it helps to collect, process and offer an accurate estimation of the delivery time. This reduces the overall drop rate as the initial prediction is accurate and that the delays are communicated effectively.
We built a model which will take the food delivery details as input and outputs the accurate delivery time. Initially, we looked at the delivery process it involved 3 stages,
- Pick up the food from the supplier(restaurants).
- Travel from the supplier to the customer place.
- Deliver the food to the customer.
To predict the delivery time, we combined all the above time interval (i.e. Pick-up time, Point-to-Point time, Delivery time). So as an input, typically we have three categories: restaurant info, customer details, and previous delivery results.
The calculations for pick-up and delivery time is comparatively easier than point-to-point time. As estimating the time travel from the supplier to customer location is a tricky task. It is influenced by a number of factors like there may be a number of routes, traffic conditions that constantly changing, road closures and more.
Luckily, there are a bunch of data points available to help better understand this environment and our expert engineers did a fantastic job to process all these data points and predicted the output.
Combining all together will give the final result as we expected. Now our client has the option to predict the accurate delivery time. This helps the client to see a 3% drop in customer churn rates.