Impact at a Glance
- 90% accuracy in ETP/ETD prediction
- 15% reduction in average delivery time
- 33% increase in on-time deliveries
About the client
Our client is an Omnichannel eCommerce Platform that provides retailers with out-of-the-box solutions to power Same-Day Delivery, Curbside, In-Store Pickup, Shipping, and Post Purchase experiences. Trusted with some of the biggest names in multiple verticals of retail, the company is a leader in delivery orchestration and post purchase experiences.
Business Challenge: Addressing Complexities of Last Mile Logistics
The primary business challenge for the logistics platform is to accurately predict the estimated time of pickup (ETP) for delivery service providers at retailer locations and the estimated time of delivery (ETD) to end customers.
The challenge is further compounded by the need to offer these predictions across multiple delivery service providers, enabling customers to select the most suitable option based on their unique delivery preferences.
Achieving this level of accuracy and reliability is critical for optimizing delivery operations, enhancing customer satisfaction, and maintaining a competitive edge in the logistics industry.
- Traffic conditions can fluctuate significantly, with peak traffic times causing delays of up to 50% in travel times compared to off-peak hours.
- Delivery distances can range from a few blocks to over 50 miles, introducing significant variability in travel times.
- Delivery service providers can vary in performance by up to 30% based on factors such as speed, reliability, and historical punctuality.
- Studies show that a 10-minute delay in delivery can lead to a 5% decrease in customer satisfaction
Cognida.ai Solution: ML-powered ETP/ETD Analysis & Prediction
Our team’s extensive knowledge in data science and engineering allowed us to develop a sophisticated Machine Learning model tailored specifically to our client’s needs. This ensured high accuracy and reliability in predicting the estimated time of pickup (ETP) and estimated time of delivery (ETD).
By leveraging tools like Zunō.Predict, we were able to select and utilize the most relevant features, setting our client apart from competitors. We built multiple models using state-of-the-art machine learning algorithms and rigorously tested and evaluated each one. Through iterative testing, we identified the best-performing model that consistently delivered high accuracy in ETP and ETD predictions.
We seamlessly integrated the selected model into the client’s logistics platform. This integration allowed customers to input package details and receive real-time ETP and ETD predictions for various delivery service providers, enhancing the platform’s value proposition.
