This map shows the Deliverlitics model which combines previously siloed data across multiple sources. It creates a holistic view of the delivery theft and fraud problem, but more importantly it is a useful operational tool for predicting and preventing package theft and loss.
Naturally most of the clusters on this map come from big cities, where there are more people to get e-commerce deliveries and more people to steal them. However, knowing which locations are of higher risk, e-commerce merchants can make more informed decisions that work for their individual business needs.
The heat map of New York City below indicates high instances of delivery issues in red, with purple showing the lowest risk areas. The map is a visual representation of the model that uses aggregated data on delivery issues with other helpful geographic data to reveal risk patterns that inform the model to make accurate and actionable risk scores.
By using Deliverltics as a post-purchase shipping and delivery solution, merchants, shippers, and customers are able to benefit from more successful deliveries. These benefits include:
The Deliverlitics machine learning model helps predict where delivery issues of theft or fraud are likely to happen and therefore prevent issues from happening in the future. This is important to deliver a seamless post-purchase experience.
With the advancement of machine learning and AI, there are endless opportunities for continued developments in the AI-driven package security space, particularly as the quality of the data coming from all modes of transportation across the supply chain improves. As the end to end picture of the global supply chain becomes more clear, we believe the applications for analysis will multiply exponentially. Similar outcomes have happened with consumer marketing data and its explosion post 2000, as well as the analytics boom in professional sports. What was once colloquial knowledge becomes quantifiable and new connections can be formed. Therefore, we likely don’t know all of the ways in which these events can be predicted and therefore planned for, but we do know that as we move forward, those methods will become more clear.
By harnessing the power of machine learning and vast claims and address data, e-commerce can fight back against delivery fraud and theft.
Deliverlitics is utilizing a risk-scoring algorithm to help prevent delivery related issues in e-commerce. By knowing order risks, merchants can make better informed decisions to help deliver a better customer experience while saving costs associated with processing incomplete deliveries.
Deliverlitics’ is leveraging the discussed machine learning model to reduce e-commerce package theft and fraud - try it today.
