We launched our new Consumer Packaged Goods (CPG) Interest Targeting initiative to let advertisers target consumers on new verticals (convenience stores, groceries, DashMart, etc.) based on their restaurant order history.
Tag Archives: Computational Advertising
DashCLIP: Leveraging multimodal models for generating semantic embeddings
DoorDash’s Consumer Packaged Goods (CPG) business spans groceries, retail products, alcohol, electronics, pharmaceuticals, and more.
Smarter promotions with causal machine learning
In August 2025 at the KDD AI Conference in Toronto, Canada, we presented our published research, “Causal Machine Learning for Promotions: Industry Evidence and Applications.” In this paper, we describe a two-stage framework for improving promotion efficiency through causal machine learning – first by estimating each customer’s true response to different offers, and then by optimizing which promotions to deliver under practical business constraints.
Using LLMs to infer grocery preferences from DoorDash restaurant orders
Consumers enjoy DoorDash deliveries from a variety of merchants, ranging from restaurants to pet stores.
Augmenting Fuzzy Matching with Human Review to Maximize Precision and Recall
Even state-of-the-art classifiers cannot achieve 100% precision.
Predicting Marketing Performance from Early Attribution Indicators
DoorDash uses machine learning to determine where best to spend its advertising dollars, but a rapidly changing market combined with frequent delays in data collection hampered our optimization efforts.
Running Experiments with Google Adwords for Campaign Optimization
Running experiments on marketing channels involves many challenges, yet at DoorDash, we found a number of ways to optimize our marketing with rigorous testing on our digital ad platforms.
Optimizing DoorDash’s Marketing Spend with Machine Learning
Over a hundred years ago, John Wanamaker famously said “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half”.