Professor of Marketing
Imperial College Business School
South Kensington Campus
London SW7 2AZ
Google Scholar profile
I use data to understand how consumers make choices in settings
ranging from laundry detergent discounts to choosing a hospital for
a bypass operation. I am particularly interested in how consumers gather
information before making a purchase and what we can learn from data on
consumer search behavior.
I am an Associate Editor at Marketing Science, Quantitative Marketing and Economics,
and the Journal of Industrial Economics. I co-organize the European Quant Marketing Seminar (eQMS),
and I am a Public Editor at @QME_Journal.
Recent Research Highlights:
New machine learning tools allow researchers to process unstructured data from text and images
similarity between products from product images and different sources of text (product descriptions,
reviews, etc.) and then feed them into a demand model so that higher similarity leads to larger
cross-price elasticities between products. We apply the model to data form multiple categories and
show that it helps us recover flexible substitution patterns.
Paper on Soda Taxes featured on the "How I Wrote This" podcast.
episode of the “How I Wrote This” podcast. We discuss what motivated us to pursue this research,
the various decisions that we took when working on the paper, and how we navigated the review process.
For a short summary of the paper, check out the video posted on the right ...
The Sequential Search Model: A Framework for Empirical Research (with Accompanying Code)
The sequential search model (Weitzman, 1979) has emerged as the workhorse model for research based on
consumer search data. Papers in this growing literature adopt different specifications of utility, estimate
the model using a variety of approaches, and discuss identification in relation to a specific setting. Our aim
aspects of the sequential model that are relevant to empirical work in order to consolidate knowledge and
to provide a comprehensive introduction on the use of the sequential model, especially for researchers that
are new to working with search data and models.
We also provide a comprehensive code base that covers different methods for computing reservation utilities
and 4 different estimation approaches. Click here for access to the code.
Machine Learning and Targeted Marketing
Machine learning methods are increasingly used to design personalized marketing strategies such as targeted
coupons and personalized advertising messages. In a recent talk at the 2023 Katia Campo symposium, I talked
about what I see as three important ingredients to designing targeting strategies:
(1) the importance of incremental targeting (i.e. based on treatment effect heterogeneity)
(2) how to derive policies from machine learning estimates by using a decision-theoretic framework
(3) how to compare the performance of ML methods based on the profits that policies derived from a given model
Slide are available here 👉 Katia Campo Price Targeting Slide-deck.
Find out more about my recent research projects on my Blog.
... or follow me on Twitter ...