no code implementations • 29 Oct 2020 • Jeremy Yang, Dean Eckles, Paramveer Dhillon, Sinan Aral
We apply our approach in two large-scale proactive churn management experiments at The Boston Globe by targeting optimal discounts to its digital subscribers with the aim of maximizing long-term revenue.
no code implementations • 31 Jan 2020 • Madhav Kumar, Dean Eckles, Sinan Aral
We develop a new machine-learning-driven methodology for designing bundles in a large-scale, cross-category retail setting.
1 code implementation • 10 May 2019 • Dean Eckles, Hossein Esfandiari, Elchanan Mossel, M. Amin Rahimian
We study the task of selecting $k$ nodes, in a social network of size $n$, to seed a diffusion with maximum expected spread size, under the independent cascade model with cascade probability $p$.
Social and Information Networks Computational Complexity Probability Physics and Society
1 code implementation • 8 Oct 2018 • Dean Eckles, Elchanan Mossel, M. Amin Rahimian, Subhabrata Sen
To model the trade-off between long and short edges we analyze the rate of spread over networks that are the union of circular lattices and random graphs on $n$ nodes.
Social and Information Networks Probability Physics and Society 91D30, 05C80
1 code implementation • 14 Jun 2017 • Dean Eckles, Eytan Bakshy
Naive observational estimators overstate peer effects by 320% and commonly used variables (e. g., demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%.
no code implementations • 4 Jan 2017 • Alexander Peysakhovich, Dean Eckles
Scientific and business practices are increasingly resulting in large collections of randomized experiments.
no code implementations • 15 Oct 2014 • Dean Eckles, Maurits Kaptein
Subsequently, we detail why BTS using the online bootstrap is more scalable than regular Thompson sampling, and we show through simulation that BTS is more robust to a misspecified error distribution.
no code implementations • 19 Jun 2012 • Eytan Bakshy, Dean Eckles, Rong Yan, Itamar Rosenn
This approach can increase ad efficacy for two main reasons: peers' affiliations reflect unobserved consumer characteristics, which are correlated along the social network; and the inclusion of social cues (i. e., peers' association with a brand) alongside ads affect responses via social influence processes.
Social and Information Networks Physics and Society Applications J.4; H.1.2