Modeling Adoption and Usage of Competing Products

TL;DR

This paper introduces a continuous-time probabilistic model using temporal point processes to analyze and predict the adoption and usage patterns of competing products in social networks, capturing social influence, recency, and competition effects.

Contribution

It presents a scalable inference method for fitting the model to large social network data, enabling insights into factors influencing product adoption and usage.

Findings

Model accurately fits synthetic and real Twitter data

Provides more precise predictions than existing models

Offers interpretable parameters for understanding social influence and competition

Abstract

The emergence and wide-spread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. In this paper, we propose a continuous-time probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use.