Using General Adversarial Networks for Marketing: A Case Study of Airbnb

TL;DR

This paper explores the application of GANs in marketing by developing a new loss function to generate optimized Airbnb listing descriptions, demonstrating how generative models can enhance marketing strategies.

Contribution

Introduces the DMK loss function for GANs to incorporate user-defined keywords, enabling targeted text generation for marketing purposes.

Findings

GANs can effectively generate Airbnb listing descriptions.

The DMK loss improves keyword inclusion in generated text.

Framework can be adapted for broader marketing text generation.

Abstract

In this paper, we examine the use case of general adversarial networks (GANs) in the field of marketing. In particular, we analyze how GAN models can replicate text patterns from successful product listings on Airbnb, a peer-to-peer online market for short-term apartment rentals. To do so, we define the Diehl-Martinez-Kamalu (DMK) loss function as a new class of functions that forces the model's generated output to include a set of user-defined keywords. This allows the general adversarial network to recommend a way of rewording the phrasing of a listing description to increase the likelihood that it is booked. Although we tailor our analysis to Airbnb data, we believe this framework establishes a more general model for how generative algorithms can be used to produce text samples for the purposes of marketing.