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An Empirical Investigation of Ad Zapping Using a Large-Scale Dataset

Abstract

We empirically investigate U.S. households’ television ad zapping behavior using a nationally representative dataset. The dataset tracks the precise time when zapping occurs during the airing of an ad from over 9 million smart TVs in 2017. Specifically, we analyze 18,664,595 airings of 23,463 ads for 5,025 brands in 124 product categories across 119 national television networks. We find that, conditional on the ad being watched, on average, 10.56% of the airtime of ads is actively avoided. We also find that there is a considerable amount of variation in zapping. For instance, zapping is lower in niche networks and higher during prime time. It also varies across dayparts but the variation itself depends on the advertised product. We find that the creative content of ads can explain more of the variation in zapping than ad placement. For instance, we find that ads in Spanish, ads that are shorter, and those that are without a price promotion are zapped less. We develop managerial implications of our findings and also provide suggestions for future research.

Type

Working Paper

Author(s)

Lakshman Krishnamurthi, Caiyun Liu, Purushottam Papatla, Joonhyuk yang

Date Published

2019

Citations

Krishnamurthi, Lakshman, Caiyun Liu, Purushottam Papatla, and Joonhyuk yang. 2019. An Empirical Investigation of Ad Zapping Using a Large-Scale Dataset.

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