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Measuring the Relative Effectiveness of Specific Advertising Channels for a Retailer Predominately Selling Offline

Johnsrud, Lise; Frisch, Annika Hagen
Master thesis
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URI
https://hdl.handle.net/11250/2686681
Date
2020
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  • Master of Science [963]
Abstract
The current marketing environment is characterized by a steady increase in new

media and changing media usage patterns. This situation requires firms to

understand the interactions between diverse online and offline advertising

channels and their impact on multichannel sales. This study investigates the

presence, magnitude, and carryover of cross-channel effects for online advertising

(emails, online banner ads, and paid search advertising) and offline advertising

(TV and aggregated traditional advertising) on multichannel sales. The analysis

considers how these advertising expenditures translate directly into sales, and

indirectly through mediators (search click-throughs and online banner clickthroughs)

– thereby enabling offline advertising to be credited for their ability to

enhance online advertising’s effectiveness.

We apply a vector autoregressive (VAR) model to data from a Norwegian florist

predominately selling through the offline channel to study the effects. We find

that cross-channel effects and indirect effects are substantial and essential. Indeed,

cross-channel elasticities are larger than own-channel elasticities. The result is

primarily due to strong cross-channel effects on the offline channel. Online

advertising, and in particular, paid search advertising is more effective than offline

advertising. In sum, our research suggests that retailers predominately selling

offline can benefit from online advertising. Furthermore, ignoring cross-channel

effects and endogeneity in advertising channels can lead to a substantial

miscalculation of online advertising’s effectiveness.

Keywords: advertising elasticity, paid search advertising, online banner

advertising, email advertising, offline advertising, cross-channel effects,

synergies, vector autoregressive (VAR) model
Description
Masteroppgave(MSc) in Master of Science in Business, Marketing - Handelshøyskolen BI, 2020
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Handelshøyskolen BI

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