THE ZMOT
THESIS DEFENSE JANELLE M. DIPHOORN
MASTER MARKETING INTELLIGENCE & MANAGEMENT Supervisor: P. Van Eck
Second supervisor: P. Verhoef
“When consumers hear about a product today, their first reaction is ‘Let me search
online for it.’ And so they go on a journey of discovery: about a product, a service,
an issue, an opportunity. Today you are not behind your competition. You are not
behind the technology. You are behind your consumer”
INTRODUCTION
Digital channels broadened the availability and accessability of information (Bawden and Robinson, 2008)
Nowadays people use the internet to gather information (Thompson, 2002)
Digitalization caused a major shift in purchase behavior and made that most of the customers start their purchace decision process online (Lecinski, 2011).
THEORETICAL BACKGROUND
The Zero Moment of Truth
“the online research action which follows a consumer’s first exposure to advertising for a product,
which, in theory, had triggered his/her need” (Moran et al., 2014).
To nearly 85% of the shoppers the ZMOT shapes their purchase decision (Google/Shopper Sciences, 2011).
Consumers learn and decide during the ZMOT
A new and critical decision-making moment
Display advertising
Display advertising leads to higher sales (Dinner et al., 2014).
Interests of someone are determined by his or hers previous internet behaviour
RELEVANCE
It is a crucial moment in the purchase journey where marketing and information collection happen
(Lecinski, 2011)
It affects the succes and the failure of almost every company because it is the period of time where
CONCEPTUAL MODEL AND RESEARCH QUESTION
METHODOLOGY
Weekly aggregated level data
115 observations
Multiplicative model
Allows for interaction
Accounts for decreasing return to scales
ZMOT: Branded search terms, number of pageviews & eWOM
RESULTS
No significant effects for ZMOT
No significant effects for display advertsing
Moderating effect of display advertising:
MANAGERIAL IMPLICATIONS
It is not likely the effects of ZMOT are non-existent – the bias-variance trade-off might suggest using
lower level of aggregation/larger dataset (Wedel and Kannen 2016).
Different way of measuring the ZMOT
Managers should take aspects other than costs into account, since these determine the effectiveness
LIMITATIONS AND FUTURE RESEARCH
Weekly aggregated level data:
One could only imagine that when individual daily-level data is available, one can estimate the effects of ZMOT and display
advertising more precisely and personal characteristics could be taken into account.
Sub-features:
Lack of information about the content and timing of the display advertisements, or type of website it is advertised on.
Size of the dataset The type of industry
Look at the effects of ZMOT in different industries to be able to generalize the outcomes and see if the effects hold in
