Unlike sales data, data on intermediate stages of the purchase funnel
(e.g., how many consumers have searched for information about a product
before purchase) are much more difficult to acquire. Consequently, most
advertising response models have focused directly on sales and ignored
other purchase funnel activities. The authors demonstrate, in the
context of the U.S. automotive market, how consumer online search volume
data from Google Trends can be combined with sales data to decompose
advertising's overall impact into two underlying components: its impacts
on (1) generating consumer interest in prepurchase information search
and (2) converting that interest into sales. The authors show that this
decompositional approach, implemented through a novel state-space model
that simultaneously examines sales and search volumes, offers important
advantages over a benchmark model that considers sales data alone.
First, the approach improves goodness-of-fit, both in and out of sample.
Second, it improves diagnosticity by distinguishing advertising
effectiveness in interest generation from its effectiveness in interest
conversion. Third, the authors find that overall advertising elasticity
can be biased if researchers consider only sales data. [ABSTRACT FROM
AUTHOR]