THE BEHAVIORAL ECONOMICS GUIDE 2016

approaches. Finally, big data (data science) can help improve nudging or the design of behavior
change initiatives through segmentation or targeting.
Jim Guszcza (2015) reflected on this last point in a piece on what he calls a “last mile problem.”
The challenge, according to Guszcza, is to combine predictive data analytics (data science) and
behavioral science. Analytical models can be used to target or segment individuals and
behavioral nudges, and then trigger the desired behavior. Such an approach could be used with
people who lack health insurance, for example, as Timothy Murphy (2016) suggests. Using data
to divide people into low-, medium-, and high-effort segments would help policymakers allocate
resources more efficiently and effectively. Resource-intensive interventions that require
education and traditional economic incentives would be assigned to the high-effort segment,
and low-cost nudges (such as defaults or commitment devices) would be used in the low-effort
segment.
As with many other technological innovations, commercial forces have been at the forefront of
data science advances. Turning real-world behavior into insights is nothing new in the
increasingly data-driven world of marketing, whether it’s about understanding consumers’
buying habits through supermarket loyalty card schemes or targeting people based on their
online behavior. The latter has been termed behavioral marketing, or behavioral targeting. The
intersection of marketing, data science, and behavioral science has become more frequently
reflected in topics covered by digital marketing and marketing analytics conferences. The UK
Market Research Society’s (MRS) 2016 conference raised questions about the role of technology
in drawing insights from data and how to extract meaning from data with ever-growing sources
and volume, and it also discussed whether there is a new era in which researchers act as ‘data
curators’.
Consumer Behavior and Neuroscience
The 2015 Behavioral Marketing Forum (the ‘behavioral marketing’ term was used here in a
behavioral science rather than in the targeting sense) in New York showed that behavioral
science in marketing now has a multitude of disciplinary and methodological bedfellows. This
includes ‘System 1 measurement’, exemplified by implicit attitude tests and biometrics (e.g.
facial response), as well as more technologically demanding neuromarketing, such as fMRI or
EEG-based methodologies.
The Journal of Marketing Research devoted a special issue to neuroscience in 2015. Two of the
issue’s articles dealt with consumer preferences evident in product choice. The first of these
studies (Telpaz et al., 2015) presents the first ever EEG 3 study to predict product choices without
relying on consumer responses. Their research recorded EEG data when consumers were
viewing consumer goods in isolation. The resulting data’s prediction rate for binary product
choices was 0.64 and significantly above chance. EEG measures—which are relatively
inexpensive, compared to some other neuroscience techniques—eliminate the need to elicit
preferences directly from consumers and thereby avoid elicitation biases and interferences in
the valuation process.
3 An EEG, or electroencephalogram, is a recording of brain activity. The test picks up and records signals through small
(generally noninvasive) sensors on the scalp. The signals represent fluctuations in electrical currents in the brain.
Behavioral Economics Guide 2016 14