Page 56 - Decoding Decisions ~ Making sense of the messy middle

56 CHAPTER 4 INFLUENCING THE MESSY MIDDLE

Within the frame, shoppers were presented with two boxes, Prefer A and
Prefer B. During the simulation, these boxes contained eight smaller boxes,
which displayed the logos of the brands being tested and information about
the product that the shopper might find during exploration. In our simulation,
all of this information was contained on one screen rather than being
revealed over the course of several sequential clicks and screens.

It was this supplementary product information to which our behavioural
science principles were applied during testing. For example, star ratings were

varied to test different applications of the social proof principle, or different
recommendation types to measure the importance of authority bias. Each of
the expressions featured in these information boxes had up to three levels of
intensity (for example three-star, four-star, and five-star reviews) for comparison.
The expressions of our biases were modelled on real-world instances, but were
quite basic in their execution, lacking any sort of creative gloss.

With both brand logos and all relevant information in place, the shopper was

asked to choose which they preferred. They were instructed not to overthink
the decision, but to follow the same process of discernment they would when
making a real-life purchase. From the collated results, we’re able to measure
the impact of any single element or combination of elements, quantifying the
impact of each change as an increased or decreased share of preference for
the respective brand.

The power of showing up

Implicit in the structure of our experiment (and marketing in general for that
matter) is the idea that to take preference share away from a competitor
brand, you have to be present when consumers are deliberating.

This might seem obvious, but it’s such a fundamental point that we don’t
want its importance to be mistaken. And as we’ll see, there is surprising

power in just showing up at the right moment.

In our first analysis of the simulation data, we compared first- and second-
preference brands, with all other expressions of our biases statistically
controlled to remain neutral.