7. Field Experiment II
In our last experiment, we study how the response of buyers to
additional search frictions varies depending on price sensitivity. As with
Field Experience I, we expose consumers to different versions of the online
store, each with an additional search friction element. We compare the results
against the control over retailers' performance measures and use our predictive
models from the previous section to characterize the heterogeneity of consumer
response.
We carried out this experience from June 1 to 15, 2019 on the
desktop and tablet versions of the online store. In the following analysis, we
use the data from June 2 to 14 to eliminate the possibility of contamination
from the start and end of implantation. All consumers were randomly assigned to
the control group or one of the four treatment groups with equal
probability. While in Field Experience I,we only included new
visitors entering through the
main landing pages, here we include new consumers as well as
returning consumers, regardless
of which page they are viewing in first. The processing
conditions are as follows:
Treatment 1: Removal of links from main pages to points
of sale and sales sections of the
website
Treatment 2: Removal of discount flags
Treatment 3: Removal of the sorting option for
discounts
Treatment 4: Replacement of discount banners with
non-discount banners
Unlike experiment 1, we separate the removal of the discount
flags and the sorting options into two different treatments for reasons of
completeness. We are also adding a fourth treatment, the use of banners without
discounts throughout the site because this communication approach is the
equivalent on the website of the email treatments used in the previous
validation experience.
Results
Before examining the impact of price sensitivity on consumers'
propensity to find and buy
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discounted items, we perform the same analysis as in Table 8,
which groups all types of consumers. By further validating the main conclusions
of experiment I, this time by including current customers rather than only new
customers, we find that the removal of the discount flags, sorting by discount
and discount banners (treatments 2 to 4) decreases both the average discount of
items purchased and the impact of purchasing items on sale. As in the past,
this objective is achieved without reducing conversion rates. An exception to
this rule, and contrary to the conclusions of experiment I, is the null effect
of the removal of the links to the points of sale and the sales from the home
page (treatment 1). It appears that current customers were not as dissuaded as
new customers from finding the high discounts at the point of sale section of
the website. This is not so surprising since many buyers probably already knew
of the existence of the point of sale and only had to go through an additional
click to find it. In summary, except for this processing, the addition of
research costs has a very similar qualitative impact on new and existing
customers.
Table 8: Main results
|
Treatment Group
Control
|
Number of visitors
|
Average discount of sold items
|
Percent of items
bought at full price
|
Number of orders (Conversion rate)
|
|
Treatment 1 No
outlet and sales
links
|
68,343
|
18.25%
|
49%
|
1,351 (1.98%)
|
|
Treatment 2 No
discount markers
|
70,058
|
17.32%
|
50%
|
1,599 (2.28%)
|
|
Treatment 3 No
discount sorting
|
70,025
|
16.69%
|
52%
|
1,605 (2.29%)
|
|
Treatment 4 No
discount banners
|
69,859
|
17.09%
|
51%
|
1,605 (2.30%)
|
A more precise test of our forecasts is to show an interaction
between a buyer's price sensitivity and their willingness to incur search costs
to find discounted items. The use of regular customers, while changing the
«navigability» of the website is, in our opinion, a very rigorous
test of this prediction. First of all, customers have memories and we expect
them to remember that very discounted items exist on the platform. Second, our
manipulations are quite subtle (that is, they slightly increase search costs)
and do not cause any change in sales prices or the assortment of products.
Third, fashion retail is a category in which buyers have a pretty
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good idea of when prices are high or a good deal and maybe
more motivated to leave the website if they can't find a discount. Despite
these challenges, we find that price sensitivity still plays a moderating role
in the impact of research frictions on the likelihood of purchasing items at
reduced prices.
Table 9: Proportion of items bought at full
price
|
Price sensitivity
|
Control
|
Treatment 1 No outlet and sale links
|
Treatment 2 No discount markers
|
Treatment 3 No discount sorting
|
Treatment 4 No discount banners
|
|
Low
|
58.7%
|
67.8%
|
66.6%
|
63.9%
|
67.5%
|
|
Medium
|
54.0%
|
52.1%
|
57.0%
|
53.1%
|
57.0%
|
|
High
|
36.3%
|
40.8%
|
38.4%
|
32.6%
|
33.2%
|
In Table 9, we group consumers into three quartiles based on
their price sensitivity, as set out in Section 5. We find that
price-insensitive consumers are more likely to buy items at full price across
the entire market (see the first row). In three of the four processing
conditions, we observe a statistically significant increase in the proportion
of full-price items purchased by customers with little price sensitivity.
Equally remarkable, this is not the case for consumers sensitive to average or
high prices, who willingly incur research costs to benefit from discounts. This
result provides additional evidence, by including current users and adding
other forms of search costs to the website, online retailers can improve
margins and, therefore, their profitability, by deliberately adding costs
friction.
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