3. Overview of Research Design
We use both field experience and historical purchase data
analysis in our research plan. All data is provided by an online kitchen and
tableware retailer in Benin. The online retailer sells branded products as well
as articles under its brand. The company offers the largest choice of kitchen
and tableware in the country. The articles are listed on the website under
three catalogs: main catalog, sales catalog, and point-of-sale catalog. The
main catalog contains all full price offers as well as some items at slightly
reduced prices. The sales catalog contains moderate priced items, while the
retail catalog contains very low-priced items, where the precise
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thresholds between «light», «moderate» and
«deep» vary over time. All products offered by the company are first
listed in the main catalog and then progressively updated and listed in the
other catalogs as new products are introduced.
Our research strategy has four components. The first is an
exploratory field experience in which new visitors to the online store are
randomly exposed to additional research frictions. In the second part, we use a
regression model to classify existing consumers according to their price
sensitivity. Third, we validate our classification by measuring the response
rates to randomly assigned electronic bulletins. Fourth, we expose new and
existing customers at the online store to additional research frictions and
measure how treatment effects affect purchase rates, discounts and margins
differently, depending on the sensitivity to the price estimated by the buyers.
We cover the design and results in the following four sections.
4. Field Experiment I
In this experiment, we are looking for preliminary evidence
that minor changes in website design can have significant effects on buyer
behavior and purchasing results. We vary the presence of website features that
may facilitate or hinder buyers from finding discounted items. We only include
new visitors to the desktop version of the online store because they have
relatively little information about the distribution of available products and
prices. In assessing the results, we are particularly interested in the effects
of processing on the discount levels of the transactions concluded and on the
overall conversion rate. We anticipate that increased search costs will
decrease the likelihood that price-insensitive customers will make the extra
effort required to find products at very low prices, and that these customers
will replace the full-priced items instead.
We experimented on the retailer's website for 15 days. During
this period, all new website visitors were randomly assigned to the control
group or one of three treatment groups with equal probability. New visitors are
defined as customers who do not have the retailer's cookies on their machines
and who register for a new account before making any purchase. Only visitors
who used a desktop computer, laptop, or tablet were included in the study. A
total of 195,806 customers were included in the experiment. Also, only
consumers who access the site through the main home page have been included,
excluding consumers who have visited the site using an electronic coupon,
newsletter or link from a third-party website. During the
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experiment, no other changes were made to the website.
Descriptions of the testing and processing conditions follow. In each of the
treatment conditions, neither the assortment of products available nor the
prices of the products were different from those of the control condition.
Control
The control condition was simply the website as it was at the
time of the study. The website has elements designed to make it easier for
consumers to find discounted items. Customers have three ways to find discount
items: by clicking on a prominent link from the home page to the store catalog,
sorting products by discount level in each catalog using a drop-down option,
and by consulting the markers which highlight the discounts greater than 40%.
In each of the processing conditions, we eliminate these elements to increase
the effort required to locate items at reduced prices.
Treatment 1: No link to outlet catalog from the main
landing page
In this condition, we are eliminating the simplest path to
discounts: the exit link from genre-specific landing pages. Other links to the
outlet catalog can be found in the «selling» section of the website,
requiring an additional click from a buyer to access the outlet catalog
compared to those of the comparison group. This treatment represents a very
slight increase in research friction which constitutes a solid test of our
hypothesis.
Treatment 2: No discount filter and no discount
markers
Here we are removing the second easiest way to find discount
items after the point of sale link: the ability for consumers to order product
listings based on the level of discount. We are also removing the accompanying
discount markers, which provide visual cues to identify high discounts. These
items are widely used by online retailers to facilitate the search and
navigation of buyers.
Treatment 3: No outlet link, no discount filter, and
no discount markers
In this last treatment, we implement the largest increase in
search frictions by deleting all the elements of the site deleted piecemeal in
the first two treatments. This is an effort to significantly increase the
magnitude of the research costs charged to buyers looking for discounts.
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Outcome Variables
Our main goal is to find out whether obstructing consumers'
search for high discount products leads some of them to buy fewer discount
products and replace them with regular priced items to improve the retailer
profitability. To assess the effects of each treatment, we take into account
several variables:
· Average discount: the average ratio of sales prices to
original prices on items purchased in each processing group. Since each
treatment makes it more difficult to locate discounts, we expect the percentage
of discounts to be lower in processing conditions compared to control on
average.
· Percent full-priced purchases: the proportion of items
purchased sold without discount. Historically, more than 50% of purchases on
the site are made at a high price. An increase in this rate in our treatment
groups would support our hypothesis while maintaining a constant conversion
rate.
· Conversion rate: percentage of consumers who choose to
purchase on the website during the trial period. Since a large part of the
seller's assortment becomes more difficult to see in the processing conditions,
it is reasonable to expect the conversion rate to decrease.
Results
Table 1 shows that clients in the three treatment groups
purchased items at significantly lower discounts on average (9.5 to 11.3% off
versus 15.5% off) and that they purchased more items at full price (65.5% to
68.7% versus 60.8%). Consequently, the average selling prices of the items
purchased in the three treatments were considerably higher than in the control
state, which confirms our initial hypothesis.
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Table 1: Results of Field Experiment I
|
Group
|
Sample size
|
Average discount
|
Percent of full- priced purchases
|
Average selling price
|
|
Control
|
30,015
|
15.7%
|
60.8%
|
397
|
|
Treatment 1
|
30,159
|
9.5%
|
68.7%
|
495
|
|
Treatment 2
|
30,243
|
11.3%
|
65.5%
|
675
|
|
Treatment 3
|
30,050
|
9.7%
|
67.0%
|
650
|
A natural concern is that if the search frictions for finding
discount items are too large, then the expected result of reducing discount
purchases could also be accompanied by lower conversion rates. This is of
particular concern for new price-sensitive buyers. However, we did not find any
significant decrease in conversion rates, measured by the number of
transactions carried out. There was no significant difference in conversion
rates between treatment group 3 and the control group. And the conversion rates
were even slightly higher in treatments 1 and 2.
To verify the robustness of our main finding, we carried out a
comparison between the treatments and the control groups at the level of the
shopping basket (compared to the article) to compare the differences in
purchasing behavior compared to the size and composition of the basket.
Confirming the main results at the item level, Table 2 shows that the average
discount on baskets purchased by consumers in two of the three treatments is
significantly lower than that of the control group (12.1% to 13.2% compared to
14.8%). For treatment 3, it is slightly lower. The average basket size in all
treatment groups was not significantly smaller than in the control group.
Table 2: Basket level results from Field Experiment
I
|
Group
|
Average discount
|
Average basket size
|
|
Control
|
14.8%
|
1011
|
|
Treatment 1
|
12.1%
|
1277
|
|
Treatment 2
|
13.2%
|
1682
|
|
Treatment 3
|
12.0%
|
1388
|
These results strongly support our hypothesis and demonstrate
the effectiveness of the manipulations we have chosen. Our results show that
online retailers can increase their margins without sacrificing conversion by
slightly increasing the search frictions associated with their
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reduced offers. In an environment without search frictions,
price-insensitive consumers can locate discounted options «for free».
By adding search frictions, online retailers can provide a semi-permeable way
to close these consumers down to full-price options while offering discount
options available to price-conscious buyers extra efforts to find them. In the
following sections, we examine in more detail the mechanism underlying our main
results.
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