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The effect of raising searching obstacles on online purchasing behavior: proof from field experiment

par Boris Helios Zocete LOKONON KOUDOGBO
Taiyuan University of Technology - Master of Business Administration 2020

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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


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


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.


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.


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.


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.


Table 1: Results of Field Experiment I


Sample size

Average discount

Percent of full- priced purchases

Average selling price






Treatment 1





Treatment 2





Treatment 3





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


Average discount

Average basket size




Treatment 1



Treatment 2



Treatment 3



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


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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