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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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5. Measuring Price Sensitivity

A full test of our hypothesis requires assessing the differential impact of research frictions on price-insensitive customers compared to price-sensitive customers. The expected impact on purchasing behavior is expected to disproportionately affect the former. In this section, we develop a sparse empirical model of price sensitivity for buyers in our context. This serves two purposes. First, it uses a new and unique set of data to identify the relevant determinants of price sensitivity. Second, it provides us with a way to pre-classify consumers based on their expected price sensitivity. We then use the predicted values from this model as an approximation of a buyer's price sensitivity. After estimating the model, we assess its predictive accuracy by comparing the behavior of groups of pre-classified buyers in a validation experiment in the field.

Data

The data in this analysis consists of historical sales registers at the retailer's transaction level since its creation in October 2016 until October 2019. Over 2.1 million individual items were sold, 318,050 consumers have made more than a million transactions during this period. Each record (an item sold) contains buyer attributes, product attributes, and transaction attributes. Tables 3 and 4 describe the available data and basic summary statistics on operations.

Table 3: Classification data set summary statistics

Start date

3 October 2016

End date

30 October 2019

Number of records (items sold)

2,100,002

Number of transactions

1,000,004

Number of unique customers

300,050

Number of unique items

400,875

Number of unique brands

1,881

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Table 4: Transaction summary statistics

 

Mean

S.D.

Item selling price

551.49

600.50

Item original price

803.43

987.00

Discount percent

15.80

19.94

Items per transaction Basket size

2.17

2.30

Item selling price

1,147.28

1,541.37

Model

We estimate a simple price sensitivity model to determine, through field monitoring experience, whether price sensitive (rather than insensitive) buyers are more willing to bear the search costs of finding discounted items online. As the primary objective of the estimation is not to identify the primitives of consumer behavior, but to distinguish consumers who are price-insensitive from price-sensitive consumers, we adopt a parsimonious model that aims to explain the discount at the level from the basket of transactions made. The underlying assumption is that very price-sensitive buyer is more likely to buy discounted items than price-sensitive buyers. The categories of variables explaining the preference for discounts include demographic characteristics, past transaction behavior, and purchasing conditions known to be associated with the search for discount behavior. For each buyer I in month t, we have:

discount_preference?? t=?? (consumer_attributes??, shopping_behavior??, t-1, shopping_conditions?? t)

The covariates available to us for each the three categories are as follows:

Shopper attributes

Prior transaction behavior

Current shopping conditions

Gender

Prior markdowns

Period (month)

Age

Prior coupon redemption

Positive store credit

Billing region

Number of completed orders

Coupon redemption

 

Store brand ratio

 
 

Time since the first transaction

 

We carry out a series of Tobit regressions of the average basket discount on these covariates and present the estimates in Table 5. We use the Tobit model since the markdowns on the baskets are a censored approximation to the left (at zero) of the price sensitivity, our conceptual

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variable of interest. To assess the relative importance of demographics, past transaction behavior, and current purchasing conditions, we estimate separate regressions for each subcategory of explanatory variables in addition to the full model.

Model results

In general, we find that the relationships between consumer attributes, past purchasing behavior, current purchasing conditions, and current purchasing behavior are strong and robust when using different choices of covariates. Each category of explanatory variables improves the model's ability to predict preference for discounts. The observed discounts are lower for men and older customers. They are higher for customers who have previously purchased products at higher prices, used coupons, and more store-branded items. In the meantime, markdowns are lower for consumers who redeem coupons for an ongoing purchase, who use the store credit and who have more transactions already made.

We use the empirical model estimated in this section to pre-classify buyers according to their level of price sensitivity to articulate the mechanism behind our main result in Field Experience 1. Indeed, we use all the information available on consumers to obtain this classification, by assigning weights to each variable according to its estimated coefficient. We consider this to be superior to an ad hoc classification, for example, by grouping buyers according to the average discount in their purchase history. However, we also recognize the shortcomings of this approach due to the aggregation of information, the absence of purchasing or purchasing decisions, and the evolution of the assortment over time. To increase our confidence in the resulting classification, we seek to establish its external validity. In the next section, we describe how we validate our classification model using electronic newsletters as part of a field experiment.

Table 5: Tobit regression

EQUATION

VARIABLES

(1)

basket_markdown

(2)

basket_markdown

(3)

basket_markdown

(4)

basket_markdown

model

male

-0.0254***

 
 

-0.0197***

 
 

(0.000865)

 
 

(0.00108)

 

cust_age

-1.26e-06***

 
 

1.11e-06***

 
 

(1.06e-07)

 
 

(1.26e-07)

 

prev_markdown

 

0.396***

 

0.392***

 
 
 

(0.00200)

 

(0.00199)

17

 

prev_coupon

 

0.0325***

 

0.0458***

 
 
 

(0.000907)

 

(0.000948)

 

order

 

-0.000645***

 

0.000500***

 
 
 

(4.90e-05)

 

(4.92e-05)

 

prev_sb_ratio

 

0.0170***

 

0.0125***

 
 
 

(0.00125)

 

(0.00124)

 

cust_hist

 

1.74e-05***

 

1.49e-05***

 
 
 

(2.05e-06)

 

(2.04e-06)

 

wallet

 
 

0.0150***

-0.00323**

 
 
 
 

(0.00143)

(0.00147)

 

coupon

 
 

-0.0336***

-0.0544***

 
 
 
 

(0.000766)

(0.000956)

 

Constant

0.141***

-0.0166***

0.0704***

0.0278***

 
 

(0.00748)

(0.000760)

(0.00125)

(0.0100)

sigma

Constant

0.331***

0.310***

0.329***

0.307***

 
 

(0.000338)

(0.000392)

(0.000336)

(0.000388)

 
 
 
 
 
 
 

Billing region FE

yes

 
 

yes

 

Month FE

 
 
 

yes

 
 
 
 
 
 
 

Observations

1,112,297

698,456

1,112,298

698,456

 

Pseudo R2

0.00324

0.0576

0.0105

0.0729

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