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
|
15
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
16
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
|
| 
